Polypharmacology by design â a medicinal ... - ACS Publications

Perspective Cite This: J. Med. Chem. 2019, 62, 420−444

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Polypharmacology by Design: A Medicinal Chemist’s Perspective on Multitargeting Compounds Ewgenij Proschak,† Holger Stark,‡ and Daniel Merk*,†,§ †

Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Strasse 9, D-60438 Frankfurt, Germany Institute of Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Universitaetsstrasse 1, D-40225, Duesseldorf, Germany § 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

J. Med. Chem. 2019.62:420-444. Downloaded from pubs.acs.org by EASTERN KENTUCKY UNIV on 01/26/19. For personal use only.

‡

ABSTRACT: Multitargeting 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 multitarget compound development 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. foundation papers.8−14 Since then, multitarget drugs have experienced steadily growing interest owing to their various advantages. The progress of multitarget compounds as well as strategies and technologies for their development were repeatedly reviewed.15−24 Currently, neurodegenerative25,26 and psychiatric disorders,27−35 infectious diseases,36 cancer,37−40 and metabolic/cardiovascular diseases41−43 are in the focus of multitarget 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 multitarget 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

1. INTRODUCTION 1.1. History of Multitarget 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 cascades.1−5 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 action.6,7 Though never designed, such multiple activities often account for the desirable overall efficacy of a drug. While beneficial multitarget 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 multitarget activity. Starting in 2004, Morphy and Rankovic and others systematically classified multitarget agents, and reviewed strategies to multiple ligand development with several © 2018 American Chemical Society

Received: May 11, 2018 Published: July 23, 2018 420

DOI: 10.1021/acs.jmedchem.8b00760 J. Med. Chem. 2019, 62, 420−444

Journal of Medicinal Chemistry

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conserved orthosteric binding sites and might offer a way to specifically modulate GPCR function.56,57 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. section 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 increasingly employed as targeting ligands. Drug conjugates must be clearly distinguished from designed multiple ligands because their pharmacophores are not joined in a common motif (cf. section 3, Scheme 1). In the context of the central idea behind multitarget 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 multitarget 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 multitarget compound design are feasible. A key to successful multitarget compound design is discrete 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.

toolbox for multitarget drug discovery and discusses where the field is heading in the future. 1.2. Advantages of Multitarget 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 multiple signaling pathways and dysregulation of multiple physiological processes.42,46,47 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 multitarget drugs that address several biological targets and produce additive or synergistic effects, termed polypharmacology.48−51 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 discovery.52,53 As obvious from these considerations, multitarget 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 polypharmacy.23 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 synergies.48,54 Suitable target combinations may allow application of lower drug doses to produce adequate therapeutic effects because partial modulation of synergistically acting targets may be sufficient to reach full therapeutic efficacy.54 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, multitarget drug discovery also offers economic advantages because the clinical development of a single multitarget drug requires less clinical trials than multiple specific drugs. 1.3. Areas of Applicability. The central idea of multitarget 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

2. IDENTIFYING SUITABLE TARGET COMBINATIONS FOR MULTIPLE-TARGETING LIGANDS As stated above, a central issue in target selection for multitargeting drug discovery is the chemical feasibility of a multitarget ligand. It is intuitional that designing a dual-target ligand is easier for highly related targets as exemplified by designed multitarget ligands of aminergic receptors60 or kinases.61 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 antagonism,62 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 multitarget 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 ATP-competitive kinase/bromodomain 4 inhibitors, the latter of which recognizes acetylated lysins.63 Moreover, the angiotensin II 421



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clinical observation of therapeutic efficacy allows a rational design of favorable polypharmacological profiles. With the help of fragment-based (cf. section 3.4) and pharmacophore-based (cf. section 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 toward the desired multitarget profiles on clinically validated target combinations.33,84−86 Related targets comprising similar binding sites such as different aminergic class A GPCRs, kinases, or cognate nuclear receptors are well accessible for such multitarget 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, and 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 and metabolic and inflammatory diseases but also for oncologic or antimicrobial drugs where resistances raise urgent need for multifactorial interventions.47 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 penetration.90,91 2.2. Phenotypic Screening of Drug Combinations. Chemical probes, well-characterized ligands with high selectivity against related targets,92 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 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 prevalidated) target combined with the use of chemical probes is a valuable approach to reduce the number of necessary animals in screenings for synergistic target combinations.93 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 FAAH.93 Still, chemical probes are often poorly characterized in vivo and inappropriate pharmacokinetics can cause false negative results. Therefore, cellular, tissue, or nonmammalian 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 lymphoidderived hematologic malignancies.94 The 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)

