Computational polypharmacology comes of age

Searching for Novel HDAC6/Hsp90 Dual Inhibitors with Anti-Prostate Cancer Activity: In Silico Screening and In Vitro Evaluation

Prostate cancer (PCA) is one of the most prevalent types of male cancers. While current treatments for early-stage PCA are available, their efficacy is limited in advanced PCA, mainly due to drug resistance or low efficacy. In this context, novel valuable therapeutic opportunities may arise from the combined inhibition of histone deacetylase 6 (HDAC6) and heat shock protein 90 (Hsp90). These targets are mutually involved in the regulation of several processes in cancer cells, and their inhibition is demonstrated to provide synergistic effects against PCA. On these premises, we performed an extensive in silico virtual screening campaign on commercial compounds in search of dual inhibitors of HDAC6 and Hsp90. In vitro tests against recombinant enzymes and PCA cells with different levels of aggressiveness allowed the identification of a subset of compounds with inhibitory activity against HDAC6 and antiproliferative effects towards LNCaP and PC-3 cells. None of the candidates showed appreciable Hsp90 inhibition. However, the discovered compounds have low molecular weight and a chemical structure similar to that of potent Hsp90 blockers. This provides an opportunity for structural and medicinal chemistry optimization in order to obtain HDAC6/Hsp90 dual modulators with antiproliferative effects against prostate cancer. These findings were discussed in detail in the study.

Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations

The identification of genetic and chemical perturbations with similar impacts on cell morphology can elucidate compounds' mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here we create such a Resource dataset, CPJUMP1, in which each perturbed gene's product is a known target of at least two chemical compounds in the dataset. We systematically explore the directionality of correlations among perturbations that target the same protein encoded by a given gene, and we find that identifying matches between chemical and genetic perturbations is a challenging task. Our dataset and baseline analyses provide a benchmark for evaluating methods that measure perturbation similarities and impact, and more generally, learn effective representations of cellular state from microscopy images. Such advancements would accelerate the applications of image-based profiling of cellular states, such as uncovering drug mode of action or probing functional genomics.

Quantitative live cell imaging of a tauopathy model enables the identification of a polypharmacological drug candidate that restores physiological microtubule interaction

Tauopathies such as Alzheimer's disease are characterized by aggregation and increased phosphorylation of the microtubule-associated protein tau. Tau's pathological changes are closely linked to neurodegeneration, making tau a prime candidate for intervention. We developed an approach to monitor pathological changes of aggregation-prone human tau in living neurons. We identified 2-phenyloxazole (PHOX) derivatives as putative polypharmacological small molecules that interact with tau and modulate tau kinases. We found that PHOX15 inhibits tau aggregation, restores tau's physiological microtubule interaction, and reduces tau phosphorylation at disease-relevant sites. Molecular dynamics simulations highlight cryptic channel-like pockets crossing tau protofilaments and suggest that PHOX15 binding reduces the protofilament's ability to adopt a PHF-like conformation by modifying a key glycine triad. Our data demonstrate that live-cell imaging of a tauopathy model enables screening of compounds that modulate tau-microtubule interaction and allows identification of a promising polypharmacological drug candidate that simultaneously inhibits tau aggregation and reduces tau phosphorylation.

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/ .

Statins synergize with phosphodiesterase type 5 inhibitors but not with selective estrogen receptor modulators to prevent myofibroblast transformation in an in vitro model of Peyronie's disease

BACKGROUND

Peyronie's disease (PD) is a fibrotic disorder characterized by plaque formation in the tunica albuginea (TA) of the penis, and we have previously shown that inhibition of transformation of TA-derived fibroblasts to myofibroblasts using a combination phosphodiesterase type 5 (PDE5) inhibitors and selective estrogen receptor modulators (SERMs) is effective in slowing the progression of early PD.

AIM

The study sought to investigate whether combinations of statins with PDE5 inhibitors or SERMs would affect myofibroblast transformation in vitro.

METHODS

Primary fibroblasts were isolated from TA of patients with PD and stimulated with transforming growth factor β1 in the absence and presence of a range of concentrations of statins, PDE5 inhibitors, SERMs, and their combinations for 72 hours before quantifying α-smooth muscle actin using in-cell enzyme-linked immunosorbent assay.

OUTCOMES

The prevention of transforming growth factor β1-induced transformation of TA-derived fibroblasts to myofibroblasts was measured in vitro.

