Polypharmacology by Design: A Medicinal Chemist's Perspective on

Subscriber access provided by STEPHEN F AUSTIN STATE UNIV

Perspective

Polypharmacology by design – a medicinal chemist’s perspective on multi-targeting compounds Ewgenij Proschak, Holger Stark, and Daniel Merk J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.8b00760 • Publication Date (Web): 23 Jul 2018 Downloaded from http://pubs.acs.org on July 23, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Medicinal Chemistry

Polypharmacology by design – a medicinal chemist’s perspective on multitargeting compounds

Ewgenij Proschak(a), Holger Stark(b), Daniel Merk(c,a)*

(a)

Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, D-60438

Frankfurt, Germany (b)

Institute of Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf,

Universitaetsstr. 1, D-40225, Duesseldorf, Germany (c)

Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Swiss

Federal Institute of Technology (ETH) Zürich, Vladimir-Prelog-Weg 4, CH-8093 Zürich, Switzerland * Correspondence to [email protected], [email protected]

Abstract

Multi-targeting compounds comprising activity on more than a single biological target have gained remarkable relevance in drug discovery owing to the complexity of multifactorial diseases such as cancer, inflammation or the metabolic syndrome. Polypharmacological drug profiles can produce additive or synergistic effects while reducing side effects and significantly contribute to the high therapeutic success of indispensable drugs such as aspirin. While their identification has long been the result of serendipity, medicinal chemistry now tends to design polypharmacology. Modern in vitro pharmacological methods and chemical probes allow a systematic search for rational target combinations and recent innovations in computational technologies, crystallography, or fragment-based design equip multi-target compound development 1 ACS Paragon Plus Environment

Journal of Medicinal Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 100

with valuable tools. In this perspective, we analyze the relevance of multiple ligands in drug discovery and the versatile toolbox to design polypharmacology. We conclude that despite

some

characteristic

challenges

remaining

unresolved,

designed

polypharmacology holds enormous potential to secure future therapeutic innovation.

Keywords: Multiple-targeting │ multiple ligand │ promiscuity │ rational design │ drug discovery

2 ACS Paragon Plus Environment

Page 3 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1 Introduction

1.1 History of multi-target drug discovery – from “dirty drugs” to designed multiple-targeting ligands

The high therapeutic efficacy of some of our oldest drugs is ascribed to their pleiotropic activities on multiple targets. Long known small molecules such as acetyl salicylic acid, paracetamol and metformin interact with a variety of proteins leading to activity profiles that cause synergistic modulatory effects in complementary signaling pathways or enzymatic cascades1–5. The sum of numerous pharmacodynamic effects forms their often superior clinical activity profile. The same holds true for newer drugs such as statins which e.g. exhibit anti-inflammatory effects that support their clinical efficacy profile but appear not to be mediated by their main target of action6,7. Though never designed, such multiple activities often account for the desirable overall efficacy of a drug. While beneficial multi-target activity profiles that produce additive or even synergistic efficacy were generated serendipitously for a long time – for many drugs not even all known activities can be linked to a biological target – modern drug discovery has started to intentionally design small molecule drugs with defined multi-target activity. Starting in 2004, Morphy & Rankovic and others systematically classified multi-target agents and reviewed strategies to multiple ligand development with several foundation papers8–14. Since then, multi-target drugs have experienced steadily growing interest owing to their various advantages. The progress of multi-target compounds as well as



Page 4 of 100

strategies and technologies for their development were repeatedly reviewed15–24. Currently, neurodegenerative25,26 and psychiatric disorders27–35, infectious diseases36, cancer37–40 and metabolic/cardiovascular diseases41–43 are in the focus of multi-target drug discovery due to their multifactorial nature or because of therapeutic resistance development. The treatment of these diseases may strongly benefit from multiple additive or synergistic pharmacodynamic activities in terms of higher therapeutic efficacy or delayed resistance development. Moreover, targeted delivery44,45 exploiting the affinity of a small molecule homing ligand to a biological target which allows tissue or even cell selective deployment of potent agents linked to the homing ligand is increasingly gaining relevance. This article intends to evaluate the significance of multi-target design in current medicinal chemistry and give a view of what is possible in the field of designing multitarget small molecules. It analyzes strategies and technologies to access multiple ligands from rational and computational perspectives as toolbox for multi-target drug discovery and discusses where the field is heading in the future.

1.2 Advantages of multi-target drugs

Many diseases, e.g. metabolic dysbalance culminating in the metabolic syndrome, psychiatric or degenerative CNS disorders, or cancer, have a multifactorial nature that can hardly be “cured” by the specific modulation of a single target. The multifactorial processes leading to neurodegeneration, to cancer development, proliferation and metastasis, or to manifestation of diabetes and other metabolic diseases involve


Page 5 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


multiple signaling pathways and dysregulation of multiple physiological processes42,46,47. Treatment of these diseases hence requires modulation of multiple biological targets to restore the physiological balance and generate sufficient therapeutic efficacy. This is either achieved by combination therapies consisting of several target specific drugs – termed polypharmacy – or by using multi-target drugs that address several biological targets and produce additive or synergistic effects – termed polypharmacology48–51. Repetitive failures of highly potent and target specific drugs in clinical development and limited therapeutic efficacy of single-target drugs are continuously promoting multitarget compound design in rational drug discovery52,53. As obvious from these considerations, multi-target drugs offer a variety of advantages. Well-designed and optimally balanced multiple ligands may replace a series of drugs in a combination therapy and thereby markedly reduce polypharmacy23. With the resulting reduction in treatment complexity, drug side-effects, pharmacokinetic complexity and drug-drug interactions can be diminished, and compliance improved. Moreover, modulation of multiple biological targets may increase therapeutic efficacy through synergies48,54. Suitable target combinations may allow application of lower drug doses to produce adequate therapeutic effects since partial modulation of synergistically acting targets may be sufficient to reach full therapeutic efficacy54. This in turn can reduce side effects mediated by full engagement on the individual targets without losing therapeutic efficacy and can widen therapeutic windows. And finally, multi-target drug discovery also offers economic advantages since the clinical development of a single multi-target drug requires less clinical trials than multiple specific drugs.



Page 6 of 100

1.3 Areas of applicability

The central idea of multi-target compounds is modulation of multiple targets that promise additive or synergistic effects, of course. With this characteristic, multiple ligands affect many disciplines and research fields in medicinal chemistry. They hold enormous therapeutic potential for example by synergistic antibacterial36 or anticancer39,55 target combinations that could help avoiding development of antibiotic or chemotherapy resistance as global challenges of current drug discovery. In addition, multifactorial diseases involving dysregulation of several physiological functions such as neurological disorders or the metabolic syndrome may remarkably benefit from multitarget compounds that combine several suitable modes of action. However, multiple ligands can also be designed to address multiple binding sites on a single target and thereby simultaneously exhibit allosteric and orthosteric modulation. This kind of compounds with multiple biological binding sites e.g. has relevance in targeting G protein coupled receptors (GPCR) where bitopic ligands constitute a promising strategy to achieve subtype or signaling selectivity despite highly conserved orthosteric binding sites and might offer a way to specifically modulate GPCR function56,57. Another field related to multiple ligand development is targeted delivery where drug conjugates are designed to contain a targeting molecule linked to a potent active agent that is delivered to the intended site of action with the help of the targeted ligand (cf. 3.2). Such targeted drug conjugates are often composed of a monoclonal antibody and a potent cytotoxic agent to be used in cancer therapy, but also small molecules are


Page 7 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


increasingly employed as targeting ligands. Drug conjugates must be clearly distinguished from designed multiple ligands since their pharmacophores are not joined in a common motif (cf. 3, Scheme 1). In the context of the central idea behind multi-target ligands to exhibit pharmacodynamic effects on multiple biological targets, virtually every combination of targets is thinkable. The more different the targets of interest respectively the chemotype of their ligands are the more difficult is the development of compounds addressing the target combination. Therefore, the majority of designed multi-target compounds either addresses target combinations within a protein family such as multiple kinases or GPCRs, or modulates targets within enzymatic pathways such as the arachidonic acid cascade where the different enzymes accommodate structurally similar substrates. Still, examples58,59 of multiple ligands for markedly different biological targets demonstrate that also much more complex target combinations in terms of multi-target compound design are feasible. A key to successful multi-target compound design is discreet selection of target combinations that produce additive effects or even synergies. Additionally, the targets must be compatible in terms of ligand accommodation and cellular/tissue localization to allow the development of efficacious and drug-like multiple-targeting ligands. It should be stressed that distinct target localization in different cell or tissue compartments opens another level of complexity in pharmacodynamic, pharmacokinetic and also toxicological aspects in the already complex situation in drug development.

2 Identifying suitable target combinations for multiple-targeting ligands



Page 8 of 100

As stated above, a central issue in target selection for multi-targeting drug discovery is the chemical feasibility of a multi-target ligand. It is intuitional that designing a dualtarget ligand is easier for highly related targets as exemplified by designed multi-target ligands of aminergic receptors60 or kinases61. The more challenging scenario is the design of multiple ligands addressing unrelated targets. In such case, successful development of a multiple ligand depends on the ability of these unrelated targets to accommodate chemically related compounds, e.g. for cyclooxygenase-2 (COX-2) inhibition and thromboxane receptor antagonism62, multiple ligand design was achieved. Although the two targets are strongly different, their ligands arachidonic acid and thromboxane A2 are structurally related suggesting that the binding sites possess a certain degree of similarity and might accommodate a shared ligand. An even greater challenge are multi-target ligands for unrelated targets that accommodate structurally unrelated ligands. Still, there are several examples demonstrating that design of such ligands is possible, as well. Ciceri et al. for example have described dual ATPcompetitive kinase/bromodomain 4 inhibitors, the latter of which recognizes acetylated lysins63. Moreover, the angiotensin II receptor 1 (AT1R) antagonist telmisartan – a peptide mimetic – also binds to peroxisome proliferator receptor gamma (PPARγ)64 which acts as a receptor for fatty acids and respective mimetics, thereby enabling the discovery of dual AT1R antagonists/PPARγ modulators65,66 with remarkable activity in vivo. Although the theoretical feasibility determinants of target combinations for multiple ligand development have not been systematically studied, yet, many suitable methods


Page 9 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


for this task are available. Most of them rely on the chemical similarity principle postulated by Johnson and Maggiora67. Although this principle is not general by far68, a multitude of studies demonstrated its applicability to predict polypharmacology and to define relationships between distinct targets based on the chemical similarity of their corresponding ligands69. Oppositely, these methods can also be employed to prioritize promising target combinations that share similar ligands for multitarget design.

2.1 Rational target selection based on clinical observations

Unsatisfying efficacies of available therapeutic options in many diseases promoted the use of drug combinations that addressed distinct targets. In many cases, medical needs led to drug cocktails as initial therapeutic approach instead of synergistic target combinations realized in a single molecule. However, differences in pharmacokinetic properties such as half-life and distribution, and drug-drug interactions make drug combination formulations problematic for many patients and, therefore, their applicability is often limited. Still, the knowledge of enhanced therapeutic efficacy of such drug combinations despite their obvious drawbacks has promoted the rational development of multi-target agents for target combinations that were previously validated by drug cocktails. Increasing knowledge on biomarkers and their analytical accessibility open further avenues to clinically verify multi-targeting approaches70–72 with suitable drug cocktails, even for orphan diseases73,74. A prime example of multi-target drug discovery relying on clinical observations are antipsychotics. The initial therapeutic approach of inhibiting dopamine D2-like receptors



Page 10 of 100

as primary target (derived from early first-generation histamine H1 receptor antagonists) was associated with considerable side effects such as extrapyramidal motor symptoms. Subsequent inclusion of anti-serotonergic 5-HT2A activity significantly improved the therapeutic efficacy and reduced adverse effects75. Later, the modulation of efficacies on different biological targets involving partial agonism on one and inverse agonism on another receptor enabled functional selectivity on multiple receptors76,77. This rational, clinical observation-driven multi-target design reduced side effects and enhanced patient response rates as exemplified with successful atypical antipsychotic drugs such as aripiprazole or cariprazine (RGH-188)78–80. In addition to multiple ligand design guided by clinical efficacy and safety observations, clinical observations of polypharmacological drug effects have led to their repurposing or repositioning81 in different therapeutic indications82,83 (cf. 3.3.1.4). Moreover, unexpected or superior efficacy of drugs comprising polypharmacological profiles can serve as hint to favorable multi-target profiles and allow rational target selection for combination. This is further supported by increasing clinical knowledge on underlying causes of a disease combined with understanding of the dysregulated signaling pathways. The discovery of suitable molecular targets by clinical observation of therapeutic efficacy allows a rational design of favorable polypharmacological profiles. With the help of fragment-based (cf. 3.4) and pharmacophore-based (cf. 3.3.1.3) approaches, drugs (or other bioactive small molecules) with suitable structural similarity can then be identified as leads for combination and optimization towards the desired multi-target profiles on clinically validated target combinations.33,84–86


Page 11 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Related targets comprising similar binding sites such as different aminergic class A GPCRs, kinases or cognate nuclear receptors are well accessible for such multi-target approaches but may also be source of off-target cross-reactivity leading to adverse effects. In such cases, targeted promiscuity by design or cross-reactivity by serendipity cannot be fully separated. Designed combination of different targets like membrane GPCRs, transmembrane transporters, intracellular enzymes at different parts of cell architecture or nuclear targets is a more challenging endeavor. Still, several examples58,87–89 highlight the possibility of designing multiple ligands for rationally defined target combinations in psychiatric and CNS disorders, metabolic and inflammatory diseases, but also for oncologic or antimicrobial drugs where resistances raise urgent need for multifactorial interventions47. For example, activity on three clinically validated cognitive targets, namely the presynaptic histamine H3 autoreceptor, Nτ-methyltransferase and acetyl- and butyrylcholinesterase, could be united in single multitarget agents despite pharmacokinetic issues and limited brain penetration90,91.

2.2 Phenotypic screening of drug combinations

Chemical probes – well-characterized ligands with high selectivity against related targets92 – are unique tools to identify target combinations that produce synergistic effects. However, it is most inconvenient to screen for synergistic effects of two or even more targets in complex animal disease models. High numbers of necessary animals due to high numbers of putative compound/target combinations that exponentially grow with the number of employed probes is ethically intolerable. Even with a small number



Page 12 of 100

of chemical probes, different dose combinations might be required equally leading to a high number of necessary animal experiments. Genetic knockout or knockdown of one (possibly pre-validated) target combined with the use of chemical probes is a valuable approach to reduce the number of necessary animals in screenings for synergistic target combinations93. An exemplary study employed the COX inhibitor Diclofenac in inflammatory models performed in FAAH(-/-) mice in order to validate the synergy between the targets COX and FAAH93. Still, chemical probes are often poorly characterized in vivo and inappropriate pharmacokinetics can cause false negative results. Therefore, cellular, tissue, or non-mammalian animal models appear more suitable to study large numbers of compound combinations to identify synergistic targets. Such assay systems usually rely on functional read-outs like apoptosis (especially in cancer cells), viral entry (in viral diseases) or bacterial death (in infectious diseases). Especially in the field of anticancer drug discovery, where powerful combination therapies are required, drug combination screenings helped identify several target combination synergies. A recent example screened compound cocktails for efficacy in myeloid- and lymphoid-derived hematologic malignancies94. 122 primary patient samples with various hematologic malignancies were profiled against a panel of 48 drug combinations ex vivo which uncovered synergistic activity of the B-cell lymphoma (BCL2) inhibitor venetoclax and the mitogen-activated protein kinase kinase (MEK) inhibitor cobimetinib, the mouse double minute 2 homolog (MDM2) inhibitor idasanutlin, or the bromo- and extra-terminal domain (BET) inhibitor ABBV-075. Such hints for suitable drug combinations can subsequently be validated e.g. using


Page 13 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


CRISPR/Cas9 gene silencing95 and in vivo experiments to provide ideal and validated starting points for a multi-target drug discovery campaign. Biological assays that rely on complex multi-dimensional read-outs are often referred to as high-content screening or phenotypic screening when entire organisms are used. Such high-content and phenotypic screening systems are an inevitable tool in drug discovery96 and have particularly high value for multi-target design. BioMAP are wellestablished high-content screening systems97. The BioMAP cultures are low-passage primary

human

cells

providing

physiological

responses

upon

exposure

to

pharmacological agents. The disease and tissue relevance of the BioMAP cell cultures is very broad and various high-content read-outs are available for each cell type98. Another powerful high content screening system are induced pluripotent stem cells (IPSC)99 which can be converted into various cell types suitable for high content screening of chemical probes and their combinations to discover synergies100. More sophisticated screening systems employ entire organisms such as fruit fly Drosophila melanogaster or zebrafish Danio rerio. Assays relying on these organisms allow complex read-outs including even behavioral changes which can be employed to screen for synergistic target combinations in CNS-related disorders101. E.g. Sonoshita et al. used a Drosophila medullary thyroid carcinoma (MTC) model to tune the multi-kinase inhibitor sorafenib towards an improved efficacy and safety profile102.

2.3 In silico approaches to target combinations: systems biology/pharmacology



Page 14 of 100

Identification of suitable target combinations is also feasible with in silico techniques. Especially the network pharmacology approach relying on the analysis of biological target networks has been successfully employed to identify promising target combinations103. A broad panel of in silico methods104 to suggest synergistic drug combinations according to this strategy is available such as the widely used analysis of signaling networks by machine learning105. Notably, such computational approaches only provide hypotheses which subsequently need validation in vitro or in vivo to confirm predicted target combinations as suitable and favorable. Table 1 gives an overview of computational techniques that were proposed for the prediction of promising target and drug combinations. Of note, many of these approaches have not been experimentally validated for multi-target design but still appear as interesting tools. Successful studies to identify synergistic target combinations have widely relied on a combination of experimental and computational techniques. E.g. the combination of a phenotypic assay on primary CNS neurons with machine learning algorithms for deconvolution of compound-mediated effects led to identification of target combinations promoting axon growth106. Moreover, a combinatorial drug screen in dedifferentiated liposarcoma (DDL)-derived cells with subsequent computational modelling of the underlying signaling networks uncovered cyclin-dependent kinase 4 (CDK4) and insulinlike growth factor 1 receptor (IGF1R) as synergistic drug targets for DDL107. Thus, employing combinations of in vitro and in silico techniques appears as most reliable and economic way to discover synergies in multi-target modulation. The high performance of such translational approaches might result from the possibility to minimize uncertainties in the computational methods by in vitro screening data. Computational approaches


Page 15 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


mostly rely on very heterogeneous data that can be improved with the help of focused in vitro experiments. Moreover, in silico deconvolution of the targeting pathways helps to reduce the influence of potential off-target activities of the screened chemical probes. Therefore, multidisciplinary approaches can provide reliable data on target synergy as inevitable starting point for multi-target drug discovery. Table 1: Available in silico techniques to identify synergistic target combinations. For further information and details refer to reviews

104,108

Approach

.

Application

Ref.