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 for this task are available. Most of them rely on the chemical similarity principle postulated by Johnson and Maggiora.67 Although this principle is not general by far,68 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 ligands.69 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 an 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 multitarget 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 multitargeting approaches70−72 with suitable drug cocktails, even for orphan diseases.73,74 A prime example of multitarget drug discovery relying on clinical observations are antipsychotics. The initial therapeutic approach of inhibiting dopamine D2-like receptors as a 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 antiserotonergic 5-HT2A activity significantly improved the therapeutic efficacy and reduced adverse effects.75 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 receptors.76,77 This rational, clinical observationdriven multitarget design reduced side effects and enhanced patient response rates as exemplified with successful atypical antipsychotic drugs such as aripiprazole or cariprazine (RGH188).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. section 3.3.1.4). Moreover, unexpected or superior efficacy of drugs comprising polypharmacological profiles can serve as hint to favorable multitarget 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 422



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Table 1. Available in Silico Techniques to Identify Synergistic Target Combinationsa approach Statistical Approaches Bliss and Loewe interaction analysis significance analysis of mutant/drug combinations snalysis of substructure profiles probabilistic concordance index and resampled Spearman correlation Machine Learning Approaches deep learning random forest random forest semisupervised learning Pathway/Network Analysis statistical pair analysis Monte Carlo cross-validation quantitative chemical−genetic interaction map ordinary differential equation model network simulation Combination of Different Techniques Pathway analysis, machine learning, statistical analysis Combined in Silico/in Vitro Approaches RNAi screen, statistical analysis

application

ref

statistical analysis of additivity and independence combined with tailored nonparametric statistical analysis analysis of chemical-genetic data set of resistant bacteria prediction of antibiotic combination efficacy mining of NCI-DREAM Drug Synergy Prediction Challenge data set

109

analysis of drug combination screening on different cell lines learning physicochemical features and network features to predict synergistic drug combinations learning synergy of anticancer drugs in mutant BRAF melanoma network-based Laplacian regularized least square synergistic drug combination prediction of antifungal drug combinations

113 114 115 116

mining clinically relevant synthetic lethality interactions analysis of pathway cross-talk inhibition impact of knockdown of cancer-related genes on response to classes of chemotherapy prediction of drug combinations efficacy within EGFR-ERK signaling pathway synergistic drug combinations for breast cancer

117 118 119 120 121

quantitative systems pharmacology identification of cell protective pathways and drug combinations

122

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

123

110 111 112

a

For further information and details refer to reviews 104,108.

2.3. In Silico Approaches to Target Combinations: Systems Biology/Pharmacology. 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 combinations.103 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 learning.105 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 multitarget 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 growth.106 Moreover, a combinatorial drug screen in dedifferentiated liposarcoma (DDL)-derived cells with subsequent computational modeling of the underlying signaling networks uncovered cyclin-dependent kinase 4 (CDK4) and insulin-like growth factor 1 receptor (IGF1R) as synergistic drug targets for DDL.107 Thus, employing combinations of in vitro and in silico techniques appears as most reliable and economic way to discover synergies in multitarget modulation. The high performance of such translational approaches might result from the possibility to minimize uncertainties in the

inhibitor venetoclax and the mitogen-activated protein kinase kinase (MEK) inhibitor cobimetinib, the mouse double minute 2 homologue (MDM2) inhibitor idasanutlin, or the bromoand extra-terminal domain (BET) inhibitor ABBV-075. Such hints for suitable drug combinations can subsequently be validated, e.g., using CRISPR/Cas9 gene silencing95 and in vivo experiments to provide ideal and validated starting points for a multitarget drug discovery campaign. Biological assays that rely on complex multidimensional 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 multitarget design. BioMAP are well-established highcontent screening systems.97 The BioMAP cultures are lowpassage 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 type.98 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 synergies.100 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 disorders.101 For example, Sonoshita et al. used a Drosophila medullary thyroid carcinoma (MTC) model to tune the multikinase inhibitor sorafenib toward an improved efficacy and safety profile.102 423