RESULTS

Statins (simvastatin, lovastatin) inhibited myofibroblast transformation in a concentration-dependent manner with half maximal inhibitory concentration values of 0.77 ± 0.07 μM and 0.8 ± 0.13 μM, respectively. Simvastatin inhibited myofibroblast transformation in a synergistic fashion when combined with vardenafil (a PDE5 inhibitor; log alpha >0). Combination of tamoxifen (a SERM) and simvastatin did not show synergy (log alpha <0). When 3 drugs (simvastatin, vardenafil, and tamoxifen) were combined, the effect was not synergistic, but rather was additive.

CLINICAL IMPLICATIONS

A combination of a statin with a PDE5 inhibitor might be useful in the clinic to slow the progression of the disease in patients with early PD; however, caution should be taken with such a combination because of the reported myopathy as a side effect.

STRENGTHS AND LIMITATIONS

The use of primary human cells from patients with PD is a strength of this study. The mechanisms by which these drug classes exert synergy when used in combination was not investigated.

CONCLUSION

This is the first demonstration of an antifibrotic synergy between statins and PDE5 inhibitors.

Design, synthesis, biological evaluation and crystal structure determination of dual modulators of carbonic anhydrases and estrogen receptors

Multi-target compounds have become increasingly important for the development of safer and more effective drug candidates. In this work, we devised a combined ligand-based and structure-based multi-target repurposing strategy and applied it to a series of hexahydrocyclopenta[c]quinoline compounds synthesized previously. The in silico analyses identified human Carbonic Anhydrases (hCA) and Estrogen Receptors (ER) as top scoring candidates for dual modulation. hCA isoforms IX and XII, and ER subtypes ER⍺ and/or ERβ are co-expressed in various cancer cell types, including breast and prostate cancer cells. ER⍺ is the primary target of anti-estrogen therapy in breast cancer, and the hCA IX isoform is a therapeutic target in triple-negative breast cancer. ER⍺-mediated transcriptional programs and hCA activity in cancer cells promote favorable microenvironments for cell proliferation. Interestingly, several lines of evidence indicate that the combined modulation of these two targets may provide significant therapeutic benefits. Moving from these first results, two additional hexahydrocyclopenta[c]quinoline derivatives bearing a sulfonamide zinc binding group (hCA) and a phenolic hydroxyl (ER) pharmacophoric group placed at the appropriate locations were designed and synthesized. Interestingly, these compounds were able to directly modulate the activities of both hCA and ER targets. In cell-based assays, they inhibited proliferation of breast and prostate cancer cells with micromolar potency and cell type-selective efficacy. The compounds inhibited hCA activity with nanomolar potency and isoform-selectivity. In transactivation assays, they reduced estrogen-driven ER activity with micro-molar potency. Finally, crystal structures of the synthesized ligands in complex with the two targets revealed that the compounds bind directly to the hCA active site, as well as to the ER ligand-binding domain, providing structural explanation to the observed activity and a rationale for optimization of their dual activity. To the best of our knowledge, this work describes the design, synthesis and biological characterization of the first dual modulators of hCA and ER, laying the ground for the structure-based optimization of their multi-target activity.

Explaining Accurate Predictions of Multitarget Compounds with Machine Learning Models Derived for Individual Targets

In drug discovery, compounds with well-defined activity against multiple targets (multitarget compounds, MT-CPDs) provide the basis for polypharmacology and are thus of high interest. Typically, MT-CPDs for polypharmacology have been discovered serendipitously. Therefore, over the past decade, computational approaches have also been adapted for the design of MT-CPDs or their identification via computational screening. Such approaches continue to be under development and are far from being routine. Recently, different machine learning (ML) models have been derived to distinguish between MT-CPDs and corresponding compounds with activity against the individual targets (single-target compounds, ST-CPDs). When evaluating alternative models for predicting MT-CPDs, we discovered that MT-CPDs could also be accurately predicted with models derived for corresponding ST-CPDs; this was an unexpected finding that we further investigated using explainable ML. The analysis revealed that accurate predictions of ST-CPDs were determined by subsets of structural features of MT-CPDs required for their prediction. These findings provided a chemically intuitive rationale for the successful prediction of MT-CPDs using different ML models and uncovered general-feature subset relationships between MT- and ST-CPDs with activities against different targets.

Morphology and gene expression profiling provide complementary information for mapping cell state

Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb human A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOAs) and gene targets, we find that the two assays not only provide a partially shared but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.

