Polypharmacology in Drug Development: A Minireview of Current Technologies

Use of Drug Sensitisers to Improve Therapeutic Index in Cancer

The clinical management of malignant tumours is challenging, often leading to severe adverse effects and death. Drug resistance (DR) antagonises the effectiveness of treatments, and increasing drug dosage can worsen the therapeutic index (TI). Current efforts to overcome DR predominantly involve the use of drug combinations, including applying multiple anti-cancerous drugs, employing drug sensitisers, which are chemical agents that enhance pharmacokinetics (PK), including the targeting of cellular pathways and regulating pertinent membrane transporters. While combining multiple compounds may lead to drug-drug interactions (DDI) or polypharmacy effect, the use of drug sensitisers permits rapid attainment of effective treatment dosages at the disease site to prevent early DR and minimise side effects and will reduce the chance of DDI as lower drug doses are required. This review highlights the essential use of TI in evaluating drug dosage for cancer treatment and discusses the lack of a unified standard for TI within the field. Commonly used benefit-risk assessment criteria are summarised, and the critical exploration of the current use of TI in the pharmaceutical industrial sector is included. Specifically, this review leads to the discussion of drug sensitisers to facilitate improved ratios of effective dose to toxic dose directly in humans. The combination of drug and sensitiser molecules might see additional benefits to rekindle those drugs that failed late-stage clinical trials by the removal of detrimental off-target activities through the use of lower drug doses. Drug combinations and employing drug sensitisers are potential means to combat DR. The evolution of drug combinations and polypharmacy on TI are reviewed. Notably, the novel binary weapon approach is introduced as a new opportunity to improve TI. This review emphasises the urgent need for a criterion to systematically evaluate drug safety and efficiency for practical implementation in the field.

Thiazolidinedione-triazole conjugates: design, synthesis and probing of the α-amylase inhibitory potential

Aim: The primary objective of this investigation was the synthesis, spectral interpretation and evaluation of the α-amylase inhibition of rationally designed thiazolidinedione-triazole conjugates (7a-7aa). Materials & methods: The designed compounds were synthesized by stirring a mixture of thiazolidine-2,4-dione, propargyl bromide, cinnamaldehyde and azide derivatives in polyethylene glycol-400. The α-amylase inhibitory activity of the synthesized conjugates was examined by integrating in vitro and in silico studies. Results: The investigated derivatives exhibited promising α-amylase inhibitory activity, with IC50 values ranging between 0.028 and 0.088 μmol ml-1. Various computational approaches were employed to get detailed information about the inhibition mechanism. Conclusion: The thiazolidinedione-triazole conjugate 7p, with IC50 = 0.028 μmol ml-1, was identified as the best hit for inhibiting α-amylase.

ProfhEX: AI-based platform for small molecules liability profiling

Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates. In this work we present ProfhEX, an AI-driven suite of 46 OECD-compliant machine learning models that can profile small molecules on 7 relevant liability groups: cardiovascular, central nervous system, gastrointestinal, endocrine, renal, pulmonary and immune system toxicities. Experimental affinity data was collected from public and commercial data sources. The entire chemical space comprised 289'202 activity data for a total of 210'116 unique compounds, spanning over 46 targets with dataset sizes ranging from 819 to 18896. Gradient boosting and random forest algorithms were initially employed and ensembled for the selection of a champion model. Models were validated according to the OECD principles, including robust internal (cross validation, bootstrap, y-scrambling) and external validation. Champion models achieved an average Pearson correlation coefficient of 0.84 (SD of 0.05), an R² determination coefficient of 0.68 (SD = 0.1) and a root mean squared error of 0.69 (SD of 0.08). All liability groups showed good hit-detection power with an average enrichment factor at 5% of 13.1 (SD of 4.5) and AUC of 0.92 (SD of 0.05). Benchmarking against already existing tools demonstrated the predictive power of ProfhEX models for large-scale liability profiling. This platform will be further expanded with the inclusion of new targets and through complementary modelling approaches, such as structure and pharmacophore-based models. ProfhEX is freely accessible at the following address: https://profhex.exscalate.eu/ .