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computational methods by in vitro screening data. Computational approaches 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 multitarget drug discovery. Beyond computationally predicting synergistic target combinations, in silico quantitative flux modeling techniques can be employed to calculate the extent to which each target must be modulated for producing the desired therapeutic effect with minimal side effects. 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 combinations.54 In this context, the arachidonic acid cascade, which has been in the focus of multitarget drug discovery for several decades, was extensively investigated, e.g., Yang et al. performed a time-resolved 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 effects.124 Subsequently, these computational predictions were confirmed by time-resolved LC-MS/MS profiling of pro- and anti-inflammatory arachidonic acid metabolites.125 Progressive developments in the field of quantitative in silico modeling of metabolic pathways may provide many further insights to promote multitarget drug discovery.

Scheme 1. Multitarget 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. MULTIPLE-TARGETING LIGAND IDENTIFICATION AND OPTIMIZATION Multitarget 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. section 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 multitarget agents are merged multiple ligands (cf. section 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 fulfill 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 multitarget compound are very challenging tasks. 3.1. Serendipity. From the stories that were told, it is difficult to report on the percentage of multiple-targeting 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 models,127,128 thus involving serendipity and rational optimization efforts. In optimal scenarios, serendipity meets genius,

excellent knowledge, and hard work.48 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 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 structures,129,130 (multitarget) biological activity is often detected by trial and error approaches. Prominent examples of oncologic and antibiotic agents, including Vinca alkaloids and taxoles,55,131,132 penicillin, macrolides, and peptide antibiotics,133,134 were originally claimed as monotargeted but later found to address multiple targets or optimized for multiple targeting.55 The need for multitargeted 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 multitargeting compounds.131,135−140 Also in the field of multikinase inhibitors many initial findings of multitarget activity were based on serendipity. By increasing knowledge through elaborate structure−activity relationship (SAR) studies and the availability of broad structural data, multikinase inhibitors could then be rationally designed.141 The discovery of dual p38 mitogen-activated protein kinase (p38α/MAPK) and phosphodiesterase 4 (PDE4) inhibitor CBS-3595 (1, Scheme 2) exemplifies such serendipitous discovery followed by rational optimization of the multitarget compound.142 During lead profiling of a series 424



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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. 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 linker.44 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 optimization.44 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.

Scheme 2. Dual p38α/MAPK/PDE-4 Inhibitor CBS-3595 (1), Which Is the Product of a Serendipitously Discovered Multitarget Profile and Subsequent Optimization of the Dual Activity

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 landscape led to identification of 1,

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

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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 solubility,153 reduce unspecific uptake of the conjugate into cells by passive diffusion,153 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 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

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 1992,143,144 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 multitarget 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, 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. Because of 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 streptomycetes,145 and several promising synthetic examples of such drug conjugates (2a−c) have been reported recently showing remarkable potency even against multiresistant strains.146−149 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). 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 antimicrobial agents.151 Although this field is rather new, linked multitarget antibiotics might increasingly gain significance in times of limited availability of antimicrobial 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 because 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

Scheme 4. Typical Cleavable Linkers (Examples) Are Either Labile to Acidic Conditions or Reducing Agents, or Designed to Be Specifically Cleaved by Certain Enzymes

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 endopeptidases;159 β-glucuronic acid160 is cleaved by βglucuronidase). 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 spacers,155 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 vivo.155 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 noncleavable fused multiple ligands 426



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Scheme 5. Self-Immolative Spacers (Examples)155,156 Undergo Intramolecular Reactions to Entirely Release the Linked Drugs from the Conjugate

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 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 nonalcoholic 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 5a.169 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. 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 data sets 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

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 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 required.161 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 excluded.161 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 multitarget compound 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. 427



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Scheme 6. Examples for Rational Design of Merged Lead Pharmacophores in Multiple Ligand Developmenta

a

(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 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.