Harnessing systematic protein-ligand interaction fingerprints for drug discovery

Determining protein-ligand interaction characteristics and mechanisms is crucial to the drug discovery process. Here, we review recent progress and successful applications of a systematic protein-ligand interaction fingerprint (IFP) approach for investigating proteome-wide protein-ligand interactions for drug development. Specifically, we review the use of this IFP approach for revealing polypharmacology across the kinome, predicting promising targets from which to design allosteric inhibitors and covalent kinase inhibitors, uncovering the binding mechanisms of drugs of interest, and demonstrating resistant mechanisms of specific drugs. Together, we demonstrate that the IFP strategy is efficient and practical for drug design research for protein kinases as targets and is extensible to other protein families.

New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer's disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.

On the development of B-Raf inhibitors acting through innovative mechanisms

B-Raf is a protein kinase participating to the regulation of many biological processes in cells. Several studies have demonstrated that this protein is frequently upregulated in human cancers, especially when it bears activating mutations. In the last years, few ATP-competitive inhibitors of B-Raf have been marketed for the treatment of melanoma and are currently under clinical evaluation on a variety of other types of cancer. Although the introduction of drugs targeting B-Raf has provided significant advances in cancer treatment, responses to ATP-competitive inhibitors remain limited, mainly due to selectivity issues, side effects, narrow therapeutic windows, and the insurgence of drug resistance.

Impressive research efforts have been made so far towards the identification of novel ATP-competitive modulators with improved efficacy against cancers driven by mutant Raf monomers and dimers, some of them showing good promises. However, several limitations could still be envisioned for these compounds, according to literature data. Besides, increased attentions have arisen around approaches based on the design of allosteric modulators, polypharmacology, proteolysis targeting chimeras (PROTACs) and drug repurposing for the targeting of B-Raf proteins. The design of compounds acting through such innovative mechanisms is rather challenging. However, valuable therapeutic opportunities can be envisioned on these drugs, as they act through innovative mechanisms in which limitations typically observed for approved ATP-competitive B-Raf inhibitors are less prone to emerge. In this article, current approaches adopted for the design of non-ATP competitive inhibitors targeting B-Raf are described, discussing also on the possibilities, ligands acting through such innovative mechanisms could provide for the obtainment of more effective therapies.

Differentiating Inhibitors of Closely Related Protein Kinases with Single- or Multi-Target Activity via Explainable Machine Learning and Feature Analysis

Protein kinases are major drug targets. Most kinase inhibitors are directed against the adenosine triphosphate (ATP) cofactor binding site, which is largely conserved across the human kinome. Hence, such kinase inhibitors are often thought to be promiscuous. However, experimental evidence and activity data for publicly available kinase inhibitors indicate that this is not generally the case. We have investigated whether inhibitors of closely related human kinases with single- or multi-kinase activity can be differentiated on the basis of chemical structure. Therefore, a test system consisting of two distinct kinase triplets has been devised for which inhibitors with reported triple-kinase activities and corresponding single-kinase activities were assembled. Machine learning models derived on the basis of chemical structure distinguished between these multi- and single-kinase inhibitors with high accuracy. A model-independent explanatory approach was applied to identify structural features determining accurate predictions. For both kinase triplets, the analysis revealed decisive features contained in multi-kinase inhibitors. These features were found to be absent in corresponding single-kinase inhibitors, thus providing a rationale for successful machine learning. Mapping of features determining accurate predictions revealed that they formed coherent and chemically meaningful substructures that were characteristic of multi-kinase inhibitors compared with single-kinase inhibitors.

Identification of SET/EED dual binders as innovative PRC2 inhibitors

Background: The inhibition of PRC2, implicated in the pathogenesis of several tumors, can be a useful therapeutic strategy for cancer treatment. In the literature, two types of PRC2 modulators are reported: competitive inhibitors of S-adenosyl methionine binding to the catalytic subunit EZH2; and allosteric ligands that prevent the interaction of the trimethylated H3K27 lysine in histone 3 to the EED subunit. The lack of dual EZH2/EED modulators drove us to search for compounds capable of recognizing both domains. Materials & methods: This goal was pursued by combining pharmacophore- and docking-based virtual screening of the Multi-Target Ligand Chemotheca database. Prediction tools for absorption, distribution, metabolism and excretion and pan-assay interference compounds were also applied. Results: Finally, five 1,2,3-triazole derivatives were identified as promising dual EZH2/EED modulators. Conclusion: Our multistage screening protocol highlighted the great potential of Chemotheca for identifying polypharmacological agents.