Design and synthesis of uracil/thiouracil based quinoline scaffolds as topoisomerases I/II inhibitors for chemotherapy: A new hybrid navigator with DFT calculation

In this work, a novel promising hybrid mode of uracil/thiouracil based quinoline pharmacophore i.e. 5a-f was rationalized and synthesized based on rigidification and lipophilic principles, and following the reported pharmacophoric features of camptothecin & doxorubicin. Concurrently, a non-rigid mode pharmacophore i.e. 7a-f was also designed and synthesized. The anti-proliferative activity of the compounds was assessed against three different cancer cell lines, namely A549 lung cancer, MCF-7 breast adenocarcinoma, and HepG-2 hepatic carcinoma. Further, promising candidates were evaluated against A549, and MCF-7 and for their ability to inhibit topoisomerases I &II. Compound 5f was observed to be the most active congener, displaying the highest cell inhibition of 84.4% for topoisomerase I and 92%, for topoisomerase II at a concentration of 100 µM. When its cytotoxicity was evaluated against A549 cells, 5f arrested the cell cycle at the S phase and increased the apoptosis ratio by 46.31%. DFT calculation of 5f showed higher dipole moment and greater negative energy values (-247531.510 kcal/mol) with positive & negative poles, and better stability reflection. Furthermore, molecular docking of 5f to both enzymes showed good agreement with the biological assessment. This study has given insight for further consideration of the highly promising hybrid 5f.

Development and evaluation of a java-based deep neural network method for drug response predictions

Accurate prediction of drug response is a crucial step in personalized medicine. Recently, deep learning techniques have been witnessed with significant breakthroughs in a variety of areas including biomedical research and chemogenomic applications. This motivated us to develop a novel deep learning platform to accurately and reliably predict the response of cancer cells to different drug treatments. In the present work, we describe a Java-based implementation of deep neural network method, termed JavaDL, to predict cancer responses to drugs solely based on their chemical features. To this end, we devised a novel cost function and added a regularization term which suppresses overfitting. We also adopted an early stopping strategy to further reduce overfit and improve the accuracy and robustness of our models. To evaluate our method, we compared with several popular machine learning and deep neural network programs and observed that JavaDL either outperformed those methods in model building or obtained comparable predictions. Finally, JavaDL was employed to predict drug responses of several aggressive breast cancer cell lines, and the results showed robust and accurate predictions with r ² as high as 0.81.

Cell Surface Transporters and Novel Drug Developments

Despite the numerous scientific and technological advances made within the last decade the attrition rates for new drug discovery remain as high as 95% for anticancer drugs. Recent drug development has been in part guided by Lipinski's Rule of 5 (Ro5) even though many approved drugs do not comply to these rules. With Covid-19 vaccine development strategy dramatically accelerating drug development perhaps it is timely to question the generic drug development process itself to find a more efficient, cost effective, and successful approach. It is widely believed that drugs permeate cells via two methods: phospholipid bilayer diffusion and carrier mediated transporters. However, emerging evidence suggests that carrier mediated transport may be the primary mechanism of drug uptake and not diffusion as long believed. Computational biology increasingly assists drug design to achieve desirable absorption, distribution, metabolism, elimination and toxicity (ADMET) properties. Perfecting drug entry into target cells as a prerequisite to intracellular drug action is a logical and compelling route and is expected to reduce drug attrition rates, particularly gaining favour amongst chronic lifelong therapeutics. Novel drug development is rapidly expanding from the utilisation of beyond the rule of five (bRo5) to pulsatile drug delivery systems and fragment based drug design. Utilising transporters as drug targets and advocating bRo5 molecules may be the solution to increasing drug specificity, reducing dosage and toxicity and thus revolutionising drug development. This review explores the development of cell surface transporter exploitation in drug development and the relationship with improved therapeutic index.