Statistical approaches Bliss and Loewe interaction analysis

Statistical analysis of additivity and independence combined with tailored non-parametric statistical analysis

109

Significance analysis of mutant/drug combinations

Analysis of chemical-genetic dataset of resistant bacteria

110

Analysis of substructure profiles

Prediction of antibiotic combination efficacy

111

Probabilistic concordance index and resampled Spearman correlation

Mining of NCI-DREAM Drug Synergy Prediction Challenge dataset

112

Deep learning

Analysis of drug combination screening on different cell lines

113

Random Forest

Learning physicochemical features and network features to predict synergistic drug combinations

114

Random Forest

Learning synergy of anticancer drugs in mutant BRAF melanoma

115

Semi-supervised learning

Network-based Laplacian regularized Least Square Synergistic drug combination prediction of antifungal drug combinations

116

Statistical pair analysis

Mining clinically relevant synthetic lethality interactions

117

Monte Carlo cross-validation

Analysis of pathway cross-talk inhibition

118

Quantitative chemical-genetic interaction map

Impact of knockdown of cancer-related genes on response to classes of chemotherapy

119

Ordinary differential equation model

Prediction of drug combinations efficacy within EGFR-ERK Signaling Pathway

120

Machine learning approaches

Pathway/Network analysis



Network simulation

Page 16 of 100

121

Synergistic drug combinations for breast cancer

Combination of different techniques Pathway analysis, machine learning, statistical analysis

122

Quantitative Systems Pharmacology identification of cell protective pathways and drug combinations

Combined in silico/in vitro approaches RNAi screen, statistical analysis

synergistic combinations of targeted therapeutic agents with essential target genes in colorectal cancer cell lines

123

Beyond computationally predicting synergistic target combinations, in silico quantitative flux modelling techniques can be employed to calculate the extent to which each target must be modulated for producing the desired therapeutic effect with minimal sideeffects. The concept of targeting multiple pathways with low drug doses postulates that maximum therapeutic efficacy can be achieved without full target engagement on a single target but rather by partial modulation of target combinations54. In this context, the arachidonic acid cascade, which has been in the focus of multi-target drug discovery for several decades, was extensively investigated. E.g. Yang et al. performed a timeresolved flux analysis of the individual branches (cyclooxygenase and lipoxygenase) of the arachidonic acid metabolic network in human polymorphous leukocytes and thereby predicted

the

most

promising

target

combinations

for

synergistic

effects124.

Subsequently, these computational predictions were confirmed by time-resolved LCMS/MS profiling of pro- and anti-inflammatory arachidonic acid metabolites125. Progressive developments in the field of quantitative in silico modelling of metabolic pathways may provide many further insights to promote multi-target drug discovery.

3 Multiple-targeting ligand identification and optimization


Page 17 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Multi-target compounds can be classified according to their molecular and pharmacophore architecture in three general types8 (Scheme 1). The simplest manner of combining multiple activities is the conjugation of pharmacophores with a linking group. Such linked pharmacophores (cf. 3.2) tend to possess large molecular weights coupled with several disadvantages but increasingly gain relevance in targeted delivery. Conjugation of two (or more) individual pharmacophores without a linking group produces fused multiple ligands that are also prone of having undesirable characteristics such as high molecular weight and extensive lipophilicity. The most demanding but also most fruitful type of small molecule multi-target agents are merged multiple ligands (cf. 3.3) whose pharmacophores are amalgamated as far as their biological targets allow. Between fused and merged multiple ligands various intermediate forms are possible, of course. Merged multiple ligands can be designed to possess low molecular weight and to fulfil Lipinski’s Rule of Five126 and thus be considered drug-like. However, the design of a merged pharmacophore satisfying the biological targets of interest and its optimization to a potent multi-target compound are very challenging tasks.



Page 18 of 100

Scheme 1: Multi-target agents can arise from linking pharmacophores with stable or biodegradable linkers, fusing of pharmacophores or merging of pharmacophores for the biological targets of interest.

3.1 Serendipity

From the stories that were told, it is difficult to report on the percentage of multipletargeting drugs developed by rational design or found by serendipity. Concerning the early histamine H1 receptor antagonists, anesthetic and antipsychotic properties were observed and then further optimized based on enhanced tissue- and animal models127,128, thus involving serendipity and rational optimization efforts. In optimal scenarios, serendipity meets genius, excellent knowledge and hard work48. In many cases, the complexity of a compound’s profile is detected at later stages in biological screenings. Selection of compounds for further development is then made not only 18 ACS Paragon Plus Environment

Page 19 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


based on binding to the original target, but on the biological efficacy. This superior efficacy profile is then traced down to identify the multiple involved targets. A thus defined target profile subsequently enables more specific selection criteria and more clearly guided optimization processes of new lead structures. In the field of natural compounds as a rich and heterogeneous source of novel lead structures129,130, (multi-target) biological activity is often detected by trial and error approaches. Prominent examples of oncologic and antibiotic agents including Vinca alkaloids and taxoles55,131,132, penicillin, macrolides and peptide antibiotics133,134 were originally claimed as mono-targeted but later found to address multiple targets or optimized for multiple-targeting55. The need for multi-targeted agents is steadily increasing due to resistance development. Natural compound derived leads have become important starting points for partial or full synthesis, as well, to optimize a beneficial designed-in profile for more potent multi-targeting compounds131,135–140. Also in the field of multi-kinase inhibitors many initial findings of multi-target activity were based on serendipity. By increasing knowledge through elaborate structure-activity relationship (SAR) studies and the availability of broad structural data, multi-kinase inhibitors could then be rationally designed141. The discovery of dual p38 mitogenactivated protein kinase (p38α/MAPK) and phosphodiesterase 4 (PDE-4) inhibitor CBS3595 (1, Scheme 2) exemplifies such serendipitous discovery followed by rational optimization of the multi-target compound142. During lead profiling of a series of p38α/MAPK inhibitors in experimental endotoxemia, one candidate displayed significantly more pronounced in vivo efficacy than could be expected from its in vitro potency. PDE-4 was then identified as a secondary target and exploration of the SAR



Page 20 of 100

landscape led to identification of 1, an almost equipotent dual p38α/MAPK/PDE-4 inhibitor. Its dual inhibition of p38α/MAPK and PDE-4 synergistically attenuates the TNFα release in vitro an in vivo rendering 1 a promising candidate for treatment of chronic inflammatory diseases.

Scheme 2: Dual p38α/MAPK/PDE-4 inhibitor CBS-3595 (1) which is the product of a serendipitously discovered multi-target profile and subsequent optimization of the dual activity.

3.2 Linked pharmacophores

The simplest way to rationally combine pharmacophores of two (or more) distinct targets is their connection with a stable or biodegradable linker44. A resultant multitarget compound still exploits the advantages of addressing multiple targets with a single molecule. Additionally, this strategy offers a way of combining two potent agents addressing distinct targets without the need of excessive structural optimization44. However, linkage of two pharmacophores usually results in rather large molecules8,10 that may fail to provide favorable bioavailability or reach intracellular compartments and the presence of a linker can significantly hinder the interaction with target proteins rendering the kind and attachment point of linkers a crucial optimization topic. 20 ACS Paragon Plus Environment

Page 21 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Linked pharmacophores are currently of great interest in antibody drug conjugates44 where the linkage often aims the exploit one pharmacophore (e.g. the antibody) as targeting agent that delivers the second pharmacophore (a small molecule) to its desired site of action. Thereby, highly potent agents can be applied in cancer treatment which exhibit too strong cytotoxicity to be used in monotherapy. The concepts of antibody drug conjugates are increasingly transferred to small molecule drug conjugates and some lessons from this field of drug discovery can also be learned for small molecule pharmacophore linkage.44

3.2.1 Linked pharmacophore examples

The targeted delivery strategy that is explored with the linkage of antibodies and cytotoxic payloads has also been successfully established by linking two small molecules. One of the first example of two linked pharmacophores for targeted delivery to cancer cells was reported as early as 1992143,144 when cytotoxic drugs were linked to cholic acid derivatives as targeting pharmacophore which helped the compounds to be specifically taken up into targeted cells via bile acid transporters. Since then, various transport systems and cell surface receptors were used to deliver linked multi-target compounds to specific cell populations by addressing e.g. estrogen receptors, folate receptors, sigma-2 receptor, prostate-specific membrane antigen, the polyamine transport system and even glucose transporters. Moreover, exploiting the affinity of a ligand to a transport system or cell surface receptor can also serve imaging purposes,



Page 22 of 100

modulate pharmacokinetic properties or allow passage of physiological barriers such as the blood-brain-barrier.44 Currently, targeted delivery of antibiotics into bacteria by exploiting bacterial uptake mechanisms catches remarkable attention. Due to their need of iron acquisition from the surroundings, bacteria produce and excrete iron-chelating compounds, so called siderophores. Specific active uptake mechanisms then internalize the chelator-iron complexes to satisfy the bacterial need for iron. This active transport system is now successfully targeted to furnish active uptake of antibacterials by conjugation of an antibiotic agent and a siderophore with a (biodegradable) linker. There are also natural sideromycins such as albomycin and salmycin produced by streptomycetes145 and several promising synthetic examples of such drug conjugates (2a-c) have been reported recently showing remarkable potency even against multi-resistant strains146– 149

. Some sideromycins are cleaved after uptake by bacterial enzymes to release the

antibiotic pharmacophore. Cefiderocol (2c), the most advanced example of designed sideromycins, has entered clinical trials and holds promise for efficacy versus multidrugresistant Gram-negative pathogens150 (Scheme 3).


Page 23 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Scheme 3: Sideromycins (2a-c, examples)

146,147,150

, conjugates composed of a siderphore and a potent

antibiotic, exploit bacterial iron uptake mechanisms to furnish active transport of the conjugates into bacteria where the conjugate is cleaved to release the antibiotic. Cefiderocol (2c) has already entered clinical development.

Linkage of therapeutic pharmacophores that are not intended to facilitate uptake but to exhibit pharmacodynamic effects on both their targets for potential synergistic activity has also caught attention in the field of anti-microbial agents151. Though this field is



Page 24 of 100

rather new, linked multi-target antibiotics might increasingly gain significance in times of limited availability of anti-microbial agents.

3.2.2 Linker chemistry

All pharmacophore linkage approaches have in common that their linker chemistry152 is crucial to achieve the desired characteristics and effects since their behavior strongly depends on the linking group. It may significantly affect activity on the targets but also governs pharmacokinetics and defines whether and where the linked molecules can be released from each other by cleavage.44 Regiochemistry is of particular importance to retain affinity of the linked pharmacophores to their targets and information on the SAR of the linked pharmacophores must be available to identify suitable positions for linker attachment. Suitable linkers can improve polarity and solubility153, reduce unspecific uptake of the conjugate into cells by passive diffusion153 and define site and kinetics of linker cleavage e.g. by pH dependence or liability to a certain enzyme. For noncleavable linkers, attachment points to the pharmacophores as well as linker length and geometry require special attention.154


Page 25 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Scheme 4: Typical cleavable linkers (examples) are either labile to acidic conditions or reducing agents, or designed to be specifically cleaved by certain enzymes.

Scheme 5: Self-immolative spacers (examples)

155,156

undergo intramolecular reactions to entirely release

the linked drugs from the conjugate.

For conjugate agents with a cleavable linker, the intended site of cleavage and release of the linked pharmacophores is a central consideration to select the most suitable linker. Cleavable linkers156 (Scheme 4) include linking groups that are labile to acid (hydrazones, oximes, thiomaleimides157) or reducing agents (disulfides) as well as linkers that can be degraded by enzymes (peptide-based: valine-citrulline158 is cleaved by cathepsin B, imide groups are cleaved by endopeptidases159; β-glucuronic acid160 is 25 ACS Paragon Plus Environment


Page 26 of 100

cleaved by β-glucuronidase). After cleavage of the labile group in the linker, modern linkers may undergo intramolecular reactions that ultimately lead to total release of the linker group and its spacers from the linked pharmacophores (self-immolative spacers155, Scheme 5).

3.2.3 Fused multiple-targeting ligands

In addition to conjugating two individual pharmacophores with a linker, two bioactive small molecules addressing two targets can also be fused directly without a linker connecting them.8 Depending on the nature of the bond linking the two compounds, the characteristics of such fused multiple ligands can vary between cleavable and noncleavable like in linked conjugates. For example, fused multiple ligands that are connected via an ester moiety, an imine or a hydrazone may be cleaved in vivo155. Similar to linked conjugates where the site of linker attachment is a crucial optimization task, also the site and manner of compound fusion needs to be carefully evaluated and optimized. Especially non-cleavable fused multiple ligands may fail to exhibit their desired activities when fusion of pharmacophores hinders their binding to the targets. In some cases, this may be an insuperable obstacle, e.g. when the ligand binding site of a target is buried within the protein such as in nuclear receptors. Simple fusion of two pharmacophores will produce quite large molecules and may seem hardly attractive on first glance.8,10 However, for some target combinations which too strongly differ in the chemotypes of ligands they can accommodate, fusion of


Page 27 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


pharmacophores may be an ultima ratio to generate a multiple ligand with the desired pharmacological profile.

3.3 Merged multiple-targeting ligands

The most elegant way of designing small molecules that modulate more than one target is merging their pharmacophores.8,12,161 Explicitly, in such merged multiple ligands, the key pharmacophore features required to interact with the targets of interest are combined in one united pharmacophore. To allow such pharmacophore merging, a certain overlap in the pharmacophores of the individual targets is required161. Moreover, the binding sites of both targets of interest must be capable of binding the same chemotypes of molecules in terms of geometry and charge distribution. Key pharmacophore elements such as acidic functions which are e.g. present in fatty acid mimetics162 or metal binding motifs must be compatible with the second biological target. On the other hand, the danger of general promiscuity of identified dual binders/modulators must be considered and excluded161.

3.3.1 Lead identification strategies for merged ligands

3.3.1.1 Rational approaches

A key step in developing merged multiple ligands is the lead compound identification. Several strategies to discover suitable starting points for multi-target compound



Page 28 of 100

development and further optimization have been employed and validated. With the availability of actives on the individual targets of interest, common structural and pharmacophore features of similar ligands can be systematically merged in minimal common lead pharmacophores. X-ray structural information can be very valuable in this step to identify key pharmacophore features that are crucial for activity. Several projects have successfully generated merged multiple ligands by rationally and systematically combining common scaffolds and substructures contained in selective ligands of the targets of interest (Scheme 6). Woo et al.163–166 (Scheme 6A) have combined the pharmacophores for aromatase inhibition (3a) and steroid sulfatase inhibition (3b) leading to several dual inhibitors of both enzymes. Initial merging of the pharmacophores was achieved by incorporating a phenol sulfamate moiety crucial for irreversible steroid sulfatase inhibition in a known aromatase inhibitor scaffold. The pharmacophore merging strategy relied on structural information from co-crystals and molecular docking studies and resulted in dual inhibitors such as 3c with balanced intermediate nanomolar potency. Extensive SAR studies and scaffold hopping yielded various chemotypes of dual aromatase/steroid sulfatase inhibitors such as 3d with potencies ranging down to picomolar values. Schmidt et al.58 (Scheme 6B) have merged pharmacophores for farnesoid X receptor (FXR) activation (4a) and soluble epoxide hydrolase (sEH) inhibition (4b) to generate a minimized merged lead pharmacophore. Key pharmacophore elements – an amide moiety to mimic the transition state of sEH substrate hydrolysis and a carboxylic acid moiety for neutralizing interactions with the FXR ligand binding site – were compatible with a common substructure in a pair of known selective actives of FXR and sEH. One


Page 29 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


out of three merged candidate lead pharmacophores (4c) possessed moderate dual potency and was extensively optimized to a series of dual FXR/sEH modulators (4d) with low nanomolar potency as potential drug for the treatment of non-alcoholic steatohepatitis and related metabolic diseases. The atypical antipsychotic ziprasidone (5a) was rationally designed from two pharmacophores to target serotonin 5-HT2 receptors and dopamine receptors (Scheme 6C). The naphthylpiperazine 5-HT2 pharmacophore (5b)167 was merged with an indolone moiety as dopamine (5c) antagonistic pharmacophore168 resulting in dual antagonist 5d. Subsequent systematic structural optimization enabled the development of 5a169. Bautista-Aguilera et al.170 (Scheme 6D) successfully designed triple ligands of choline esterase (ChE), monoamine oxidase (MAO) and histamine H3 receptor for the use in neurodegenerative diseases by rationally merging pharmacophores for each target in a single molecule. Incorporation of a general H3 antagonism pharmacophore derived from 6a in the compatible dual ChE/MAO inhibitor 6b generated lead 6c with intermediate triple potency. Minor structural changes (6d) were then sufficient to optimize the triple activity to balanced nanomolar values.



Page 30 of 100

Scheme 6: Examples for rational design of merged lead pharmacophores in multiple ligand development: (A) Introduction of the steroid sulfatase inhibition pharmacophore phenolsulfamate from 3b into aromatase inhibitor 3a was sufficient to design merged dual aromatase/steroid sulfatase inhibitor 3c. Subsequent SAR studies and scaffold hopping yielded several series of potent dual inhibitors such as 3d. (B) Minimal pharmacophores for farnesoid X receptor (FXR) activation (4a) and soluble epoxide hydrolase (sEH) inhibition (4b) were compatible for merging in dual lead pharmacophore 4c exhibiting moderate dual potency. 4c was systematically optimized towards both targets resulting in dual modulator 4d with low nanomolar dual activity. (C) The atypical antipsychotic ziprasidone (5a) was discovered by merging pharmacophores for serotonin 5-HT2 receptor antagonism (5b) and dopamine (5c) receptor antagonism producing the dual antagonist 5d which was systematically optimized to 5a. (D) Design of the triple lead pharmacophore 6c incorporating pharmacophore elements for choline esterase (ChE) inhibition (adopted from 6b), monoamine oxidase (MAO) inhibition (6b) and histamine H3 receptor


Page 31 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


antagonism (6a) depicts that even activity on three individual targets can be rationally designed in a single small molecule. Minor structural changes were then sufficient to optimize 6c to the potent triple modulator 6d.

3.3.1.2 Descriptor-based computational approaches

Complementary to rational merging of known common pharmacophore elements, several computational approaches are available as potent alternatives. Large publicly available datasets of structure-bioactivity relationships such as ChEMBL171 or PubChem172 can be used to build drug-target interaction networks and to discover scaffolds suitable for merged multiple ligand development. For such approaches, molecular descriptors are required to capture characteristics of the bioactive compounds and various molecular fingerprints have been successfully applied to this problem161. Suitable fingerprints include simple substructure fingerprints173 and circular fingerprints174 but also ligand interaction fingerprints175–177 and pharmacophore feature descriptors178. With such molecular descriptors, the similarity of actives for a pair (or set) of targets of interest can be computed to identify similar ligands179. Thus, discovered candidates for dual activity can then be characterized in vitro on the respective other target(s) of interest for which no bioactivity data are available and actives can serve as leads for merged multiple ligand development. As alternative to systematic lead structure searches based on compound similarity, large structure-bioactivity datasets can also be used to generate quantitative structureactivity relationship (QSAR) models for prediction of bioactivity on the targets of interest. Such models can be built using neural networks, support vector machines or random 31 ACS Paragon Plus Environment


Page 32 of 100

forest models/decision trees.161,180,181 QSAR models require a considerably large dataset of actives and inactives on the targets of interest for their training but then can be used to score unknown compounds for their probability to be active. Successful examples have used QSAR models to identify dual modulators for defined target pairs with high retrieval and low false positive rates.182,183 Furthermore, generative artificial intelligence is continuously gaining relevance in early drug discovery and might also hold enormous potential in multi-target compound design. Recently, a generative artificial intelligence model trained to capture the constitution of ChEMBL annotated compounds represented as SMILES strings and finetuned on a set of ligands for two specific target families was successfully employed to discover novel dual ligands184. The advantage of this method is that it required far less training data than most other approaches for the targets of interest and invents novel chemical entities that were not annotated in the training data.