Alvarez KL, Aguilar-Pineda JA, Vera-Lopez KJ, Lino Cardenas CL. In Silico Analysis of Metabolites from Peruvian Native Plants as Potential Therapeutics against Alzheimer's Disease

Background: Despite research on the molecular bases of Alzheimer’s disease (AD), effective therapies against its progression are still needed. Recent studies have shown direct links between AD progression and neurovascular dysfunction, highlighting it as a potential target for new therapeutics development. In this work, we screened and evaluated the inhibitory effect of natural compounds from native Peruvian plants against tau protein, amyloid beta, and angiotensin II type 1 receptor (AT1R) pathologic AD markers. Methods: We applied in silico analysis, such as virtual screening, molecular docking, molecular dynamics simulation (MD), and MM/GBSA estimation, to identify metabolites from Peruvian plants with inhibitory properties, and compared them to nicotinamide, telmisartan, and grapeseed extract drugs in clinical trials. Results: Our results demonstrated the increased bioactivity of three plants’ metabolites against tau protein, amyloid beta, and AT1R. The MD simulations indicated the stability of the AT1R:floribundic acid, amyloid beta:rutin, and tau:brassicasterol systems. A polypharmaceutical potential was observed for rutin due to its high affinity to AT1R, amyloid beta, and tau. The metabolite floribundic acid showed bioactivity against the AT1R and tau, and the metabolite brassicasterol showed bioactivity against the amyloid beta and tau. Conclusions: This study has identified molecules from native Peruvian plants that have the potential to bind three pathologic markers of AD.

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.

Design and Synthesis of Hsp90 Inhibitors with B-Raf and PDHK1 Multi-Target Activity

The design of multi-target ligands has become an innovative approach for the identification of effective therapeutic treatments against complex diseases, such as cancer. Recent studies have demonstrated that the combined inhibition of Hsp90 and B-Raf provides synergistic effects against several types of cancers. Moreover, it has been reported that PDHK1, which presents an ATP-binding pocket similar to that of Hsp90, plays an important role in tumor initiation, maintenance and progression, participating also to the senescence process induced by B-Raf oncogenic proteins. Based on these premises, the simultaneous inhibition of these targets may provide several benefits for the treatment of cancer. In this work, we set up a design strategy including the assembly and integration of molecular fragments known to be important for binding to the Hsp90, PDHK1 and B-Raf targets, aided by molecular docking for the selection of a set of compounds potentially able to exert Hsp90-B-Raf-PDHK1 multi-target activities. The designed compounds were synthesized and experimentally validated in vitro. According to the in vitro assays, compounds 4 a, 4 d and 4 e potently inhibited Hsp90 and moderately inhibited the PDHK1 kinase. Finally, molecular dynamics simulations were performed to provide further insights into the structural basis of their multi-target activity.

Dual Targeting Strategies On Histone Deacetylase 6 (HDAC6) And Heat Shock Protein 90 (Hsp90)

The design of multi-target drugs acting simultaneously on multiple signaling pathways is a growing field in medicinal chemistry, especially for the treatment of complex diseases such as cancer. Histone deacetylase 6 (HDAC6) is an established anticancer drug target involved in tumor cells transformation. Being an epigenetic enzyme at the interplay of many biological processes, HDAC6 has become an attractive target for polypharmacology studies aimed at improving therapeutic efficacy of anticancer drugs. For example, the molecular chaperone Heat shock protein 90 (Hsp90) is a substrate of HDAC6 deacetylation, and several lines of evidence demonstrate that simultaneous inhibition of HDAC6 and Hsp90 promote synergistic antitumor effects on different cancer cell lines, highlighting the potential benefits of developing a single molecule endowed with multi-target activity. This review will summarize the complex interplay between HDAC6 and Hsp90, providing also useful hints for multi-target drug design and discovery approaches in this field. To this end, crystallographic structures of HDAC6 and Hsp90 complexes will be extensively reviewed in the light of discussing binding pockets features and pharmacophore requirements and providing useful guidelines for the design of dual inhibitors. The few examples of multi-target inhibitors obtained so far, mostly based on chimeric approaches, will be summarized and put into context. Finally, the main features of HDAC6 and Hsp90 inhibitors will be compared, and ligand- and structure-based strategies potentially useful for the development of small molecular weight dual inhibitors will be proposed and discussed.