Polypharmacology: The science of multi-targeting molecules

Polypharmacology is a concept where a molecule can interact with two or more targets simultaneously. It offers many advantages as compared to the conventional single-targeting molecules. A multi-targeting drug is much more efficacious due to its cumulative efficacy at all of its individual targets making it much more effective in complex and multifactorial diseases like cancer, where multiple proteins and pathways are involved in the onset and development of the disease. For a molecule to be polypharmacologic in nature, it needs to possess promiscuity which is the ability to interact with multiple targets; and at the same time avoid binding to antitargets which would otherwise result in off-target adverse effects. There are certain structural features and physicochemical properties which when present would help researchers to predict if the designed molecule would possess promiscuity or not. Promiscuity can also be identified via advanced state-of-the-art computational methods. In this review, we also elaborate on the methods by which one can intentionally incorporate promiscuity in their molecules and make them polypharmacologic. The polypharmacology paradigm of "one drug-multiple targets" has numerous applications especially in drug repurposing where an already established drug is redeveloped for a new indication. Though designing a polypharmacological drug is much more difficult than designing a single-targeting drug, with the current technologies and information regarding different diseases and chemical functional groups, it is plausible for researchers to intentionally design a polypharmacological drug and unlock its advantages.

A proteomic platform to identify off-target proteins associated with therapeutic modalities that induce protein degradation or gene silencing

Novel modalities such as PROTAC and RNAi have the ability to inadvertently alter the abundance of endogenous proteins. Currently available in vitro secondary pharmacology assays, which evaluate off-target binding or activity of small molecules, do not fully assess the off-target effects of PROTAC and are not applicable to RNAi. To address this gap, we developed a proteomics-based platform to comprehensively evaluate the abundance of off-target proteins. First, we selected off-target proteins using genetics and pharmacology evidence. This process yielded 2813 proteins, which we refer to as the “selected off-target proteome” (SOTP). An iterative algorithm was then used to identify four human cell lines out of 932. The 4 cell lines collectively expressed ~ 80% of the SOTP based on transcriptome data. Second, we used mass spectrometry to quantify the intracellular and extracellular proteins from the selected cell lines. Among over 10,000 quantifiable proteins identified, 1828 were part of the predefined SOTP. The SOTP was designed to be easily modified or expanded, owing to the rational selection process developed and the label free LC–MS/MS approach chosen. This versatility inherent to our platform is essential to design fit-for-purpose studies that can address the dynamic questions faced in investigative toxicology.

Discovery of novel pyrazolopyrimidine derivatives as potent mTOR/HDAC bi-functional inhibitors via pharmacophore-merging strategy

The mTOR and HDAC dual suppression is meaningful for counteracting drug resistance resulted from kinase mutation and bypass mechanisms. Herein, we communicate our recent discovery of a novel structural series of mTOR/HDAC bi-functional inhibitors featuring the pyrazolopyrimidine core via pharmacophore-merging strategy. More than half of them exerted potent dual-target inhibitory activities. In particular, compound 50 exhibited IC₅₀ values of 0.49 and 0.91 nM against mTOR and HDAC1, respectively, along with remarkably enhanced anti-proliferative activity (IC₅₀ = 1.74 μM) against MV4-11 cell line than mTOR inhibitor MLN-0128 (IC₅₀ = 5.84 μM) and HDAC inhibitor SAHA (IC₅₀ = 8.44 μM). Its intracellular intervention of both mTOR signaling and HDAC was validated by the Western blot analysis. Moreover, as the first disclosed mTOR/HDAC dual inhibitor with selectivity for some specific HDAC subtypes, it has the potential to alleviate the adverse effects resulted from pan-HDAC inhibition. Attributed to its favorable in vitro performance, compound 50 is valuable for further functional investigation as a polypharmacological anti-cancer agent.

