Rational Design of Multi-Target-Directed Ligands: Strategies and

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Rational Design of Multi-Target-Directed Ligands: Strategies and Emerging Paradigms Junting Zhou, Xueyang Jiang, Siyu He, Hongli Jiang, Feng Feng, Wenyuan Liu, Wei Qu, and Haopeng Sun J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.9b00017 • Publication Date (Web): 13 May 2019 Downloaded from http://pubs.acs.org on May 13, 2019

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Journal of Medicinal Chemistry

Rational Design of Multi-Target-Directed Ligands: Strategies and Emerging Paradigms

Junting Zhou a, b, Xueyang Jiang a, b, Siyu He a, Hongli Jiang a, b, Feng Feng b, c, Wenyuan Liu d, Wei Qu b *, Haopeng Sun a *

a

Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing,

211198, People’s Republic of China b

Department of Natural Medicinal Chemistry, China Pharmaceutical University,

Nanjing, 211198, People’s Republic of China c

Jiangsu Food and Pharmaceutical Science College, Huaian, 223003, People’s

Republic of China d

Department of Analytical Chemistry, China Pharmaceutical University, Nanjing,

210009, People’s Republic of China

Correspondence: [email protected] (Haopeng Sun); [email protected] (Wei Qu).

ACS Paragon Plus Environment

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Abstract: Due to the complexity of multifactorial diseases, single-target drugs do not always exhibit satisfactory efficacy. Recently, increasing evidence indicates that simultaneous modulation of multiple targets may improve both therapeutic safety and efficacy, compared with single-target drugs. However, few multi-target drugs are on market or in clinical trials, despite the best efforts of medicinal chemists. This article discusses the systematic establishment of target combination, lead generation, and optimization of multi-target-directed ligands (MTDLs). Moreover, we analyze some MTDLs research cases for several complex diseases in recent years, and the physicochemical properties of 117 clinical multi-target drugs, with the aim to reveal the trends and insights of the potential use of MTDLs.

1. Introduction The doctrine of “one molecule, one target, one disease” dominated the pharmaceutical industry in the 20th century1. Plenty of successful selective drugs have been developed from this doctrine, which will predominate for certain diseases in the future2. However, for multifactorial diseases such as cancer3-6, neurodegenerative disease7, 8, cardiovascular disease9, 10 and infection11, the initiation and progression involve multiple receptors or signaling pathways. For multifactorial diseases, manipulating a single target does not always lead to satisfactory efficacy, despite the best research efforts12. Hence, there is an increasing interest of developing therapeutics which simultaneously manipulate multiple targets, for improving therapeutic efficacy and (or) safety13. There are two approaches of multi-target therapeutics: the first approach is a


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mixture of monotherapies, including drug cocktail (combinations of drugs with a single active ingredient, respectively) and combination drug (one formula includes multiple active ingredients); whereas the second approach is multi-target-directed ligands (MTDLs, one tablet, one active ingredient)14. The former approach may provide better dose flexibility and lower treatment cost. However, it’s often suffered from adverse effects and poor patient compliance15. For instance, the combination of venlafaxine and fluoxetine for depression therapy may cause severe anticholinergic adverse effects16. Furthermore, it often leads to undesired effects such as dose-limiting toxicities, drugdrug interactions, complex and unpredictable pharmacokinetic (PK)/pharmacodynamic (PD) profiles17. Since 2004, Morphy et al. started to systematically review strategies for multifunctional ligands development18-22. MTDLs are agents which treat multifactorial diseases by interacting with multiple targets involved in pathogenesis23. This strategy could avoid many pitfalls24-26. In Table 1, the advantages and disadvantages of the above two approaches are discussed.


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Table 1. Comparison of the advantages and disadvantages of two multi-target therapeutics14. Approaches

Mixture of monotherapies

Multiple ligands 1. They are new chemical entity; 2. The programs of development and regulatory approval are standard,

1. Can offer better dose flexibility by directly adjust ratio of drugs in same as the single target drugs; mixture; 3. As a single active component, the PK/PD properties are easier to Advantages 2. Can achieve sequenced administration or adjusting target exposure; formulate compared with a mixture; 3. Clinical trials may be faster due to clinical experience, and the 4. Therapeutic efficacy may increase through synergies even at low treatment cost may be lower. dosages; 5. Reduced adverse effects enable wider therapeutic windows. 1. “Combination versus parts” factorial trial might be conducted; Disadvantages 2. Should align PK/PD properties of the multiple components; 3. More likely to cause drug–drug interaction.

1. Achieving balanced and multi-selective potency towards multiple targets is challenging; 2. Difficult to achieve sequenced administration at the targets.


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To date, some outstanding MTDLs have been in clinical trial stage or approved by regulatory agencies12. However, many of them were found by serendipity, such as imatinib (a multi-target kinase inhibitor)27. The “simple” strategy of making MTDLs by linking two active ligands together usually resulted in very large molecular weight (MW)7. For example, many researches have published about hybrids of tacrine and other active ligands, which were obtained in linked pharmacophore approach28, 29. Moreover, some lead compounds discovered through phenotypic screening lack definite targets, with high promiscuity and severe adverse effects30. These problems explain the failures of many MTDLs. Thus, we believe that multi-target drugs should be designed in a more rational and holistic view of target combination, ligands selection and the balance of desired activities to maximize developability, efficacy and safety. In this review, we discuss the above critical issues, analyze the physicochemical properties of 117 clinical multitarget drugs, and go over important references about MTDLs published in recent years, to provide a comprehensive compendium of the rational design of MTDLs. 2. Rational Combination of Multiple Targets for MTDLs The design and validation of target combination is crucial for MTDLs discovery— ideal target combination may provide superior therapeutic efficacy through synergies. It is generally easier to design multi-target ligands for highly related targets in the same superfamily31, 32. If targets belong to different superfamilies, their endogenous ligand had better be similar, or even identical (often a monoamine or an eicosanoid)19. If so, the binding sites of multiple targets are more likely to accommodate a shared ligand



frame19. Interestingly, there are also some rare examples, whose targets come from different superfamilies, and endogenous ligands are also unrelated33, 34. In this case, the realistic method could be combining pharmacophores via exploiting the tolerant positions of each component. Nonetheless, for highly similar targets, achieving multiple activities is easy, but formulating optimized selectivity at closely related but undesired targets is the challenge35. Theoretically, undesired but relevant targets had better be as few as possible. But a few examples showed that it was not impossible36, 37. 2.1 Target Combination Based on Clinical Observations Combination therapies such as drug cocktails are common topics of clinical studies, but the issues of complex PK profiles, drug-drug interaction often limit their clinical applications14. However, the increasing knowledge of enhanced efficacy suggests that the target combination (validated by drug cocktails) may be possible26, 38,39

. A typical example of target combination based on clinical observations is

antipsychotic. Initially, the antipsychotics targeting dopamine D2-like receptors often cause considerable adverse effects, for example, the extrapyramidal motor symptoms40. The following clinical research found that additional inclusion of anti-serotonergic 5HT2A led to improved treatment effect and reduced adverse effects41. Subsequently, the adjustment of potencies towards multiple targets, including agonism on one receptor and partial agonism on another was achieved42, 43. Such multi-target design driven by clinical observation significantly improved efficacy and reduced adverse effects, and


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antipsychotic drugs such as cariprazine and aripiprazole are the successful examples4446

.

2.2 Target Combination Based on Phenotypic Screening Phenotypic screening is another approach for target combination47, 48. Cellular, tissue and animal models can all be used for screening large numbers of compound combinations for synergies49. But high throughput of putative compounds or target combination requires tremendous animal experiments. Even only a few compounds are tested, different dose combinations would require large animal sample sizes. Therefore, genetic knockdown or knockout of one target is a feasible approach to reduce animal usage, due to the absence of dose combinations17. For instance, a study used diclofenac (a COX inhibitor) in an inflammatory model using FAAH (-/-) mice to validate the synergistic effect of these two targets50. Biological assays that generate multidimensional read-outs are usually known as high-content screening, which are valuable approaches for MTDLs discovery51. If the screening systems are more complex, entire animals may be employed, enabling sophisticated read-outs including even behavioral changes52. For example, fruit fly or zebrafish can be used for the complex screening system53. 2.3 Target Combination Based on in-Silico Technique In-silico technique is also a feasible approach for screening suitable target combination54. Many in-silico methods55 are available, such as analysis of biological target networks via machine learning56. The network pharmacology method by analyzing signaling networks is especially valuable57. However, the hypothetic target



combination predicted by computational approaches needs wet-lab validation to confirm its biological foundation and feasibility. For example, the target combination of insulin-like growth factor 1 receptor (IGF1R) and cyclin-dependent kinase 4 (CDK4) was predicted by computational modeling, and validated to be synergistic drug targets for DDL in dedifferentiated liposarcoma (DDL)-derived cells58. 3. Lead Generation For exploring MTDL lead compounds, there are generally two approaches: screening approaches and knowledge-based approaches19, 20, 59. They usually analyze information in the literature or patent. 3.1 Knowledge-Based Approach Knowledge-based approach is also known as pharmacophore-based approach, which is currently the predominant method for generating MTDLs59. This approach combines pharmacophores of selective ligands for multiple targets into a single compound, which integrates the activities of these ligands. In the order of pharmacophore overlap incrementation, the MTDLs are classified into linked, fused and merged types (Figure 1)22. Actually, the level of overlap of the pharmacophores forms a continuum: one extreme is the linked MTDLs with high MW and lengthy linker; whereas the other extreme is the merged MTDLs, which is the result of a high degree of overlap of pharmacophores and obtains smaller MW and simpler structure22.


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Figure 1. Multi-target drug design strategy based on pharmacophore22. In linked MTDLs, the pharmacophores are connected by stable or biodegradable linkers; in fused MTDLs, the pharmacophores are attached directly; in merged MTDLs, the pharmacophores are merged together. 3.1.1 Linked Pharmacophores A linked MTDL typically contains underlying pharmacophores separated by a linker which doesn’t exist in any one of the original ligands22. Although this is a straightforward strategy, the position, length, and composition of linkers can be designed to retain the activity of the template scaffolds60. For instance, the linker should not join a ligand at the position with steric effect. Sometimes, the linker itself works as pharmacophore by interacting with target. Therefore, linkers should be tailored so as to enhance interactions between the targets and linkers60. According to the property of linkers, the linked MTDLs are further divided into two categories: cleavable or noncleavable19. However, linked MTDLs are usually too large for favorable bioavailability, or accessing intracellular compartments. Moreover, the linker may hinder the interaction between targets and ligands17. Recently, linked pharmacophores become a popular



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concept: a targeting ligand (e.g., the antibody) is linked to an active molecule, then the targeting ligand delivers the active ligand to a desired target to exert efficacy61. The cleavable MTDLs employ a linker which can be degraded to release ligands for corresponding targets, respectively19. In most cases, cleavable MTDLs have an ester linker which can be degraded by plasma esterase, as shown in the example in Scheme 1A. Although cleavage might make the PK/PD relationship complex, cleavable MTDLs are regarded as a single active ingredient in the stage of administration. This is one advantage compared with cocktails and multicomponent drugs. Compound 3 was reported as a new multi-target therapeutic candidate, which incorporated the pharmacophores of cinnamic acids 1 (lipoxygenase inhibitors, antioxidants and anti-inflammatories)62 and paracetamol 2 (a nonsteroidal antiinflammatory drug)63 via an ester linker. The compound 3 showed high analgesic activity (91%) suggesting the application in treating peripheral nerve injuries62 (Scheme 1A). This kind of MTDLs may be referred as “mutual prodrug”: after degradation of ester linker, the released ligands will elicit therapeutic effects in distinct pathways. Most reported linked MTDLs are actually non-cleavable MTDLs, whose linker is stable in vivo. As single active ingredients, non-cleavable MTDLs are able to interact multiple targets and responsible for corresponding activity. Compound 6 is a pharmacological hybrid combining H1 receptor (H1R) and H2R antagonistic activities. It was designed through linking roxatidine-type H2R antagonist (5) pharmacophore with mepyramine-type H1R antagonist (4) motif

64

. The N-

desmethylmepyramine was linked by a poly-methylene linker, to form the


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cyanoguanidine motif that was the “urea equivalent” of the H2R antagonist moiety. Compound 6 was the most potent hybrid with pKb values of 8.42 for H1R and 6.43 for H2R64 (Scheme 1B). Scheme 1. The Two Categories of Linked MTDLs: The Cleavable MTDLs and Non-cleavable MTDLs

a

(A) Compound 3 is a cleavable MTDL, with an ester linker that can be hydrolyzed in

vivo62. (B) Compound 6 is a non-cleavable MTDL, the linker of which is stable in vivo64. 3.1.2 Fused Pharmacophores The MTDLs with partially overlapped pharmacophores are classified as fused MTDLs. Compound 9 was designed by fusing the phenyl from rivastigming (7) and the dihydro-indene ring from rasagiline (8). The compound 9 exhibited both acetylcholinesterase

(AChE)

and

monoamine

oxidases

(MAOs)

activities

simultaneously (Scheme 2A)65. Compound 12 is a hybrid of curcumin 10 and melatonin 11, two well-studied natural products in Alzheimer’s disease (AD) models. Hybrid 12



was discovered to exhibit significant neuroprotection and anti-oxidative activities66 (Scheme 2B). Scheme 2. Representative Structures of Fused Pharmacophoresa

a

The two pharmacophores are in blue and red; purple illustrates the fused/merged

pharmacophore from two selective ligands. (A) Dual cholinesterase/MAO-B inhibitor (9)65. (B) Neuroprotection/anti-oxidative bisfunctional agent (12)66. Both compounds 9 and 12 are the products of fused pharmacophores approach. 3.1.3 Merged Pharmacophores The MTDLs with merged pharmacophores have the highest level of pharmacophore overlapping. By identifying “tolerant region” for each receptor and taking advantage of commonalities in each framework of the template ligands, the pharmacophores are highly integrated to obtain smaller MW and simpler structures with optimized physicochemical profiles. Through incorporating the pharmacophore of 3-hydroxy-4-pydinone (for metal chelation) from deferiprone and aminopropoxyphenyl scaffold from a H3R antagonist into one molecule, compound 16 was designed, synthesized and biologically characterized67. It shows excellent selective H3R antagonism (from compound 13),


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amyloid-β (Aβ) aggregation inhibition (from compound 14), metal ion chelation, and radical scavenging (from compound 15)67 (Scheme 3A). A site-activated multifunctional candidate for treating AD was reported68. This research integrated their newly developed bifunctional chelator HLA20 (17) and two marketed drugs, rivastigmine (7) and donepezil (18, an AChE inhibitor). Three moieties (phenyl, carbamyl and ethylmethylamino moieties) of rivastigmine bound to the active site of AChE,

meanwhile,

the

pharmacophore

[(5,

6-dimethoxy-1-indanon-2-yl)

methylpiperidine] from donepezil interacted with the middle of AChE gorge and the PAS. The above moieties were merged into the scaffold of 17. Compound 19 showed less cytotoxicity than 17, and negligible affinity to metal ions, unless it inhibited BuChE and AChE (Scheme 3B). Scheme 3. Representative Structures of Merged Pharmacophoresa

a(A)

Multifunctional agent (16) is designed by merging the pharmacophores of the

aminopropoxyphenyl scaffolds from compound 13, 14 and 1567. (B) Bisfunctional compound 19 is designed via merging the pharmacophores from chelator HLA20 (17), rivastigmine (7) and donepezil (18)68.