3.3.1.3 Pharmacophore models

In addition to aforementioned descriptor-based approaches to multiple ligand identification,

pharmacophore

models

provide

a

complementary

computational

approach. Pharmacophore models describing the “ensemble of steric and electronic features of a compound that is necessary for the interaction with a biological target” can be computationally deduced from either a set of known ligands of the target (ligandbased) or from co-crystal structures (structure-based). They represent a threedimensional arrangement of features such as hydrogen bond donors/acceptors,


Page 33 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


charged residues, aromatic interactions or lipophilic contacts. Moreover, excluded volumes may be defined in these three-dimensional models to avoid clashes with the biological

target

leading

to

inactive

compounds

even

though

the

positive

pharmacophore features are matched185–187. Ligand-based pharmacophores are generated from sets of known actives for a target of interest. For this, the known actives are aligned in order to identify common pharmacophore features to be included in the pharmacophore model. Since the bioactive conformations of these ligands are not known without structural information, a number of preferred energy-minimized conformations have to be included for every ligand which may lead to considerable computation efforts. However, modern algorithms in pharmacophore modelling software are competent to cover the threedimensional conformational space of small molecules. In addition to sampling preferred conformations of the ligands, their alignment is an elaborate computational task. For example, small molecules can be aligned by superimposing single atoms, atom ensembles (fragments) or chemical/pharmacophore features with least-squares fitting but also descriptor-based methods are available and modern alignment techniques use e.g. pattern recognition. This large number of factors influencing the pharmacophore model generation in addition to the selection of training ligands can lead to remarkable variation in the final models and, therefore, ligand-based pharmacophore modelling is a challenging task even for a single target185,188–190. Structure-based generation of pharmacophore models provides the strong advantage that an optimal three-dimensional conformation of the ligand can be extracted from the co-crystal structure185,191–194. Moreover, pharmacophore features can be defined from



Page 34 of 100

the interactions of the ligand with biological target and excluded volumes result from the binding site surface. However, structure-based pharmacophore models derived from a single co-crystal structure cannot cover the full chemical space of small molecules that might be interacting with the target of interest. A co-crystal structure represents only one possible “frozen” conformation of the protein and, thus, conformational adaption to a ligand is not reflected by a structure-based pharmacophore model185. Both, ligand- and structure-based pharmacophore models have their limitations and often suffer from high rates of false-positives in screening approaches leading to few retrieved actives. Obstacles in pharmacophore-based screening amongst others include poor data quality, limited number of known inactives, erroneous crystal structures and restricted computational implementation of some interaction types such as halogen bonding185. To optimize the output to a maximum hit rate of true actives, pharmacophore models should be carefully adjusted and cross-validated if sufficient data is available190,195. Although pharmacophore models are often outperformed by descriptor-based approaches for individual targets, they represent a valuable approach to multiple ligand identification. Strategies to obtain multiple ligands from pharmacophore models involve virtual screening of compound libraries to identify potential binders or de-novo design of small molecules matching the pharmacophore model(s). For multiple ligand discovery by pharmacophore-based virtual screening, two individual screenings are required using pharmacophore models of both targets of interest. In an optimal scenario, the screened compound libraries contain a set of preferred energy-minimized conformations of each structure to identify conformations matching the pharmacophore model though this may


Page 35 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


significantly increase computation time. A suitable tolerance radius must be defined for the individual pharmacophore features to be matched by the screening compounds and some features should possibly be defined as optional185,190. Candidate compounds matching the pharmacophores of both targets of interest should then be analyzed in more detail using for example molecular docking to further validate them in silico before selected compounds are tested experimentally. Successful workflows196,197 following this strategy individually generated pharmacophore models for the ligand binding sites of both targets of interest and then screened compound libraries for candidates matching both pharmacophore models. These molecules were then docked into the binding sites of both targets and top-scoring hits were experimentally validated with good dual hit rates. Alternatively, pharmacophore models can also be developed to cover the common pharmacophore features of both targets of interest198–200. Such consensus models can originate from structural information derived from co-crystals of each target, from known dual ligands of both targets or from a combination of both. An exemplary consensus dual pharmacophore model was built by docking known dual ligands into the structures of both targets to identify common pharmacophore elements, which were then used to define the common motifs for the model196. A purely ligand-based dual pharmacophore approach200 individually generated pharmacophore models for sets of inhibitors of soluble epoxide hydrolase (sEH) and 5lipoxigenase (5-LO). Subsequently, the models were aligned pairwise in a graph-based manner to yield dual pharmacophore models. Importantly, the alignment allowed varying distances between individual pharmacophore features according to the



Page 36 of 100

assumption that targets may share ligands due to shared pharmacophore elements even if these elements have distinct spatial distances. Pharmacophore-based virtual screening followed by scaffold analysis and shape/electrostatics-based refinement successfully uncovered a novel dual inhibitor (7) of sEH and 5-LO with fragment-like size and intermediate dual potency (Figure 1).

Figure 1: A ligand-based dual pharmacophore approach

200

individually generated pharmacophore-

models for soluble epoxide hydrolase inhibitors and 5-lipoexigenase inhibitors. Subsequent graph-based


Page 37 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


alignment of the resulting models produced a dual consensus pharmacophore that was used in virtual screening which yielded a novel dual inhibitor (7) of both enzymes.

Pharmacophore-based de novo design as alternative strategy to obtain active small molecules from pharmacophore models holds the great advantage that new chemical entities are developed instead of identifying ligands from libraries of known compounds. Numerous techniques have been developed to computationally “invent” such novel compounds including reaction-driven approaches201 that fuse building blocks, fusion of fragment

structures202

and

applications

of

generative

artificial

intelligence184.

Independently from their source, de novo designs are then ranked or scored using pharmacophore models for the targets of interest or a consensus pharmacophore model to select candidate compounds for synthesis and biological evaluation. The drawback of this approach may be the challenging synthesis of de novo designs generated by some techniques203,204.

3.3.1.4 Identification of multiple-targeting ligands from clinical observations

In addition to ligand-/drug- and structure-based approaches to identify multiple ligand leads,

disease-based

approaches

are

at

least

similarly

promising.

Network

analysis205,206 of biological target patterns can not only reveal synergistic target combinations (cf. 2.3) but together with observations on drug effects in clinical development or practice may uncover unanticipated activities and targets for known bioactive compounds that potentially display superior efficacy. Such side-activities of known drugs can be optimized to new selective agents in a classical selective 37 ACS Paragon Plus Environment


Page 38 of 100

optimization of side-activities (SOSA)207,208 campaign or developed to new multiple ligands. Translational research involving expertise from various professions is a prerequisite to identify multiple ligand lead compounds amongst drugs from clinical observations, but such starting points may be of extraordinary value for multiple ligand discovery as they already constitute extensively optimized bioactive compounds and are drug-like by definition. The discovery of PPARγ modulating activity of the AT1R antagonist telmisartan (8a) is a prime example of lead identification for multi-target drug discovery from clinical observations. Initially, 8a was found to reduce free plasma glucose and free plasma insulin209 when compared to the competitor AT1R antagonist losartan. This activity was traced down to a partial activation of PPARγ at pharmacologically relevant concentrations64. A recent meta-analysis of 21 randomized clinical trials revealed that telmisartan improves insulin resistance in terms of fasting blood glucose and insulin levels210. Structure-activity relationship studies of 8a derivatives as partial PPARγ agonists revealed crucial structural features required for PPARγ binding and activation211,212. Both, AT1R and PPARγ displayed similar pharmacophore requirements leading to the identification of potent dual AT1R antagonists/partial PPARγ agonists such as 8b with in vivo efficacy by minor structural modifications of the original drug (Scheme 7)66.


Page 39 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Scheme 7: Clinical observations of improved insulin sensitivity under telmisartan (8a) treatment led to identification of its PPARγ activating potential which was then optimized by minor structural changes (8b) while conserving the original AT1R antagonistic activity.

3.3.2 Optimization strategies for merged multiple ligands

After a multiple ligand lead compound has been identified by whichever technique, a challenging structural optimization process will follow with the objective to generate high potency on two molecular targets and to provide a balanced activity profile on these targets of interest. The challenge of complying with the structural requirements of two targets to achieve high affinity often results in high lipophilicity and compound size and poor pharmacokinetics. Merged lead pharmacophores should, therefore, comprise low molecular weight to provide a sufficient margin for structural variation and enlargement. Moreover, a key focus in the multi-target optimization must lie on conserving low lipophilicity and avoiding extensive structural enlargement8,10,161. 39 ACS Paragon Plus Environment


Page 40 of 100

The multi-target lead optimization must also optimize the potency ratio of the multiple ligand on its targets. In the best case, the optimal ratio also considers exposure of relevant tissues to the multi-target agent in vivo and often animal disease models will be necessary to find the optimal balance in potency on the targets of interest8. Besnard et al.213 have employed a fully automated multiple ligand development approach to the discovery of compounds addressing various neurotransmitter receptors (Figure 2A). Using Bayesian probabilistic activity models built on ChEMBL data, e.g. multi-target activity of the ChE inhibitor donepezil (9a) on dopamine receptors was discovered. Continuously relying on Bayesian models, derivatives of donepezil were stepwise optimized towards potent selective as well as multi-target ligands (e.g. 9b) by selecting compounds for synthesis based on the computational predictions. Besides activity predictions for serotonin, dopamine and adrenergic receptors, the employed models also covered compound characteristics such as blood-brain-barrier penetration. A rational optimization strategy for multiple ligands that does not rely on structural data or computational models is systematic exploration of the SAR on each individual target. Starting from the multiple lead pharmacophore 4c, methyl groups and chlorine atoms were systematically introduced in every possible position of the scaffold. Thereby substituent vectors that are tolerated by both targets of interest were identified. Additionally, the preferred electronic environment can be evaluated for each position by comparing electron-releasing methyl groups with electron-withdrawing chlorine atoms. Preferred substitution vectors that positively affect activity on both targets of interest (or are at least tolerated) can then be analyzed more deeply with various substituents. In such manner, every position can be systematically optimized. The SAR on multiple


Page 41 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


targets will, of course, not be congruent for every position and it may become necessary to combine structural elements that individually improve potency on a single target. In such cases, the dual SAR can be visualized by plotting the compounds’ pEC50/pIC50 values for the two targets and select extreme examples for combination if their structures are compatible (Figure 2B). By this strategy, a highly potent dual FXR/sEH modulator (4d) was developed58. Multiple ligand optimization can also successfully rely on structural information or molecular docking. Zafirlukast (10a) was identified as triple modulator comprising antagonism on cysteinyl leukotriene 1 receptor (CysLT1R), PPARγ agonism and sEH inhibition in vitro but no balanced activity profile. This profile was optimized purely based on molecular docking results. By close inspection of the compound’s proposed binding modes to PPARγ and sEH, very minor structural changes were identified that proved sufficient for increasing PPARγ and sEH potency and thereby balancing the triple modulator’s (10b) activity profile (Figure 2C)214.



Page 42 of 100

Figure 2: Successful structural optimization of multiple ligands has been achieved by various strategies: (A) A fully automated optimization approach of donepezil (9a) to multiple ligands of serotonin and dopamine receptors employing Bayesian probability models resulted in several highly potent multiple ligands such as 9b as well as selective ligands of neurotransmitter receptors. (B) Systematic exploration of substituent vectors allowing dual optimization on multiple ligands scaffold 4c allowed stepwise


Page 43 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


optimization of dual FXR/sEH modulation. Final combination of all SAR knowledge yielded the highly potent dual modulator 4d. (C) Relying on molecular docking results, zafirlukast (10a) was optimized to a potent triple ligand (10b) of peroxisome proliferator-activated receptor γ (PPARγ), soluble epoxide hydrolase (sEH) and cysteinylleukotriene receptor 1 (CysLT1R) by very minor structural changes.

3.3.3 Design-in approach

Complimentary to the search for a merged lead pharmacophore that already satisfies the structural requirements for modulation both targets of interest, also a pair of potent compounds that selectively modulate the two targets can serve as starting point for multiple ligand development. In such approach, the pharmacophore (and activity on target) of one compound is designed into the other in a design-in strategy8. By stepwise adaption of one structure towards the other and introduction of structural elements required for activity on the complimentary target, dual potency is generated. Of note, the design-in approach and the rational generation of merged consensus pharmacophores (cf. 3.3.1.1) cannot always be clearly separated but describe a common strategy of uniting two selective compounds in terms of molecular architecture, substituent vectors and functional groups that are crucial for interaction with the biological targets of interest. The challenge of such strategy lies in increasing potency for the complimentary target while conserving potency on the original target for which the molecular architecture of both selective lead compounds needs to provide sufficient similarity. In the optimal scenario, the two selective leads share a common central scaffold and differ merely in the decoration of this moiety with substituents. Then, systematic evaluation of suitable 43 ACS Paragon Plus Environment


Page 44 of 100

substitution patterns for dual/multiple potencies can lead to success in multiple-targeting ligand development and optimization. The design-in approach is a challenging task and may face insuperable obstacles when the two selective compounds used as leads are not sufficiently compatible. On the other hand, designing one existing pharmacophore into another one also offers advantages. When two optimized selective modulators are chosen to be combined, considerable knowledge on their SAR is usually available which can be used for the optimization process towards a multiple ligand. Such SAR data can provide hints, in which positions further or alternative substituents are tolerated and whether scaffold hopping towards a more compatible molecular architecture might be feasible.

3.3.4 Design-out approach

The design-out strategy towards multiple ligands with designed target modulation profile follows the opposite direction as the design-in approach8. Instead of using selective compounds and merge their pharmacophores by stepwise structural adaption, the design-out strategy starts from an unselective ligand that modulates multiple biological targets including the targets of interest. By reducing the potency on the undesired targets through directed structural changes, the design-out approach generates designed multiple ligands by increasing their selectivity towards the desired target combination. This strategy can profit from X-ray structural data even more than designin or pharmacophore merging approaches since it relies on a single lead compound that already interacts with all targets of interest with high affinity. Therefore, co-crystal


Page 45 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


structures may provide insights where the binding mode of the lead to an undesired target significantly differs from the interaction with the desired targets and where e.g. clashes with the protein could be generated that disrupt potency on the “off”-target(s). Design-out approaches are less common since suitable lead compounds that possess multiple potent activities but constitute no promiscuous binders are rare. The design-out approach may still be attractive when target combinations involving more than two individual proteins shall be addressed which with every additional target increases the difficulties and obstacles of the design-in strategy or pharmacophore merging.

3.4 Fragment-based approaches to multiple-targeting ligands

The concept of fragment-based discovery of multi-target drugs was suggested by Morphy and Rankovic11 based on reports by Hann et al.215, which retrospectively show that selectivity tends to increase with molecular complexity. Moreover, Hopkins et al.14 demonstrated an inverse correlation between molecular weight and number of targets, to which a compound binds. Taken together, these observations suggest that fragmentbased screening for multiple targets to identify multi-target fragment leads may offer higher chances than screening approaches with larger compounds. Although fragment-based discovery of multiple ligands appears as a highly promising approach84, the number of successful prospective studies is limited. Most of these rely on in silico pre-selection of fragments. Achenbach et al.216 performed a fragment-based screening to identify dual inhibitors of sEH and 5-LO. A commercially available fragment library was virtually screened using an adapted self-organizing map (SOM) algorithm. It



Page 46 of 100

involved two self-organizing maps that were trained on molecular fragments derived from known active compounds on the two desired targets. As a background distribution, fragments from FDA approved drugs were used for both SOMs. After training, the SOMs were aligned by minimizing the distance between the background (FDA approved compounds). Thereby regions populated with sEH fragments or 5-LO fragments were identified. Both regions shared an overlapping space whose visual inspection yielded 24 fragments with predicted activity towards both targets for experimental validation in STD-NMR and activity-based assays. Five out of 24 fragments showed inhibitory activity towards both enzymes in activity assays, while two structures displayed binding to both targets in the STD-NMR setup. One selected fragment was expanded to a potent dual inhibitor with high affinity (nanomolar concentration range) to both targets. A structure-based virtual screening to multi-target fragment identification has successfully yielded dual inhibitors of β-secretase 1 (BACE-1) and glycogen synthase kinase 3β (GSK-3β)217. Bottegoni et al. based this approach on the hypothesis of common H-bonds required for targeting both enzymes and selected fragments by visual inspection after an unbiased docking procedure. In a subsequent screening step, the selected fragments were enriched by structurally similar compounds and the procedure was repeated to yield 27 fragments for experimental evaluation. NMR-based functional screening of the selected fragments confirmed 11 as inhibitor of both enzymes (Scheme 8). Rational generation of a common pharmacophore hypothesis currently appears as bestestablished technique to identify multi-target fragments. Prati et al. applied such


Page 47 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


approach to dual BACE-1/GSK-3β inhibitors218. The authors postulated that a cyclic amide is crucial for GSK-3β inhibition while a guanidine is required to inhibit BACE-1. This rationale led to the combination of both structural motifs in triazinone fragment 12 with dual inhibitory activity. However, exploration of the starting fragment’s SAR failed to significantly improve inhibitory potency. Shang et al.219 rationally designed fragments for dual targeting of COX-2 and leukotriene A4 hydrolase (LTA4H) by analyzing selective ligands for both targets and claimed that 9 out of 21 fragments exhibited dual inhibitory activity at 1 µM concentration. Structure-based fragment growing then enabled potency optimization of one fragment in a micromolar range. The authors of the studies described above claim that fragment-based discovery of multi-target drugs is a promising approach but the results merely depict that its practical implementation is still rather immature. Fragment-based design for single targets is very well established, but this is not the case for multi-target drug design as only few studies have reported a full screening of fragment-based libraries towards a rationally selected target combination. Artis et al. described the fragment-based discovery of the pan-PPAR agonist indeglitazar 13220 starting from the multi-target fragment 5-methoxyindole-3-propionic acid 13a which was identified in an in vitro fragment library screen using proximitybased co-activator recruitment assays. X-ray structures of 13a with all three PPAR subtypes were then generated to define a rational optimization hypothesis and eventually enable the development of 13. Thereby, the successful discovery of 13 highlights the major drawbacks in present approaches towards fragment-based multitarget approaches: It seems clearly possible to identify fragment hits addressing



Page 48 of 100

multiple targets, but structural information will be required for successful fragment growing and fragment-based design221. However, few laboratories working in the field have the possibility to generate reliable structural information. Several approaches to fragment-based multi-target design tried to overcome this issue by molecular docking, but most of these attempts failed to achieve significant activity improvement which might be due to the limited predictive power of commonly used scoring functions222. Recent advances in high-throughput crystallography223 may allow a major breakthrough in fragment-based multi-target drug discovery in near future.