Discovery of Novel Pyrrolo[2,3-d]pyrimidine-based Derivatives as Potent JAK/HDAC Dual Inhibitors for the Treatment of Refractory Solid Tumors

It remains a big challenge to develop HDAC inhibitors effective for solid tumors. Previous studies have suggested that the feedback activation of JAK-STAT3 pathway represents a key mechanism leading to resistance to HDAC inhibitors in breast cancer, suggesting the therapeutic promise of JAK/HDAC dual inhibitors. In this work, we discovered a series of pyrrolo[2,3-d]pyrimidine-based derivatives as potent JAK and HDAC dual inhibitors. Especially, compounds 15d and 15h potently inhibited JAK1/2/3 and HDAC1/6 and displayed antiproliferative and proapoptotic activities in triple-negative breast cancer cell lines. Besides, compounds 15d and 15h also diminished the activation of LIFR-JAK-STAT signaling triggered by tumor-associated fibroblasts, which suggests that these compounds could potentially overcome the drug resistance caused by the tumor microenvironment. More importantly, compound 15d effectively inhibited the tumor growth in MDA-MB-231 xenograft tumor model. Overall, this work provides valuable leads and novel antitumor mechanisms for the treatment of the SAHA-resistant triple-negative breast cancers.

An up-to-date overview of computational polypharmacology in modern drug discovery

INTRODUCTION

In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED

In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION

Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

Discovery of Novel Inhibitors Targeting Multi-UDP-hexose Pyrophosphorylases as Anticancer Agents

To minimize treatment toxicities, recent anti-cancer research efforts have switched from broad-based chemotherapy to targeted therapy, and emerging data show that altered cellular metabolism in cancerous cells can be exploited as new venues for targeted intervention. In this study, we focused on, among the altered metabolic processes in cancerous cells, altered glycosylation due to its documented roles in cancer tumorigenesis, metastasis and drug resistance. We hypothesize that the enzymes required for the biosynthesis of UDP-hexoses, glycosyl donors for glycan synthesis, could serve as therapeutic targets for cancers. Through structure-based virtual screening and kinetic assay, we identified a drug-like chemical fragment, GAL-012, that inhibit a small family of UDP-hexose pyrophosphorylases-galactose pyro-phosphorylase (GALT), UDP-glucose pyrophosphorylase (UGP2) and UDP-N-acetylglucosamine pyrophosphorylase (AGX1/UAP1) with an IC₅₀ of 30 µM. The computational docking studies supported the interaction of GAL-012 to the binding sites of GALT at Trp190 and Ser192, UGP2 at Gly116 and Lys127, and AGX1/UAP1 at Asn327 and Lys407, respectively. One of GAL-012 derivatives GAL-012-2 also demonstrated the inhibitory activity against GALT and UGP2. Moreover, we showed that GAL-012 suppressed the growth of PC3 cells in a dose-dependent manner with an EC₅₀ of 75 µM with no effects on normal skin fibroblasts at 200 µM. Western blot analysis revealed reduced expression of pAKT (Ser473), pAKT (Thr308) by 77% and 72%, respectively in the treated cells. siRNA experiments against the respective genes encoding the pyrophosphorylases were also performed and the results further validated the proposed roles in cancer growth inhibition. Finally, synergistic relationships between GAL-012 and tunicamycin, as well as bortezomib (BTZ) in killing cultured cancer cells were observed, respectively. With its unique scaffold and relatively small size, GAL-012 serves as a promising early chemotype for optimization to become a safe, effective, multi-target anti-cancer drug candidate which could be used alone or in combination with known therapeutics.