3. 2 Screening Approach The screening approach is another commonly reported strategy for MTDL lead generation69. Actually, focused screening is the main-stream screening strategy, instead of “irrational” high throughput screening (HTS). In focused screening, compound classes already identified to be effective towards one of the targets of interest are subsequently screened for another one. It is a favorable approach for targets that are kinases: MTDLs are often identified via cross-screening of compounds in kinase panel profiling70. MTDLs produced by screening methods may obtain all activities towards both targets of interest (A and B)20. But the resulting MTDLs are highly unlikely to possess the balanced affinity to multiple targets, therefore, the affinity would need to be optimized on all targets (cf. section 4.3). Moreover, the lead may also have activity towards undesired targets, which must be “design-out” during optimization (cf. section 4.2) (Figure 2).

Figure 2. The screening of compounds libraries may generate a lead compound which obtain all activities towards both targets of interest (A and B). (a) But a single compound may be unlikely to have balanced affinity to all targets, therefore its activities should be optimized. (b) In another case, the lead compound also has activity towards


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undesired targets, which must be “design-out” during optimization20. 4. Lead Optimization After the lead generation phase, the next challenge is to optimize the lead ligand for balanced activities and better physicochemical profiles. In this lead optimization phase, there are mainly two approaches: the design-in and design-out approaches17. 4.1 Design-in approach In design-in approach, the pharmacophore of one ligand is designed into the other according to molecular architecture and functional groups important for interacting with the biological targets19. Preferably, the two selective lead compounds share sufficient similarity. The challenge of this approach lies in enhancing affinity to one target while retaining the affinity to the other target. Therefore, systematic structureactivity relationship (SAR) of substitution for multiple potencies should be modeled71. The SAR data of selective template ligands may provide many hints for the optimization of the hybrid, e.g. whether scaffold hopping are workable, and whether substituents are feasible, etc. Of note, instead of two distinctly separated approaches, the rational generation of merged pharmacophores and the design-in approach can be referred as a common strategy20. Using design-in approach, Woo et al.72-77 designed a phenol sulfamate moiety from compound 21 into the scaffold of compound 20. The phenol sulfamate moiety of compound 21 is responsible for irreversible steroid sulfatase (STS) inhibition, and the scaffold of 20 is for aromatase inhibition. The initial optimization relied on molecular docking and co-crystals studies created the hybrid compound 22. Further SAR



researches and scaffold hopping developed several chemotypes of MTDLs, among which the compound 23 targeted both enzymes with activities down to picomolar values (Scheme 4). Scheme 4. Representative Example of Design-in Strategy a

a

Based on design-in approach, the pharmacophore of 20 is designed into that of 21,

according to molecular docking and co-crystals information75, 76. 4.2 Design-out approach Generally, design-out approach begins with a compound which simultaneously acts on all targets of interest, involving undesired ones. By reducing the affinity to undesired targets, selectivity to the targets of interest will be improved78. Compared with design-in and merged pharmacophores approaches, this approach may benefit more from X-ray structure analysis, because it starts from a single ligand which already modulates with all desired targets18. The binding mode of the lead compound toward undesired targets can be characterized via co-crystal structures analysis. The


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identification of the binding mode differences between desired and undesired targets paves the way for the disruption of the potency on the “off-target(s)”. Such approach is less common due to the rarity of available leads with multi-target activities. However, it’s still attractive if the numbers of targets of interest exceed two, because every additional target may exponentially increase the challenges of design-in approach17. The design-out approach has been applied successfully to develop MTDLs. For instance, Reichard et al.79 designed a neurokinin 1 (NK1)/NK2 dual antagonist in this approach. Compound 24 is a broad-spectrum NK antagonist. Its Ki values are 1.3 nM, 0.4 nM and 0.3 nM for NK1, NK2 and NK3, respectively. However, NK3 receptor may have a critical impact on androgen/secretion/gonadotrophin production. Targeting NK3 may lead to an anti-androgen therapy, but their intention was to develop a NK1/NK2 dual antagonist for asthma. Therefore, they minimized NK3 binding affinity of the lead to avoid potential NK3-mediated effects. By applying a stereo-simplification strategy, a strict SAR study of piperidine pharmacophore from 24 was carried out via an efficient synthesis of substituted cyclic urea, which resulted in the development of compound 25, which showed selective NK1/NK2 affinity (the Ki values were 1.9, 4.1 and 945 nM for NK1, NK2 and NK3, respectively) (Scheme 5).



Scheme 5. Representative Example of Design-out Approach

aIn

design-out approach, Compound 25 is obtained by reducing the affinity to the

undesired target NK3, meanwhile, retaining the affinities on NK1 and NK279. 4.3 Balancing of Activities Establishing the optimal ratio of affinities to multiple targets via in vitro assays is critical: ideally, each target should be modulated to a suitable level in vivo at similar brain or plasma concentrations, to maximize clinical relevance19. For a MTDL with large difference in affinities in vitro towards multiple targets, it may well only exert multiple effects at high doses. However, it often cause side-effects that limit dosage upper limit80,

81

. However, recent examples showed the feasibility of balancing

activities through synergistic effect with differences in in vitro activities82. So the phenotypic experiments in vivo activity validation are necessary22. In most cases, the goal is to balance in vitro activities within an order of magnitude for multiple targets, under the presumption that it may achieve similar degrees of in vivo receptor occupancy. However, the biodistribution of compound, the level of receptor occupancy for each target, receptor and (or) enzyme densities are different in various tissues20. Therefore, clinical knowledge and input are very important for optimizing in vitro activities. If


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clinical knowledge or input is insufficient, animal pathological models may be helpful to find the optimal balanced ratio22. Ideally, the template ligands to be integrated should contain similar physicochemical profiles and in vivo activities at the same order of magnitude. Because it’s hard to obtain a workable MTDL if the template ligands are completely different from each other in terms of PK/PD profiles and activities20. 5. MTDL Synthesis Since MTDLs are often hybrids integrated by two or more selective ligands, their synthesis will be undoubtedly more difficult than traditional small molecule drugs. Moreover, the generation of effective molecules as MTDLs relies on the nature of template ligands, linkage site, linker length, etc. Therefore, developing efficient synthetic strategies for MTDLs is crucial for MTDLs development. For linked MTDLs, linker may be the key in designing leads, because the length, position, and structure of linkers may influence the activity. In some cases, linkers can participate in target binding, and even work as pharmacophores83. The click chemistry paradigm84 has been quickly applied to medicinal chemistry because it meets three important criteria to be an ideal reaction: versatility, selectivity and efficiency. Recent application of click chemistry mainly focused on coppercatalyzed azide-alkyne cycloaddition (CuAAC), the thiol-yne click reaction (TYC) / thiol-ene click reaction (TEC) and the Diels-Alder (DA) reaction84. Among them, CuAAC reactions are most widely applied in MTDLs synthesis (Scheme 6A). The simplicity and tolerance of CuAAC reaction, with the relatively



inertial triazole ring, suggested its capability of linking two or more ligands. For instance, a “click chemistry” method was described for the efficient synthesis of proteosis-targeting chimera (PROTAC)85. This reaction was applied in linking the bromodomain and extra-terminal domain-4 (BRD4) ligand, and ligase binders targeting Von Hippel-lindau (VHL) and cereblon (CRBN) proteins (Scheme 6B). Furthermore, the structure of 1,2,3-Triazoles formed through CuAAC reaction not only contributes to linking entities, but also works biologically active pharmacophores in treating cancer86, acquired immune deficiency syndrome epidemic87, tuberculosis88, fungal infections89, 90. However, few 1, 2, 3-triazole-bearing therapeutics are on market or in clinical trials. Hopefully, we will witness the emergence of them on market as the click reactions are used more and more widely. Scheme 6. General Scheme and Representative Example of CuAAC Reactiona

a(A)

the 1, 3-dipolar cycloaddition between alkynes and azides; (B) the representative

example of CuAAC reaction85. Compared with CuAAC reaction, TEC/TYC and DA reactions are less applied in


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the synthesis of MTDLs. DA reaction can be applied between a substituted alkene (usually termed the dienophile) and a conjugated diene91, to form a substituted cyclohexene derivative (Scheme 7A). Based on merged pharmacophores approach, Arepalli

et

al.92

designed

a

series

of

novel

1,3-diphenylbenzo[f][1,

7]benzonaphthyrdines as potent cytotoxic agents and Topoisomerase IIα inhibitors, through one-pot intermolecular imino DA reaction (Scheme 7C). This reaction has many advantages, such as economical, atom-economy, multi-component reaction, region-selective, and broad substrate scope92. TEC/TYC reaction only has a few applications in the synthesis of MTDLs (Scheme 7B). Scheme 7. General Schemes and Representative Example of DA and Thiol-ene/yne Click Reactiona

a(A)

The Diels-Alder (DA) reaction; (B) the TEC reaction (top) and the TYC reaction

(bottom); (C) the representative example of DA reaction92. Apart from the click chemistry, cross-coupling reactions are emerging as an effective method of organic transformations with diverse applications in the systhesis of carbon-carbon (or carbon-heteroatom) bonds93. They have enabled synthesizing



complex organic molecules, with extensive applications in the preparation of MTDLs94. Such reactions have several advantages: (i) mild conditions; (ii) high yields; (iii) easy application under both aqueous and heterogeneous conditions; (iv) toleration of a broad range of functional groups; (v) robustness to steric hindrance, and so on95. The high predictability, operational-simplicity, and superb functional group tolerance of crosscoupling reactions may contribute to efficient strategies development for MTDLs synthesis. The Suzuki cross-coupling represents the most powerful C-C bond forming reaction in organic synthesis96. Traditional Suzuki cross-coupling contains the coupling of an aryl halide (pseudohalide) and an organoboron reagent, and is most commonly employed for the synthesis of biaryls by a C(sp2)-C(sp2) disconnection using a nickel or palladium catalyst (Scheme 8A). Acyl Suzuki cross-coupling contains the coupling of an acyl electrophile (acyl halide, anhydride, ester, amide) and an organoboron reagent (Scheme 8B). Hou et al.97 developed an efficient and convenient protocol for synthesizing ABT-869 (a MTDL for receptor tyrosine kinases), and the Suzuki coupling reaction was applied in the key step of ABT-869 synthesis (Scheme 8C). The synthetic process was more suitable for industrial production due to the advantages of simplicity, high yield and purity97.


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Scheme 8. General Scheme and Representative Example of Suzuki Reaction

a (A)

The aryl Suzuki reaction: C(sp2-sp2) cross-coupling; (B) the acyl Suzuki reaction:

C(acyl-sp2) cross-coupling; (C) the representative example of Suzuki reaction97. The cross-coupling reaction of organic electrophiles with organostannanes is usually referred as the Stille reaction (Scheme 9A). The process of Stille reaction is mild, and it can tolerate of a broad range of functional groups, which is therefore widely applied in the synthesis of complex molecules98. Further, organostannanes are generally insensitive to oxygen and moisture, tolerant to harsher conditions, and can be achieved through various methods98. The R1 group attached to the trialkyltin is generally sp2hybridized, involving aryl, and alkenes groups; but conditions have been designed to incorporate both sp3-hybridized groups, such as benzylic and allylic substituents, and sp-hybridized alkynes. X is generally a halide (such as Cl, Br, or I), but pseudohalides



(such as triflates), sulfonates and phosphates can also be used99. Rasolofonjatovo et al.100 designed a series of hybrids through merging combretastatin A-4 and isocombretastatin A-4 cores. The Stille reaction was applied in the key synthesis progress (Scheme 9B). Scheme 9. General Scheme and Representative Example of Stille Reactiona

a(A)

The Stille reaction; (B)representative example of Stille reaction100. The palladium catalyzed C-C bond formation, which couples a sp2 carbon of an

aryl or vinyl halide (or triflate) with a terminal sp hybridized carbon (from an alkyne), is generally referred as Sonogashira cross-coupling reaction (Scheme 10A). It is one of the most frequently used for sp2–sp C–C bond construction, widely applied in natural products synthesis, dendrimers, heterocycles and conjugated polymers101. Khatyr et al.102 obtained the multi-target ligands through chemioselective palladium-catalyzed Sonogashira reaction (Scheme 10B). The main advantage of this method is adaptability and synthetic versatility for synthesizing multitopic metal-binding ligands with various bridging units and phenyl substituents102.


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Scheme 10. General Scheme and Representative Example of Sonogashira Reactiona

a(A)

The Sonogashira reaction; (B)the representative example of Sonogashira

reaction102. The Heck reaction is the reaction of an alkene with an unsaturated halide (or triflate), which was catalyzed by a palladium or palladium nanomaterial, to create a substituted alkene (Scheme 11A)103. It is attractive in terms of synthesis, because high chemo-selectivity and mild conditions are associated with low cost of the reagents and low toxicity104. Based on fused pharmacophores approach, Palem et al.105 reported a novel quinazolinones with allylphenyl quinoxaline hybrid designed for antiproliferative activity, utilizing palladium-catalyzed Heck reaction in the final synthesis step (Scheme 11B).