LigAdvisor: a versatile and user-friendly web-platform for drug design

Although several tools facilitating in silico drug design are available, their results are usually difficult to integrate with publicly available information or require further processing to be fully exploited. The rational design of multi-target ligands (polypharmacology) and the repositioning of known drugs towards unmet therapeutic needs (drug repurposing) have raised increasing attention in drug discovery, although they usually require careful planning of tailored drug design strategies. Computational tools and data-driven approaches can help to reveal novel valuable opportunities in these contexts, as they enable to efficiently mine publicly available chemical, biological, clinical, and disease-related data. Based on these premises, we developed LigAdvisor, a data-driven webserver which integrates information reported in DrugBank, Protein Data Bank, UniProt, Clinical Trials and Therapeutic Target Database into an intuitive platform, to facilitate drug discovery tasks as drug repurposing, polypharmacology, target fishing and profiling. As designed, LigAdvisor enables easy integration of similarity estimation results with clinical data, thereby allowing a more efficient exploitation of information in different drug discovery contexts. Users can also develop customizable drug design tasks on their own molecules, by means of ligand- and target-based search modes, and download their results. LigAdvisor is publicly available at https://ligadvisor.unimore.it/.

The Emerging Role of COX-2, 15-LOX and PPARγ in Metabolic Diseases and Cancer: An Introduction to Novel Multi-target Directed Ligands (MTDLs)

Emerging evidence supports an intertwining framework for the involvement of different inflammatory pathways in a common pathological background for a number of disorders. Of importance are pathways involving arachidonic acid metabolism by cyclooxygenase-2 (COX-2) and 15-lipoxygenase (15-LOX). Both enzyme activities and their products are implicated in a range of pathophysiological processes encompassing metabolic impairment leading to adipose inflammation and the subsequent vascular and neurological disorders, in addition to various pro- and antitumorigenic effects. A further layer of complexity is encountered by the disparate, and often reciprocal, modulatory effect COX-2 and 15-LOX activities and metabolites exert on each other or on other cellular targets, the most prominent of which is peroxisome proliferator-activated receptor gamma (PPARγ). Thus, effective therapeutic intervention with such multifaceted disorders requires the simultaneous modulation of more than one target. Here, we describe the role of COX-2, 15-LOX, and PPARγ in cancer and complications of metabolic disorders, highlight the value of designing multi-target directed ligands (MTDLs) modifying their activity, and summarizing the available literature regarding the rationale and feasibility of design and synthesis of these ligands together with their known biological effects. We speculate on the potential impact of MTDLs in these disorders as well as emphasize the need for structured future effort to translate these early results facilitating the adoption of these, and similar, molecules in clinical research.

GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds

The current study describes the construction of various ligand-based machine learning models to be used for drug-repurposing against the family of G-Protein Coupled Receptors (GPCRs). In building these models, we collected > 500,000 data points, encompassing experimentally measured molecular association data of > 160,000 unique ligands against > 250 GPCRs. These data points were retrieved from the GPCR-Ligand Association (GLASS) database. We have used diverse molecular featurization methods to describe the input molecules. Multiple supervised ML algorithms were developed, tested and compared for their accuracy, F scores, as well as for their Matthews' correlation coefficient scores (MCC). Our data suggest that combined with molecular fingerprinting, ensemble decision trees and gradient boosted trees ML algorithms are on the accuracy border of the rather sophisticated deep neural nets (DNNs)-based algorithms. On a test dataset, these models displayed an excellent performance, reaching a ~ 90% classification accuracy. Additionally, we showcase a few examples where our models were able to identify interesting connections between known drugs from the Drug-Bank database and members of the GPCR family of receptors. Our findings are in excellent agreement with previously reported experimental observations in the literature. We hope the models presented in this paper synergize with the currently ongoing interest of applying machine learning modeling in the field of drug repurposing and computational drug discovery in general.

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug's activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.

Multi-Targeting Bioactive Compounds Extracted from Essential Oils as Kinase Inhibitors

Essential oils (EOs) are popular in aromatherapy, a branch of alternative medicine that claims their curative effects. Moreover, several studies reported EOs as potential anti-cancer agents by inducing apoptosis in different cancer cell models. In this study, we have considered EOs as a potential resource of new kinase inhibitors with a polypharmacological profile. On the other hand, computational methods offer the possibility to predict the theoretical activity profile of ligands, discovering dangerous off-targets and/or synergistic effects due to the potential multi-target action. With this aim, we performed a Structure-Based Virtual Screening (SBVS) against X-ray models of several protein kinases selected from the Protein Data Bank (PDB) by using a chemoinformatics database of EOs. By evaluating theoretical binding affinity, 13 molecules were detected among EOs as new potential kinase inhibitors with a multi-target profile. The two compounds with higher percentages in the EOs were studied more in depth by means Induced Fit Docking (IFD) protocol, in order to better predict their binding modes taking into account also structural changes in the receptor. Finally, given its good binding affinity towards five different kinases, cinnamyl cinnamate was biologically tested on different cell lines with the aim to verify the antiproliferative activity. Thus, this work represents a starting point for the optimization of the most promising EOs structure as kinase inhibitors with multi-target features.