Multi-target-directed-ligands acting as enzyme inhibitors and receptor ligands

In this review, we present the latest advances in the field of multi-target-directed ligand (MTDL) design for the treatment of various complex pathologies of multifactorial origin. In particular, latest findings in the field of MTDL design targeting both an enzyme and a receptor are presented for different diseases such as Alzheimer's disease (AD), depression, addiction, glaucoma, non-alcoholic steatohepatitis and pain and inflammation. The ethology of the diseases is briefly described, with special emphasis on how the MTDL can evolve into novel therapies that replace the classic pharmacological dogma "one target one disease". Considering the current needs for therapy adherence improvement, it is exposed as from the medicinal chemistry, different molecular scaffolds are studied. With the use of structure activity relationship studies and molecular optimization, new hybrid molecules are generated with improved biological properties acting at two biologically very distinct targets.

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

Docking calculations can accelerate drug discovery by predicting the bound poses of ligands for a targeted protein. However, it is not clear which docking methods work best. Furthermore, predicting poses requires steps outside the docking algorithm itself, such as preparation of the protein and ligand, and it is not known which components are most in need of improvement. The Continuous Evaluation of Ligand Protein Predictions (CELPP) is a blinded prediction challenge designed to address these issues. Participants create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow's predictions and posts the scores online. The results can be used to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.

xCELLanalyzer: A Framework for the Analysis of Cellular Impedance Measurements for Mode of Action Discovery

Mode of action (MoA) identification of bioactive compounds is very often a challenging and time-consuming task. We used a label-free kinetic profiling method based on an impedance readout to monitor the time-dependent cellular response profiles for the interaction of bioactive natural products and other small molecules with mammalian cells. Such approaches have been rarely used so far due to the lack of data mining tools to properly capture the characteristics of the impedance curves. We developed a data analysis pipeline for the xCELLigence Real-Time Cell Analysis detection platform to process the data, assess and score their reproducibility, and provide rank-based MoA predictions for a reference set of 60 bioactive compounds. The method can reveal additional, previously unknown targets, as exemplified by the identification of tubulin-destabilizing activities of the RNA synthesis inhibitor actinomycin D and the effects on DNA replication of vioprolide A. The data analysis pipeline is based on the statistical programming language R and is available to the scientific community through a GitHub repository.

Interaction between nanoparticles and charged phospholipid membranes

Charged lipids in cell membranes and subcellular organelles are arranged in the form of a bilayer with the hydrocarbon tails sequestered away from the water and the polar head groups exposed to the aqueous environment. Most of them bear net negative charges leading to the negatively charged cell membranes. Charged lipid-lipid and lipid-protein interactions are generally dynamic and heavily depend on their local molecular concentrations. To examine the electrostatic properties of charged lipid layers in contact with an electrolyte solution, we incorporate the single chain mean field theory with Poisson-Boltzmann theory to explore the equilibrium structure of charged phospholipid membranes. Using the three bead coarse-grained model we reproduced the essential equilibrium properties of the charged phospholipid bilayer. We also investigate the influence of the mobile ions on the thickness of the layer, the area per lipid (APL), and the electrostatic potential of the membrane. Then we investigate the attraction-repulsion property of two charged nanoparticles which are stuck on the charged lipid molecules surrounded with mobile ions. After that we simulated the interaction between the Pleckstrin homology domain (PH domain) of Akt and the cytoplasmic membrane. Taking into account the electrostatic interaction, we observe the structure changes of the membrane at different concentrations of mobile ions in its equilibrium state. Also we discuss the influence of mobile ions on the size of the pore opened in the membrane by the charged protein. Such an observation may shed light on the activation of oncogenic Akt (or protein kinase B) around the membrane at the molecular level.