Scheme 11. General Scheme and Representative Example of Heck Reactiona

a(A)

The Heck reaction; (B)the representative example of Heck reaction105. The synthesis of MTDLs is generally more difficult than single-target ligands. The

synthetic reactions discussed above are all efficient and mild methods for MTDLs synthesis, what’s more, they all have high product yields and are tolerant of a broad range of functional groups. Thus, these efficient reactions may enable convenient synthesis of MTDLs. 6. Developability and Physicochemical Challenges Many factors contribute to the developability of MTDLs, particularly the physicochemical properties relevant to the molecular size and complexity12. Physicochemical properties will further influence the PK profiles and oral bioavailability, which is the most favorable route of administration20. In general, optimizing the PK profiles while keeping a balanced activity is the most difficult challenging in the lead optimizing phase. As a result of combination of selective ligands, MTDLs may be more lipophilic and have higher molecular weight than approved drugs on market20. Many MTDLs have MW> 500 and the clogP >522, which are major challenges to developability. A database of 117 MTDLs on market or in clinical trials was compiled by


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searching the website https://integrity.thomson-pharma.com. According to the classification on this website, the full database was divided into subsets by therapeutic fields. For each MTDL, seven physicochemical properties were calculated: MW, cLogP, the number of hydrogen bond acceptors (HBA), the number of hydrogen bond donors (HBD), polar surface area (PSA), the number of rotatable bonds (RB) and the number of heteroatoms106, 107. The mean and median values of above seven properties for the full set and each subset are shown in Table 2. Table 2. Physicochemical Properties Data of MTDLs on Market or in Clinical Trailsa. Therapeutic fields MW

Cancer

Nervous Cardiovascular system disease disease

Infectious disease

Full MTDL set

476.51 (471.26) 335.06 (314.91) 408.08 (391.98) 407.90 (324.34) 385.96 (371.54)

cLogP

3.81 (4.43)

3.16 (3.51)

3.27 (3.29)

2.58 (2.48)

3.22 (3.39)

HBA

6.18 (6.00)

3.75 (3.00)

5.20 (5.00)

5.95 (4.00)

4.81 (4.00)

HBD

1.95 (2.00)

1.35 (1.00)

2.50 (2.00)

3.00 (3.00)

1.93 (2.00)

PSA

99.06 (94.00)

62.27 (54.64)

102.11 (93.89)

115.91 (96.79)

84.70 (77.23)

RB

6.32 (6.00)

5.46 (5.00)

8.20 (7.50)

6.84 (5.00)

6.32 (5.00)

Heteroatom

9.36 (9.50)

5.41 (5.00)

7.25 (6.50)

8.68 (8.00)

7.00 (6.00)

Approved drug numbers

14

13

10

12

49

Sample numbers

22

56

20

19

117

a

The values outside the brackets represent the mean values, and the inside ones represent the median values.

We found significant differences in physicochemical properties among the



MTDLs in the four therapeutic fields. For instance, the mean (median) values of MW are 335.06 (314.91) for MTDLs in the field of nervous system disease, and 476.51 (471.26) for MTDLs in the field of cancer. It is remarkable that nervous system disease cluster occupied the lowest mean (median) values of all seven properties, especially the MW and lipophilicity, which are critical for blood-brain barrier (BBB) permeability. It is consistent with the fact that few MTDLs in the field of neurodegenerative diseases (especially for AD) reached clinical trial stage or on market, though many researches have been conducted. Through literature search, we found that the MTDLs for AD in basic research had significant different targets (such as AChE and Aβ aggregation inhibition). Most of these MTDLs have MW>500 Da, which may rise concerns on their oral bioavailability, and the ability to cross BBB. For instance, many researched have been published about hybrids generated simply via linking active pharmacophore to tacrine108. Expect few exceptions, most of MTDLs in basic researches for AD have never been tested for the ability to cross the BBB or oral bioavailability. If tests to address these problems were conducted, they were confined to in vitro experiments by the parallel artificial membrane permeability assay (PAMPA). However, PAMPA which is based on an artificial membrane-mimic system is just a passive model for testing BBB permeability, whose results may not agree with actual in vivo situation. Conversely, the mean (median) values for MTDLs in the field of cancer are all higher or equal to that of full MTDL set. Furthermore, the MTDLs in the field of cancer have the highest MW, cLogP, HBA and heteroatom mean (median) values, in consistence with the structure complexity of targets involved in cancers. So far, many


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MTDLs for cancer treatment are on market or in clinical trial stage, however, only few of them were originally designed as multi-target drugs, such as axitinib109 and lapatinib110. Whereas the rest are just serendipity27: the multiple activities were discovered during kinase activity profiling, or they were designed for other therapeutic areas. In other words, they were designed for single targets, but actually act on multiple targets. The discovery of imatinib is a typical example27. From the successful case of imatinib, high throughput in vitro screening may facilitate discovering more MTDLs ever considered as single-target drug candidate. Regarding to the MTDLs in the fields of cardiovascular and infectious diseases, the mean (median) values are generally higher than that of full MTDL set (except for the cLogP median values of cardiovascular disease). This also indicates to some extent the complexity of the multi-target drugs for cardiovascular disease and infectious disease. Therefore, as a general principle of MTDL design, the complexity and size of the template ligands had better be minimized, and the pharmacophores overlap had better be maximized. Removing functionality and rotatable bonds to simplifying structures have often been required in lead optimization111. If the pharmacophores are completely different, it may be impossible to integrate into a compact compound, and a high MW may be unavoidable. In this case, the high MW will be one of the main reason of failure in clinical. Therefore, similar targets, in terms of possessing identical or highly similar endogenous ligand, or being from the same superfamily, are preferred to rationally design multiple ligands. On the other hand, where possible, the template ligands are



highly desirable with favorable physicochemical profiles. Because it’s hard to obtain a worthy MTDL if the template ligands suffer physicochemical liabilities. Moreover, it’s easier to discover high-quality lead compounds by taking screening and pharmacophore combination approaches simultaneously, because the lead compounds obtained by screening usually have small molecular size and more favorable physicochemical profiles. If a drug candidate is given via intravenously administration, or the aim is to discover pharmacological tools to explore the feasibility of novel target combination, the oral bioavailability is less of an issue112. 7. Recent Progress of MTDLs in Drug Discovery Campaign A full description of recently discovered MTDLs is beyond the scope of this article. So we just discuss the MTDLs’ application in some common multifactorial diseases, to provide better insights of translational research. 7.1 MTDLs for Cancer Currently, cancer is the focus of MTDL discovery because of the multifactorial nature and frequently developed resistance to chemotherapy113. The synergistic activities of MTDLs may contribute to higher therapeutic efficacy and delayed resistance development3-6. 7.1.1 MTDLs Targeting Histone Deacetylase and Other Targets Histone deacetylase (HDAC) is an epigenetic enzyme associated with tumorigenesis and development. HDAC is therefore regarded as an important target for cancer therapy114, 115. Generally, HDAC inhibitors (HDACi) consist of three parts: (i) a


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zinc binding group (ZBG) to chelate the catalytic zinc ion; (ii) a hydrophobic linker to occupy the tunnel of the active site; (iii) a surface cap recognize the amino acid residues around the entrance of the active site116. So far, four HDACi have been approved, including vorinostat (SAHA)117, belinostat (PXD101)118, romidepsin (FK228)119 and panobinostat (LBH589)120. However, HDACi only demonstrated the efficacy in hematologic malignancies, but not solid tumors121. Hopefully, combination therapies would lead to more clinical applications of HDACi, and emerging interest was drawn to this field122. Moreover, the HDACi pharmacophores can tolerate diverse cap groups to enable MTDLs strategies. Currently, HDACi have been combined with a variety of anticancer drugs in clinical studies, and the target combination selection of HDACi-based multi-target drug candidates was mainly based on these clinical experience123-126. Proteasome inhibitor (PI) bortezomib123 was applied in combination with HDACi panobinostat in clinical studies, to induce simultaneous blockage of the aggresome pathways and ubiquitin degradation. Bhatia et al.127 reported a first-in-class dual HDAC-proteasome inhibitor. Rather than covalent binders, they focused on noncovalent scaffolds of PIs, in order to avoid the pitfalls of insufficient specificity, insufficient stability, excessive reactivity, as well as chemical incompatibilities due to highly reactive electrophilic warheads128. The high affinity of compound 26 was mainly due to a P3-neopentyl-Asn residue (Scheme 12A)129. Moreover, the available crystal structures of proteasome and corresponding peptidic ligands reveal the bulky residue, as an excellent side chain, can occupy the whole S3 specificity pocket in the



Page 32 of 140

chymotrypsin-like site of the 20S core particle. So they utilized compound 26 as template to design and synthesize dual-HDAC-PIs. The X-ray structure of 26 revealed the exposure of P4 residue to the solvent. So they decided to incorporate the HDACi part in the P4 position (Scheme 12A). On the other hand, the most effective synergy was derived from the combination of PIs and HDAC6. Compound 27 is a representative selective HDAC6 inhibitor on the basis of an N-dydroxybenzamide scaffold which provided HDAC6 selectivity. They replaced the solvent-exposed 4-picolyl scaffold by a methyl group in the P2 position to reduce MW. The above hybridization strategy led to the HDAC-proteasome inhibitor compound 28. Biochemical assays, cellular assays, and X-ray crystal structures analysis (HDAC6 and 20S proteasome complexed with compound 28) confirmed that compound 28 inhibited both targets simultaneously. Compound 28 was tested for the activity of proteasome inhibition, while ricolinostat (HDAC6 inhibitor) or vorinostat (pan-HDAC inhibitor) were the negative controls, bortezomib was the positive control. Furthermore, compound 28 can block the activity of chymotrypsin-like proteasome in selected leukemic cell lines (HL-60, SUP-B15r, and SEM), while vorinostat and ricolinostat can’t (Table 3). Table 3. Biological Activities of MTDLs Targeting HDAC and Proteasome127. Compound

HDAC6 IC50 (μM)

HL60 IC50 (nM)

SEM IC50 (nM)

SUP-B15r IC50 (nM)

28

0.27 ±0.01

260.70 ±3.12

394.20 ±9.85

820.50 ±13.94

Bortezomib

-

6.67 ±0.16

4.43 ±0.13

28.09 ±1.15

Ricolinostat

-

> 25000

> 25000

> 25000

Vornostat

-

> 25000

> 25000

> 25000


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Regarding to the fused-pharmacophores strategy, He et al.130 have reported novel HDACs and p53-human murine double minute 2 (MDM2) interaction dual-inhibitors (Scheme 12B). The interaction with MDM2 protein is a major inhibitory mechanism of of p53131. Blocking the interaction of p53 and MDM2 can reactivate the function of p53 and is therefore believed to be a promising strategy for cancer therapy. By analyzing the binding models of known MDM2 inhibitors and HDACs, they found that three functional groups on the imidazole scaffold of compound 29 can mimick Phe19, Leu26 and Trp23 residues in p53, which stretched into the binding pocket of MDM2. Therefore, they used the SAHA (30) and 29 as templates to design and synthesize MDM2-HDAC dual inhibitors. The N3 substitution of 29 exposed to the solvent was designed to introduce a linker (Scheme 12B). The resulting compound 31 showed remarkable selectivity for HDAC6 and high affinity to MDM2 (Table 4). Moreover, it exhibited superb in vivo antitumor efficacy in A549 xenograft model (tumor growth inhibition (TGI) = 74.5%) and was orally available. Table 4. Biological Activities of MTDLs Targeting HDAC and MDM2130. Compound

MDM2 Ki (μM)

HDAC1 IC50 (nM)

HDAC6 IC50 (nM)

29, Nutlin-3

0.14 ±0.04

-

-

30, SAHA

> 20

45.0 ±3.1

16.3 ±1.6

31

0.11 ±0.03

821 ±12

17.5 ±1.5

The combination of pazopanib124 (a multiple vascular endothelial growth factor receptor (VEGFR) inhibitor for advanced renal cell carcinoma treatment) and HDACi exhibited good preclinical results125. Therefore, another series of dual HDAC-VEGFR inhibitor were designed down the road132. Zang et al.132 incorporated pazopanib (32)



into the surface recognition cap of HDACi pharmacophore from MS-275 (33, an HDACi) (Scheme 12C), to overcome developed drug resistance in almost all patients treated with VEGFR inhibitors133,

134

. Vascular endothelial growth factor

(VEGF)/VEGFR signaling pathway are regarded as the main antiangiogenesis target for cancer therapy135. They selected pazopanib as a template based on promising preclinical study results of combination of pazopanib and HDACi124, 125. To decide which site of compound 32 can be substituted without losing the affinity to VEGFR-2, they analyzed the binding pattern of compound 32 within the ATP pocket of VEGFR2. They found that the 2-aminopyrimidine scaffold of 32 formed two critical hydrogen bonds with Cys917 residue in the hinge region, and the indazole moiety inserted into the inner pocket of VEGFR-2. Therefore, they kept these two important scaffolds without modification. They figured out that the solvent-exposed benzenesulfonamide moiety could be modified with the HDACi motif without compromising the VEGFR-2 inhibition. Therefore, they conjugated two privileged ZBG (ortho-aminoanilide and hydroxamic acid) to the solvent-exposed phenyl group straightway or by diverse linkers, resulting in a series of dual HDAC-VEGFR-2 inhibitors. The resulting compound 34 showed HDACs inhibition comparable to entinostat (an HDACi in phase III), and uncompromised antiangiogenic activities compared with pazopanib (Table 5). Moreover, compound 34 has ideal PK profiles, up to 72% oral bioavailability in SD rats, and desirable in vivo antitumor efficacy in HT-29 xenograft model (TGI = 40%).


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Table 5. Biological Activities of MTDLs Targeting HDAC and VEGFR-2132. Compound

VEGFR-2 Inh% (0.2 μM)

HDAC IC50 (μM)

32, Pazopanib

95

-

33, MS-275

-

1.8

34

100

4.6

SAHA

-

0.13

The combination of Janus kinases 2 (JAK2) and HDAC inhibitors has been reported in clinical studies126. On this basis, Huang et al.136 reported designing dual JAK2-HDAC inhibitors for invasive fungal infections (IFIs) and leukemia treatment simultaneously (Scheme 12D). JAK2 is a validated target for multiple cancers, including hematological malignancies137. But the application of JAK2 inhibitors has been in compliance with drug resistance and the complication of IFIs138. In general, a JAK2 inhibitor consists of three parts: two hydrophobic groups, hinge-region binder (typically an aminopyrimidine), and a solvent-exposed motif. The aminopyrimidine proved to be an important motif for JAK2 inhibition. The first series were designed via merging the central motif of the template compound 35 with the ZBG group from compound 30 (benzamide or hydroxamic acid) by diverse linkers. Since N-phenylmethanesulfonamide derivatives of compound 35 showed similar activity for JAK2 inhibition, the second series were designed and synthesized with the N-cyanomethylbenzamide/N-phenylmethanesulfonamide

substitution.