successfully applied to this problem.161 Suitable fingerprints include simple substructure fingerprints173 and circular fingerprints174 but also ligand interaction fingerprints175−177 and pharmacophore feature descriptors.178 With such molecular descriptors, the similarity of actives for a pair (or set) of targets of interest can be computed to identify similar ligands.179 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 an alternative to systematic lead structure searches based on compound similarity, large structure−bioactivity data sets can also be used to generate quantitative structure−activity

relationship (QSAR) models for prediction of bioactivity on the targets of interest. Such models can be built using neural networks or support vector machines or random forest models/decision trees.161,180,181 QSAR models require a considerably large data set 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 multitarget compound design. Recently, 428



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others include poor data quality, limited number of known inactives, erroneous crystal structures, and restricted computational implementation of some interaction types such as halogen bonding.185 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 available.190,195 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 although this may 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 optional.185,190 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 interest.198−200 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 model.196 A purely ligand-based dual pharmacophore approach200 individually generated pharmacophore models for sets of inhibitors of soluble epoxide hydrolase (sEH) and 5lipoxygenase (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 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). Pharmacophore-based de novo design as an 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 devel-

a generative artificial intelligence model trained to capture the constitution of ChEMBL annotated compounds represented as SMILES strings and fine-tuned on a set of ligands for two specific target families was successfully employed to discover novel dual ligands.184 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 (ligand-based) or from cocrystal structures (structure-based). They represent a threedimensional arrangement of features such as hydrogen bond donors/acceptors, 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 positive pharmacophore features are matched.185−187 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. Because 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 modeling software are competent to cover the three-dimensional 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 modeling is a challenging task even for a single target.185,188−190 Structure-based generation of pharmacophore models provides the strong advantage that an optimal three-dimensional conformation of the ligand can be extracted from the cocrystal structure.185,191−194 Moreover, pharmacophore features can be defined from the interactions of the ligand with a 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 model.185 Both, ligand- and structure-based pharmacophore models have their limitations and often suffer from high rates of falsepositives in screening approaches, leading to few retrieved actives. Obstacles in pharmacophore-based screening among 429



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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 multitarget 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 concentrations.64 A recent meta-analysis of 21 randomized clinical trials revealed that telmisartan improves insulin resistance in terms of fasting blood glucose and insulin levels.210 Structure−activity relationship studies of 8a derivatives as partial PPARγ agonists revealed crucial structural features required for PPARγ binding and activation.211,212 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 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

Figure 1. A ligand-based dual pharmacophore approach200 individually generated pharmacophore models for soluble epoxide hydrolase inhibitors and 5-lipoexigenase inhibitors. Subsequent graph-based 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.

oped to computationally “invent” such novel compounds including reaction-driven approaches201 that fuse building blocks, fusion of fragment structures,202 and applications of generative artificial intelligence.184 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 techniques.203,204 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. section 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 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 among drugs from clinical observations, but such starting points may be of extraordinary value for multiple ligand discovery as they

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 multitarget optimization must lie on conserving low lipophilicity and avoiding extensive structural enlargement.8,10,161 The multitarget 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 multitarget agent in vivo and often animal disease models will be necessary to find the optimal balance in potency on the targets of interest.8 430



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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 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.

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., multitarget activity of the ChE inhibitor donepezil (9a) on dopamine receptors was discovered. Continuously relying on Bayesian models, derivatives of donepezil were stepwise optimized toward potent selective as well as 431



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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 toward a multiple ligand. Such SAR data can provide hints in which positions further or alternative substituents are tolerated and whether scaffold hopping toward a more compatible molecular architecture might be feasible. 3.3.4. Design-Out Approach. The design-out strategy toward multiple ligands with designed target modulation profile follows the opposite direction as the design-in approach.8 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 toward the desired target combination. This strategy can profit from X-ray structural data even more than design-in or pharmacophore merging approaches because it relies on a single lead compound that already interacts with all targets of interest with high affinity. Therefore, co-crystal 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 because 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 multitarget 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 fragment-based screening for multiple targets to identify multitarget fragment leads may offer higher chances than screening approaches with larger compounds. Although fragment-based discovery of multiple ligands appears as a highly promising approach,84 the number of successful prospective studies is limited. Most of these rely on in silico preselection 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 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