Cardiovascular Profile of Xanthone-Based 1,4 Dihydropyridines Bearing a Lidoflazine Pharmacophore Fragment

As a follow-up to our previous studies on differently substituted 1,4-dihydropyridines endowed with a peculiar cardiac selectivity, in this paper, a small series of hybrid compounds bearing the pharmacophore fragment of lidoflazine in position 2 or 3 on a 4-(xanthen-9-one)-dihydropyridine core was reported. Lidoflazine was selected due to our promising previously reported data, and the xanthen-9-one substituent was introduced in position 4 of the dihydropyridine scaffold based on the cardiac selectivity observed in several of our studies. The new hybrid compounds were tested to assess cardiac and vascular activities, and the data were evaluated in comparison with those previously obtained for 4-(xanthen-9-one)-dihydropyridines and lidoflazine–nifedipine hybrid compounds. The functional studies indicated an interesting peculiar selectivity for the cardiac parameter inotropy, in particular when the lidoflazine fragment was introduced in position 2 of the dihydropyridine scaffold (4a–e), and thus a possible preferential binding with the Ca_v 1.2 isoform of l-type calcium channels, which are mainly involved in cardiac contractility.

Rational application of drug promiscuity in medicinal chemistry

‘Drug promiscuity’ refers to a drug that can act on multiple molecular targets, exhibiting similar or different pharmacological effects. Drugs may interact with unwanted targets, leading to off-target effects (one of the main reasons for side effects). Thus, intervention to prevent off-target effects in the early stages of drug discovery could reduce the risk of failure. The conversion between target and off-target effects is important for drug repurposing. Drug repurposing strategies could reduce research and development costs. This review details the research progress in the rational application of drug promiscuity for the discovery of multi-target drugs, drug repurposing and improving druggability in medicinal chemistry over the last 5 years.

Novel Pyrimidines as Antitubercular Agents

Mycobacterium tuberculosis infection is responsible for a global pandemic. New drugs are needed that do not show cross-resistance with the existing front-line therapeutics. A triazine antitubercular hit led to the design of a related pyrimidine family. The synthesis of a focused series of these analogs facilitated exploration of their in vitro activity, in vitro cytotoxicity, and physiochemical and absorption-distribution-metabolism-excretion properties. Select pyrimidines were then evaluated for their pharmacokinetic profiles in mice. The findings suggest a rationale for the further evolution of this promising series of antitubercular small molecules, which appear to share some similarities with the clinical compound PA-824 in terms of activation, while highlighting more general guidelines for the optimization of small-molecule antitubercular agents.

Exploiting polypharmacology for improving therapeutic outcome of kinase inhibitors (KIs): An update of recent medicinal chemistry efforts

Polypharmacology has been increasingly advocated for the therapeutic intervention in complex pathological conditions, exemplified by cancer. Although kinase inhibitors (KIs) have revolutionized the treatment for certain types of malignancies, some major medical needs remain unmet due to the relentless advance of drug resistance and insufficient efficacy of mono-target KIs. Hence, "multiple targets, multi-dimensional activities" represents an emerging paradigm for innovative anti-cancer drug discovery. Over recent years, considerable leaps have been made in pursuit of kinase-centric polypharmacological anti-cancer therapeutics, providing avenues to tackling the limitation of mono-target KIs. In the review, we summarize the clinically important mechanisms inducing KI resistance and depict a landscape of recent medicinal chemistry efforts on exploring kinase-centric polypharmacological anti-cancer agents that targeting multiple cancer-related processes. In parallel, some inevitable challenges are emphasized for the sake of more accurate and efficient drug discovery in the field.

Global organization of a binding site network gives insight into evolution and structure-function relationships of proteins

The global organization of protein binding sites is analyzed by constructing a weighted network of binding sites based on their structural similarities and detecting communities of structurally similar binding sites based on the minimum description length principle. The analysis reveals that there are two central binding site communities that play the roles of the network hubs of smaller peripheral communities. The sizes of communities follow a power-law distribution, which indicates that the binding sites included in larger communities may be older and have been evolutionary structural scaffolds of more recent ones. Structurally similar binding sites in the same community bind to diverse ligands promiscuously and they are also embedded in diverse domain structures. Understanding the general principles of binding site interplay will pave the way for improved drug design and protein design.