A

minor

substitution in the hydrophobic area of compound 35 could be tolerated, thus benzamide or hydroxamic acid were introduced in this position. The optimized compound 36 in second series showed highly selective JAK2/HDAC6 inhibition (Table 6). Importantly,



Page 36 of 140

compound 36 had shown efficacy in several in vivo models, such as human embryonic lung fibroblasts (HEL) xenograft model. Table 6. Biological Activities of MTDLs Targeting HDAC and JAK2136. Compound

JAK2 IC50 (nM)

HDAC1 IC50 (nM)

HDAC6 IC50 (nM)

30, SAHA

-

40 ±9

-

35

41 ±5

-

-

36

8.4 ±0.7

250 ±25

46 ±1.7


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Scheme

a

12.

MTDLs

Targeting

HDAC

and

Other

Targetsa

Most HDACi consist of three parts: a ZBG, a hydrophobic linker, and a surface

cognition cap138. The HDACi pharmacophores can tolerate diverse cap groups, enabling MTDLs strategies.



7.1.2 MTDLs Targeting Src and MEK Kinases As a non-receptor type tyrosine kinase, Src regulates many activities of cancer cells, such as proliferation, migration, and invasion139, 140. Therefore, Src is believed to be a valuable target to treat cancer. On the other hand, MEK kinase can influence mitogen-activated protein kinase (MAPK) signaling pathway and regulate apoptosis and oncogenic transformation141, and is therefore another important anticancer target. Unfortunately, merely 22% patients in the presence of the BRAF gene mutations can be treated with MEK inhibitor (Trametinib) for melanoma in clinical trials 142. There is a need to apply combination therapies to improve the clinical response143. Recent clinical researches showed that the simultaneous inhibition of MEK and Src led to improved efficacy in cancer patients144. In previous work, Cui et al.145 found that acridine derivatives OA (37) exhibited inhibition on Src. But the inhibition of 37 on Src is weak, likely because there is merely one hydrogen-bond interaction between 37 and the hinge of Src protein. In addition, studies have shown that molecules which bear a moiety can improve the affinity of kinase inhibition obviously, such as urea bonds which had the ability to bind the DFGout conformation of c-Src146. For examples, some kinase inhibitors approved or in clinical trial stage, such as imatinib147, ponatinib148, and regorafenib149, all have amido or urea moieties capable of forming hydrogen bonds with the amino-acid residues of the DFG-out pocket of kinases150. Taking these into considerations, they introduced the phenyl-urea moieties to the 9-anilinoacridine substituents by fused-pharmacophores approach (Scheme 13). Among the derivatives, compound 39 inhibited 59.67% of Src


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activity and exhibited inhibition rates on MEK and PI3K of 43.23% and 44.41% at 10 μM, respectively. The anti-proliferative activity of 39 was determined on HepG-2 and K562 cells using MTT assay, and imatinib was the positive control. For compounds 37 and 39, the IC50 of anti-proliferative activities on HepG-2 and K562 cells were showed in Table 7. Compound 39 is a novel MTDL for dual Src and MEK inhibition and can induce cancer cell apoptosis150. Scheme 13. MTDLs Targeting Src and MEK Kinases145

Table 7. Biological Activities of MTDLs Targeting Src and MEK Kinases145. Compound

Src Inh%

MEK Inh%

K562 IC50 (μM)

HepG-2 IC50 (μM)

37

8.02 at 50 μM

44.03 at 50 μM

5.8

>50

39

59.67 at 10 μM

43.23 at 10 μM

4.08

9.41

Imatinib

-

-

0.53

>25

7.1.3 MTDLs Targeting ERα and VEGFR-2 In breast cancer, estrogen receptor α (ERα) is charge of estrogen-induced proliferation151. VEGFR-2 is also considered as a dominant receptor in the angiogenesis pathway152. However, VEGFR-2 inhibitors have limited efficacy as monotherapy152, 153. A combination of Tamoxifen (41, a selective estrogen receptor modulators (SERM)) and only low doses of Brivanib alaninate (a VEGFR-2 inhibitor), was able to improve therapeutic efficacy, at the same time prevent SERM resistant tumour growth154.



Furthermore, VEGFR-2 inhibitors that contain indol-2-one scaffold share some structural similarities with SERMs, such as YM231146 (42) and sunitinib. Hereby, Tang et al.155 designed and synthesized series of dual-target inhibitors toward VEGFR-2 and ERα through linked-pharmacophore approach. They integrated the 2H-1-benzopyran pharmacophore form acolbifene (40) with functional groups capable of inhibiting VEGFR-2 selectively (Scheme 14). Among the ERα/VEGFR-2 dual inhibitors, compound 43 had the highest level of inhibitory activities for ERα and VEGFR-2 (Table 8). Following studies demonstrated potential anti-proliferation activity of compound 43 on MCF-7 breast cancer cells, as well as good in vivo antiangiogenesis activity. In pursuit of diverse scaffolds as MTDLs for ERα and VEGFR-2 inhibition, they further designed series of 6-aryl-indenoisoquinolone analogues containing side chains156. Compound 44 contained two hydroxyl groups, which mimicked estradiol to form two hydrogen-bond interactions with ERα. Compound 44 was also tested for inhibitory activity of human breast cancer cell lines MCF-7 (ER+). Compound 44 proved to be an ERα/VEGFR-2 dual inhibitor and its IC50 values are showed in Table 8. This work identified a novel approach to design multi-target drugs with pharmacophores from two families' targets.


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Scheme 14. MTDLs Targeting ERα and VEGFR-2155

Table 8. Biological Activities of MTDLs Targeting ERα and VEGFR-2155. Compound

ERα Inh% (0.1 mg/ml)

MCF-7 IC50 (μM)

VEGFR-2 Inh% (0.1 mg/ml)

43

99.89

2.73

100.26

44

7.2 μM

1.2

0.099 μMb

41, Tamoxifen

100.0

1.89

-

Sunitinib

-

-

100 (0.14 μMb)

a

The values represent the IC50 values for ERα inhibition; bThe values represent the IC50 values for VEGFR-2 inhibition. a

7.1.4 MTDLs Targeting Microtubule and Tyrosine Kinase (RTK) Combination chemotherapy with cytotoxic and anti-angiogenic drug (especially RTK inhibitors) has shown promising results in clinical studies157-159. Compound 46 is a dual inhibitor for VEGFR-2 and platelet-derived growth factor receptor β (PDGFRβ), with furo[2,3-d] pyrimidine scaffold160, 161. Furthermore, 6-methyl cyclopenta fused pyrimidines 45 showed strong tubulin depolymerization activity, as well as both in vitro and in vivo anticancer activities162-164.



As an initial research, Zhang et al.165 tried to integrate tubulin inhibitory activity of compound 45 into the existing RTK inhibitor compound 46. They replaced the 5substitution on to the 4-NH moiety of compound 48 and placed a 6-CH3 group on compound 46, to generate a hybrid furo[2,3-d]pyrimidine scaffold (Scheme 15). This scaffold has anti-tubulin activity compared to compound 45, and it can also access the hydrophobic region on the basic pharmacophore for RTKs (VEGFR-2 and PDGFR-β). Among the derivatives generated by the 2-substitution, the 2-methyl group-substituted compound 47 showed EC50 value of 3.9 μM for angiogenesis inhibition. The inhibitory efficiency of compound 47 was 9-fold higher than erlotinib, and approximately equals half of sunitinib. Moreover, 47 was the most active compound as tubulin depolymerizer, and for EGFR, VEGFR-2, and PDGFR-β inhibition (Table 9). SAR analysis of this compound series indicated that N-alkyl group is the key for microtubule depolymerization activity. In order to explore the bioactive conformation, they further designed series of 4-substituted 2, 6-dimethylfuro[2,3-d]pyrimidines which were conformationally restricted. Compared with compound 47, optimized compound 48 exhibited a remarkable improved inhibitory efficiency on RTKs and microtubule assembly166. Scheme 15. MTDLs Targeting Tyrosine Kinase and Microtubule165


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Table 9. Biological Activities of MTDLs Targeting RTKs and Microtubule165. Compound

PDGFR-β IC50 (nM)

VEGFR-2 IC50 (nM)

Microtubule depolymerization EC50 (nM)

47

22.8 ±4.9

19.1 ±3.0

103

48

58.2 ±8.0

33. 2 ±5.0

21

Sunitinib

83.1 ±10.1

18.9 ±2.7

-

Combretastatin A-4

-

-

9.8

7.2 MTDLs for Central Nervous System Diseases Owing to the complex etiology of central nervous system diseases, and development of new multifunctional drugs in this therapeutic field has become more attractive in recent years167, 168. 7.2.1 MTDLs Targeting Glycogen Synthase Kinase 3β and Tau-Aggregation for AD In 2018, Gandini et al.169 developed 2, 4-thiazolidinedione derivatives with dual inhibitory activities on the glycogen synthase kinase-3β (GSK-3β, a phosphorylating tau kinase) and tau aggregation process (Scheme 16). It is not easy to design a ligand binding two targets which shared no binding site similarity, as the case of tau protein and GSK-3β170. Moreover, combining molecular skeletons from two different molecules in one small molecule could be very challenging for PK optimization171. Initially, they found some known GSK-3β inhibitors had common five-membered heterocyclic fragments with hydrogen bond donor and acceptor functionalities172, 173. Thiadiazolidinedione (TDZD) is the first class reported non-ATP competitive inhibitor of GSK-3β174, and tideglusib (49) is currently under clinical development175. Interestingly, thiohydantoin, hydantoin, and rhodanine were discovered as effective



scaffolds to inhibit tau antiaggregation176. Thus, they speculated that the cross-talk of the above scaffolds may be applied for the two selected targets. They didn’t select rhodanine as lead due to the pan-assay interference compounds (PAINS) behavior177. Conversely, they selected the 2, 4-thiazolidinedion (TZD) fragment without PAINS (Scheme 12)178, 179, even though TZD scaffold has never been explored as either GSK3β or tau anti-aggregation fragment. Previous works showed that (i) 5-arylidene substitution could improve efficacy of the 2-iminothiazolidin-4-one GSK-3β inhibitors; (ii) their volume and size did not fit in similar regions of homologous kinases, suggesting that selectivity for GSK-3β could be optimized173. Thus, they modified position 5 using diverse heteroaromatic and aromatic substituents, and obtained series of 5-arylidene-2, 4-thiazolidinediones. The resulting compound 50 and 51 exhibited (i) micromolar IC50 values for GSK-3β (Table 10), and increased the cell viability in cell model induced by okadaic acid; (ii) PAMPA-BBB permeability and suitable cellular safety profile; (iii) satisfactory PK properties, and balanced activities with low MW.


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Scheme 16. MTDLs Targeting GSK-3β and Tau-Aggregation for AD169

Table 10. Biological Activities of MTDLs Targeting GSK-3β and TauAggregation169.

a

Compound

GSK-3β IC50 (μM)

Tau K18 self-aggregation Inh% (10 μM)

50

4.93 ±0.66

-

51

0.89 ±0.21

35a

49, Tideglusib

0.69 ±0.09

-

Compared to tau K18 self-aggregation alone.

7.2.2 MTDLs Targeting ChE and Other Targets for AD Tacrine is an acetylcholinesterase inhibitors (AChEIs) approved by the FDA180, but its hepatotoxicity limited further clinical applications. Nowadays, tacrine is widely used as a template for designing MTDLs for AD therapy181, 182. It’s worth mentioning that many of tacrine hybrids have decreased toxicity. Neurofibrillary tangles

(NFTs)

represents

one


of

the

most

relevant


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histopathological hallmarks of AD. According to the tau hypothesis, GSK-3β is a multifunctional serine/threonine kinase that regulates tau phosphorylation and precipitation as NFTs. On the basis of linked-pharmacophore strategy, our group designed a series of GSK-3β/AChE dual inhibitors183. The hybrid compound 54 exhibited the promising profile: (i) the IC50 for hAChE and hGSK-3β inhibition are 6.5 nM and 66 nM, respectively (Table 11); (ii) in vivo studies indicated that compound 54 effectively improved the learning and memory of scopolamine-treated ICR mice; (iii) with lower hepatotoxicity compared with tacrine. The research identified a novel approach for developing dual GSK-3β/AChE inhibitors as drug candidates for AD therapy (Scheme 17). Table 11. Biological Activities of MTDLs Targeting GSK-3β and ChE183. Compound

hGSK-3β IC50 (nM)

hAChE IC50 (nM)

hBuChE IC50 (nM)

52, Tacrine

-

230 ±31

40 ±3.7

53

1.1

-

-

54

66 ±6.2

6.4 ±0.3

260 ±32

Melatonin (55) is a pineal neurohormone and its level decreases with age, notably in AD patients. Studies have shown that melatonin had strong antioxidant activities and could eliminate various reactive oxygen species184. Furthermore, it can protect microglial cells from Aβ-induced apoptosis, and improve cognitive disorders in rats185, 186

. Rodriguez-Franco et al.187 developed novel melatonin-tacrine hybrids based on the

linked-pharmacophore approach. They linked the two pharmacophores with different length of linkers, which can be stretched into the enzyme cavity (Scheme 17).


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Compound 56 was the strongest inhibitor for hAChE with 40000-fold inhibitory efficiency of tacrine. Interestingly, it showed high selectivity of 1000-fold on AChE over hBuChE. Moreover, compound 56 had 2.5-fold higher oxygen radical absorbance capacity than trolox (a vitamin E analogue) (Table 12). Table 12. Biological Activities of MTDLs Targeting ChE and Oxidation187.

a

Compound

hAChE IC50 (nM)

hBuChE IC50 (nM)

Trolox equiva

52, Tacrine

350 ±10

40 ±2

< 0.01

55

-

-

2.3 ±0.1

56

0.008 ±0.0004

7.8 ±0.4

2.5 ±0.1

Data are expressed as µmol of trolox equivalent/µmol of tested compound.