multitarget 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 electronreleasing 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 a manner, every position can be systematically optimized. The SAR on multiple 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 developed.58 Multiple ligand optimization can also successfully rely on structural information or molecular docking. Zafirlukast (10a) was identified as a 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 3.3.3. Design-In Approach. Complementary to the search for a merged lead pharmacophore that already satisfies the structural requirements for modulation of 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 an approach, the pharmacophore (and activity on target) of one compound is designed into the other in a design-in strategy.8 By stepwise adaption of one structure toward the other and introduction of structural elements required for activity on the complementary target, dual potency is generated. Of note, the design-in approach and the rational generation of merged consensus pharmacophores (cf. section 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 a strategy lies in increasing potency for the complementary 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 substitution patterns for dual/multiple potencies can 432



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Scheme 8. Multitarget Fragment Examplesa

a 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.

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 toward both targets for experimental validation in STD-NMR and activity-based assays. Five out of 24 fragments showed inhibitory activity toward 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 multitarget fragment identification has successfully yielded dual inhibitors of βsecretase 1 (BACE-1) and glycogen synthase kinase 3β (GSK3β).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 best-established technique to identify multitarget fragments. Prati et al. applied such an approach to dual BACE-1/GSK-3β inhibitors.218 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 multitarget 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 multitarget drug design as only few studies have reported a full screening of fragment-based libraries toward a rationally selected target combination.

Artis et al. described the fragment-based discovery of the pan-PPAR agonist indeglitazar 13220 starting from the multitarget fragment 5-methoxyindole-3-propionic acid 13a, which was identified in an in vitro fragment library screen using proximity-based coactivator 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 toward fragment-based multitarget approaches: it seems clearly possible to identify fragment hits addressing multiple targets, but structural information will be required for successful fragment growing and fragment-based design.221 However, few laboratories working in the field have the possibility to generate reliable structural information. Several approaches to fragment-based multitarget 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 functions.222 Recent advances in highthroughput crystallography223 may allow a major breakthrough in fragment-based multitarget drug discovery in near future. Another important application of fragment-based design is multikinase inhibitor development. Originally, selectivity rather than designed multitarget activity has been a 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 toward kinase inhibitors has changed the selectivity paradigm and boosted polypharmacological kinase inhibitor design.224 Therein, the concept of kinase-privileged fragments facilitates identification of multikinase inhibitor leads.225 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) inhibitor.226 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 433



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subproteome. Subsequent addition of the multitarget kinase inhibitor of interest displaces its targets which can be analyzed by quantitative mass spectrometry. 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 multitarget compounds, of course. High content screenings (cf. section 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 anti-inflammatory drugs.238 In this study, shunting effects that occurred for selective inhibitors were ameliorated by inhibitor combinations, an outcome which would be favored for multitarget inhibitors of the arachidonic acid cascade.239 Thus, “-omics” techniques are suitable to demonstrate functional properties of multitarget compounds in cells or tissues. Concepts to address target interactions caused by receptor homo- or heterodimerization/oligomerization and other strategies to achieve ligand-specific signaling mechanism are also arising.240 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 multitarget 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 be of particular interest concerning the late formation of anti-inflammatory protectins or resolvins, which are not detected in these assays.241,242 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 multitargeting 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 a model of Parkinson’s disease only partly resembles the multifactorial disease state in human.243,244 In some instances, phenotypic screenings (cf. section 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.

screening approach efficiently identified multitarget candidates from a library of kinase-privileged compounds with favorable balance of efficacy and toxicity.227