A simple mathematical approach to the analysis of polypharmacology and polyspecificity data

There many possible types of drug-target interactions, because there are a surprising number of ways in which drugs and their targets can associate with one another.  These relationships are expressed as polypharmacology and polyspecificity.  Polypharmacology is the capability of a given drug to exhibit activity with respect to multiple drug targets, which are not necessarily in the same activity class. Adverse drug reactions ('side effects') are its principal manifestation, but polypharmacology is also playing a role in the repositioning of existing drugs for new therapeutic indications.  Polyspecificity, on the other hand, is the capability of a given target to exhibit activity with respect to multiple, structurally dissimilar drugs.  That these concepts are closely related to one another is, surprisingly, not well known.  It will be shown in this work that they are, in fact, mathematically related to one another and are in essence 'two sides of the same coin'.  Hence, information on polypharmacology provides equivalent information on polyspecificity, and vice versa. Networks are playing an increasingly important role in biological research. Drug-target networks, in particular, are made up of drug nodes that are linked to specific target nodes if a given drug is active with respect to that target.  Such networks provide a graphic depiction of polypharmacology and polyspecificity.  However, by their very nature they can obscure information that may be useful in their interpretation and analysis.  This work will show how such latent information can be used to determine bounds for the degrees of polypharmacology and polyspecificity, and how to estimate other useful features associated with the lack of completeness of most drug-target datasets.

Rapid identification of dual p53-MDM2/MDMX interaction inhibitors through virtual screening and hit-based substructure search

A virtual screening method coupled with hit-based substructure search strategy was developed to identify dual inhibitors of the p53-MDM2/MDMX interactions and a series of novel scaffolds with moderate inhibitory activity were obtained.

Computational proteome-wide screening predicts neurotoxic drug-protein interactome for the investigational analgesic BIA 10-2474

The investigational compound BIA 10-2474, designed as a long-acting and reversible inhibitor of fatty acid amide hydrolase for the treatment of neuropathic pain, led to the death of one participant and hospitalization of five others due to intracranial hemorrhage in a Phase I clinical trial. Putative off-target activities of BIA 10-2474 have been suggested to be major contributing factors to the observed neurotoxicity in humans, motivating our study's proteome-wide screening approach to investigate its polypharmacology. Accordingly, we performed an in silico screen against 80,923 protein structures reported in the Protein Data Bank. The resulting list of 284 unique human interactors was further refined using target-disease association analyses to a subset of proteins previously linked to neurological, intracranial, inflammatory, hemorrhagic or clotting processes and/or diseases. Eleven proteins were identified as potential targets of BIA 10-2474, and the two highest-scoring proteins, Factor VII and thrombin, both essential blood-clotting factors, were predicted to be inhibited by BIA 10-2474 and suggest a plausible mechanism of toxicity. Once this small molecule becomes commercially available, future studies will be conducted to evaluate the predicted inhibitory effect of BIA 10-2474 on blood clot formation specifically in the brain.

Comprehensive Modeling and Discovery of Mebendazole as a Novel TRAF2- and NCK-interacting Kinase Inhibitor

TRAF2- and NCK-interacting kinase (TNIK) represents one of the crucial targets for Wnt-activated colorectal cancer. In this study, we curated two datasets and conducted a comprehensive modeling study to explore novel TNIK inhibitors with desirable biopharmaceutical properties. With Dataset I, we derived Comparative Molecular Similarity Indices Analysis (CoMSIA) and variable-selection k-nearest neighbor models, from which 3D-molecular fields and 2D-descriptors critical for the TNIK inhibitor activity were revealed. Based on Dataset II, predictive CoMSIA-SIMCA (Soft Independent Modelling by Class Analogy) models were obtained and employed to screen 1,448 FDA-approved small molecule drugs. Upon experimental evaluations, we discovered that mebendazole, an approved anthelmintic drug, could selectively inhibit TNIK kinase activity with a dissociation constant K_d = ~1 μM. The subsequent CoMSIA and kNN analyses indicated that mebendazole bears the favorable molecular features that are needed to bind and inhibit TNIK.