MAOs are considered to be valuable targets for AD therapy, because its high expression level leads to oxidative damage188. Selegiline189 (57) is a selective and irreversible MAO-B inhibitor and shows neuroprotective effect in in vitro and in vivo AD models. Based on linked-pharmacophore approach, Lu et al.190 designed series of selegiline-tacrine hybrids through linking the two pharmacophores using carbon spacers with different lengths (Scheme 17). Compound 58 showed balanced activities towards the all targets (Table 13). Table 13. Biological Activities of MTDLs Targeting ChE and MAO190. Compound

AChE IC50 (nM)

BuChE IC50 (nM)

hMAO-A IC50 (μM)

hMAO-B IC50 (μM)

52, Tacrine

110.2 ±7.30

21.6 ±1.77

-

-

58

22.6 ±3.04

9.37 ±0.75

0.3724 ±0.0249

0.1810 ±0.0300

Clorgyline

-

-

0.0041 ±0.0002

-

Pargyline

-

-

-

0.1180 ±0.0160



Scheme 17. MTDLs Targeting ChE and Other Targets for AD183, 187, 190

Recent studies suggested that depression present in nearly 40% of dementia patients191 can be treated with serotonin transporter (SERT) inhibitors192. Nowadays, fluoxetine (59, an antidepressant)193 and paroxetine194, 195 are two of the most widely used SERT inhibitors without anticholinergic adverse effects195. Thus, the combination of AChE and SERT inhibition might have great benefits: cognitive impairment might be reduced, and dose-related side effects due to undue AChE inhibition might be avoided195, 196. To design such a MTDL, a hypothetical model of AChE active center was constructed based on crystal structure of AChE and donepezil195. Rivastigmine (7), a marketed AChE inhibitor, was chosen as the template. They speculated that there were three parts of rivastigmine responsible for binding to AChE in hypothetic binding sites: (i) a carbamate for catalytic triad site; (ii) an aryl for hydrophobic binding site A; (iii) and an amine for the ionic site.


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Compound 59 was selected as the element to access the hydrophobic binding site of AChE. Moreover, fluoxetine can inhibit SERT activity strongly, and has an ethylamine group to overlap with rivastigmine (Scheme 18). Therefore, a novel dual AChE-SERT inhibitor compound 60 with balanced AChE-SERT inhibitory activities was developed. The conformationally restricted compound 61 was further designed and synthesized to improve the inhibition efficiency (Table 14). Scheme 18. MTDLs Targeting AChE and SERT for AD195

Table 14. Biological Activities of MTDLs Targeting AChE and SERT195. Compound

AChE IC50 (nM)

SERT IC50 (nM)

7, Rivastigmine

11000

> 1000

19, Donepezil

10

> 1000

59, Fluoxetine

> 10000

180

60

101

42

61

14

6

7.2.3 MTDLs Targeting Metal Chelation, Aβ, Metal-Aβ, and ROS for AD Lee et al.197, 198 first reported a single molecule that inhibited multiple pathologic mechanisms of AD, involving metal-induced Aβ aggregation, metal-free, toxicity induced by Aβ and metal-Aβ, free radical reaction, and ROS generation (Scheme 19). To block metal chelation and Aβ/metal-Aβ interactions, the hybrid was designed via



merging compound 63, a known Aβ imaging tool199, with compound 62, a molecule targeting metal-Aβ200, and compound 64, a metal chelator201. Nitrogen and oxygen donor atoms, as well as an hydroxyl group, were introduced to form tetradentate hybrid, to increase metal binding ability. In this conformation, the ligand can inhibit ROS generation by disrupting the favorable tetrahedral geometry of Cu(I). Substituents (such as phenolic and quinoline groups)202 as validated antioxidants were introduced into hybrid for antioxidation. Finally, polar groups (such as amino and hydroxyl groups) were added to improve water solubility. Series of chemical studies indicated that hybrid 65 targeted metal free and metal-Aβ, inhibited in vitro Aβ aggregation, as well as reduced the toxicity caused by Aβ. Moreover, compound 65 was water-soluble with potential BBB permeability, and regulated the formation and presence of free radicals (Table 15). Scheme 19 MTDLs Targeting Metal Chelation, Aβ, Metal-Aβ, and ROS for AD197


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Table 15. Biological Activities of MTDLs Targeting Metal Chelation, Aβ, MetalAβ, and ROS197. Target Aβ and metal-Aβ Aβ42 fibers Metal

Multiple Activities of Compound 65 65 can bind to Aβ40 and Aβ42; 65 stoichiometry, and bind to metal-Aβ42 and chelate Cu(II) from Aβ42 competitively. 65 can interact with Aβ42 fibers and bind to Zn(II)-Aβ42 fibers. 65 can bind to Cu(II) and Zn(II) selectively, and chelate metal ions surrounded by soluble Aβ.

Early Oligomerization 65 can inhibit the formation of dodecamer and hexamer . of Aβ Metal-Associated Aβ65 may regulate Aβ/metal-Aβ-induced toxicity. Induced Toxicity ROS formation Oxidantion BBB permeability

65 may control Cu-triggered formation of hydroxyl radicals. 65 can scavenge free radicals more effectively Trolox (1.41 ±0.15 for 65; 1.00 ±0.08 for Trolox) by the TEAC assay. 65 may cross the BBB in PAMPA assay.

7.2.4 MTDLs Targeting MAO-B and Other Targets for Parkinson’s Disease Parkinson’s disease (PD) is a well-known nervous system disease. MAOs plays an important role in neurotransmitter degradation. In human brain, the MAO-B mainly degrades dopamine and therefore serves as a target for PD203. The human histamine H3 receptor (hH3R) is mainly expressed in the central nervous system (CNS). The activation of presynaptic H3R might reduce the release neurotransmitters including dopamine 204, 205, indicating its value as a promising target for PD. Compounds 68 and 69 are potent and selective reversible MAO-B inhibitors. Ciproxifan (66) is a H3R inverse agonist with simultaneous MAO-B inhibitory activity. In the linked-pharmacophore approach, Affini et al.206 designed and synthetized MAO-B/H3R dual-target ligands for PD therapy. The alkyloxyphenyl linker of



compound 66 was kept, whereas the imidazole moiety was replaced by piperidine (from UCL2190, 67) which fits H3R pharmacophore more favorably. The series I was designed by introducing of the H3R pharmacophore to the C5 or C6 position of the indanone scaffold of compound 68. The series II was designed by introducing the H3R motif to the C5 or C6 position of the more lipophilic 2-benzylidene-indanone derivative 69. The series III was designed by introducing the H3R motif to the benzylidene ring B at the C4’ position (Scheme 20). Extensive SAR studies generated various chemotypes of dual MAO-B/ hH3R inhibitors. Compound 70 (from series III) showed strong activities on MAO-B and hH3R, as well as selectivity on MAO-B over A (SI > 36) (Table 16). Thus, indanone-substituted derivatives are potent lead compounds for MAO-B/hH3R dual-target candidates for the treatment of PD.


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Scheme 20. MTDLs Targeting MAO-B and H3R for PD206

Table 16. Biological Activities of MTDLs Targeting MAO-B and H3R206.

a

Compound

MAO-A IC50 (nM)

MAO-B IC50 (nM)

SIa

H3R Ki (nM)

66, Ciproxifan

2100

11000

0.19

46-180

67, UCL2190

-

-

11

68

39

3

13

-

69

> 1000000

9.2

-

-

70

> 10000

276

36

10

Selectivity index (SI) = IC50 MAO-A/IC50 MAO-B.

Among the validated targets for PD treatment, A2A adenosine receptor (AR) and MAO-B are two most important ones207. Combined with levodopa, MAO-B inhibitors can inhibit dopamine metabolism in the brain and therefore increase the levels of



Page 54 of 140

dopamine. Compound 71 is a strong A2A antagonist208. Caffeine (72) is a nonselective AR antagonists with cognitive enhancer efficacy in treating PD and AD209. In 2014, the first compound is reported to inhibit all three targets of interest, A1, A2AARs and MAOB210. In 2016, Brunschweiger et al. 211 further developed a series of novel derivatives based on compound 71 via SAR investigation. The benzyl group of compound 71 was substituted by diverse aromatic groups, and there substituent groups were introduced directly or through various linkers to the N8 of the pyrazinopurine scaffold (Scheme 21). Among these compounds, compound 73 showed comparable inhibitory activities on A1, A2AARs and MAO-B (Table 17). Scheme 21. MTDLs Targeting MAO-B and A1/A2A AR for PD211

Table 17. Biological Activities of MTDLs Targeting MAO-B and A1/A2A AR211. Compound

A1 AR Ki (μM)

A2A AR Ki (μM)

hMAO-A IC50 (μM)

hMAO-B IC50 (μM)

71

0.265 ±0.068

1.06 ±0.30

-

> 10.0

72

44.9

23.4

> 50.0

> 50.0

73

0.393 ±0.101

0.595 ±0.051

> 10.0

0.210 ±0.041

7.2.5 MTDLs Targeting SERT/5-HT1A/5-HT7 for Depression Depression is one of the leading causes of disability in the world212. However,


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there are still significant challenges for depression treatment, such as limited patient response rate and tremendous adverse effects213, 214. The combination of SERT and 5HT receptor inhibition may be an effective strategy for treating depression215. Recent research indicated that pindolol could enhance selective serotonin reuptake inhibitors (SSRIs) effects and simultaneously target 5-HT1A 216. Additionally, the combination of selective 5-HT7 receptor antagonist with even a low dose of SSRI was effective in depression models. Hence, MTDLs towards SERT/5-HT1A/5-HT7 may be valuable for treating depression. The benzofuran derivative 77 showed high binding affinity to the 5-HT1A receptor and SERT217. 2-biphenyl piperazine derivatives 74-76 exhibited high affinity to 5-HT1A as well as 5-HT7 receptors218-220. Based on the knowledge above, Gu et al.221 reported a series of novel aralkyl-piperazine compounds in merged-pharmacophore approach. To explore the affinity to 5-HT1A and 5-HT7 receptors, the N1 position was introduced by diverse aromatic groups. And they tested if replacing the benzofuran scaffold with indole scaffold could enhance serotonin reuptake inhibition (Scheme 22). Among the series of compounds, compound 78 exhibited high affinity to 5-HT1A/5-HT7 and strong serotonin reuptake inhibition (RUI) (Table 18).



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Scheme 22. MTDLs Targeting SERT/5-HT1A/5-HT7 for Depression221

Table 18. Biological Activities of MTDLs Targeting SERT/5-HT1A/5-HT7221. Compound

5-HT1A Ki (nM)

5-HT7 Ki (nM)

SERT IC50 (nM)

74

60.9

0.13

-

75

188

0.58

-

76

99

1.4

-

77

1.94

-

25.3

78

28

3.3

25

7.2.6 MTDLs Targeting Dopamine D2 receptor and Serotonin reuptake for Schizophrenia Schizophrenia is an overwhelming mental disease222. Though typical antipsychotics show improvement in mitigating positive symptoms by blocking dopamine D2 receptors (D2Rs)223, they may also lead to extrapyramidal symptoms (EPS) like PD and tardive dyskinesia224. The combination of a neuroleptic together with a SSRI showed improved efficacy for schizophrenia, at the same time a decrease in depression, without exacerbating EPS225. Therefore, Smid et al.226 designed hybrids based on the templates of a well-known dopamine D2 receptor inhibitor (eltoprazine227,


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80) and a serotonin reuptake (SR) inhibitor (indalpine, 79) (Scheme 23). They took some factors into consideration for receptor affinity: (i) the length of the alkyl linker between the aryl and indole piperazine groups; (ii) the substitution pattern of the indole scaffold; (iii) the bicyclic heteroaryl structure. Some key candidates showed potent in vitro and in vivo pharmacological activities, especially compound 81. Molecular docking also indicated that compound 81 can adopt two different conformations, which fit the SR pharmacophore and dopamine D2 receptor pharmacophore, respectively (Table 19). Scheme 23. MTDLs Targeting Dopamine D2 receptor and Serotonin reuptake for Schizophrenia226

Table 19. Biological Activities of MTDLs Targeting Dopamine D2 receptor and Serotonin reuptake226. In vitro

In vivo

Compound

hD2 Ki (μM)

rSR Ki (μM)

APO EC50 (mg/kg)

5-HTPb EC50 (mg/kg)

79, Indalpine

2.0 ±0.1

-

0.1

-

80, Eltoprazine

-

2.0 ±0.1

-

-

81

6.9 ±1.8

0.2 ±0.1

0.08

0.15

a

a

Antagonizing apomorphine induced climbing behavior in mice (po). b 5-HTP induced serotonin syndrome like behavior in mice (po).



7.3 MTDLs for Cardiovascular Diseases Cardiovascular diseases are the leading causes of death in humans228, and the pathological process of which is multifactorial228. The development of MTDLs may bring great benefits for cardiovascular disease patients229. 7.3.1 MTDLs Targeting AT1 and ETA for Hypertension In renin-angiotensin-aldosterone system (RAAS), angiotensin II (Ang II) binds to two G-protein coupled receptors (GPCRs), angiotensin II type 1 (AT1) and AT2, leading to vasoconstriction and the release of vasopressin and aldosterone230, 231. Therefore, an antagonist for AT1 and AT2 might be useful for treating cardiovascular diseases9, 232. The endothelin receptors ETA and ETB are also potential targets for cardiovascular diseases233, 234. ETA regulates vasoconstriction, whereas ETB counteracts these effects by facilitating the synthesis of vasodilators prostacycline and nitric oxide235, 236. In merged-pharmacophore approach, Murugesan et al.37 reported a dual-target AT1 and ETA antagonist. Compound 82 (a known ETA antagonist) was selected as the template. Irbesartan (compound 83) is an AT1 antagonist approved by many regulatory agencies. They merged the pharmacophores of 82 (a biphenyl sulfonamide attached to a 5-isoxazole) and 83 (a biphenyl imidazolone). Moreover, the 5-isoxazole was replaced by a 3-isoxazole to improve the metabolic stability (Scheme 24). The resulting compound 84 exhibited balanced activities for the two targets (Table 20). PK profiles such as oral bioavailability, Cmax/Tmax and half-life in plasma were also tested. Compared to 82 and 83, 84 exhibited further decreased blood pressure in rats and more durable effects.