Scheme 8: Multi-target fragment examples: The dual inhibitor of β-secretase 1 (BACE-1) and glycogen synthase kinase 3β (GSK-3β) 11 was computationally identified based on a common H-bond hypothesis and confirmed as inhibitor of both enzymes by NMR-based functional screening. Triazinone 12 was rationally designed as dual inhibitor of BACE-1 and GSK-3β by combining structural motifs required for interaction with the two enzymes. The approved drug indeglitazar (13) activating the nuclear receptors PPARα, PPARγ and PPARδ was developed from fragment 13a which had been identified as active on the three receptors in a fragment library screening. The structural optimization was guided by co-crystal structure data for 13a with all three PPARs.

Another important application of fragment-based design is multikinase inhibitor development. Originally, selectivity rather than designed multitarget activity has been a 48 ACS Paragon Plus Environment

Page 49 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


dominating issue in ATP mimicking orthosetric kinase inhibitor design. However, most approved kinase inhibitors are not selective for a single target but comprise activity on a small subset of the kinome. Despite being designed as selective agents, kinase inhibitors often owe their therapeutic efficacy to these polypharmacological profiles. Recent understanding of cancer resistance towards kinase inhibitors has changed the selectivity paradigm and boosted polypharmacological kinase inhibitor design224. Therein, the concept of kinase-privileged fragments facilitates identification of multikinase inhibitor leads225. Frett et al. demonstrated the successful application of this approach in the discovery of a dual rearranged during transfection kinase (RET) and vascular endothelial growth factor receptor 2 (VEGFR2) inhibitor226. A suitable lead was retrieved by screening a kinase focused fragment collection. Lipophilic interactions with the targets and the PK profile were subsequently optimized to obtain a clinical candidate with the desired polypharmacological profile. A characteristic challenge in multikinase inhibitor design is balancing potency on desired kinase targets and unwanted kinase off-targets. The combination of a phenotypic Drosophila model of multiple endocrine neoplasia type 2 and kinome-wide activity profiling successfully addressed this issue. The dual screening approach efficiently identified multi-target candidates from a library of kinase-privileged compounds with favorable balance of efficacy and toxicity227.

4 Test systems to characterize multiple ligands



Page 50 of 100

Functional characterization of multiple ligands and confirmation of their simultaneous multi-target engagement as well as synergistic pharmacodynamic effects requires sophisticated in vitro assay systems and in vivo disease models. Since multiple ligands are often intended to produce superior synergistic effects, the employed in vitro and in vivo models must be competent to reveal such activity. Although animal models may appear more suitable for such complex requirements, in vitro experiments should preferably be conducted in advance to give first hints on the potential superiority of a multiple ligand over selective compounds before more tedious in vivo experiments are initiated. Both, in vitro and in vivo systems suitable for multiple ligand characterization have been developed but especially the available animal models cannot fully reflect multifactorial human diseases for multiple ligand testing, yet.

4.1 Cellular assay systems

The challenge in cellular characterization of multiple ligands lies in demonstrating simultaneous multi-target engagement in a single test system. Techniques to identify multiple targets for the same molecule are widely employed in the field of target identification and have been reviewed extensively228. Most of them are only of qualitative nature, however, and/or require modification of the test compound by a tagging system. Still, some label-free, quantitative approaches have been reported, as well, which can serve to demonstrate simultaneous target engagement in cells or tissues229. Drug affinity responsive target stability (DARTS)230, a method relying on target stabilization against protease degradation upon ligand binding holds much


Page 51 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


promise in multi-target compound characterization. Although DARTS is a powerful technique, it still has the drawback that test compounds are added to the cell lysate and neither cell penetration nor compound localization in the cell are taken into account. These drawbacks were overcome by the cellular thermal shift assay (CETSA)231 where stabilization of a target against thermal denaturation upon ligand binding is analyzed directly in cellular environment. Of note, the target proteins can be detected in multiple different ways including Western blot analysis, mass spectroscopy, ELISA, or AlphaScreen. Schulz-Fincke et al. reported an application of CETSA to confirm target engagement of a multi-target lysine-specific demethylase 1 (LSD1)/MAO lead compound232. Although this study could only demonstrate engagement on LSD1, CETSA has great potential as method to study simultaneous target engagement for multiple ligands. Based on a similar principle, the technique thermal proteome profiling employs multiplexed quantitative mass spectrometry to quantify the amount of nondenatured targets. The method was successfully used to demonstrate target engagement of multi-target pan-HDAC inhibitor panabinostat233 and to profile interactions of small molecules with transmembrane proteins234. In studying target engagement of multi-kinase inhibitors, both thermal proteome profiling235 and CETSA236 proved suitable. In addition, the Kinobeads technique237 has been successfully applied to demonstrate multiple target engagement of kinase inhibitors. It employs immobilized nonselective kinase inhibitors to bind and separate the kinase sub-proteome. Subsequent addition of the multi-target kinase inhibitor of interest displaces its targets which can be analyzed by quantitative mass spectrometry.



Page 52 of 100

In addition to studying target engagement - in other words confirming an interaction of the multiple ligand with the targets of interest - functional readouts are also essential for in vitro characterization of multi-target compounds, of course. High content screenings (cf. 2.2) are very useful for this purpose and can also already give a hint on potential synergy. The detection of a synergistic effect in a cellular context is straightforward if the modulation of two targets results in a single synergistic cellular effect, e.g. apoptosis. When two independent effects are expected it is markedly more demanding. However, recent advances in metabolomics allow the quantification of a broad spectrum of analytes from complex biological samples. An exemplary study demonstrated whole blood screening of arachidonic acid metabolites for different combinations of antiinflammatory drugs238. In this study, shunting effects that occurred for selective inhibitors were ameliorated by inhibitor combinations – an outcome which would be favored for multi-target inhibitors of the arachidonic acid cascade239. Thus, “-omics” techniques are suitable to demonstrate functional properties of multi-target compounds in cells or tissues. Concepts to address target interactions caused by receptor homo- or heterodimerization/oligomerisation and other strategies to achieve ligand-specific signaling mechanism are also arising240. Here, multiple-targeting may open so-far unseen therapeutic options, but such aspects are out of the scope of this perspective. An important drawback of cell-based assays for multi-target compound characterization is the missing adoption to the disease state, e.g. inflammatory processes cause overexpression of COX-2 isoenzymes and, therefore, the enzyme concentration may be different from the assay conditions which may significantly affect the read-out. This may


Page 53 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


be of particular interest concerning the late formation of anti-inflammatory protectins or resolvins, which are not detected in these assays241,242. More complex tissue-based test systems or advanced animal disease models provide more appropriate and functional read-outs to study complex biochemical cascades and disease-modifying effects of multiple-targeting ligands.

4.2 Animal models

With improving in vivo disease models for the screening and characterization of multitarget compounds, one must be aware that the main focus is fighting a disease and not developing a screening system with the highest response to the designed compounds. Read-outs selected for multi-targeting approaches should, therefore, be oriented to an optimal disease simulation and not high compound effects. Animal models are supposed to simplify a disease with complex causes and multiple symptoms to simple and measurable effects. Adding further pathological dysregulations, biological targets and read-outs brings additional complexity into erstwhile minimalized models, e.g. the 6hydroxydopamine (6-OHDA)-induced unilateral movement disorder in rats as model of Parkinson’s disease only partly resembles the multifactorial disease state in human243,244. In some instances, phenotypic screenings (cf. 2.2) in simple organisms may be a more economic entry into preclinical development and proof-of-concept studies, especially when the primary objective of the animal model is confirmation of dual activity and a resulting synergistic effect.



Page 54 of 100

Cross-breeding of animals each reflecting single pathophysiological phenotypes may enable more complex and realistic models of multifactorial diseases that require treatment with multi-targeting compounds. One such complex animal model of the metabolic syndrome, the spontaneously hypertensive obese (SHROB) rat245, reflects almost all symptoms of the human disease. It allows efficient in vivo characterization of drug combinations (e.g., antihypertensive and antidiabetic drugs) as well as multi-target compounds. Earlier studies of such multi-target agents required multiple models. Casimiro-Garcia et al. e.g. reported the discovery of imidazo[4,5-b]pyridines with dual activity on AT1R and PPARγ but had to demonstrate their in vivo efficacy in two distinct models of hypertension (spontaneously hypertensive rat (SHR)) and insulin resistance (Zucker diabetic fatty (ZDF) rat)246. More recent studies tested combinations of sEH inhibitors and PPARγ agonists in a SHROB model to reveal synergistic effects and further benefits through more complex read-outs covering the whole range of metabolic parameters. Still, more complex parameters and models are needed for a realistic animal disease representation useful for multiple targeting247–250 and in some cases, non-human primate screening (marmoset) may be required. As such preclinical trials need many prerequisites, combination of different animal models with various read-outs are often preferred for the initial in vivo characterization of multiple ligands when no suitable complex model is available.

5 Conclusion and outlook


Page 55 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Innovative technologies such as systems biology and artificial intelligence coupled with constantly growing knowledge on drug targets, molecular disease determinants and predictive biomarkers are promoting drug discovery to new heights. Despite this expanding tool box for all aspects of drug discovery, the success of small molecule drugs is not satisfactory in many diseases, however. For several disorders that arise from complex multifactorial pathologies, this may be ascribed to the need for multiple pharmacological interventions to achieve sufficient therapeutic efficacy. As a result, many severe and complex diseases are treated with drug cocktails instead of single efficacious drugs, but such polypharmacy holds numerous disadvantages. Moreover, chemotherapy resistance development arises as global drug discovery challenge in the fields of anti-cancer and anti-infectious disease treatment. Multi-target compounds that simultaneously modulate more than a single molecular target hold great potential to overcome both unmet medical needs in enabling therapeutic breakthroughs in severe multifactorial diseases and in preventing development of resistances. Multiple targeting is, therefore, an increasingly productive discipline of medicinal chemistry. It strongly profits from past and recent innovations in computational techniques, in vitro test systems and animal models that provide the technologies for multi-target compound development. Computational methods and models combined with enormous available datasets are key contributors to the growing success of multi-target compound development and affect virtually every step of this discipline. Signaling network analysis and quantitative flux modeling enable generation of robust hypotheses for potentially synergistic target combinations. Despite often lacking experimental validation, several in silico



Page 56 of 100

approaches for this central step in multi-target design have been developed and further examples of their prospective application will provide further evidence which technologies hold most potential. In silico pharmacophore modeling, docking algorithms and computational de novo design are crucial in silico tools for multi-target lead identification and can be valuable in structural optimization of multiple ligands, as well. And finally, experimental efforts in in vitro and in vivo characterization of multi-target compounds can be reduced with computational support. With growing computational resources and the increasing role of machine learning/artificial intelligence techniques, computational methods will constantly gain even more relevance and improve handling of big data. Especially sound combination of artificial intelligence and focused experiments to reduce model uncertainties has the potential to further improve the quality of computational models and help selecting the “right” experiments. Multi-target design is not only supported by innovations in computational techniques, however. The increasing number of high-quality chemical probes to study the roles of molecular targets and their interplay in pathologies allows robust experimental validation of target synergies and may also reveal unexpected connections between targets in a disease. In addition, several breakthroughs in experimental technologies may strongly promote

and

accelerate

multi-target

compound

development.

High-throughput

crystallography might equip multi-target compound discovery with valuable structural information that can improve pharmacophore model generation and significantly support structural optimization when hypotheses for compatible modifications can be retrieved from co-crystal structures. In addition, assay technologies are arising that are competent of revealing simultaneous target engagement and synergistic effects of multi-


Page 57 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


target modulation. Sophisticated in vitro assays will especially be necessary for multiple ligands that cause effects which cannot be observed in a common convergent read-out (such as apoptosis) but induce parallel activities. Therefore, the predictive value of in vitro assays for complex disease modifications must be carefully (re-)analyzed for complex polypharmacological approaches. Possibly, also innovative technologies such as organs on-a-chip will allow future improvements in functional characterization of multi-target compounds. An unresolved issue of in vitro characterization of multiple ligands remains the lack of test systems that reflect adaptive mechanisms such as overexpression of a target arising from either a disease or as consequence of multitarget modulation. Meaningful in vivo characterization of multi-target compounds often remains a challenge, as well. When multiple ligands are designed to achieve superior therapeutic efficacy in multifactorial diseases through synergistic effects, animal models are required that reflect the multifactorial nature of the human disease and can reveal synergies. Such sophisticated in vivo models are only available for few diseases, but first examples have been established and cross-breeding of animals reflecting individual aspects of multifactorial pathologies holds promise for future breakthroughs in this field, as well. With the increasing spectrum of available methods and applications, multi-target compound design, optimization and functional characterization involves many disciplines and demands strong expertise in each field. Multi-disciplinary collaborative projects involving solid expertise not only in medicinal chemistry but also in computational techniques, in vitro test systems and in vivo pharmacology, therefore



Page 58 of 100

appear necessary for successful development of therapeutically relevant multiple ligands. In summary, improved understanding of pathophysiological conditions that uncovers the multiple factors underlying many diseases highlights the enormous potential of multitarget agents to open new therapeutic avenues. They can overcome polypharmacy and enhance therapeutic efficacy of small molecule drugs. Increasing knowledge on molecular targets, their cross-talk and -regulations combined with the ability to handle big data and the availability of innovative in silico, in vitro and in vivo technologies equip medicinal chemistry with a versatile toolbox for multi-target design. However, despite the availability of enormous datasets, reliable computational methods involving artificial intelligence and automated steps e.g. in compound design and optimization, multi-target design and multiple ligand optimization indispensably requires expert knowledge as key decision maker. Big data analysis and evaluation of drug-target interaction patterns indicate a steadily growing trend towards drugs that address more than one single target.205,206 In addition, increasing disease complexity and improved understanding of pathophysiological correlations promote multi-target compound development and render multiple ligands a key for securing innovation in future drug discovery.


Page 59 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Acknowledgments

The authors apologize for the numerous excellent works, which have not been included in this Perspective. E.P. was supported by the Deutsche Forschungsgemeinschaft (DFG;

Sachbeihilfe

PR1405/2-2;

Heisenberg-Professur

PR1405/4-1,

SFB1039

Teilprojekt A07). H.S. acknowledges support by the EU COST Action CA15135 MuTaLig. D.M. was supported by an ETH Zurich Fellowship (grant no. 16-2 FEL-07).

Author Information

Corresponding author Daniel Merk. phone: +41 44 6337439, +49 69 79829327, mail: [email protected], [email protected]

Notes The authors declare no competing interests

Biographies E.P.: Ewgenij Proschak is Professor for Drug Design at the Institute of Pharmaceutical Chemistry at the Goethe University of Frankfurt. After his doctoral and postdoctoral studies at Goethe University, he became Independent Group Leader at the Lipid



Page 60 of 100

Signaling Research Center (LIFF) in Frankfurt. Currently, the German Research Council (DFG) awarded him with a Heisenberg Professorship. He has worked on hit identification and hit-to-lead optimization for fatty acid mimetics including inhibitors of 5LO, mPGES-1, sEH, and LTA4H and modulators of PPARs and FXR. His current research interests are the design and synthesis of multitarget drugs for the treatment of inflammatory conditions and metabolic syndrome.

H.S.: Holger Stark studied pharmacy in Berlin, Germany. In 2000 he became full professor at Goethe University in Frankfurt, Germany, and went in 2013 to Heinrich Heine University Düsseldorf, Germany, for his actual position. He founded start-up companies on cancer therapeutics (Warburg Glycomed, PSites Pharma) and has received prizes for successful research as well as for teaching. On more than 350 book contributions, original papers, reviews and patents he has focused on neurotransmitter with selectivity or multiple targeting, mainly at histamine and dopamine receptor subtypes, as well as on lipid signaling and enzyme research. He is co-inventor of pitolisant (Wakix®), the first histamine H3 receptor antagonist with market approval. H. Stark is editor-in-chief of Archiv der Pharmazie and received honorary doctorate from University of Nis, Serbia.

D.M.: Daniel Merk graduated in Pharmacy and Pharmaceutical Sciences at the LudwigMaximilians-University Munich and received his Ph.D. in Pharmaceutical/Medicinal Chemistry from Goethe University Frankfurt. Since 2015, he is Junior Group Leader at


Page 61 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


the Institute of Pharmaceutical Chemistry of Goethe University Frankfurt and since 2017 is an ETH-Fellowship scholar at the Swiss Federal Institute of Technology (ETH) Zurich. His research focuses on the exploration of nuclear receptors as pharmaceutical targets with special emphasis on FXR, PPARs, RXRs and orphan receptors, the medicinal chemistry of their natural and synthetic modulators, computer-assisted and naturalproduct-inspired drug discovery and multi-target compound design.

Abbreviations used

5-HT2, serotonin receptor 2; 5-HT2A, serotonin receptor 2A; 5-LO, 5-lipoxigenase; 6OHDA, 6-hydroxytryptamine; AT1R, angiotensin II receptor 1; BACE-1, β-secretase 1; BCL, B-cell lymphoma; BET, bromo- and extra-terminal domain; CDK4, cyclindependent kinase 4; ChE, choline esterase; CETSA, cellular thermal shift assay; CNS, central nervous system; COX, cyclooxygenase; CRISPR/Cas9, Clustered Regularly Interspaced Short Palindromic Repeats associated endonuclease 9; CysLT1R, cysteinyl leukotriene 1 receptor; D2, dopamine receptor 2; DARTS, drug affinity responsive target stability; DDL, dedifferentiated liposarcoma; FXR, farnesoid X receptor; GPCR, Gprotein coupled receptor; GSK-3β, glycogen synthase kinase 3β; H1, histamine receptor 1; H3, histamine receptor 3; IGF1R, insulin-like growth factor 1 receptor; LTA4H, leukotriene A4 hydrolase; LSD1, lysine-specific demethylase 1; MAO, monoamine oxidase; MDM2, mouse double minute 2 homolog; MEK, mitogen-activated protein kinase kinase; MTC, medullary thyroid carcinoma; NMR, nuclear magnetic resonance; p38/MAPK, p38 mitogen-activated protein kinase; PDE-4, phosphodiesterase 4;



Page 62 of 100

PEPT1, peptide transporter 1; PPAR, peroxisome proliferator-activated receptor; RET, rearranged during transfection kinase; SAR, structure-activity relationship; SHR, spontaneously hypertensive rat; SHROB rat, spontaneously hypertensive obese rat; SOM, self-organizing map; sEH, soluble epoxide hydrolase; SOSA, selective optimization of side-activities; STD-NMR, saturation-transfer difference NMR; QSAR, quantitative structure-activity relationship; VEGFR2, vascular endothelial growth factor receptor 2; ZDF rat, Zucker diabetic fatty rat.

References (1)

Amin, A. R.; Attur, M. G.; Pillinger, M.; Abramson, S. B. The Pleiotropic Functions of Aspirin: Mechanisms of Action. Cell. Mol. Life Sci. 1999, 56 (3–4), 305–312.

(2)

Dzeshka, M. S.; Shantsila, A.; Lip, G. Y. H. Effects of Aspirin on Endothelial Function and Hypertension. Curr. Hypertens. Rep. 2016, 18 (11), 83.