Identification of Target Associations for Polypharmacology from Analysis of Crystallographic Ligands of the Protein Data Bank

The design of a chemical entity that potently and selectively binds to a biological target of therapeutic relevance has dominated the scene of drug discovery so far. However, recent findings suggest that multitarget ligands may be endowed with superior efficacy and be less prone to drug resistance. The Protein Data Bank (PDB) provides experimentally validated structural information about targets and bound ligands. Therefore, it represents a valuable source of information to help identifying active sites, understanding pharmacophore requirements, designing novel ligands, and inferring structure-activity relationships. In this study, we performed a large-scale analysis of the PDB by integrating different ligand-based and structure-based approaches, with the aim of identifying promising target associations for polypharmacology based on reported crystal structure information. First, the 2D and 3D similarity profiles of the crystallographic ligands were evaluated using different ligand-based methods. Then, activity data of pairs of similar ligands binding to different targets were inspected by comparing structural information with bioactivity annotations reported in the ChEMBL, BindingDB, BindingMOAD, and PDBbind databases. Afterward, extensive docking screenings of ligands in the identified cross-targets were made in order to validate and refine the ligand-based results. Finally, the therapeutic relevance of the identified target combinations for polypharmacology was evaluated from comparison with information on therapeutic targets reported in the Therapeutic Target Database (TTD). The results led to the identification of several target associations with high therapeutic potential for polypharmacology.

Molecular Docking: Shifting Paradigms in Drug Discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

In Silico Repositioning of Cannabigerol as a Novel Inhibitor of the Enoyl Acyl Carrier Protein (ACP) Reductase (InhA)

Cannabigerol (CBG) and cannabichromene (CBC) are non-psychoactive cannabinoids that have raised increasing interest in recent years. These compounds exhibit good tolerability and low toxicity, representing promising candidates for drug repositioning. To identify novel potential therapeutic targets for CBG and CBC, an integrated ligand-based and structure-based study was performed. The results of the analysis led to the identification of CBG as a low micromolar inhibitor of the Enoyl acyl carrier protein (ACP) reductase (InhA) enzyme.

Polypharmacological study of Ceritinib using a structure based in silico approach

Drug repurposing has gained mass recognition over the past few years as it has paved new therapeutic applications for already approved FDA drugs. It focuses on finding new molecular targets of drugs for medical uses different than the one originally proposed. Ceritinib, an Anaplastic Lymphoma Kinase (ALK) inhibitor is given orally in the treatment of non-small cell lung cancer (NSCLC). This treatment has been reported to be associated with a number of side effects such as hyperglycemia, convulsion, pneumonitis etc. The side effects are usually due to the unintended interaction of the drug with other protein targets. In silico polypharmacological studies of Ceritinib suggests that it binds to multiple targets other than the intended one which may largely be due to different proteins possessing similar binding sites. ProBis server was used to retrieve probable off-targets of Ceritinib based on presence of structurally similar protein binding sites as that of ALK. Ceritinib was found to bind effectively to three proteins namely Lymphocyte Cell-Specific Protein-Tyrosine Kinase, Tropomyosin receptor kinase B and Aurora kinase B having favorable binding energies and inhibition constants, with no reported side-effects as compared to their marketed drugs. Therefore, it is concluded from the present study that Ceritinib may act as an effective therapeutic target against its polypharmacological targets.

Chemoproteomic identification of molecular targets of antifungal prototypes, thiosemicarbazide and a camphene derivative of thiosemicarbazide, in Paracoccidioides brasiliensis

Paracoccidioidomycosis (PCM) is a neglected human systemic disease caused by species of the genus Paracoccidioides. The disease attacks the host’s lungs and may disseminate to many other organs. Treatment involves amphotericin B, sulfadiazine, trimethoprim-sulfamethoxazole, itraconazole, ketoconazole, or fluconazole. The treatment duration is usually long, from 6 months to 2 years, and many adverse effects may occur in relation to the treatment; co-morbidities and poor treatment adherence have been noted. Therefore, the discovery of more effective and less toxic drugs is needed. Thiosemicarbazide (TSC) and a camphene derivative of thiosemicarbazide (TSC-C) were able to inhibit P. brasiliensis growth at a low dosage and were not toxic to fibroblast cells. In order to investigate the mode of action of those compounds, we used a chemoproteomic approach to determine which fungal proteins were bound to each of these compounds. The compounds were able to inhibit the activities of the enzyme formamidase and interfered in P. brasiliensis dimorphism. In comparison with the transcriptomic and proteomic data previously obtained by our group, we determined that TSC and TSC-C were multitarget compounds that exerted effects on the electron-transport chain and cell cycle regulation, increased ROS formation, inhibited proteasomes and peptidases, modulated glycolysis, lipid, protein and carbohydrate metabolisms, and caused suppressed the mycelium to yeast transition.