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Scheme 24. MTDLs targeting AT1 and ETA for hypertension37

Table 20. Biological Activities of MTDLs Targeting AT1 and ETA37. Compound

ETA Ki (nM)

AT1 Ki (nM)

82

1.4

> 10000

83, Irbesartan

> 10000

0.8

84

9.3

0.8

The maximal efficacy of darglitazone in the PPARγ activation assay was defined as 100%.

a

7.3.2 MTDLs Targeting PPARγ and AT2 for Cardiovascular Disease The peroxisome proliferator-activated receptor γ (PPARγ), as a critical target for the prevention of diabetes, is an intracellular nuclear hormone receptor regulating insulin and glucose metabolism237. The American Heart Association indicated that there’s a strong association between cardiovascular diseases and diabetes, patients with one disease are prone to suffer from the other238. Therefore, a drug candidate with dual activities to AT1 and PPARγ may potentially treat symptoms that are cardiovascular risk factors, e.g. hypertension, insulin resistance, and hypertriglyceridemia239. In merged-pharmacophore approach, Casimiro-Garcia et al. 239 developed a dualactive PPARγ agonist and AT1 antagonist. Telmisartan (compound 85) is an approved AT1 antagonist and compound 86 is a PPARγ agonist. A general pharmacophore to address two targets was identified by cross-screening: an acidic head group, a



Page 60 of 140

hydrophobic tail and a nonpolar phenyl linker (Scheme 25). The carboxylic acid was substituted with bioisostere tetrazole ring with a large size to fully occupy the binding pocket and keep the favorable interactions. Further, they complemented the biphenyl linker of compound 85 with a cyclopentyl group to reduce polarity. The template benzimidazole was substituted with a pyridine imidazole to enhance the interactions with the charged binding site (Scheme 25). Among the 15 synthesized molecules, compound 87 has optimal activity profile on both targets (Table 21). In two in vivo models, compounded 87 also reduced blood pressure for a longer duration compared with the approved drug telmisartan. Scheme 25. Design of Multi-Targeted Agents Targeting PPARγ and AT1 for Hypertension239

Table 21. Biological Activities of MTDLs Targeting PPARγ and AT1239. Compound

AT1 Ki (nM)

hPPARγ EC50 (nM) (% max)a

85, Telmisartan

0.49

1520

Pioglitazone

-

1280

87

1.6

212


a


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7.3.3 MTDLs Targeting ACE and DPP4 for Hypertension and Diabetes, respectively It has been estimated that about 70% of patients with type 2 diabetes are hypertensive240. On the other hand, the risk of suffering from type 2 diabetes is far higher in patients with hypertension241. For the patients with both hypertension and diabetes, taking angiotensin converting enzyme (ACE) inhibitors and dipeptidyl peptidase-4 (DPP4) inhibitors is the common practice. However, the combinations of multiple prescription drugs could be challenging for patient compliance. Therefore, dual-target inhibitors for ACE and DPP4 may further improve the treatment of cardiovascular disease. In 2017, Sattigeri et al.242 reported a new series of dual inhibitors for ACE and DPP4. Initially, they analyzed ligand-ACE and ligand-DPP4 co-crystal structures to orient the design of dual inhibitors243-246. Thus, ACE inhibitors need a carboxy group, a zinc chelating group (SH or COOH) and a hydrophobic group to occupy the pocket of ACE. DPP4 inhibitors typically need a hydrophobic moiety (such as trifluorophenyl, to interact with a serine) and a basic amine (such as amino, to form a salt bridge with glutamic acid). The structure analysis of fosinoprilat (89) and zofenopril (90) suggested the proline ring of enalaprilat (88) can be further substituted. A similar analysis of DPP4 inhibitors (91 and 92) suggested that the pocket of DDP4 was large enough to accommodate certain substitutions, and ACE ligand can be merged at the triazolopiperidine ring of compound 91 (Scheme 26A). Thus, they removed the triazolopiperidine motif of compound 91, and incorporated all necessary scaffolds for



Page 62 of 140

ACE and DPP4 inhibition via an amide (Scheme 26B). Among the hybrids, compound 93 was the strongest dual inhibitor of DPP4 and ACE (Table 22), and showed good PK properties in plasma (AUC = 23.49 μg.h/ml, Cmax = 2.51 μg/ml). Scheme 26. MTDLs Targeting ACE and DPP4 for Hypertension and Diabetesa

a(A)

The representative scaffold of ACE and DPP4 inhibitors242. (B) ACE/ DPP4 dual

inhibitors merged by enalaprilat 82 and sitagliptin 85. Table 22. Biological Activities of MTDLs Targeting ACE and DPP4242. ACE Inhibitona IC50 (nM)

DPP4 Inhibitona IC50 (nM)

Rat

Mouse

Human

Rat

Mouse

Human

88, Enalaprilat

9.6

11.5

2.5

-

-

> 100 μM

91, Sitagliptin

-

-

11 μM

33

46

20

93

2800

2210

51

1230

7170

102

Compound

a

Plasma from Wistar rat, ob/ob mouse and human was used as source of ACE and DPP4 enzymes.


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7.4 MTDLs for Infectious Disease 7.4.1 MTDLs Targeting Integrase and Reverse transcriptase for AIDS Human immunodeficiency virus (HIV) is

the

pathogen of acquired

immunodeficiency syndrome (AIDS). Integrase (IN) and reverse transcriptase (RT) are two enzymes for indispensable HIV-1 replication247. IN integrates viral DNA into the host genome, whereas RT transcribes single-stranded RNA viral genome into doublestranded DNA248. Recently, designing MTDLs for IN and RT dual inhibition is emerging for anti-HIV research248. Wang et al.249 provided the first IN/RT dual inhibitors. TNK-651 (94) is a strong non-nucleoside RT inhibitor, whose N-1 substituent group stretches from the NNRTI binding site to the protein/solvent interface250 (Scheme 27). It would tolerate the integration of another pharmacophore for IN251. GS-9137 (95)252 is a very strong IN inhibitor with a quinolone carboxylic acid core. Therefore, linking quinolone pharmacophore and pyrimidine generated IN/RT dual inhibitors, e.g. compound 96 (Table 23). Scheme 27. MTDLs Targeting IN and RT for AIDS249



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Table 23. Biological Activities of MTDLs Targeting IN and RT249. Compound

IN IC50 (μM)

RT IC50 (μM)

HIV EC50 (μM)

94, TNK-651

2.4

0.057

0.033

95, GS-9137

0.0072

-

0.0009

96

35

0.19

0.22

Subsequently, they reported another IN/RT inhibitor247 (Scheme 28). In Similar way, through crystallographic analysis, they founded that the methylsulfonamide motif at the position C-5 of delavirdine (98, an RT inhibitor)253 was located on the protein/solvent interface, which can tolerate functional substituent groups. Therefore, they substituted the sulfonamide group with a diketoacid (DKA) group (from compound 97) and obtained IN/RT dual inhibitors. Except for a DKA type of metalchelating functionality, IN binding also needs a hydrophobic aromatic ring adjacent to the chelator that can be satisfied by the indole ring of the hybrid. These hybrids all strongly inhibited both IN and RT enzymatic activities, and HIV proliferation in cell assay. Compound 99 had the strongest activities among these hybrids (Table 24). Scheme 28. MTDLs Targeting IN and RT for AIDS247


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Table 24. Biological Activities of MTDLs Targeting IN and RT247. Compound

IN IC50 (μM)

RT IC50 (μM)

HIV EC50 (μM)

97

0.093

> 100

0.16

98, Delavirdine

> 100

0.036

0.021

99

11

0.059

0.52

7.4.2 MTDLs Targeting PfATP6 and Host Haemoglobin Digestion for Malaria Malaria is a severe disease which leads to over one million deaths per year. More seriously, the parasite has developed resistance to the majority of monotherapy254. For this reason, combination chemotherapy of artemisinin (100) with other antimalarials like quinine (101)255 is recommended256. Quinine can effectively inhibit the asexual erythrocytic forms of malaria, possibly due to the interference with host haemoglobin digestion257. A Fe2+-activated artemisinin has strong PfATP6 (a critical parasite Ca2+ transporter) inhibitory activity256. Based on the knowledge above, Walsh et al.256 designed a artemisinin-quinine hybrid which was linked covalently (102) (Scheme 29). The vinyl group from quinine was modified for the introduction from artemisinin. This novel hybrid had strong in vitro activity on the (drug-resistant) FcB1 and 3D7 strains of Plasmodium falciparum. The activity was better than quinine alone, artemisinin alone, or a 1:1 mixture of quinine and artemisinin (Table 25).



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Scheme 29. MTDLs Targeting PfATP6 and Host Haemoglobin Digestion for Malaria

Table 25. Biological Activities of MTDLs Targeting PfATP6 and Host Haemoglobin Digestion256. Compound

3D7 (48h) IC50 (nM)

3D7 (72h) IC50 (nM)

FcB1 (48h) IC50 (nM)


100, Artemisinin

49.4

45.5

50.0

55.0

101, Quinine

149

73.5

96.8

75.3

102

8.95

10.4

9.59

10.2

Quinine+ Artemisinina

31.8

28.6

27.9

26.3

a

Values represent concentrations of each of quinine and artemisinin in a 1:1 ratio.

7.5 MTDLs Targeting 15-LOX and COX-2for Inflammation MTDLs comprising of various targets for inflammation exhibited good therapeutic potentials258. The activation of lipoxygenases (LOX) and cyclooxygenases (COX) are both crucial for generating multiple downstream mediators for inflammation259. Overexpression of 15-LOX is related to some inflammatory diseases due to downstream production of 15(S)-hydroxy-eicosatetraenoic acid (15-HETE) and eoxins260. As for COX, it has two isoforms: COX-1 and COX-2261. COX-1 participates in the prostaglandins synthesis, which are important for normal physiology of some organs, such as gastrointestinal tract and kidney. Whereas COX-2 mainly participates


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in the process of inflammation262. Yasser et al.36 reported an anti-inflammatory MTDL for 15-LOX and COX-2 dual inhibition. They surmised that the hybridization of the pharmacophores 4thiazolidinone (from 103, a 15-LOX inhibitor) and 1,3,4-Thiadiazole (from 104, a COX-2 inhibitor) could strongly inhibit inflammation with minimal adverse effects. Merging both scaffolds was expected to provide better selectivity on COX-2 over COX1, because the large size of the hybrid compound may not fit in the COX-1 binding pocket, which is smaller than COX-2 (Scheme 30)263. Adopting fused-pharmacophore approach, compound 105 showed activities against COX-2 and 15-LOX (Table 26). Additionally, it showed in vitro inhibitory activity on LOX significantly higher than zileuton, and was well tolerated in animal models (Table 26). Scheme 30. MTDLs Targeting 15-LOX and COX-2 for Inflammation36

Table 26. Biological Activities of MTDLs Targeting 15-LOX and COX-236.

a

Compound

15-LOX IC50 (μM)

COX-1 IC50 (μM)

COX-2 IC50 (μM)

SIa

103

8.24

-

-

-

104

-

8.94

0.33

27

105

11.87

15.42

0.07

220.29

Selectivity index (SI) = IC50 COX-1/IC50 COX-2.



8. Conclusion In the past few decades, the “one molecule, one target, one disease” paradigm dominated the pharmaceutical industry. But the increasing researches indicate that perhaps we are entering an exciting new era of systematic discovery of MTDLs60. So far, the major challenges for rational design of MTDLs are target selection, achieving appropriate ratio of desired activities, and optimizing the PK profiles. Thus, future rational, successful MTDLs discovery calls for a holistic view rather than the reductionistic approach12. This review provided an overview of recent development of MTDLs for the treatment of multifactorial diseases. The identification and validation of novel target combination in disease-relevance and developability perspectives will be critical for success. Balancing the multi-target activities, optimizing the physicochemical and PK profiles, and achieving high selectivity over undesired targets are the most challenging aspects. In addition to the methods discussed above, there are many other computational methods for rational drug design, e.g. the field of quantitative systems pharmacology: a computer-based methodology which combines preclinical neurophysiology, neuropharmacology and available clinical information, enabling testing of combinations in virtual patients264. QSAR methodology has been used to identify molecular factors for target interaction, and to propose the best molecules for the following studies265. Virtual screening methods have been applied to identify leads in huge in-silico libraries of compounds266. On the basis of molecular docking, dynamic


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studies may evaluate the stability of the ligand–protein complexes, and determine important interactions in the binding patterns of the ligands267. Apart from the typical MTDLs inhibiting or activating two or more targets simultaneously, it may be possible to further develop dual functional molecules targeting different systems of the body. For instance, the antibody-drug conjugates (ADCs), hydrophobic tag (HyT), PROTAC are emerging technologies, which are structurally characterized by bifunctionality. For a typical MTDL, it acts on two (or more) different targets to excert additive or synergistic effects. Different from typical MTDLs, molecules designed with the above three technologies can recruit protein of interest (POI), then induce protein-protein interaction (PPI) and POI degradation. ADCs consist of recombinant monoclonal antibodies (mAbs) and cytotoxic chemicals (known as payloads) by linkers268. The mAb binds to specific antigens or receptors on cancer cell, then the whole ADC is internalized into the cancer cell, where the payloads exert cytotoxic effects269. After about 50 years of research, several ADCs have been approved: trastuzumab emtansine270 and brentuximab vedotin271 have paved the way for clinical trials of more ADCs. Hydrophobic tags technology is a small molecule which mimics a misfolded protein conformation. The synthetic hydrophobic groups (such as adamantane, a bulky hydrocarbon) form a covalent bond to the surface of target proteins, inducing their degradation via the proteasomes272. An example is Fluvestrant273 approved by the FDA in 2002. Moreover, the PROTAC technology is another strategy for protein degradation. PROTAC is a special kind of MTDL which integrates a target-binding ligand and an E3 ligase ligand into one molecule via a linker.



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By accessing rather than inhibiting active E3 ligases, PROTAC induces ubiquitination and degradation of the target protein by the proteasome274

275

. Therefore, we can see

that the future application of MTDL will become even more diversified. Therefore, we foresee that the new generation of MTDLs, characterized by the chemical, biological and theoretical level, provide a road to treat complex diseases effectively.