(3)

Wu, T.; Horowitz, M.; Rayner, C. K. New Insights into the Anti-Diabetic Actions of Metformin: From the Liver to the Gut. Expert Rev. Gastroenterol. Hepatol. 2017, 11 (2), 157–166.

(4)

Varjabedian, L.; Bourji, M.; Pourafkari, L.; Nader, N. D. Cardioprotection by Metformin: Beneficial Effects Beyond Glucose Reduction. Am. J. Cardiovasc. Drugs 2018, 1–13.

(5)

Rogliani, P.; Ora, J.; Di Daniele, N.; Lauro, D. Pleiotropic Effects of Hypoglycemic Agents: Implications in Asthma and COPD. Curr. Opin. Pharmacol. 2018, 40, 34– 38.

(6)

Sirtori, C. R. The Pharmacology of Statins. Pharmacol. Res. 2014, 88, 3–11.


Page 63 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(7)

Veillard, N. R.; Mach, F. Statins: The New Aspirin? Cell. Mol. Life Sci. 2002, 59 (11), 1771–1786.

(8)

Morphy, R.; Rankovic, Z. Designed Multiple Ligands. An Emerging Drug Discovery Paradigm. J. Med. Chem. 2005, 48 (21), 6523–6543.

(9)

Morphy, R.; Rankovic, Z. Designing Multiple Ligands - Medicinal Chemistry Strategies and Challenges. Curr. Pharm. Des. 2009, 15 (6), 587–600.

(10) Morphy, R.; Rankovic, Z. The Physicochemical Challenges of Designing Multiple Ligands. J. Med. Chem. 2006, 49 (16), 4961–4970. (11) Morphy, R.; Rankovic, Z. Fragments, Network Biology and Designing Multiple Ligands. Drug Discov. Today 2007, 12 (3–4), 156–160. (12) Morphy, R.; Kay, C.; Rankovic, Z. From Magic Bullets to Designed Multiple Ligands. Drug Discov. Today 2004, 9 (15), 641–651. (13) Stark, H. Turning from Monogamy to Strategic Promiscuity. Drug Discov. Today 2004, 9 (17), 736–737. (14) Hopkins, A. L.; Mason, J. S.; Overington, J. P. Can We Rationally Design Promiscuous Drugs? Curr. Opin. Struct. Biol. 2006, 16 (1), 127–136. (15) Handl, H. L.; Vagner, J.; Han, H.; Mash, E.; Hruby, V. J.; Gillies, R. J. Hitting Multiple Targets with Multimeric Ligands. Expert Opin. Ther. Targets 2004, 8 (6), 565–586. (16) Zimmermann, G. R.; Lehár, J.; Keith, C. T. Multi-Target Therapeutics: When the Whole Is Greater than the Sum of the Parts. Drug Discov. Today 2007, 12 (1–2), 34–42. (17) Costantino, L.; Barlocco, D. Designed Multiple Ligands: Basic Research vs



Page 64 of 100

Clinical Outcomes. Curr. Med. Chem. 2012, 19 (20), 3353–3387. (18) Bansal, Y.; Silakari, O. Multifunctional Compounds: Smart Molecules for Multifactorial Diseases. Eur. J. Med. Chem. 2014, 76, 31–42. (19) Zhang, W.; Pei, J.; Lai, L. Computational Multitarget Drug Design. J. Chem. Inf. Model. 2017, 57 (3), 403–412. (20) de Oliveira Pedrosa, M.; Duarte da Cruz, R. M.; de Oliveira Viana, J.; de Moura, R. O.; Ishiki, H. M.; Barbosa Filho, J. M.; Diniz, M. F. F. M.; Scotti, M. T.; Scotti, L.; Bezerra Mendonca, F. J. Hybrid Compounds as Direct Multitarget Ligands: A Review. Curr. Top. Med. Chem. 2017, 17 (9), 1044–1079. (21) Korcsmáros, T.; Szalay, M. S.; Böde, C.; Kovács, I. A.; Csermely, P. How to Design Multi-Target Drugs. Expert Opin. Drug Discov. 2007, 2 (6), 799–808. (22) Ravikumar, B.; Aittokallio, T. Improving the Efficacy-Safety Balance of Polypharmacology in Multi-Target Drug Discovery. Expert Opin. Drug Discov. 2018, 13 (2), 179–192. (23) Peters, J. U. Polypharmacology - Foe or Friend? J. Med. Chem. 2013, 56 (22), 8955–8971. (24) Peters, J. U. Polypharmacology in Drug Discovery; Peters, J.-U., Ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2012. (25) Wang, L.; Esteban, G.; Ojima, M.; Bautista-Aguilera, O. M.; Inokuchi, T.; Moraleda, I.; Iriepa, I.; Samadi, A.; Youdim, M. B. H.; Romero, A.; Soriano, E.; Herrero, R.; Fernández Fernández, A. P.; Ricardo-Martínez-Murillo; MarcoContelles, J.; Unzeta, M. Donepezil + Propargylamine + 8-Hydroxyquinoline Hybrids as New Multifunctional Metal-Chelators, ChE and MAO Inhibitors for the


Page 65 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Potential Treatment of Alzheimer’s Disease. Eur. J. Med. Chem. 2014, 80, 543– 561. (26) Unzeta, M.; Esteban, G.; Bolea, I.; Fogel, W. A.; Ramsay, R. R.; Youdim, M. B. H.; Tipton, K. F.; Marco-Contelles, J. Multi-Target Directed Donepezil-Like Ligands for Alzheimer’s Disease. Front. Neurosci. 2016, 10, 205. (27) Simone Tranches Dias, K.; Viegas, C. Multi-Target Directed Drugs: A Modern Approach for Design of New Drugs for the Treatment of Alzheimer’s Disease. Curr. Neuropharmacol. 2014, 12 (3), 239–255. (28) Ramsay, R. R.; Majekova, M.; Medina, M.; Valoti, M. Key Targets for Multi-Target Ligands Designed to Combat Neurodegeneration. Front. Neurosci. 2016, 10, 375. (29) Prati, F.; Cavalli, A.; Bolognesi, M. L. Navigating the Chemical Space of Multitarget-Directed Ligands: From Hybrids to Fragments in Alzheimer’s Disease. Molecules 2016, 21 (4), 466. (30) Bolognesi, M. L.; Cavalli, A.; Melchiorre, C. Memoquin: A Multi-Target-Directed Ligand as an Innovative Therapeutic Opportunity for Alzheimer’s Disease. Neurotherapeutics 2009, 6 (1), 152–162. (31) Geldenhuys, W. J.; Van der Schyf, C. J. Rationally Designed Multi-Targeted Agents against Neurodegenerative Diseases. Curr. Med. Chem. 2013, 20 (13), 1662–1672. (32) Bawa, P.; Pradeep, P.; Kumar, P.; Choonara, Y. E.; Modi, G.; Pillay, V. MultiTarget Therapeutics for Neuropsychiatric and Neurodegenerative Disorders. Drug Discov. Today 2016, 21 (12), 1886–1914. (33) Hughes, R. E.; Nikolic, K.; Ramsay, R. R. One for All? Hitting Multiple Alzheimer’s



Page 66 of 100

Disease Targets with One Drug. Front. Neurosci. 2016, 10 (APR), 177. (34) Dekeyne, A.; Brocco, M.; Loiseau, F.; Gobert, A.; Rivet, J.-M.; Di Cara, B.; Cremers, T. I.; Flik, G.; Fone, K. C. F.; Watson, D. J. G.; Papp, M.; Sharp, T.; Serres, F.; Cespuglio, R.; Olivier, B.; Chan, J. S. W.; Lavielle, G.; Millan, M. J. S32212, a Novel Serotonin Type 2C Receptor Inverse Agonist/ 2-Adrenoceptor Antagonist and Potential Antidepressant: II. A Behavioral, Neurochemical, and Electrophysiological Characterization. J. Pharmacol. Exp. Ther. 2012, 340 (3), 765–780. (35) Millan, M. J.; Mannoury la Cour, C.; Chanrion, B.; Dupuis, D. S.; Di Cara, B.; Audinot, V.; Cussac, D.; Newman-Tancredi, A.; Kamal, M.; Boutin, J. A.; Jockers, R.; Marin, P.; Bockaert, J.; Muller, O.; Dekeyne, A.; Lavielle, G. S32212, a Novel Serotonin Type 2C Receptor Inverse Agonist/ 2-Adrenoceptor Antagonist and Potential Antidepressant: I. A Mechanistic Characterization. J. Pharmacol. Exp. Ther. 2012, 340 (3), 750–764. (36) East, S. P.; Silver, L. L. Multitarget Ligands in Antibacterial Research: Progress and Opportunities. Expert Opin. Drug Discov. 2013, 8 (2), 143–156. (37) Fu, R.-G.; Sun, Y.; Sheng, W.-B.; Liao, D.-F. Designing Multi-Targeted Agents: An Emerging Anticancer Drug Discovery Paradigm. Eur. J. Med. Chem. 2017, 136, 195–211. (38) Raghavendra, N. M.; Pingili, D.; Kadasi, S.; Mettu, A.; Prasad, S. V. U. M. Dual or Multi-Targeting Inhibitors: The next Generation Anticancer Agents. Eur. J. Med. Chem. 2018, 143, 1277–1300. (39) Petrelli, A.; Valabrega, G. Multitarget Drugs: The Present and the Future of


Page 67 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Cancer Therapy. Expert Opin. Pharmacother. 2009, 10 (4), 589–600. (40) Tao, L.; Zhu, F.; Xu, F.; Chen, Z.; Jiang, Y. Y.; Chen, Y. Z. Co-Targeting Cancer Drug Escape Pathways Confers Clinical Advantage for Multi-Target Anticancer Drugs. Pharmacol. Res. 2015, 102, 123–131. (41) Gattrell, W.; Johnstone, C.; Patel, S.; Smith, C. S.; Scheel, A.; Schindler, M. Designed Multiple Ligands in Metabolic Disease Research: From Concept to Platform. Drug Discov. Today 2013, 18 (15–16), 692–696. (42) Gellrich, L.; Merk, D. Therapeutic Potential of Peroxisome Proliferator-Activated Receptor Modulation in Non-Alcoholic Fatty Liver Disease and Non-Alcoholic Steatohepatitis. Nucl. Recept. Res. 2017, 4, 101310. (43) Bisi, A.; Gobbi, S.; Belluti, F.; Rampa, A. Design of Multifunctional Compounds for Cardiovascular Disease: From Natural Scaffolds to “Classical” Multitarget Approach. Curr. Med. Chem. 2013, 20 (13), 1759–1782. (44) Merk, D.; Schubert-Zsilavecz, M. The Linker Approach: Drug Conjugates. In Drug Selectivity; Wiley‐VCH Verlag GmbH & Co. KGaA: Weinheim, DE, 2017; pp 207– 245. (45) Chittasupho, C. Multivalent Ligand: Design Principle for Targeted Therapeutic Delivery Approach. Ther. Deliv. 2012, 3 (10), 1171–1187. (46) Cavalli, A.; Bolognesi, M. L.; Minarini, A.; Rosini, M.; Tumiatti, V.; Recanatini, M.; Melchiorre, C. Multi-Target-Directed Ligands to Combat Neurodegenerative Diseases. J. Med. Chem. 2008, 51 (3), 347–372. (47) Antolin, A. A.; Workman, P.; Mestres, J.; Al-Lazikani, B. Polypharmacology in Precision Oncology: Current Applications and Future Prospects. Curr. Pharm.



Page 68 of 100

Des. 2016, 22 (46), 6935–6945. (48) Anighoro, A.; Bajorath, J.; Rastelli, G. Polypharmacology: Challenges and Opportunities in Drug Discovery. J. Med. Chem. 2014, 57 (19), 7874–7887. (49) Medina-Franco, J. L.; Giulianotti, M. A.; Welmaker, G. S.; Houghten, R. A. Shifting from the Single to the Multitarget Paradigm in Drug Discovery. Drug Discov. Today 2013, 18 (9–10), 495–501. (50) Aronson, J. K. In Defence of Polypharmacy. Br. J. Clin. Pharmacol. 2004, 57 (2), 119–120. (51) Butini, S.; Nikolic, K.; Kassel, S.; Brückmann, H.; Filipic, S.; Agbaba, D.; Gemma, S.; Brogi, S.; Brindisi, M.; Campiani, G.; Stark, H. Polypharmacology of Dopamine Receptor Ligands. Prog. Neurobiol. 2016, 142, 68–103. (52) Cook, D.; Brown, D.; Alexander, R.; March, R.; Morgan, P.; Satterthwaite, G.; Pangalos, M. N. Lessons Learned from the Fate of AstraZeneca’s Drug Pipeline: A Five-Dimensional Framework. Nat. Rev. Drug Discov. 2014, 13 (6), 419–431. (53) Franci, G.; Miceli, M.; Altucci, L. Targeting Epigenetic Networks with Polypharmacology: A New Avenue to Tackle Cancer. Epigenomics 2010, 2 (6), 731–742. (54) Lötsch, J.; Geisslinger, G. Low-Dose Drug Combinations along Molecular Pathways Could Maximize Therapeutic Effectiveness While Minimizing Collateral Adverse Effects. Drug Discov. Today 2011, 16 (23–24), 1001–1006. (55) Li, Y. H.; Wang, P. P.; Li, X. X.; Yu, C. Y.; Yang, H.; Zhou, J.; Xue, W. W.; Tan, J.; Zhu, F. The Human Kinome Targeted by FDA Approved Multi-Target Drugs and Combination Products: A Comparative Study from the Drug-Target Interaction


Page 69 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Network Perspective. PLoS One 2016, 11 (11), e0165737. (56) Valant, C.; Sexton, P. M.; Christopoulos, A. Orthosteric/Allosteric Bitopic Ligands: Going Hybrid at GPCRs. Mol. Interv. 2009, 9 (3), 125–135. (57) Fronik, P.; Gaiser, B. I.; Sejer Pedersen, D. Bitopic Ligands and Metastable Binding Sites: Opportunities for G Protein-Coupled Receptor (GPCR) Medicinal Chemistry. J. Med. Chem. 2017, 60 (10), 4126–4134. (58) Schmidt, J.; Rotter, M.; Weiser, T.; Wittmann, S.; Weizel, L.; Kaiser, A.; Heering, J.; Goebel, T.; Angioni, C.; Wurglics, M.; Paulke, A.; Geisslinger, G.; Kahnt, A.; Steinhilber, D.; Proschak, E.; Merk, D. A Dual Modulator of Farnesoid X Receptor and Soluble Epoxide Hydrolase to Counter Nonalcoholic Steatohepatitis. J. Med. Chem. 2017, 60 (18), 7703–7724. (59) Dolles, D.; Hoffmann, M.; Gunesch, S.; Marinelli, O.; Möller, J.; Santoni, G.; Chatonnet, A.; Lohse, M. J.; Wittmann, H. J.; Strasser, A.; Nabissi, M.; Maurice, T.; Decker, M. Structure-Activity Relationships and Computational Investigations into the Development of Potent and Balanced Dual-Acting Butyrylcholinesterase Inhibitors and Human Cannabinoid Receptor 2 Ligands with Pro-Cognitive in Vivo Profiles. J. Med. Chem. 2018, 61 (4), 1646–1663. (60) Éliás, O.; Kovács, Z.; Wágner, G.; Némethy, Z.; Tarcsay, Á.; Greiner, I. Charting the Chemical Space around the (Iso)indoline Scaffold, a Comprehensive Approach towards Multitarget Directed Ligands. Bioorg. Med. Chem. Lett. 2016, 26 (17), 4211–4215. (61) Apsel, B.; Blair, J. A.; Gonzalez, B.; Nazif, T. M.; Feldman, M. E.; Aizenstein, B.; Hoffman, R.; Williams, R. L.; Shokat, K. M.; Knight, Z. A. Targeted



Page 70 of 100

Polypharmacology: Discovery of Dual Inhibitors of Tyrosine and Phosphoinositide Kinases. Nat. Chem. Biol. 2008, 4 (11), 691–699. (62) Bertinaria, M.; Shaikh, M. A. A. G.; Buccellati, C.; Cena, C.; Rolando, B.; Lazzarato, L.; Fruttero, R.; Gasco, A.; Hoxha, M.; Capra, V.; Sala, A.; Rovati, G. E. Designing Multitarget Anti-Inflammatory Agents: Chemical Modulation of the Lumiracoxib Structure toward Dual Thromboxane Antagonists-COX-2 Inhibitors. ChemMedChem 2012, 7 (9), 1647–1660. (63) Ciceri, P.; Müller, S.; O’Mahony, A.; Fedorov, O.; Filippakopoulos, P.; Hunt, J. P.; Lasater, E. A.; Pallares, G.; Picaud, S.; Wells, C.; Martin, S.; Wodicka, L. M.; Shah, N. P.; Treiber, D. K.; Knapp, S. Dual Kinase-Bromodomain Inhibitors for Rationally Designed Polypharmacology. Nat. Chem. Biol. 2014, 10 (4), 305–312. (64) Benson, S. C.; Pershadsingh, H. A.; Ho, C. I.; Chittiboyina, A.; Desai, P.; Pravenec, M.; Qi, N.; Wang, J.; Avery, M. A.; Kurtz, T. W. Identification of Telmisartan as a Unique Angiotensin II Receptor Antagonist with Selective PPARγ-Modulating Activity. Hypertension 2004, 43 (5), 993–1002. (65) Casimiro-Garcia, A.; Heemstra, R. J.; Bigge, C. F.; Chen, J.; Ciske, F. A.; Davis, J. A.; Ellis, T.; Esmaeil, N.; Flynn, D.; Han, S.; Jalaie, M.; Ohren, J. F.; Powell, N. A. Design, Synthesis, and Evaluation of imidazo[4,5-C]pyridin-4-One Derivatives with Dual Activity at Angiotensin II Type 1 Receptor and Peroxisome ProliferatorActivated Receptor-γ. Bioorg. Med. Chem. Lett. 2013, 23 (3), 767–772. (66) Lamotte, Y.; Faucher, N.; Sançon, J.; Pineau, O.; Sautet, S.; Fouchet, M.-H.; Beneton, V.; Tousaint, J.-J.; Saintillan, Y.; Ancellin, N.; Nicodeme, E.; Grillot, D.; Martres, P. Discovery of Novel Indazole Derivatives as Dual Angiotensin II