Selection of protein conformations for structure-based polypharmacology studies

Several drugs exert their therapeutic effect through the modulation of multiple targets. Structure-based approaches hold great promise for identifying compounds with the desired polypharmacological profiles. These methods use knowledge of the protein binding sites to identify stereoelectronically complementary ligands. The selection of the most suitable protein conformations to be used in the design process is vital, especially for multitarget drug design in which the same ligand has to be accommodated in multiple binding pockets. Herein, we focus on currently available techniques for the selection of the most suitable protein conformations for multitarget drug design, compare the potential advantages and limitations of each method, and comment on how their combination could help in polypharmacology drug design.

Polypharmacology of Berberine Based on Multi-Target Binding Motifs

Background: Polypharmacology is emerging as the next paradigm in drug discovery. However, considerable challenges still exist for polypharmacology modeling. In this study, we developed a rational design to identify highly potential targets (HPTs) for polypharmacological drugs, such as berberine. Methods and Results: All the proven co-crystal structures locate berberine in the active cavities of a redundancy of aromatic, aliphatic, and acidic residues. The side chains from residues provide hydrophobic and electronic interactions to aid in neutralization for the positive charge of berberine. Accordingly, we generated multi-target binding motifs (MBM) for berberine, and established a new mathematical model to identify HPTs based on MBM. Remarkably, the berberine MBM was embodied in 13 HPTs, including beta-secretase 1 (BACE1) and amyloid-β_1-42 (Aβ_1-42). Further study indicated that berberine acted as a high-affinity BACE1 inhibitor and prevented Aβ_1-42 aggregation to delay the pathological process of Alzheimer's disease. Conclusion: Here, we proposed a MBM-based drug-target space model to analyze the underlying mechanism of multi-target drugs against polypharmacological profiles, and demonstrated the role of berberine in Alzheimer's disease. This approach can be useful in derivation of rules, which will illuminate our understanding of drug action in diseases.

MTLD, a Database of Multiple Target Ligands, the Updated Version

Polypharmacology plays an important role in drug discovery and polypharmacology drug strategies provide a novel path in drug design. However, to develop a polypharmacology drug with the desired profile remains a challenge. Previously, we developed a free web-accessible database called Multiple Target Ligand Database (MTLD, www.mtdcadd.com). Herein, the MTLD database has been updated, containing 2444 Multiple Target Ligands (MTLs) that bind with 21,424 binding sites from 18,231 crystal structures. Of the MTLs, 304 entries are approved drugs, and 1911 entries are drug-like compounds. Also, we added new functions such as multiple conditional search and linkage visualization. Through querying the updated database, extremely useful information for the development of polypharmacology drugs may be provided.

Molecular Connectivity Predefines Polypharmacology: Aliphatic Rings, Chirality, and sp3 Centers Enhance Target Selectivity

Dark chemical matter compounds are small molecules that have been recently identified as highly potent and selective hits. For this reason, they constitute a promising class of possible candidates in the process of drug discovery and raise the interest of the scientific community. To this purpose, Wassermann et al. (2015) have described the application of 2D descriptors to characterize dark chemical matter. However, their definition was based on the number of reported positive assays rather than the number of known targets. As there might be multiple assays for one single target, the number of assays does not fully describe target selectivity. Here, we propose an alternative classification of active molecules that is based on the number of known targets. We cluster molecules in four classes: black, gray, and white compounds are active on one, two to four, and more than four targets respectively, whilst inactive compounds are found to be inactive in the considered assays. In this study, black and inactive compounds are found to have not only higher solubility, but also a higher number of chiral centers, sp3 carbon atoms and aliphatic rings. On the contrary, white compounds contain a higher number of double bonds and fused aromatic rings. Therefore, the design of a screening compound library should consider these molecular properties in order to achieve target selectivity or polypharmacology. Furthermore, analysis of four main target classes (GPCRs, kinases, proteases, and ion channels) shows that GPCR ligands are more selective than the other classes, as the number of black compounds is higher in this target superfamily. On the other side, ligands that hit kinases, proteases, and ion channels bind to GPCRs more likely than to other target classes. Consequently, depending on the target protein family, appropriate screening libraries can be designed in order to minimize the likelihood of unwanted side effects early in the drug discovery process. Additionally, synergistic effects may be obtained by library design toward polypharmacology.