4. TEST SYSTEMS TO CHARACTERIZE MULTIPLE LIGANDS Functional characterization of multiple ligands and confirmation of their simultaneous multitarget engagement as well as synergistic pharmacodynamic effects requires sophisticated in vitro assay systems and in vivo disease models. Because 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 multitarget 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 extensively.228 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 tissues. 229 Drug affinity responsive target stability (DARTS),230 a method relying on target stabilization against protease degradation upon ligand binding, holds much promise in multitarget 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 a 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 multitarget lysine-specific demethylase 1 (LSD1)/MAO lead compound.232 Although this study could only demonstrate engagement on LSD1, CETSA has great potential as a method to study simultaneous target engagement for multiple ligands. On the basis of 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 multitarget pan-HDAC inhibitor panabinostat233 and to profile interactions of small molecules with transmembrane proteins.234 In studying target engagement of multikinase 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 434



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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 multitarget 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 multitarget 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. Multitarget 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 multitarget compound development. High-throughput crystallography might equip multitarget 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 for revealing simultaneous target engagement and synergistic effects of multitarget 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 multitarget 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 multitarget 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 a 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, multitarget compound design, optimization and functional characterization involves many disciplines and demands strong expertise in each field. Multidisciplinary collaborative projects involving solid expertise not only in medicinal chemistry but also in computational techniques, in vitro test systems, and in vivo pharmacology and therefore appear necessary for successful development of therapeutically relevant multiple ligands.

Cross-breeding of animals each reflecting single pathophysiological phenotypes may enable more complex and realistic models of multifactorial diseases that require treatment with multitargeting compounds. One such complex animal model of the metabolic syndrome, the spontaneously hypertensive obese (SHROB) rat,245 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 multitarget compounds. Earlier studies of such multitarget 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.247,248 Still, more complex parameters and models are needed for a realistic animal disease representation useful for multiple targeting249−252 and in some cases, nonhuman primate screening (marmoset) may be required. As such preclinical trials need many prerequisites, a 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 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. 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 anticancer and anti-infectious disease treatment. Multitarget 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 multitarget compound development. Computational methods and models combined with enormous available data sets are key contributors to the growing success of multitarget 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 approaches for this central step in multitarget design have been developed and further examples of their prospective 435



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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 multitarget design. However, despite the availability of enormous data sets, reliable computational methods involving artificial intelligence and automated steps, e.g., in compound design and optimization, multitarget 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 toward drugs that address more than one single target.205,206 In addition, increasing disease complexity and improved understanding of pathophysiological correlations promote multitarget compound development and render multiple ligands a key for securing innovation in future drug discovery.

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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 multitarget compound design.

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ACKNOWLEDGMENTS We 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).

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ABBREVIATIONS USED

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REFERENCES

5-HT2, serotonin receptor 2; 5-HT2A, serotonin receptor 2A; 5-LO, 5-lipoxigenase; 6-OHDA, 6-hydroxytryptamine; AT1R, angiotensin II receptor 1; BACE-1, β-secretase 1; BCL, B-cell lymphoma; BET, bromo- and extra-terminal domain; CDK4, cyclin-dependent 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, G-protein 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 homologue; MEK, mitogen-activated protein kinase kinase; MTC, medullary thyroid carcinoma; NMR, nuclear magnetic resonance; p38/MAPK, p38 mitogenactivated protein kinase; PDE-4, phosphodiesterase 4; 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 sideactivities; STD-NMR, saturation-transfer difference NMR; QSAR, quantitative structure−activity relationship; VEGFR2, vascular endothelial growth factor receptor 2; ZDF rat, Zucker diabetic fatty rat

AUTHOR INFORMATION

Corresponding Author

*Phone: +41 44 6337439, +49 69 79829327. E-mail: daniel. [email protected], [email protected]. ORCID

Ewgenij Proschak: 0000-0003-1961-1859 Daniel Merk: 0000-0002-5359-8128 Notes

The authors declare no competing financial interest. Biographies 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 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 5-LO, 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. Holger Stark studied pharmacy in Berlin, Germany. In 2000, he became a 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 coinventor of pitolisant (Wakix), the first histamine H3 receptor antagonist with market approval. He is editor-in-chief of Archiv der Pharmazie and received an honorary doctorate from University of Nis, Serbia.

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Daniel Merk graduated in Pharmacy and Pharmaceutical Sciences at the Ludwig-Maximilians-University Munich and received his Ph.D. in Pharmaceutical/Medicinal Chemistry from Goethe University Frankfurt. Since 2015, he is Junior Group Leader at the Institute of 436



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