Page 71 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Antagonists and Partial PPARγ Agonists. Bioorg. Med. Chem. Lett. 2014, 24 (4), 1098–1103. (67) Johnson, M.; Maggiora, G. Concepts and Applications of Molecular Similarity; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 1990. (68) Martin, Y. C.; Kofron, J. L.; Traphagen, L. M. Do Structurally Similar Molecules Have Similar Biological Activity? J. Med. Chem. 2002, 45 (19), 4350–4358. (69) Lavecchia, A.; Cerchia, C. In Silico Methods to Address Polypharmacology: Current Status, Applications and Future Perspectives. Drug Discov. Today 2016, 21 (2), 288–298. (70) Smith, A. D.; Roda, D.; Yap, T. A. Strategies for Modern Biomarker and Drug Development in Oncology. J. Hematol. Oncol. 2014, 7 (1), 70. (71) Jørgensen, J. T. The Importance of Predictive Biomarkers in Oncology Drug Development. Expert Rev. Mol. Diagn. 2016, 16 (8), 807–809. (72) Poornima, P.; Kumar, J. D.; Zhao, Q.; Blunder, M.; Efferth, T. Network Pharmacology of Cancer: From Understanding of Complex Interactomes to the Design of Multi-Target Specific Therapeutics from Nature. Pharmacol. Res. 2016, 111, 290–302. (73) Sun, W.; Zheng, W.; Simeonov, A. Drug Discovery and Development for Rare Genetic Disorders. Am. J. Med. Genet. Part A 2017, 173 (9), 2307–2322. (74) Bakkar, N.; Boehringer, A.; Bowser, R. Use of Biomarkers in ALS Drug Development and Clinical Trials. Brain Res. 2015, 1607, 94–107. (75) Wong, E. H. F.; Tarazi, F. I.; Shahid, M. The Effectiveness of Multi-Target Agents in Schizophrenia and Mood Disorders: Relevance of Receptor Signature to



Page 72 of 100

Clinical Action. Pharmacol. Ther. 2010, 126 (2), 173–185. (76) Roth, B. L.; Sheffler, D. J.; Kroeze, W. K. Magic Shotguns versus Magic Bullets: Selectively Non-Selective Drugs for Mood Disorders and Schizophrenia. Nat. Rev. Drug Discov. 2004, 3 (4), 353–359. (77) Takeshima, M. Treating Mixed Mania/hypomania: A Review and Synthesis of the Evidence. CNS Spectr. 2017, 22 (2), 177–185. (78) Tuplin, E. W.; Holahan, M. R. Aripiprazole, A Drug That Displays Partial Agonism and Functional Selectivity. Curr. Neuropharmacol. 2017, 15 (8), 1192–1207. (79) Caraci, F.; Leggio, G. M.; Salomone, S.; Drago, F. New Drugs in Psychiatry: Focus on New Pharmacological Targets. F1000Research 2017, 6, 397. (80) Krause, M.; Zhu, Y.; Huhn, M.; Schneider-Thoma, J.; Bighelli, I.; Nikolakopoulou, A.; Leucht, S. Antipsychotic Drugs for Patients with Schizophrenia and Predominant or Prominent Negative Symptoms: A Systematic Review and MetaAnalysis. Eur. Arch. Psychiatry Clin. Neurosci. 2018, 1–15. (81) Langedijk, J.; Mantel-Teeuwisse, A. K.; Slijkerman, D. S.; Schutjens, M.-H. D. B. Drug Repositioning and Repurposing: Terminology and Definitions in Literature. Drug Discov. Today 2015, 20 (8), 1027–1034. (82) Aubé, J. Drug Repurposing and the Medicinal Chemist. ACS Med. Chem. Lett. 2012, 3 (6), 442–444. (83) Ekins, S.; Williams, A. J.; Krasowski, M. D.; Freundlich, J. S. In Silico Repositioning of Approved Drugs for Rare and Neglected Diseases. Drug Discov. Today 2011, 16 (7–8), 298–310. (84) Bottegoni, G.; Favia, A. D.; Recanatini, M.; Cavalli, A. The Role of Fragment-


Page 73 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Based and Computational Methods in Polypharmacology. Drug Discov. Today 2012, 17 (1–2), 23–34. (85) Ramsay, R. R.; Popovic-Nikolic, M. R.; Nikolic, K.; Uliassi, E.; Bolognesi, M. L. A Perspective on Multi-Target Drug Discovery and Design for Complex Diseases. Clin. Transl. Med. 2018, 7 (1), 3. (86) Talevi, A. Multi-Target Pharmacology: Possibilities and Limitations of The “skeleton Key Approach” from a Medicinal Chemist Perspective. Front. Pharmacol. 2015, 6 (SEP), 205. (87) Schaller, D.; Hagenow, S.; Alpert, G.; Naß, A.; Schulz, R.; Bermudez, M.; Stark, H.; Wolber, G. Systematic Data Mining Reveals Synergistic H3R/MCHR1 Ligands. ACS Med. Chem. Lett. 2017, 8 (6), 648–653. (88) Apelt, J.; Ligneau, X.; Pertz, H. H.; Arrang, J. M.; Ganellin, C. R.; Schwartz, J. C.; Schunack, W.; Stark, H. Development of a New Class of Nonimidazole Histamine H3receptor Ligands with Combined Inhibitory Histamine N-Methyltransferase Activity. J. Med. Chem. 2002, 45 (5), 1128–1141. (89) Blöcher, R.; Lamers, C.; Wittmann, S. K.; Merk, D.; Hartmann, M.; Weizel, L.; Diehl, O.; Brüggerhoff, A.; Boß, M.; Kaiser, A.; Schader, T.; Göbel, T.; Grundmann, M.; Angioni, C.; Heering, J.; Geisslinger, G.; Wurglics, M.; Kostenis, E.; Brüne, B.; Steinhilber, D.; Schubert-Zsilavecz, M.; Kahnt, A. S.; Proschak, E. N-Benzylbenzamides: A Novel Merged Scaffold for Orally Available Dual Soluble Epoxide Hydrolase/Peroxisome Proliferator-Activated Receptor γ Modulators. J. Med. Chem. 2016, 59 (1), 61–81. (90) Graßmann, S.; Apelt, J.; Ligneau, X.; Pertz, H. H.; Arrang, J. M.; Ganellin, C. R.;



Page 74 of 100

Schwartz, J. C.; Schunack, W.; Stark, H. Search for Histamine H3 Receptor Ligands with Combined Inhibitory Potency at Histamine N-Methyltransferase: ωPiperidinoalkanamine Derivatives. Arch. Pharm. (Weinheim). 2004, 337 (10), 533–545. (91) Khan, N.; Saad, A.; Nurulain, S. M.; Darras, F. H.; Decker, M.; Sadek, B. The Dual-Acting H3 Receptor Antagonist and AChE Inhibitor UW-MD-71 DoseDependently Enhances Memory Retrieval and Reverses Dizocilpine-Induced Memory Impairment in Rats. Behav. Brain Res. 2016, 297, 155–164. (92) Arrowsmith, C. H.; Audia, J. E.; Austin, C.; Baell, J.; Bennett, J.; Blagg, J.; Bountra, C.; Brennan, P. E.; Brown, P. J.; Bunnage, M. E.; Buser-Doepner, C.; Campbell, R. M.; Carter, A. J.; Cohen, P.; Copeland, R. A.; Cravatt, B.; Dahlin, J. L.; Dhanak, D.; Edwards, A. M.; Frederiksen, M.; Frye, S. V; Gray, N.; Grimshaw, C. E.; Hepworth, D.; Howe, T.; Huber, K. V. M.; Jin, J.; Knapp, S.; Kotz, J. D.; Kruger, R. G.; Lowe, D.; Mader, M. M.; Marsden, B.; Mueller-Fahrnow, A.; Müller, S.; O’Hagan, R. C.; Overington, J. P.; Owen, D. R.; Rosenberg, S. H.; Roth, B.; Roth, B.; Ross, R.; Schapira, M.; Schreiber, S. L.; Shoichet, B.; Sundström, M.; Superti-Furga, G.; Taunton, J.; Toledo-Sherman, L.; Walpole, C.; Walters, M. A.; Willson, T. M.; Workman, P.; Young, R. N.; Zuercher, W. J. The Promise and Peril of Chemical Probes. Nat. Chem. Biol. 2015, 11 (8), 536–541. (93) Naidu, P. S.; Booker, L.; Cravatt, B. F.; Lichtman, A. H. Synergy between Enzyme Inhibitors of Fatty Acid Amide Hydrolase and Cyclooxygenase in Visceral Nociception. J. Pharmacol. Exp. Ther. 2009, 329 (1), 48–56. (94) Kurtz, S. E.; Eide, C. A.; Kaempf, A.; Khanna, V.; Savage, S. L.; Rofelty, A.;


Page 75 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


English, I.; Ho, H.; Pandya, R.; Bolosky, W. J.; Poon, H.; Deininger, M. W.; Collins, R.; Swords, R. T.; Watts, J.; Pollyea, D. A.; Medeiros, B. C.; Traer, E.; Tognon, C. E.; Mori, M.; Druker, B. J.; Tyner, J. W. Molecularly Targeted Drug Combinations Demonstrate Selective Effectiveness for Myeloid- and LymphoidDerived Hematologic Malignancies. Proc. Natl. Acad. Sci. U. S. A. 2017, 114 (36), E7554–E7563. (95) Doudna, J. A.; Charpentier, E. Genome Editing. The New Frontier of Genome Engineering with CRISPR-Cas9. Science 2014, 346 (6213), 1258096. (96) Lee, J. A.; Uhlik, M. T.; Moxham, C. M.; Tomandl, D.; Sall, D. J. Modern Phenotypic Drug Discovery Is a Viable, Neoclassic Pharma Strategy. J. Med. Chem. 2012, 55 (10), 4527–4538. (97) Houck, K. A.; Dix, D. J.; Judson, R. S.; Kavlock, R. J.; Yang, J.; Berg, E. L. Profiling Bioactivity of the ToxCast Chemical Library Using BioMAP Primary Human Cell Systems. J. Biomol. Screen. 2009, 14 (9), 1054–1066. (98) Kleinstreuer, N. C.; Yang, J.; Berg, E. L.; Knudsen, T. B.; Richard, A. M.; Martin, M. T.; Reif, D. M.; Judson, R. S.; Polokoff, M.; Dix, D. J.; Kavlock, R. J.; Houck, K. A. Phenotypic Screening of the ToxCast Chemical Library to Classify Toxic and Therapeutic Mechanisms. Nat. Biotechnol. 2014, 32 (6), 583–591. (99) Takahashi, K.; Yamanaka, S. Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors. Cell 2006, 126 (4), 663–676. (100) Barbaric, I.; Gokhale, P. J.; Andrews, P. W. High-Content Screening of Small Compounds on Human Embryonic Stem Cells. Biochem. Soc. Trans. 2010, 38



Page 76 of 100

(4), 1046–1050. (101) McCarroll, M. N.; Gendelev, L.; Keiser, M. J.; Kokel, D. Leveraging Large-Scale Behavioral Profiling in Zebrafish to Explore Neuroactive Polypharmacology. ACS Chem. Biol. 2016, 11 (4), 842–849. (102) Sonoshita, M.; Scopton, A. P.; Ung, P. M. U.; Murray, M. A.; Silber, L.; Maldonado, A. Y.; Real, A.; Schlessinger, A.; Cagan, R. L.; Dar, A. C. A WholeAnimal Platform to Advance a Clinical Kinase Inhibitor into New Disease Space. Nat. Chem. Biol. 2018, 14 (3), 291–298. (103) Hopkins, A. L. Network Pharmacology: The next Paradigm in Drug Discovery. Nat. Chem. Biol. 2008, 4 (11), 682–690. (104) Sheng, Z.; Sun, Y.; Yin, Z.; Tang, K.; Cao, Z. Advances in Computational Approaches in Identifying Synergistic Drug Combinations. Brief. Bioinform. 2017, doi: 10.1093/bib/bbx047. (105) Chua, H. E.; Bhowmick, S. S.; Tucker-Kellogg, L. Synergistic Target Combination Prediction from Curated Signaling Networks: Machine Learning Meets Systems Biology and Pharmacology. Methods 2017, 129, 60–80. (106) Al-Ali, H.; Lee, D.-H.; Danzi, M. C.; Nassif, H.; Gautam, P.; Wennerberg, K.; Zuercher, B.; Drewry, D. H.; Lee, J. K.; Lemmon, V. P.; Bixby, J. L. Rational Polypharmacology: Systematically Identifying and Engaging Multiple Drug Targets To Promote Axon Growth. ACS Chem. Biol. 2015, 10 (8), 1939–1951. (107) Miller, M. L.; Molinelli, E. J.; Nair, J. S.; Sheikh, T.; Samy, R.; Jing, X.; He, Q.; Korkut, A.; Crago, A. M.; Singer, S.; Schwartz, G. K.; Sander, C. Drug Synergy Screen and Network Modeling in Dedifferentiated Liposarcoma Identifies CDK4


Page 77 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


and IGF1R as Synergistic Drug Targets. Sci. Signal. 2013, 6 (294), ra85. (108) He, L.; Kulesskiy, E.; Saarela, J.; Turunen, L.; Wennerberg, K.; Aittokallio, T.; Tang, J. Methods for High-Throughput Drug Combination Screening and Synergy Scoring. Methods Mol. Biol. 2018, 1711, 351–398. (109) Kashif, M.; Andersson, C.; Mansoori, S.; Larsson, R.; Nygren, P.; Gustafsson, M. G. Bliss and Loewe Interaction Analyses of Clinically Relevant Drug Combinations in Human Colon Cancer Cell Lines Reveal Complex Patterns of Synergy and Antagonism. Oncotarget 2017, 8 (61), 103952–103967. (110) Wambaugh, M. A.; Shakya, V. P. S.; Lewis, A. J.; Mulvey, M. A.; Brown, J. C. S. High-Throughput Identification and Rational Design of Synergistic Small-Molecule Pairs for Combating and Bypassing Antibiotic Resistance. PLoS Biol. 2017, 15 (6), e2001644. (111) Mason, D. J.; Stott, I.; Ashenden, S.; Weinstein, Z. B.; Karakoc, I.; Meral, S.; Kuru, N.; Bender, A.; Cokol, M. Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure. J. Med. Chem. 2017, 60 (9), 3902– 3912. (112) Liu, Y.; Zhao, H. Predicting Synergistic Effects between Compounds through Their Structural Similarity and Effects on Transcriptomes. Bioinformatics 2016, 32 (24), 3782–3789. (113) Preuer, K.; Lewis, R. P. I.; Hochreiter, S.; Bender, A.; Bulusu, K. C.; Klambauer, G. DeepSynergy: Predicting Anti-Cancer Drug Synergy with Deep Learning. Bioinformatics 2018, 34 (9), 1538–1546. (114) Li, X.; Xu, Y.; Cui, H.; Huang, T.; Wang, D.; Lian, B.; Li, W.; Qin, G.; Chen, L.; Xie,



Page 78 of 100

L. Prediction of Synergistic Anti-Cancer Drug Combinations Based on Drug Target Network and Drug Induced Gene Expression Profiles. Artif. Intell. Med. 2017, 83, 35–43. (115) Gayvert, K. M.; Aly, O.; Platt, J.; Bosenberg, M. W.; Stern, D. F.; Elemento, O. A Computational Approach for Identifying Synergistic Drug Combinations. PLoS Comput. Biol. 2017, 13 (1), e1005308. (116) Chen, X.; Ren, B.; Chen, M.; Wang, Q.; Zhang, L.; Yan, G. NLLSS: Predicting Synergistic Drug Combinations Based on Semi-Supervised Learning. PLoS Comput. Biol. 2016, 12 (7), e1004975. (117) Lee, J. S.; Das, A.; Jerby-Arnon, L.; Arafeh, R.; Auslander, N.; Davidson, M.; McGarry, L.; James, D.; Amzallag, A.; Park, S. G.; Cheng, K.; Robinson, W.; Atias, D.; Stossel, C.; Buzhor, E.; Stein, G.; Waterfall, J. J.; Meltzer, P. S.; Golan, T.; Hannenhalli, S.; Gottlieb, E.; Benes, C. H.; Samuels, Y.; Shanks, E.; Ruppin, E. Harnessing Synthetic Lethality to Predict the Response to Cancer Treatment. Nat. Commun. 2018, 9 (1), 2546. (118) Cava, C.; Bertoli, G.; Castiglioni, I. In Silico Identification of Drug Target Pathways in Breast Cancer Subtypes Using Pathway Cross-Talk Inhibition. J. Transl. Med. 2018, 16 (1), 154. (119) Hu, H.-M.; Zhao, X.; Kaushik, S.; Robillard, L.; Barthelet, A.; Lin, K. K.; Shah, K. N.; Simmons, A. D.; Raponi, M.; Harding, T. C.; Bandyopadhyay, S. A Quantitative Chemotherapy Genetic Interaction Map Reveals Factors Associated with PARP Inhibitor Resistance. Cell Rep. 2018, 23 (3), 918–929. (120) Huang, L.; Jiang, Y.; Chen, Y. Predicting Drug Combination Index and Simulating


Page 79 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway. Sci. Rep. 2017, 7 (1), 40752. (121) Jaeger, S.; Igea, A.; Arroyo, R.; Alcalde, V.; Canovas, B.; Orozco, M.; Nebreda, A. R.; Aloy, P. Quantification of Pathway Cross-Talk Reveals Novel Synergistic Drug Combinations for Breast Cancer. Cancer Res. 2017, 77 (2), 459–469. (122) Pei, F.; Li, H.; Henderson, M. J.; Titus, S. A.; Jadhav, A.; Simeonov, A.; Cobanoglu, M. C.; Mousavi, S. H.; Shun, T.; McDermott, L.; Iyer, P.; Fioravanti, M.; Carlisle, D.; Friedlander, R. M.; Bahar, I.; Taylor, D. L.; Lezon, T. R.; Stern, A. M.; Schurdak, M. E. Connecting Neuronal Cell Protective Pathways and Drug Combinations in a Huntington’s Disease Model through the Application of Quantitative Systems Pharmacology. Sci. Rep. 2017, 7 (1), 17803. (123) Williams, S. P.; Barthorpe, A. S.; Lightfoot, H.; Garnett, M. J.; McDermott, U. High-Throughput RNAi Screen for Essential Genes and Drug Synergistic Combinations in Colorectal Cancer. Sci. data 2017, 4, 170139. (124) Yang, K.; Ma, W.; Liang, H.; Ouyang, Q.; Tang, C.; Lai, L. Dynamic Simulations on the Arachidonic Acid Metabolic Network. PLoS Comput. Biol. 2007, 3 (3), e55. (125) He, C.; Wu, Y.; Lai, Y.; Cai, Z.; Liu, Y.; Lai, L. Dynamic Eicosanoid Responses upon Different Inhibitor and Combination Treatments on the Arachidonic Acid Metabolic Network. Mol. Biosyst. 2012, 8 (5), 1585–1594. (126) Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings. Adv. Drug Deliv. Rev. 1997, 23 (1–3), 3– 25.