Novel 3-(1,2,3,6-Tetrahydropyridin-4-yl)-1H-indole-Based Multifunctional Ligands with Antipsychotic-Like, Mood-Modulating, and Procognitive Activity

The most troublesome aspects of behavioral and psychological symptoms of dementia (BPSD) are nowadays addressed by antidepressant, anxiolytic, and antipsychotic drugs, often administered off-label. Considering their modest effectiveness in dementia patients, the increased risk of adverse events and cognitive decline, there is an unmet need for well-tolerated and effective therapy of BPSD. We designed and synthesized multifunctional ligands characterized in vitro as high-affinity partial agonists of D₂R, antagonists of 5-HT₆R, and blockers of SERT. Moreover, the molecules activated 5-HT_1AR and blocked 5-HT₇R while having no relevant affinity for off-target M₁R and hERG channel. Compound 16 (N-{2-[4-(5-chloro-1H-indol-3-yl)-1,2,3,6-tetrahydropyridin-1-yl]ethyl}-3-methylbenzene-1-sulfonamide) exhibited a broad antipsychotic-, antidepressant-, and anxiolytic-like activity, not eliciting motor impairments in mice. Most importantly, 16 showed memory-enhancing properties and it ameliorated memory deficits induced by scopolamine. The molecule outperformed most important comparators in selected tests, indicating its potential in the treatment of both cognitive and noncognitive (behavioral and psychological) symptoms of dementia.

Computational polypharmacology: a new paradigm for drug discovery

INTRODUCTION

Over the past couple of years, the cost of drug development has sharply increased along with the high rate of clinical trial failures. Such increase in expenses is partially due to the inability of the "one drug - one target" approach to predict drug side effects and toxicities. To tackle this issue, an alternative approach, known as polypharmacology, is being adopted to study small molecule interactions with multiple targets. Apart from developing more potent and effective drugs, this approach allows for studies of off-target activities and the facilitation of drug repositioning. Although exhaustive polypharmacology studies in-vitro or in-vivo are not practical, computational methods of predicting unknown targets or side effects are being developed. Areas covered: This article describes various computational approaches that have been developed to study polypharmacology profiles of small molecules. It also provides a brief description of the algorithms used in these state-of-the-art methods. Expert opinion: Recent success in computational prediction of multi-targeting drugs has established polypharmacology as a promising alternative approach to tackle some of the daunting complications in drug discovery. This will not only help discover more effective agents, but also present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.

Heat shock protein 90 and serine/threonine kinase B-Raf inhibitors have overlapping chemical space

With the aid of computational design, we show that Hsp90 and B-Raf inhibitors have overlapping chemical space and we disclose the first-in-class dual inhibitors.

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for “Big Data” in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the “Big Data” using advanced machine‐learning methods, and their applications in polypharmacology prediction and target de‐convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi‐party or multi‐party data sharing. Data sharing is important in the context of the recent trend of “open innovation” in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so‐called “precompetitive” collaboration between pharma companies. At the end we highlight the importance of education in “Big Data” for further progress of this area.

Drug combination therapy increases successful drug repositioning

Repositioning of approved drugs has recently gained new momentum for rapid identification and development of new therapeutics for diseases that lack effective drug treatment. Reported repurposing screens have increased dramatically in number in the past five years. However, many newly identified compounds have low potency; this limits their immediate clinical applications because the known, tolerated plasma drug concentrations are lower than the required therapeutic drug concentrations. Drug combinations of two or more compounds with different mechanisms of action are an alternative approach to increase the success rate of drug repositioning.

Polypharmacology in Drug Development: A Minireview of Current Technologies

Polypharmacology, the process in which a single drug is able to bind to multiple targets specifically and simultaneously, is an emerging paradigm in drug development. The potency of a given drug can be increased through the engagement of multiple targets involved in a certain disease. Polypharmacology may also help identify novel applications of existing drugs through drug repositioning. However, many problems and challenges remain in this field. Rather than covering all aspects of polypharmacology, this Minireview is focused primarily on recently reported techniques, from bioinformatics technologies to cheminformatics approaches as well as text-mining-based methods, all of which have made significant contributions to the research of polypharmacology.

Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design

Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.