ASSOCIATED CONTENT Supporting Information The name, indication, MW, cLogP, PSA, HBA, HBD, RB, numbers of heteroatom and clinical phase of 117 MTDLs on market or in clinical trails are provided in the Supporting Information. (PDF)

AUTHOR INFORMATION Corresponding Authors HP. S.: *E-mail: [email protected]; Tel: +86-13951934235. W. Q: *E-mail: [email protected]; Tel: +86-13852294378. ORCID Haopeng Sun: 0000-0002-5109-0304 Author Contributions Haopeng Sun, Wei Qu and Junting Zhou were responsible for writing this manuscript. All authors have participated in the checking and revision of the manuscript.


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Notes The authors declare no competing financial interest. Biographies Junting Zhou received her Bachelor’s degree from China Pharmaceutical University in 2016. She is currently a postgraduate student at the Department of Natural Medicinal Chemistry (China Pharmaceutical University) under the supervision of Prof. Feng Feng. Her main research focuses on the discovery, synthesis and biological evaluation of designed multiple ligands targeting Alzheimer’s Diseases. Xueyang Jiang studied Pharmacy at Anhui University of Chinese Medicine and received his Bachelor degree in 2013. He pursues his doctorate in nature medicinal chemistry in China Pharmaceutical University under the supervision of Prof. Feng Feng. His research is focused on the structural optimization and mechanism study of the natural product in the field of Alzheimer disease. Siyu He received Bachelor’s degree from China Pharmaceutical University in 2016. She is currently a postgraduate student at the Department of Medicinal Chemistry (China Pharmaceutical University) under the supervision of Associate Prof. Haopeng Sun. Her research mainly focuses on the discovery, synthesis and biological evaluation of small molecules targeting autophagy machinery. Hongli Jiang received her Bachelor’s degree from China Pharmaceutical University in 2017. She is currently a postgraduate student at the Department of Natural Medicinal Chemistry (China Pharmaceutical University) under the supervision of Associate Prof. Wei Qu. Her research mainly focuses on the design, synthesis and biological evaluation



of small molecules for AD therapy. Feng Feng graduated in Chemistry at Shaanxi Normal University in 1991. He received his Ph.D in 2001 in Pharmacy, with a thesis about natural products with anti-tumor activities supervised by Prof. S. X. Zhao. He studied in the University of California, Irvine as visiting scholar in 2005. In 2010, he was promoted to Professor of Natural Medicinal Chemistry at China Pharmaceutical University. So far, he has published more than 70 papers on journals indexed by Science Citation Index. His major research interests include extraction and isolation of chemical constituents from natural medicines, structural modification of active compositions and drug analysis in vivo. In addition, he is focusing on the prevention and treatment of cancer and neurodegenerative agents. Wenyuan Liu graduated in Chemistry at Shaanxi Normal University in 1991. She received her Ph.D in 2004 in Pharmaceutical Analysis, supervised by Prof. Z. X. Zhang. She studied in the University of California, San Diego as visiting scholar in 2008. In 2010, she was promoted to Professor of Pharmaceutical Analysis, at China Pharmaceutical University. So far, she has published more 50 research papers on Journal of Chromatography A, Journal of Chromatography B, Journal of Pharmaceutical and Biomedical Analysis, etc. Her major research interests include the analysis of pharmaceutical instruments. Wei Qu graduated in Traditional Chinese Pharmacy at the China Pharmaceutical University in 2004. She received her Ph.D. in 2009 in natural medicinal chemistry, with a thesis about natural products with anti-tumor activities supervised by Prof. J. Y. Liang.


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In 2016, she was promoted to Associate Professor of Natural Medicinal Chemistry at China Pharmaceutical University. Her current research interests focus on design, synthesis and biological evaluation of natural compounds, and active chemical constituents from natural medicines, in particular, agents targeting neurodegenerative diseases and cancer. Haopeng Sun graduated in Pharmacy at the China Pharmaceutical University in 2006. He received his Ph.D. in 2011 in medicinal chemistry, with a thesis about the structural optimization and mechanism study of the natural product. Advisor Prof. Q. D. You. In 2014, he was promoted to Associate Professor of Medicinal Chemistry at China Pharmaceutical University. So far, he has published more than 110 papers on peerreview journals indexed by Science Citation Index. His major research interests include the design, synthesis and biological evaluation of small molecule bioactive compounds, in particular, agents targeting neurodegenerative diseases. In addition, he is focusing in the field of anti-cancer and anti-inflammatory agents.

ACKNOWLEDGMENTS We gratefully thank the support from the grants (Nos. 81573281 and 81830105) of National Natural Science Foundation of China. We also thank the support from “Double First-Class” initiative innovation team project of China Pharmaceutical University (Nos. CPU2018GF11 and CPU2018GY34). We also appreciated for the support from the Joint Laboratory of China Pharmaceutical University and Taian City Central Hospital.



ABBREVIATIONS USED AD, Alzheimer’s disease; MTDLs, Multi-target-directed ligands; PK, pharmacokinetic; PD, pharmacodynamics; MW, molecular weight; AChE, acetylcholinesterase; BBB, blood-brain barrier; DDL, dedifferentiated liposarcoma; H1R, H1 receptor; IGF1R, insulin-like growth factor 1 receptor; CDK4, cyclin-dependent kinase 4; MAOs, monoamine oxidases; SAR, structure-activity relationship; NK, neurokinin; CuAAC, cycloaddition; TEC, the thiol-ene click reaction; TYC, thiol-yne click reaction; DA, Diels-Alder reaction; BRD4, extra-terminal domain-4; VHL, Von Hippel-lindau; CRBN, cereblon; PSA, polar surface area; RB, rotatable bonds; HBD, hydrogen bond donors; HBA, hydrogen bond acceptors; HDACs, histone deacetylases; HDACi, histone deacetylases inhibitor; ZBG, zinc binding group; PIs, proteasome inhibitors; MDM2, murine double minute 2; VEGFR, vascular endothelial growth factor receptor; VEGF, vascular endothelial growth factor; IFIs, invasive fungal infections; MAPK, mitogen-activated protein kinase; JAK, Janus kinases; ERα, Estrogen receptor α; SERM, selective estrogen receptor modulators; PDGFR-β, platelet-derived growth factor receptor β; TDZDs, Thiadiazolidinediones; PAINS, pan assay interference compounds; TZD, thiazolidinedione; Aβ, amyloid-β; NFT, neurofibrillary tangles; ACh, acetylcholine; AChEIs, acetylcholinesterase inhibitors; GSK-3β, glycogen synthase kinase-3β; SERT, serotonin transporter; CNS, central nervous system; PD, Parkinson’s disease; hH3R, human histamine H3 receptor; AR, adenosine receptor; SSRIs, selective serotonin reuptake inhibitors; RUI, reuptake inhibition; D2Rs , dopamine D2 receptors; Ang II, angiotensin II; GPCRs, G-protein coupled receptors; PPARγ, proliferator-


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activated receptor γ; DPP4, dipeptidyl peptidase-4; ACE, angiotensin converting enzyme; COX, cyclooxygenases; LOX, lipoxygenases; 15-HETE, 15(S)-hydroxyeicosatetraenoic acid; ADCs, antibody-drug conjugates; HyT, hydrophobic tag; PROTAC, proteosis-targeting chimera; PPI, protein-protein interaction; POI, protein of interest; mAbs, monoclonal antibodies; BED4, bromodomain and extra-terminal domain-4; TGI, tumor growth inhibition; STS, steroid sulfatase; EPS, extrapyramidal symptoms; SR, serotonin reuptake; AT1, angiotensin II type 1; HEL, human embryonic lung fibroblasts.

Table of Contents graphic

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(245) Zhong, H. P.; Xiao, F. L.; Kenton, L.; Geldern, T. W., Von; Wiedeman, P. E.; Lubben, T. H.; Zinker, B. A.; Kent, S.; Ballaron, S. J.; Stashko, M. A. Discovery, Structure-Activity Relationship, and Pharmacological Evaluation of (5-SubstitutedPyrrolidinyl-2-Carbonyl)-2-Cyanopyrrolidines as Potent Dipeptidyl Peptidase IV Inhibitors. J. Med. Chem. 2006, 49(12), 3520-3535. (246) Kim, D.; Wang, L.; Beconi, M.; Eiermann, G. J.; Fisher, M. H.; He, H.; Hickey, G. J.; Kowalchick, J. E.; Leiting, B.; Lyons, K. (2R)-4-Oxo-4-[3-(Trifluoromethyl)-5,6Dihydro[1,2,4]Triazolo[4,3-a]Pyrazin-7(8H)-yl]-1-(2,4,5-Trifluorophenyl)Butan-2Amine: A Potent, Orally Active Dipeptidyl Peptidase IV Inhibitor for the Treatment of Type 2 Diabetes. J. Med. Chem. 2005, 48(1), 141-145. (247) Zheng, Q. W.; Robert, V. Design and Synthesis of Dual Inhibitors of HIV Reverse Transcriptase and Integrase: Introducing a Diketoacid Functionality into Delavirdine. Bioorg. Med. Chem. 2008, 16(7), 3587-3595. (248) Gu, S. X.; Xue, P.; Ju, X. L.; Zhu, Y. Y. Advances in Rationally Designed Dual Inhibitors of HIV-1 Reverse Transcriptase and Integrase. Bioorg. Med. Chem. 2016, 24(21), 5007-5016. (249) Wang, Z.; Vince, R. Synthesis of Pyrimidine and Quinolone Conjugates as a Scaffold for Dual Inhibitors of HIV Reverse Transcriptase and Integrase. Bioorg. Med. Chem. Lett. 2008, 18(4), 1293-1296. (250) Hopkins, A. L.; Ren, J.; Esnouf, R. M.; Willcox, B. E.; Jones, E. Y.; Ross, C.; Miyasaka, T.; Walker, R. T.; Tanaka, H.; Stammers, D. K. Complexes of HIV-1 Reverse Transcriptase with Inhibitors of the HEPT Series Reveal Conformational Changes



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Rational Design of Multi-Target-Directed Ligands: Strategies and Emerging Paradigms

Junting Zhou a, b, Xueyang Jiang a, b, Siyu He a, Hongli Jiang a, b, Feng Feng b, c, Wenyuan Liu d, Wei Qu b *, Haopeng Sun a *

a

Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing,

211198, People’s Republic of China b

Department of Natural Medicinal Chemistry, China Pharmaceutical University,

Nanjing, 211198, People’s Republic of China c

Jiangsu Food and Pharmaceutical Science College, Huaian, 223003, People’s

Republic of China d

Department of Analytical Chemistry, China Pharmaceutical University, Nanjing,

210009, People’s Republic of China

Correspondence: [email protected] (Haopeng Sun); [email protected] (Wei Qu).


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Table 1. Comparison of the advantages and disadvantages of two multi-target therapeutics14. Approaches

Mixture of monotherapies

Multiple ligands 1. They are new chemical entity;

1. Can offer better dose flexibility by directly adjust ratio of drugs in mixture; Advantages 2. Can achieve sequenced administration or adjusting target exposure; 3. Clinical trials may be faster due to clinical experience, and the treatment cost may be lower.

2. The programs of development and regulatory approval are standard, same as the single target drugs; 3. As a single active component, the PK/PD properties are easier to formulate compared with a mixture; 4. Therapeutic efficacy may increase through synergies even at low dosages; 5. Reduced adverse effects enable wider therapeutic windows.

1. “Combination versus parts” factorial trial might be conducted; Disadvantages 2. Should align PK/PD properties of the multiple components; 3. More likely to cause drug–drug interaction.

1. Achieving balanced and multi-selective potency towards multiple targets is challenging; 2. Difficult to achieve sequenced administation at the targets.



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Table 2. Physicochemical Properties Data of MTDLs on Market or in Clinical Trailsa. Therapeutic fields MW

Nervous Cardiovascular system disease disease

Cancer

Infectious disease

Full MTDL set

476.51 (471.26) 335.06 (314.91) 408.08 (391.98) 407.90 (324.34) 385.96 (371.54)

cLogP

3.81 (4.43)

3.16 (3.51)

3.27 (3.29)

2.58 (2.48)

3.22 (3.39)

HBA

6.18 (6.00)

3.75 (3.00)

5.20 (5.00)

5.95 (4.00)

4.81 (4.00)

HBD

1.95 (2.00)

1.35 (1.00)

2.50 (2.00)

3.00 (3.00)

1.93 (2.00)

PSA

99.06 (94.00)

62.27 (54.64)

102.11 (93.89)

115.91 (96.79)

84.70 (77.23)

RB

6.32 (6.00)

5.46 (5.00)

8.20 (7.50)

6.84 (5.00)

6.32 (5.00)

Heteroatom

9.36 (9.50)

5.41 (5.00)

7.25 (6.50)

8.68 (8.00)

7.00 (6.00)

Approved 14 13 10 12 49 drug numbers Sample 22 56 20 19 117 numbers a The values outside the brackets represent the mean values, and the inside ones represent the median values.

Table 3. Biological Activities of MTDLs Targeting HDAC and Proteasome127.

28

HDAC6 IC50 (μM) 0.27 ±0.01

HL60 IC50 (nM) 260.70 ±3.12

SEM IC50 (nM) 394.20 ±9.85

SUP-B15r IC50 (nM) 820.50 ±13.94

Bortezomib

-

6.67 ±0.16

4.43 ±0.13

28.09 ±1.15

Ricolinostat

-

> 25000

> 25000

> 25000

Vornostat

-

> 25000

> 25000

> 25000

Compound

Table 4. Biological Activities of MTDLs Targeting HDAC and MDM2130.

29, Nutlin-3

MDM2 Ki (μM) 0.14 ±0.04

HDAC1 IC50 (nM) -

HDAC6 IC50 (nM) -

30, SAHA

> 20

45.0 ±3.1

16.3 ±1.6

31

0.11 ±0.03

821 ±12

17.5 ±1.5

Compound


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Table 5. Biological Activities of MTDLs Targeting HDAC and VEGFR-2132.