Page 80 of 100

(127) Shen, W. W. A History of Antipsychotic Drug Development. Compr. Psychiatry 1999, 40 (6), 407–414. (128) Ramachandraiah, C. T.; Subramaniam, N.; Tancer, M. The Story of Antipsychotics: Past and Present. Indian J. Psychiatry 2009, 51 (4), 324–326. (129) Pistollato, F.; Calderón Iglesias, R.; Ruiz, R.; Aparicio, S.; Crespo, J.; Dzul Lopez, L.; Giampieri, F.; Battino, M. The Use of Natural Compounds for the Targeting and Chemoprevention of Ovarian Cancer. Cancer Lett. 2017, 411, 191–200. (130) Rejhová, A.; Opattová, A.; Čumová, A.; Slíva, D.; Vodička, P. Natural Compounds and Combination Therapy in Colorectal Cancer Treatment. Eur. J. Med. Chem. 2018, 144, 582–594. (131) Amelio, I.; Lisitsa, A.; Knight, R.; Melino, G.; Antonov, A. Polypharmacology of Approved Anticancer Drugs. Curr. Drug Targets 2017, 18 (5), 534–543. (132) de Lera, A. R.; Ganesan, A. Epigenetic Polypharmacology: From Combination Therapy to Multitargeted Drugs. Clin. Epigenetics 2016, 8 (1), 105. (133) Walsh, C. T.; Wencewicz, T. A. Prospects for New Antibiotics: A MoleculeCentered Perspective. J. Antibiot. (Tokyo). 2014, 67 (1), 7–22. (134) Silver, L. L. Multi-Targeting by Monotherapeutic Antibacterials. Nat. Rev. Drug Discov. 2007, 6 (1), 41–55. (135) Liao, J. J.-L.; Andrews, R. C. Targeting Protein Multiple Conformations: A Structure-Based Strategy for Kinase Drug Design. Curr. Top. Med. Chem. 2007, 7 (14), 1394–1407. (136) Kelloff, G. J.; Sigman, C. C. Cancer Biomarkers: Selecting the Right Drug for the Right Patient. Nat. Rev. Drug Discov. 2012, 11 (3), 201–214.


Page 81 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(137) Zumla, A.; Rao, M.; Dodoo, E.; Maeurer, M. Potential of Immunomodulatory Agents as Adjunct Host-Directed Therapies for Multidrug-Resistant Tuberculosis. BMC Med. 2016, 14 (1), 89. (138) Zhan, P.; Liu, X. Designed Multiple Ligands: An Emerging Anti-HIV Drug Discovery Paradigm. Curr. Pharm. Des. 2009, 15 (16), 1893–1917. (139) Prasad, S.; Gupta, S. C.; Aggarwal, B. B. Serendipity in Cancer Drug Discovery: Rational or Coincidence? Trends Pharmacol. Sci. 2016, 37 (6), 435–450. (140) Markman, M.; Peereboom, D. M. From Serendipity to Design: The Evolution of Drug Development in Oncology. Cleve. Clin. J. Med. 1997, 64 (3), 155–163. (141) Karlsson, A.; García-Echeverría, C. Chapter 13. Identification and Optimization of Dual PI3K/mTOR Inhibitors. In Designing Multi-Target Drugs; Royal Society of Chemistry: Cambridge, UK, 2012; pp 206–220. (142) Albrecht, W.; Unger, A.; Bauer, S. M.; Laufer, S. A. Discovery of N-{4-[5-(4Fluorophenyl)-3-Methyl-2-Methylsulfanyl-3H-Imidazol-4-Yl]-Pyridin-2-Yl}Acetamide (CBS-3595), a Dual p38α MAPK/PDE-4 Inhibitor with Activity against TNFα-Related Diseases. J. Med. Chem. 2017, 60 (13), 5290–5305. (143) Wess, G.; Kramer, W.; Schubert, G.; Enhsen, A.; Baringhaus, K. H.; Glombik, H.; Müllner, S.; Bock, K.; Kleine, H.; John, M.; Neckermann, O.; Hoffmann, A. Synthesis of Bile Acid - Drug Conjugates: Potential Drug - Shuttles for Liver Specific Targeting. Tetrahedron Lett. 1993, 34 (5), 819–822. (144) Kramer, W.; Wess, G.; Schubert, G.; Bickel, M.; Girbig, F.; Gutjahr, U.; Kowalewski, S.; Baringhaus, K. H.; Enhsen, A.; Glombik, H. Liver-Specific Drug Targeting by Coupling to Bile Acids. J. Biol. Chem. 1992, 267 (26), 18598–18604.



Page 82 of 100

(145) Braun, V.; Pramanik, A.; Gwinner, T.; Köberle, M.; Bohn, E. Sideromycins: Tools and Antibiotics. Biometals 2009, 22 (1), 3–13. (146) Ghosh, M.; Miller, P. A.; Möllmann, U.; Claypool, W. D.; Schroeder, V. A.; Wolter, W. R.; Suckow, M.; Yu, H.; Li, S.; Huang, W.; Zajicek, J.; Miller, M. J. Targeted Antibiotic Delivery: Selective Siderophore Conjugation with Daptomycin Confers Potent Activity against Multidrug Resistant Acinetobacter Baumannii Both in Vitro and in Vivo. J. Med. Chem. 2017, 60 (11), 4577–4583. (147) Liu, R.; Miller, P. A.; Vakulenko, S. B.; Stewart, N. K.; Boggess, W. C.; Miller, M. J. A Synthetic Dual Drug Sideromycin Induces Gram-Negative Bacteria To Commit Suicide with a Gram-Positive Antibiotic. J. Med. Chem. 2018, 61 (9), 3845–3854. (148) Wencewicz, T. A.; Long, T. E.; Möllmann, U.; Miller, M. J. Trihydroxamate Siderophore-Fluoroquinolone Conjugates Are Selective Sideromycin Antibiotics That Target Staphylococcus Aureus. Bioconjug. Chem. 2013, 24 (3), 473–486. (149) Wencewicz, T. A.; Miller, M. J. Biscatecholate-Monohydroxamate Mixed Ligand Siderophore-Carbacephalosporin Conjugates Are Selective Sideromycin Antibiotics That Target Acinetobacter Baumannii. J. Med. Chem. 2013, 56 (10), 4044–4052. (150) Choi, J. J.; McCarthy, M. W. Cefiderocol: A Novel Siderophore Cephalosporin. Expert Opin. Investig. Drugs 2018, 27 (2), 193–197. (151) Wainwright, M.; Shah, A.; Meegan, K.; Loughran, C.; Smith, A.; Valli, N.; Dempster, N. Phenothiazinium-Fluoroquinolone Drug Conjugates. Int. J. Antimicrob. Agents 2010, 35 (4), 405–409.


Page 83 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(152) Tsuchikama, K.; An, Z. Antibody-Drug Conjugates: Recent Advances in Conjugation and Linker Chemistries. Protein Cell 2018, 9 (1), 33–46. (153) Srinivasarao, M.; Galliford, C. V; Low, P. S. Principles in the Design of LigandTargeted Cancer Therapeutics and Imaging Agents. Nat. Rev. Drug Discov. 2015, 14 (3), 203–219. (154) Walles, M.; Connor, A.; Hainzl, D. ADME and Safety Aspects of Non-Cleavable Linkers in Drug Discovery and Development. Curr. Top. Med. Chem. 2018, 17 (32), 3463–3475. (155) Seidi, F.; Jenjob, R.; Crespy, D. Designing Smart Polymer Conjugates for Controlled Release of Payloads. Chem. Rev. 2018, 118 (7), 3965–4036. (156) Frigerio, M.; Kyle, A. F. The Chemical Design and Synthesis of Linkers Used in Antibody Drug Conjugates. Curr. Top. Med. Chem. 2018, 17 (32), 3393–3424. (157) Castañeda, L.; Maruani, A.; Schumacher, F. F.; Miranda, E.; Chudasama, V.; Chester, K. A.; Baker, J. R.; Smith, M. E. B.; Caddick, S. Acid-Cleavable Thiomaleamic Acid Linker for Homogeneous Antibody–drug Conjugation. Chem. Commun. 2013, 49 (74), 8187. (158) Dubowchik, G. M.; Firestone, R. A.; Padilla, L.; Willner, D.; Hofstead, S. J.; Mosure, K.; Knipe, J. O.; Lasch, S. J.; Trail, P. A. Cathepsin B-Labile Dipeptide Linkers for Lysosomal Release of Doxorubicin from Internalizing Immunoconjugates: Model Studies of Enzymatic Drug Release and AntigenSpecific in Vitro Anticancer Activity. Bioconjug. Chem. 2002, 13 (4), 855–869. (159) Mhidia, R.; Bézière, N.; Blanpain, A.; Pommery, N.; Melnyk, O. Assembly/disassembly of Drug Conjugates Using Imide Ligation. Org. Lett. 2010,



Page 84 of 100

12 (18), 3982–3985. (160) Jeffrey, S. C.; Andreyka, J. B.; Bernhardt, S. X.; Kissler, K. M.; Kline, T.; Lenox, J. S.; Moser, R. F.; Nguyen, M. T.; Okeley, N. M.; Stone, I. J.; Zhang, X.; Senter, P. D. Development and Properties of β-Glucuronide Linkers for Monoclonal Antibody-Drug Conjugates. Bioconjug. Chem. 2006, 17 (3), 831–840. (161) Chen, H.; Bauer, U.; Engkvist, O. Merged Multiple Ligands. In Drug Selectivity; Wiley‐VCH Verlag GmbH & Co. KGaA: Weinheim, DE, 2017; pp 247–274. (162) Proschak, E.; Heitel, P.; Kalinowsky, L.; Merk, D. Opportunities and Challenges for Fatty Acid Mimetics in Drug Discovery. J. Med. Chem. 2017, 60 (13), 5235– 5266. (163) Wood, P. M.; Woo, L. W. L.; Thomas, M. P.; Mahon, M. F.; Purohit, A.; Potter, B. V. L. Aromatase and Dual Aromatase-Steroid Sulfatase Inhibitors from the Letrozole and Vorozole Templates. ChemMedChem 2011, 6 (8), 1423–1438. (164) Woo, L. W. L.; Bubert, C.; Purohit, A.; Potter, B. V. L. Hybrid Dual AromataseSteroid Sulfatase Inhibitors with Exquisite Picomolar Inhibitory Activity. ACS Med. Chem. Lett. 2011, 2 (3), 243–247. (165) Woo, L. W. L.; Wood, P. M.; Bubert, C.; Thomas, M. P.; Purohit, A.; Potter, B. V. L. Synthesis and Structure-Activity Relationship Studies of Derivatives of the Dual Aromatase-Sulfatase Inhibitor 4-{[(4-Cyanophenyl)(4H-1,2,4-Triazol-4Yl)amino]methyl}phenyl Sulfamate. ChemMedChem 2013, 8 (5), 779–799. (166) Woo, L. W. L.; Bubert, C.; Sutcliffe, O. B.; Smith, A.; Chander, S. K.; Mahon, M. F.; Purohit, A.; Reed, M. J.; Potter, B. V. L. Dual Aromatase-Steroid Sulfatase Inhibitors. J. Med. Chem. 2007, 50 (15), 3540–3560.


Page 85 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(167) Glennon, R. A.; Slusher, R. M.; McKenney, J. D.; Lyon, R. A.; Titeler, M. 5-HT1 and 5-HT2 Binding Characteristics of Some Quipazine Analogues. J. Med. Chem. 1986, 29 (11), 2375–2380. (168) Lowe, J. A.; Seeger, T. F.; Nagel, A. A.; Howard, H. R.; Seymour, P. A.; Heym, J. H.; Ewing, F. E.; Newman, M. E.; Schmidt, A. W.; Furman, J. S.; Vincent, L. A.; Maloney, P. R.; Robinson, G. L.; Reynolds, L. S.; Vinick, F. J. 1Naphthylpiperazine Derivatives as Potential Atypical Antipsychotic Agents. J. Med. Chem. 1991, 34 (6), 1860–1866. (169) Howard, H. R.; Lowe, J. A.; Seeger, T. F.; Seymour, P. A.; Zorn, S. H.; Maloney, P. R.; Ewing, F. E.; Newman, M. E.; Schmidt, A. W.; Furman, J. S.; Robinson, G. L.; Jackson, E.; Johnson, C.; Morrone, J. 3-Benzisothiazolylpiperazine Derivatives as Potential Atypical Antipsychotic Agents. J. Med. Chem. 1996, 39 (1), 143–148. (170) Bautista-Aguilera, Ó. M.; Hagenow, S.; Palomino-Antolin, A.; Farré-Alins, V.; Ismaili, L.; Joffrin, P. L.; Jimeno, M. L.; Soukup, O.; Janočková, J.; Kalinowsky, L.; Proschak, E.; Iriepa, I.; Moraleda, I.; Schwed, J. S.; Romero Martínez, A.; LópezMuñoz, F.; Chioua, M.; Egea, J.; Ramsay, R. R.; Marco-Contelles, J.; Stark, H. Multitarget-Directed Ligands Combining Cholinesterase and Monoamine Oxidase Inhibition with Histamine H3R Antagonism for Neurodegenerative Diseases. Angew. Chemie - Int. Ed. 2017, 56 (41), 12765–12769. (171) Bento, A. P.; Gaulton, A.; Hersey, A.; Bellis, L. J.; Chambers, J.; Davies, M.; Krüger, F. A.; Light, Y.; Mak, L.; McGlinchey, S.; Nowotka, M.; Papadatos, G.; Santos, R.; Overington, J. P. The ChEMBL Bioactivity Database: An Update. Nucleic Acids Res. 2014, 42 (Database issue), D1083–D1090.



Page 86 of 100

(172) Wang, Y.; Bryant, S. H.; Cheng, T.; Wang, J.; Gindulyte, A.; Shoemaker, B. A.; Thiessen, P. A.; He, S.; Zhang, J. PubChem BioAssay: 2017 Update. Nucleic Acids Res. 2017, 45 (D1), D955–D963. (173) Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints. J. Chem. Inf. Model. 2010, 50 (5), 742–754. (174) Glen, R. C.; Bender, A.; Arnby, C. H.; Carlsson, L.; Boyer, S.; Smith, J. Circular Fingerprints: Flexible Molecular Descriptors with Applications from Physical Chemistry to ADME. IDrugs 2006, 9 (3), 199–204. (175) Jasper, J. B.; Humbeck, L.; Brinkjost, T.; Koch, O. A Novel Interaction Fingerprint Derived from per Atom Score Contributions: Exhaustive Evaluation of Interaction Fingerprint Performance in Docking Based Virtual Screening. J. Cheminformatics 2018 101 2018, 10 (1), 15. (176) Méndez-Lucio, O.; Kooistra, A. J.; Graaf, C. de; Bender, A.; Medina-Franco, J. L. Analyzing Multitarget Activity Landscapes Using Protein-Ligand Interaction Fingerprints: Interaction Cliffs. J. Chem. Inf. Model. 2015, 55 (2), 251–262. (177) Da, C.; Kireev, D. Structural Protein-Ligand Interaction Fingerprints (SPLIF) for Structure-Based Virtual Screening: Method and Benchmark Study. J. Chem. Inf. Model. 2014, 54 (9), 2555–2561. (178) Reutlinger, M.; Koch, C. P.; Reker, D.; Todoroff, N.; Schneider, P.; Rodrigues, T.; Schneider, G. Chemically Advanced Template Search (CATS) for ScaffoldHopping and Prospective Target Prediction for “Orphan” Molecules. Mol. Inf. 2013, 32 (2), 133–138. (179) Nikolic, K.; Mavridis, L.; Bautista-Aguilera, O. M.; Marco-Contelles, J.; Stark, H.;


Page 87 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


do Carmo Carreiras, M.; Rossi, I.; Massarelli, P.; Agbaba, D.; Ramsay, R. R.; Mitchell, J. B. O. Predicting Targets of Compounds against Neurological Diseases Using Cheminformatic Methodology. J. Comput. Aided. Mol. Des. 2015, 29 (2), 183–198. (180) Wale, N.; Karypis, G. Target Fishing for Chemical Compounds Using TargetLigand Activity Data and Ranking Based Methods. J. Chem. Inf. Model. 2009, 49 (10), 2190–2201. (181) Raevsky, O. A.; Mukhametov, A.; Grigorev, V. Y.; Ustyugov, A.; Tsay, S.-C.; JihRu Hwu, R.; Yarla, N. S.; Barreto, G. E.; Aliev, G.; Bachurin, S. O. Applications of Multi-Target Computer-Aided Methodologies in Molecular Design of CNS Drugs. Curr. Med. Chem. 2017, doi: 10.2174/0929867324666170920154111. (182) Kalash, L.; Val, C.; Azuaje, J.; Loza, M. I.; Svensson, F.; Zoufir, A.; Mervin, L.; Ladds, G.; Brea, J.; Glen, R.; Sotelo, E.; Bender, A. Computer-Aided Design of Multi-Target Ligands at A1R, A2AR and PDE10A, Key Proteins in Neurodegenerative Diseases. J. Cheminform. 2017, 9 (1), 67. (183) Klabunde, T.; Giegerich, C.; Evers, A. MARS: Computing Three-Dimensional Alignments for Multiple Ligands Using Pairwise Similarities. J. Chem. Inf. Model. 2012, 52 (8), 2022–2030. (184) Merk, D.; Friedrich, L.; Grisoni, F.; Schneider, G. De Novo Design of Bioactive Small Molecules by Artificial Intelligence. Mol. Inform. 2018, 37 (1), 1700153. (185) Handler, N. Pharmacophore Generation for Multiple Ligands. In Drug Selectivity; Wiley‐VCH Verlag GmbH & Co. KGaA: Weinheim, DE, 2017; pp 275–312. (186) Wolber, G.; Seidel, T.; Bendix, F.; Langer, T. Molecule-Pharmacophore



Page 88 of 100

Superpositioning and Pattern Matching in Computational Drug Design. Drug Discov. Today 2008, 13 (1–2), 23–29. (187) Yang, S. Y. Pharmacophore Modeling and Applications in Drug Discovery: Challenges and Recent Advances. Drug Discov. Today 2010, 15 (11–12), 444– 450. (188) Koutsoukas, A.; Simms, B.; Kirchmair, J.; Bond, P. J.; Whitmore, A. V; Zimmer, S.; Young, M. P.; Jenkins, J. L.; Glick, M.; Glen, R. C.; Bender, A. From in Silico Target Prediction to Multi-Target Drug Design: Current Databases, Methods and Applications. J. Proteomics 2011, 74 (12), 2554–2574. (189) Rollinger, J. M. Accessing Target Information by Virtual Parallel Screening-The Impact on Natural Product Research. Phytochem. Lett. 2009, 2 (2), 53–58. (190) Sun, H. Pharmacophore-Based Virtual Screening. Curr. Med. Chem. 2008, 15 (10), 1018–1024. (191) Wolber, G.; Langer, T. LigandScout: 3-D Pharmacophores Derived from ProteinBound Ligands and Their Use as Virtual Screening Filters. J. Chem. Inf. Model. 2005, 45 (1), 160–169. (192) Barillari, C.; Marcou, G.; Rognan, D. Hot-Spots-Guided Receptor-Based Pharmacophores (HS-Pharm): A Knowledge-Based Approach to Identify LigandAnchoring Atoms in Protein Cavities and Prioritize Structure-Based Pharmacophores. J. Chem. Inf. Model. 2008, 48 (7), 1396–1410. (193) Tintori, C.; Corradi, V.; Magnani, M.; Manetti, F.; Botta, M. Targets Looking for Drugs: A Multistep Computational Protocol for the Development of StructureBased Pharmacophores and Their Applications for Hit Discovery. J. Chem. Inf.