32, Pazopanib

VEGFR-2 Inh% (0.2 μM) 95

HDAC IC50 (μM) -

33, MS-275

-

1.8

34

100

4.6

SAHA

-

0.13

Compound

Table 6. Biological Activities of MTDLs Targeting HDAC and JAK2136. Compound

JAK2 IC50 (nM)

HDAC1 IC50 (nM)

HDAC6 IC50 (nM)

30, SAHA

-

40 ±9

-

35

41 ±5

-

-

36

8.4 ±0.7

250 ±25

46 ±1.7

Table 7. Biological Activities of MTDLs Targeting Src and MEK Kinases145. Compound

Src Inh%

MEK Inh%

K562 IC50 (μM)

HepG-2 IC50 (μM)

37, OA

8.02 at 50 μM

44.03 at 50 μM

5.8

>50

39

59.67 at 10 μM

43.23 at 10 μM

4.08

9.41

Imatinib

-

-

0.53

>25

Table 8. Biological Activities of MTDLs Targeting ERα and VEGFR-2155.

43

ERα Inh% (0.1 mg/ml) 99.89

MCF-7 IC50 (μM) 2.73

VEGFR-2 Inh% (0.1 mg/ml) 100.26

44

7.2 μMa

1.2

0.099 μMb

41, Tamoxifen

100.0

1.89

-

Sunitinib

-

-

100 (0.14 μMb)

Compound

The values represent the IC50 values for ERα inhibition; bThe values represent the IC50 values for VEGFR-2 inhibition. a



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Table 9. Biological Activities of MTDLs Targeting RTKs and Microtubule165.

47

PDGFR-β IC50 (nM) 22.8 ±4.9

VEGFR-2 IC50 (nM) 19.1 ±3.0

Microtubule depolymerization EC50 (nM) 103

48

58.2 ±8.0

33. 2 ±5.0

21

Sunitinib

83.1 ±10.1

18.9 ±2.7

-

Combretastatin A-4

-

-

9.8

Compound

Table 10. Biological Activities of MTDLs Targeting GSK-3β and TauAggregation169.

a

Compound

GSK-3β IC50 (μM)

Tau K18 self-aggregation Inh% (10 μM)

50

4.93 ±0.66

-

51

0.89 ±0.21

35a

49, Tideglusib

0.69 ±0.09

-

Compared to tau K18 self-aggregation alone.

Table 11. Biological Activities of MTDLs Targeting GSK-3β and ChE183. Compound

hGSK-3β IC50 (nM)

hAChE IC50 (nM)

hBuChE IC50 (nM)

52, Tacrine

-

230 ±31

40 ±3.7

53

1.1

-

-

54

66 ±6.2

6.4 ±0.3

260 ±32

Table 12. Biological Activities of MTDLs Targeting ChE and Oxidation187.

52, Tacrine

hAChE IC50 (nM) 350 ±10

hBuChE IC50 (nM) 40 ±2

Trolox equiva < 0.01

55

-

-

2.3 ±0.1

56

0.008 ±0.0004

7.8 ±0.4

2.5 ±0.1

Compound

a

Data are expressed as µmol of trolox equivalent/µmol of tested compound and are the mean.


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Table 13. Biological Activities of MTDLs Targeting ChE and MAO190. Compound

AChE IC50 (nM)

BuChE IC50 (nM)

hMAO-A IC50 (μM)

hMAO-B IC50 (μM)

52, Tacrine

110.2 ±7.30

21.6 ±1.77

-

-

58

22.6 ±3.04

9.37 ±0.75

0.3724 ±0.0249

0.1810 ±0.0300

Clorgyline

-

-

0.0041 ±0.0002

-

Pargyline

-

-

-

0.1180 ±0.0160

Table 14. Biological Activities of MTDLs Targeting AChE and SERT195.

7, Rivastigmine

AChE IC50 (nM) 11000

SERT IC50 (nM) > 1000

19, Donepezil

10

> 1000

59, Fluoxetine

> 10000

180

60

101

42

61

14

6

Compound

Table 15. Biological Activities of MTDLs Targeting Metal Chelation, Aβ, MetalAβ, and ROS197. Target Aβ and metal-Aβ Aβ42 fibers Metal

Multiple Activities of Compound 65 65 can bind to Aβ40 and Aβ42; 65 stoichiometry, and bind to metal-Aβ42 and chelate Cu(II) from Aβ42 competitively. 65 can interact with Aβ42 fibers and bind to Zn(II)-Aβ42 fibers. 65 can bind to Cu(II) and Zn(II) selectively, and chelate metal ions surrounded by soluble Aβ.

Early Oligomerization 65 can inhibit the formation of dodecamer and hexamer . of Aβ Metal-Associated Aβ65 may regulate Aβ/metal-Aβ-induced toxicity. Induced Toxicity ROS formation 65 may control Cu-triggered formation of hydroxyl radicals. Oxidantion BBB permeability

65 can scavenge free radicals more effectively Trolox (1.41 ±0.15 for 65; 1.00 ±0.08 for Trolox) by the TEAC assay. 65 may cross the BBB in PAMPA assay.



Page 122 of 140

Table 16. Biological Activities of MTDLs Targeting MAO-B and H3R206.

a

Compound

MAO-A IC50 (nM)

MAO-B IC50 (nM)

SIa

H3R Ki (nM)

66, Ciproxifan

2100

11000

0.19

46-180

67, UCL2190

-

-

11

68

39

3

13

-

69

> 1000000

9.2

-

-

70

> 10000

276

36

10

Selectivity index (SI) = IC50 MAO-A/IC50 MAO-B.

Table 17. Biological Activities of MTDLs Targeting MAO-B and A1/A2A AR211. Compound

A1 AR Ki (μM)

A2A AR Ki (μM)

hMAO-A IC50 (μM)

hMAO-B IC50 (μM)

71

0.265 ±0.068

1.06 ±0.30

-

> 10.0

72

44.9

23.4

> 50.0

> 50.0

73

0.393 ±0.101

0.595 ±0.051

> 10.0

0.210 ±0.041

Table 18. Biological Activities of MTDLs Targeting SERT/5-HT1A/5-HT7221. Compound

5-HT1A Ki (nM)

5-HT7 Ki (nM)

SERT IC50 (nM)

74

60.9

0.13

-

75

188

0.58

-

76

99

1.4

-

77

1.94

-

25.3

78

28

3.3

25

Table 19. Biological Activities of MTDLs Targeting Dopamine D2 receptor and Serotonin reuptake226. In vitro

In vivo

Compound

hD2 Ki (μM)

rSR Ki (μM)

APOa EC50 (mg/kg)

5-HTPb EC50 (mg/kg)

79, Indalpine

2.0 ±0.1

-

0.1

-

80, Eltoprazine

-

2.0 ±0.1

-

-

6.9 ±1.8 0.2 ±0.1 0.08 0.15 81 b Antagonizing apomorphine induced climbing behavior in mice (po). 5-HTP induced serotonin syndrome like behavior in mice (po). a


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Table 20. Biological Activities of MTDLs Targeting AT1 and ETA37. Compound

ETA Ki (nM)

AT1 Ki (nM)

82

1.4

> 10000

83, Irbesartan

> 10000

0.8

84

9.3

0.8


a

Table 21. Biological Activities of MTDLs Targeting PPARγ and AT1239. Compound

AT1 Ki (nM)

hPPARγ EC50 (nM) (% max)a

85, Telmisartan

0.49

1520

Pioglitazone

-

1280

87

1.6

212


a

Table 22. Biological Activities of MTDLs Targeting ACE and DPP4242. ACE Inhibitona IC50 (nM)

DPP4 Inhibitona IC50 (nM)

Rat

Mouse

Human

Rat

Mouse

Human

88, Enalaprilat

9.6

11.5

2.5

-

-

> 100 μM

91, Sitagliptin

-

-

11 μM

33

46

20

93

2800

2210

51

1230

7170

102

Compound

a

Plasma from Wistar rat, ob/ob mouse and human was used as source of ACE and DPP4 enzymes.

Table 23. Biological Activities of MTDLs Targeting IN and RT 249.

94, TNK-651

IN IC50 (μM) 2.4

RT IC50 (μM) 0.057

HIV EC50 (μM) 0.033

95, GS-9137

0.0072

-

0.0009

96

35

0.19

0.22

Compound



Page 124 of 140

Table 24. Biological Activities of MTDLs Targeting IN and RT247.

97

IN IC50 (μM) 0.093

RT IC50 (μM) > 100

HIV EC50 (μM) 0.16

98, Delavirdine

> 100

0.036

0.021

99

11

0.059

0.52

Compound

Table 25. Biological Activities of MTDLs Targeting PfATP6 and Host Haemoglobin Digestion256. Compound

3D7 (48h) IC50 (nM)

3D7 (72h) IC50 (nM)



100, Artemisinin

49.4

45.5

50.0

55.0

101, Quinine

149

73.5

96.8

75.3

102

8.95

10.4

9.59

10.2

Quinine+ Artemisinina

31.8

28.6

27.9

26.3

a

Values represent concentrations of each of quinine and artemisinin in a 1:1 ratio.

Table 26. Biological Activities of MTDLs Targeting 15-LOX and COX-236. COX-1 IC50 (μM) -

COX-2 IC50 (μM) -

SIa

103

15-LOX IC50 (μM) 8.24

104

-

8.94

0.33

27

105

11.87

15.42

0.07

220.29

Compound

a

Selectivity index (SI) = IC50 COX-1/IC50 COX-2.


-

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Figure 1. Multi-target drug design strategy based on pharmacophore22. In linked MTDLs, the pharmacophores are connected by stable or biodegradable linkers; in fused MTDLs, the pharmacophores are attached directly; in merged MTDLs, the pharmacophores are merged together.

Figure 2. The screening of compounds libraries may generate a lead compound which obtain all activities towards both targets of interest (A and B). (a) But a single compound may be unlikely to have balanced affinity to all targets, therefore its activities should be optimized. (b) In another case, the lead compound also has activity towards undesired targets, which must be “design-out” during optimization20.



Scheme 1. The Two Categories of Linked MTDLs: The Cleavable MTDLs and Non-cleavable MTDLs

a

(A) Compound 3 is a cleavable MTDL, with an ester linker that can be hydrolyzed in

vivo62. (B) Compound 6 is a non-cleavale MTDL, the linker of which is stable in vivo64.

Scheme 2. Representative Structures of Fused Pharmacophoresa

a

The two pharmacophores are in blue and red; purple illustrates the fused/merged

pharmacophore from two selective ligands. (A) Dual cholinesterase/MAO-B inhibitor (9)65. (B) Neuroprotection/anti-oxidative bisfunctional agent (12)66. Both compounds 9 and 12 are the products of fused pharmacophores approach.


Page 126 of 140

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Scheme 3. Representative Structures of Merged Pharmacophoresa

a(A)

Multifunctional agent (16) is designed by merging the pharmacophores of the

aminopropoxyphenyl scaffolds from compound 13, 14 and 1567. (B) Bisfunctional compound 19 is designed via merging the pharmacophores from chelator HLA20 (17), rivastigmine (7) and donepezil (18)68.

Scheme 4. Representative Example of Design-in Strategy a

a

Based on design-in approach, the pharmacophore of 20 is designed into that of 21,

according to molecular docking and co-crystals information75, 76.



Scheme 5. Representative Example of Design-out Approach

aIn

design-out approach, Compound 25 is obtained by reducing the affinity to the

undesired target NK3, meanwhile, retaining the affinities on NK1 and NK279.

Scheme 6. General Scheme and Representative Example of CuAAC Reactiona

a(A)

the 1, 3-dipolar cycloaddition between alkynes and azides; (B) the representative

example of CuAAC reaction85.


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Scheme 7. General Schemes and Representative Example of DA and Thiol-ene/yne Click Reactiona

a(A)

The Diels-Alder (DA) reaction; (B) the TEC reaction (top) and the TYC reaction

(bottom); (C) the representative example of DA reaction92.

Scheme 8. General Scheme and Representative Example of Suzuki Reaction

a (A)

The aryl Suzuki reaction: C(sp2-sp2) cross-coupling; (B) the acyl Suzuki reaction:

C(acyl-sp2) cross-coupling; (C) the representative example of Suzuki reaction97.



Scheme 9. General Scheme and Representative Example of Stille Reactiona

a(A)

The Stille reaction; (B)the representative example of Stille reaction100.

Scheme 10. General Scheme and Representative Example of Sonogashira Reactiona

a(A)

The Sonogashira reaction; (B)the representative example of Sonogashira

reaction102.


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Scheme 11. General Scheme and Representative Example of Heck Reactiona

a(A)

The Heck reaction; (B)the representative example of Heck reaction105.



Scheme 12. MTDLs Targeting HDAC and Other Targetsa

a

Most HDACi consist of three parts: a ZBG, a hydrophobic linker, and a surface

cognition cap138. The HDACi pharmacophores can tolerate diverse cap groups, enabling MTDLs strategies.


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Scheme 13. MTDLs Targeting Src and MEK Kinases145

Scheme 14. MTDLs Targeting ERα and VEGFR-2155

Scheme 15. MTDLs Targeting Tyrosine Kinase and Microtubule165



Scheme 16. MTDLs Targeting GSK-3β and Tau-Aggregation for AD169

Scheme 17. MTDLs Targeting ChE and Other Targets for AD183, 187, 190


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Scheme 18. MTDLs Targeting AChE and SERT for AD195

Scheme 19 MTDLs Targeting Metal Chelation, Aβ, Metal-Aβ, and ROS for AD197



Scheme 20. MTDLs Targeting MAO-B and H3R for PD206

Scheme 21. MTDLs Targeting MAO-B and A1/A2A AR for PD211


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Scheme 22. MTDLs Targeting SERT/5-HT1A/5-HT7 for Depression221

Scheme 23. MTDLs Targeting Dopamine D2 receptor and Serotonin reuptake for Schizophrenia226

Scheme 24. MTDLs targeting AT1 and ETA for hypertension37



Scheme 25. Design of Multi-Targeted Agents Targeting PPARγ and AT1 for Hypertension239

Scheme 26. MTDLs Targeting ACE and DPP4 for Hypertension and Diabetesa

a(A)

The representative scaffold of ACE and DPP4 inhibitors242. (B) DPP4-ACE dual

inhibitors from the merger of enalaprilat 82 and sitagliptin 85.


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Scheme 27. MTDLs Targeting IN and RT for AIDS249

Scheme 28. MTDLs Targeting IN and RT for AIDS247

Scheme 29. MTDLs Targeting PfATP6 and Host Haemoglobin Digestion for Malaria

Scheme 30. MTDLs Targeting 15-LOX and COX-2 for Inflammation36



Table of Contents graphic


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Rational Design of Multi-Target-Directed Ligands: Strategies and

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