Page 89 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Model. 2008, 48 (11), 2166–2179. (194) Sanders, M. P. A.; Barbosa, A. J. M.; Zarzycka, B.; Nicolaes, G. A. F.; Klomp, J. P. G.; De Vlieg, J.; Del Rio, A. Comparative Analysis of Pharmacophore Screening Tools. J. Chem. Inf. Model. 2012, 52 (6), 1607–1620. (195) Triballeau, N.; Bertrand, H. O.; Acher, F. Are You Sure You Have a Good Model? In Pharmacophores and Pharmacophore Searches; Wiley-Blackwell, 2006; pp 325–364. (196) Wei, D.; Jiang, X.; Zhou, L.; Chen, J.; Chen, Z.; He, C.; Yang, K.; Liu, Y.; Pei, J.; Lai, L. Discovery of Multitarget Inhibitors by Combining Molecular Docking with Common Pharmacophore Matching. J. Med. Chem. 2008, 51 (24), 7882–7888. (197) Arooj, M.; Sakkiah, S.; Cao, G. ping; Lee, K. W. An Innovative Strategy for Dual Inhibitor Design and Its Application in Dual Inhibition of Human Thymidylate Synthase and Dihydrofolate Reductase Enzymes. PLoS One 2013, 8 (4), e60470. (198) Morissette, G.; Bouthillier, J.; Marceau, F. Dual Antagonists of the Bradykinin B1 and B2 Receptors Based on a Postulated Common Pharmacophore from Existing Non-Peptide Antagonists. Biol. Chem. 2006, 387 (2), 189–194. (199) Sindhu, T.; Srinivasan, P. Identification of Potential Dual Agonists of FXR and TGR5 Using E-Pharmacophore Based Virtual Screening. Mol. BioSyst. 2015, 11 (5), 1305–1318. (200) Moser, D.; Wisniewska, J. M.; Hahn, S.; Achenbach, J.; Buscató, E. la; Klingler, F.-M.; Hofmann, B.; Steinhilber, D.; Proschak, E. Dual-Target Virtual Screening by Pharmacophore Elucidation and Molecular Shape Filtering. ACS Med. Chem. Lett. 2012, 3 (2), 155–158.



Page 90 of 100

(201) Hartenfeller, M.; Zettl, H.; Walter, M.; Rupp, M.; Reisen, F.; Proschak, E.; Weggen, S.; Stark, H.; Schneider, G. DOGS: Reaction-Driven de Novo Design of Bioactive Compounds. PLoS Comput. Biol. 2012, 8 (2), e1002380. (202) Tschinke, V.; Cohen, N. C. The NEWLEAD Program: A New Method for the Design of Candidate Structures from Pharmacophoric Hypotheses. J. Med. Chem. 1993, 36 (24), 3863–3870. (203) Hartenfeller, M.; Schneider, G. De Novo Drug Design. Methods Mol. Biol. 2010, 672, 299–323. (204) Schneider, P.; Schneider, G. De Novo Design at the Edge of Chaos. J. Med. Chem. 2016, 59 (9), 4077–4086. (205) Lin, H.-H.; Zhang, L.-L.; Yan, R.; Lu, J.-J.; Hu, Y. Network Analysis of Drug-Target Interactions: A Study on FDA-Approved New Molecular Entities Between 2000 to 2015. Sci. Rep. 2017, 7 (1), 12230. (206) Lu, J.-J.; Pan, W.; Hu, Y.-J.; Wang, Y.-T. Multi-Target Drugs: The Trend of Drug Research and Development. PLoS One 2012, 7 (6), e40262. (207) Wermuth, C. G. Selective Optimization of Side Activities: Another Way for Drug Discovery. J. Med. Chem. 2004, 47 (6), 1303–1314. (208) Langer, T.; Wermuth, C.-G. Selective Optimization of Side Activities (SOSA): A Promising Way for Drug Discovery. In Polypharmacology in Drug Discovery; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2012; pp 227–243. (209) Vitale, C.; Mercuro, G.; Castiglioni, C.; Cornoldi, A.; Tulli, A.; Fini, M.; Volterrani, M.; Rosano, G. M. C. Metabolic Effect of Telmisartan and Losartan in Hypertensive Patients with Metabolic Syndrome. Cardiovasc. Diabetol. 2005, 4


Page 91 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(1), 6. (210) Wang, Y.; Qiao, S.; Han, D.-W.; Rong, X.-R.; Wang, Y.-X.; Xue, J.-J.; Yang, J. Telmisartan Improves Insulin Resistance: A Meta-Analysis. Am. J. Ther. 2018, doi: 10.1097/MJT.0000000000000733. (211) Goebel, M.; Staels, B.; Unger, T.; Kintscher, U.; Gust, R. Characterization of New PPARgamma Agonists: Benzimidazole Derivatives - the Importance of Position 2. ChemMedChem 2009, 4 (7), 1136–1142. (212) Goebel, M.; Clemenz, M.; Staels, B.; Unger, T.; Kintscher, U.; Gust, R. Characterization of New PPARgamma Agonists: Analysis of Telmisartan’s Structural Components. ChemMedChem 2009, 4 (3), 445–456. (213) Besnard, J.; Ruda, G. F.; Setola, V.; Abecassis, K.; Rodriguiz, R. M.; Huang, X.P.; Norval, S.; Sassano, M. F.; Shin, A. I.; Webster, L. A.; Simeons, F. R. C.; Stojanovski, L.; Prat, A.; Seidah, N. G.; Constam, D. B.; Bickerton, G. R.; Read, K. D.; Wetsel, W. C.; Gilbert, I. H.; Roth, B. L.; Hopkins, A. L. Automated Design of Ligands to Polypharmacological Profiles. Nature 2012, 492 (7428), 215–220. (214) Schierle, S.; Flauaus, C.; Heitel, P.; Willems, S.; Schmidt, J.; Kaiser, A.; Weizel, L.; Goebel, T.; Kahnt, A. S.; Geisslinger, G.; Steinhilber, D.; Wurglics, M.; Rovati, G. E.; Schmidtko, A.; Proschak, E.; Merk, D. Boosting Anti-Inflammatory Potency of Zafirlukast by Designed Polypharmacology. J. Med. Chem. 2018, 61 (13), 5758–5764. (215) Hann, M. M.; Leach, A. R.; Harper, G. Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery. J. Chem. Inf. Comput. Sci. 2001, 41 (3), 856–864.



Page 92 of 100

(216) Achenbach, J.; Klingler, F. M.; Blöcher, R.; Moser, D.; Häfner, A. K.; Rödl, C. B.; Kretschmer, S.; Krüger, B.; Löhr, F.; Stark, H.; Hofmann, B.; Steinhilber, D.; Proschak, E. Exploring the Chemical Space of Multitarget Ligands Using Aligned Self-Organizing Maps. ACS Med. Chem. Lett. 2013, 4 (12), 1169–1172. (217) Bottegoni, G.; Veronesi, M.; Bisignano, P.; Kacker, P.; Favia, A. D.; Cavalli, A. Development and Application of a Virtual Screening Protocol for the Identification of Multitarget Fragments. ChemMedChem 2016, 11 (12), 1259–1263. (218) Prati, F.; De Simone, A.; Armirotti, A.; Summa, M.; Pizzirani, D.; Scarpelli, R.; Bertozzi, S. M.; Perez, D. I.; Andrisano, V.; Perez-Castillo, A.; Monti, B.; Massenzio, F.; Polito, L.; Racchi, M.; Sabatino, P.; Bottegoni, G.; Martinez, A.; Cavalli, A.; Bolognesi, M. L. 3,4-Dihydro-1,3,5-Triazin-2(1H)-Ones as the First Dual BACE-1/GSK-3β Fragment Hits against Alzheimer’s Disease. ACS Chem. Neurosci. 2015, 6 (10), 1665–1682. (219) Shang, E.; Yuan, Y.; Chen, X.; Liu, Y.; Pei, J.; Lai, L. De Novo Design of Multitarget Ligands with an Iterative Fragment-Growing Strategy. J. Chem. Inf. Model. 2014, 54 (4), 1235–1241. (220) Artis, D. R.; Lin, J. J.; Zhang, C.; Wang, W.; Mehra, U.; Perreault, M.; Erbe, D.; Krupka, H. I.; England, B. P.; Arnold, J.; Plotnikov, A. N.; Marimuthu, A.; Nguyen, H.; Will, S.; Signaevsky, M.; Kral, J.; Cantwell, J.; Settachatgull, C.; Yan, D. S.; Fong, D.; Oh, A.; Shi, S.; Womack, P.; Powell, B.; Habets, G.; West, B. L.; Zhang, K. Y. J.; Milburn, M. V; Vlasuk, G. P.; Hirth, K. P.; Nolop, K.; Bollag, G.; Ibrahim, P. N.; Tobin, J. F. Scaffold-Based Discovery of Indeglitazar, a PPAR Pan-Active Anti-Diabetic Agent. Proc. Natl. Acad. Sci. U. S. A. 2009, 106 (1), 262–267.


Page 93 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(221) Blundell, T. L.; Jhoti, H.; Abell, C. High-Throughput Crystallography for Lead Discovery in Drug Design. Nat. Rev. Drug Discov. 2002, 1 (1), 45–54. (222) Li, Y.; Liu, Z.; Li, J.; Han, L.; Liu, J.; Zhao, Z.; Wang, R. Comparative Assessment of Scoring Functions on an Updated Benchmark: 1. Compilation of the Test Set. J. Chem. Inf. Model. 2014, 54 (6), 1700–1716. (223) Schiebel, J.; Krimmer, S. G.; Röwer, K.; Knörlein, A.; Wang, X.; Park, A. Y.; Stieler, M.; Ehrmann, F. R.; Fu, K.; Radeva, N.; Krug, M.; Huschmann, F. U.; Glöckner, S.; Weiss, M. S.; Mueller, U.; Klebe, G.; Heine, A. High-Throughput Crystallography: Reliable and Efficient Identification of Fragment Hits. Structure 2016, 24 (8), 1398–1409. (224) Knight, Z. A.; Lin, H.; Shokat, K. M. Targeting the Cancer Kinome through Polypharmacology. Nat. Rev. Cancer 2010, 10 (2), 130–137. (225) Aronov, A. M.; McClain, B.; Moody, C. S.; Murcko, M. A. Kinase-Likeness and Kinase-Privileged Fragments: Toward Virtual Polypharmacology. J. Med. Chem. 2008, 51 (5), 1214–1222. (226) Frett, B.; Carlomagno, F.; Moccia, M. L.; Brescia, A.; Federico, G.; De Falco, V.; Admire, B.; Chen, Z.; Qi, W.; Santoro, M.; Li, H. Fragment-Based Discovery of a Dual Pan-RET/VEGFR2 Kinase Inhibitor Optimized for Single-Agent Polypharmacology. Angew. Chem. Int. Ed. Engl. 2015, 54 (30), 8717–8721. (227) Dar, A. C.; Das, T. K.; Shokat, K. M.; Cagan, R. L. Chemical Genetic Discovery of Targets and Anti-Targets for Cancer Polypharmacology. Nature 2012, 486 (7401), 80–84. (228) Ziegler, S.; Pries, V.; Hedberg, C.; Waldmann, H. Target Identification for Small



Page 94 of 100

Bioactive Molecules: Finding the Needle in the Haystack. Angew. Chemie - Int. Ed. 2013, 52 (10), 2744–2792. (229) Saxena, C. Identification of Protein Binding Partners of Small Molecules Using Label-Free Methods. Expert Opin. Drug Discov. 2016, 11 (10), 1017–1025. (230) Lomenick, B.; Hao, R.; Jonai, N.; Chin, R. M.; Aghajan, M.; Warburton, S.; Wang, J.; Wu, R. P.; Gomez, F.; Loo, J. A.; Wohlschlegel, J. A.; Vondriska, T. M.; Pelletier, J.; Herschman, H. R.; Clardy, J.; Clarke, C. F.; Huang, J. Target Identification Using Drug Affinity Responsive Target Stability (DARTS). Proc. Natl. Acad. Sci. U. S. A. 2009, 106 (51), 21984–21989. (231) Martinez Molina, D.; Jafari, R.; Ignatushchenko, M.; Seki, T.; Larsson, E. A.; Dan, C.; Sreekumar, L.; Cao, Y.; Nordlund, P. Monitoring Drug Target Engagement in Cells and Tissues Using the Cellular Thermal Shift Assay. Science 2013, 341 (6141), 84–87. (232) Schulz-Fincke, J.; Hau, M.; Barth, J.; Robaa, D.; Willmann, D.; Kürner, A.; Haas, J.; Greve, G.; Haydn, T.; Fulda, S.; Lübbert, M.; Lüdeke, S.; Berg, T.; Sippl, W.; Schüle, R.; Jung, M. Structure-Activity Studies on N-Substituted Tranylcypromine Derivatives Lead to Selective Inhibitors of Lysine Specific Demethylase 1 (LSD1) and Potent Inducers of Leukemic Cell Differentiation. Eur. J. Med. Chem. 2018, 144, 52–67. (233) Franken, H.; Mathieson, T.; Childs, D.; Sweetman, G. M. A.; Werner, T.; Tögel, I.; Doce, C.; Gade, S.; Bantscheff, M.; Drewes, G.; Reinhard, F. B. M.; Huber, W.; Savitski, M. M. Thermal Proteome Profiling for Unbiased Identification of Direct and Indirect Drug Targets Using Multiplexed Quantitative Mass Spectrometry.


Page 95 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Nat. Protoc. 2015, 10 (10), 1567–1593. (234) Reinhard, F. B. M.; Eberhard, D.; Werner, T.; Franken, H.; Childs, D.; Doce, C.; Savitski, M. F.; Huber, W.; Bantscheff, M.; Savitski, M. M.; Drewes, G. Thermal Proteome Profiling Monitors Ligand Interactions with Cellular Membrane Proteins. Nat. Methods 2015, 12 (12), 1129–1131. (235) Savitski, M. M.; Reinhard, F. B. M.; Franken, H.; Werner, T.; Savitski, M. F.; Eberhard, D.; Martinez Molina, D.; Jafari, R.; Dovega, R. B.; Klaeger, S.; Kuster, B.; Nordlund, P.; Bantscheff, M.; Drewes, G. Tracking Cancer Drugs in Living Cells by Thermal Profiling of the Proteome. Science 2014, 346 (6205), 1255784. (236) Hengst, J. A.; Dick, T. E.; Sharma, A.; Doi, K.; Hegde, S.; Tan, S.-F.; Geffert, L. M.; Fox, T. E.; Sharma, A. K.; Desai, D.; Amin, S.; Kester, M.; Loughran, T. P.; Paulson, R. F.; Claxton, D. F.; Wang, H.-G.; Yun, J. K. SKI-178: A Multitargeted Inhibitor of Sphingosine Kinase and Microtubule Dynamics Demonstrating Therapeutic Efficacy in Acute Myeloid Leukemia Models. Cancer Transl. Med. 2017, 3 (4), 109–121. (237) Bantscheff, M.; Eberhard, D.; Abraham, Y.; Bastuck, S.; Boesche, M.; Hobson, S.; Mathieson, T.; Perrin, J.; Raida, M.; Rau, C.; Reader, V.; Sweetman, G.; Bauer, A.; Bouwmeester, T.; Hopf, C.; Kruse, U.; Neubauer, G.; Ramsden, N.; Rick, J.; Kuster, B.; Drewes, G. Quantitative Chemical Proteomics Reveals Mechanisms of Action of Clinical ABL Kinase Inhibitors. Nat. Biotechnol. 2007, 25 (9), 1035– 1044. (238) Mazaleuskaya, L. L.; Lawson, J. A.; Li, X.; Grant, G.; Mesaros, C.; Grosser, T.; Blair, I. A.; Ricciotti, E.; FitzGerald, G. A. A Broad-Spectrum Lipidomics Screen of



Page 96 of 100

Antiinflammatory Drug Combinations in Human Blood. JCI Insight 2016, 1 (12), doi: 10.1172/jci.insight.87031. (239) Meirer, K.; Steinhilber, D.; Proschak, E. Inhibitors of the Arachidonic Acid Cascade: Interfering with Multiple Pathways. Basic Clin. Pharmacol. Toxicol. 2014, 114 (1), 83–91. (240) Dijkman, P. M.; Castell, O. K.; Goddard, A. D.; Munoz-Garcia, J. C.; de Graaf, C.; Wallace, M. I.; Watts, A. Dynamic Tuneable G Protein-Coupled Receptor Monomer-Dimer Populations. Nat. Commun. 2018, 9 (1), 1710. (241) Hanke, T.; Merk, D.; Steinhilber, D.; Geisslinger, G.; Schubert-Zsilavecz, M. Small Molecules with Anti-Inflammatory Properties in Clinical Development. Pharmacol. Ther. 2015, 157, 163–187. (242) Dalli, J.; Serhan, C. N. Identification and Structure Elucidation of the Proresolving Mediators Provides Novel Leads for Resolution Pharmacology. Br J Pharmacol 2018, doi: 10.1111/bph.14336. (243) Tronci, E.; Francardo, V. Animal Models of L-DOPA-Induced Dyskinesia: The 6OHDA-Lesioned Rat and Mouse. J. Neural Transm. 2017, 1–8. (244) Porras, G.; Li, Q.; Bezard, E. Modeling Parkinson’s Disease in Primates: The MPTP Model. Cold Spring Harb. Perspect. Med. 2012, 2 (3), a009308–a009308. (245) Ernsberger, P.; Koletsky, R. J.; Kline, D. D.; Bedol, D. M.; Friedman, J. E. The SHROB Model of Syndrome X: Effects of Excess Dietary Sucrose. Ann. N. Y. Acad. Sci. 1999, 892, 315–318. (246) Casimiro-Garcia, A.; Filzen, G. F.; Flynn, D.; Bigge, C. F.; Chen, J.; Davis, J. A.; Dudley, D. A.; Edmunds, J. J.; Esmaeil, N.; Geyer, A.; Heemstra, R. J.; Jalaie, M.;


Page 97 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Ohren, J. F.; Ostroski, R.; Ellis, T.; Schaum, R. P.; Stoner, C. Discovery of a Series of Imidazo[4,5- B]pyridines with Dual Activity at Angiotensin II Type 1 Receptor and Peroxisome Proliferator-Activated Receptor-γ. J. Med. Chem. 2011, 54 (12), 4219–4233. (247) Kotagal, V.; Spino, C.; Bohnen, N. I.; Koeppe, R. A.; Albin, R. L. Serotonin, BetaAmyloid, and Cognition in Parkinson Disease. Ann. Neurol. 2018, 83 (5), 994– 1002. (248) Huot, P.; Sgambato-Faure, V.; Fox, S. H.; McCreary, A. C. Serotonergic Approaches in Parkinson’s Disease: Translational Perspectives, an Update. ACS Chem. Neurosci. 2017, 8 (5), 973–986. (249) Bezard, E.; Imbert, C.; Gross, C. E. Experimental Models of Parkinson’s Disease: From the Static to the Dynamic. Rev. Neurosci. 1998, 9 (2), 71–90. (250) Ko, W. K. D.; Bezard, E. Experimental Animal Models of Parkinson’s Disease: A Transition from Assessing Symptomatology to α-Synuclein Targeted Disease Modification. Exp. Neurol. 2017, 298 (Pt B), 172–179.

Table of contents graphic:



378x744mm (96 x 96 DPI)

ACS Paragon Plus Environment

Page 98 of 100

Page 99 of 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


865x963mm (96 x 96 DPI)



308x171mm (96 x 96 DPI)


Page 100 of 100

Polypharmacology by Design: A Medicinal Chemist's Perspective on

Recommend Documents