Computational repurposing of oncology drugs through off-target drug binding interactions from pharmacological databases.

PURPOSE: Systematic repurposing of approved medicines for another indication may accelerate drug development in oncology. We present a strategy combining biomarker testing with drug repurposing to identify new treatments for patients with advanced cancer. METHODS: Tumours were sequenced with the Illumina TruSight Oncology 500 (TSO-500) platform or the FoundationOne CDx panel. Mutations were screened by two medical oncologists and pathogenic mutations were categorised referencing literature. Variants of unknown significance were classified as potentially pathogenic using plausible mechanisms and computational prediction of pathogenicity. Gain of function (GOF) mutations were evaluated through repurposing databases Probe Miner (PM), Broad Institute Drug Repurposing Hub (Broad Institute DRH) and TOPOGRAPH. GOF mutations were repurposing events if identified in PM, not indexed in TOPOGRAPH and excluding mutations with a known Food and Drug Administration (FDA)-approved biomarker. The computational repurposing approach was validated by evaluating its ability to identify FDA-approved biomarkers. The total repurposable genome was identified by evaluating all possible gene-FDA drug-approved combinations in the PM dataset. RESULTS: The computational repurposing approach was accurate at identifying FDA therapies with known biomarkers (94%). Using next-generation sequencing molecular reports (n = 94), a meaningful percentage of patients (14%) could have an off-label therapeutic identified. The frequency of theoretical drug repurposing events in The Cancer Genome Atlas pan-cancer dataset was 73% of the samples in the cohort. CONCLUSION: A computational drug repurposing approach may assist in identifying novel repurposing events in cancer patients with no access to standard therapies. Further validation is needed to confirm a precision oncology approach using drug repurposing.

Identification of differential biological activity and synergy between the PARP inhibitor rucaparib and its major metabolite.

The (poly)pharmacology of drug metabolites is seldom comprehensively characterized in drug discovery. However, some drug metabolites can reach high plasma concentrations and display in vivo activity. Here, we use computational and experimental methods to comprehensively characterize the kinase polypharmacology of M324, the major metabolite of the PARP1 inhibitor rucaparib. We demonstrate that M324 displays unique PLK2 inhibition at clinical concentrations. This kinase activity could have implications for the efficacy and safety of rucaparib and therefore warrants further clinical investigation. Importantly, we identify synergy between the drug and the metabolite in prostate cancer models and a complete reduction of α-synuclein accumulation in Parkinson's disease models. These activities could be harnessed in the clinic or open new drug discovery opportunities. The study reported here highlights the importance of characterizing the activity of drug metabolites to comprehensively understand drug response in the clinic and exploit our current drug arsenal in precision medicine.

Short-term exposure to environmental levels of nicotine and cotinine impairs visual motor response in zebrafish larvae through a similar mode of action: Exploring the potential role of zebrafish α7 nAChR.

The current view is that environmental levels of nicotine and cotinine, commonly in the ng/L range, are safe for aquatic organisms. In this study, 7 days post-fertilization zebrafish embryos have been exposed for 24 h to a range of environmental concentrations of nicotine (2.0 ng/L-2.5 µg/L) and cotinine (50 pg/L-10 µg/L), as well as to a binary mixture of these emerging pollutants. Nicotine exposure led to hyperactivity, decreased vibrational startle response and increased non-associative learning. However, the more consistent effect found for both nicotine and cotinine was a significant increase in light-off visual motor response (VMR). The effect of both pollutants on this behavior occurred through a similar mode of action, as the joint effects of the binary mixture of both chemicals were consistent with the concentration addition concept predictions. The results from docking studies suggest that the effect of nicotine and cotinine on light-off VMR could be mediated by zebrafish α7 nAChR expressed in retina. The results presented in this study emphasize the need to revisit the environmental risk assessment of chemicals including additional ecologically relevant sublethal endpoints.

Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium.

The Mechanism of Action (MoA) of a drug is generally represented as a small, non-tissue-specific repertoire of high-affinity binding targets. Yet, drug activity and polypharmacology are increasingly associated with a broad range of off-target and tissue-specific effector proteins. To address this challenge, we have implemented an efficient integrative experimental and computational framework leveraging the systematic generation and analysis of drug perturbational profiles representing >700 FDA-approved and experimental oncology drugs, in cell lines selected as high-fidelity models of 23 aggressive tumor subtypes. Protein activity-based analyses revealed highly reproducible, drug-mediated modulation of tissue-specific targets, leading to generation of a proteome-wide polypharmacology map, characterization of MoA-related drug clusters and off-target effects, and identification and experimental validation of novel, tissue-specific inhibitors of undruggable oncoproteins. The proposed framework, which is easily extended to elucidating the MoA of novel small-molecule libraries, could help support more systematic and quantitative approaches to precision oncology.

canSAR: update to the cancer translational research and drug discovery knowledgebase.

canSAR (https://cansar.ai) is the largest public cancer drug discovery and translational research knowledgebase. Now hosted in its new home at MD Anderson Cancer Center, canSAR integrates billions of experimental measurements from across molecular profiling, pharmacology, chemistry, structural and systems biology. Moreover, canSAR applies a unique suite of machine learning algorithms designed to inform drug discovery. Here, we describe the latest updates to the knowledgebase, including a focus on significant novel data. These include canSAR's ligandability assessment of AlphaFold; mapping of fragment-based screening data; and new chemical bioactivity data for novel targets. We also describe enhancements to the data and interface.

Identification of different side effects between PARP inhibitors and their polypharmacological multi-target rationale.

AIMS: The aim of this study was to determine the differences and potential mechanistic rationale for observed adverse drug reactions (ADRs) between four approved PARP inhibitors (PARPi). METHODS: The Medicines and Healthcare products Regulatory Authority (MHRA) Yellow Card drug analysis profiles and NHS secondary care medicines database enabled the identification of suspected ADRs associated with the PARPi in the UK from launch to 2020. The polypharmacology of the PARPi were data-mined from several public data sources. RESULTS: The overall ADRs per 100 000 Rx identified across the four PARPi are statistically significant (χ2 test, P < .001). Rucaparib has the greatest relative suspected ADRs, which can be explained by its least clean kinome and physicochemical properties. The suspected gastrointestinal ADRs of rucaparib and niraparib can be ascribed to their kinase polypharmacology. Suspected blood and lymphatic system ADRs of PARPi can be linked to their high volume of distribution (Vd ). The thrombocytopenia rate of niraparib > rucaparib > olaparib tracked with the Vd trend. Hypertension is only associated with niraparib and could be explained by the therapeutically achievable inhibition of DYRK1A and/or transporters. Arrhythmia cases are potentially linked to the structural features of hERG ion-channel inhibition found in rucaparib and niraparib. Enhanced psychiatric/nervous disorders associated with niraparib can be interpreted from the diverse neurotransporter off-targets reported. CONCLUSIONS: Despite their similar mode of action, the differential polypharmacology of PARP inhibitors influences their ADR profile.

Evolution of kinase polypharmacology across HSP90 drug discovery.

Most small molecules interact with several target proteins but this polypharmacology is seldom comprehensively investigated or explicitly exploited during drug discovery. Here, we use computational and experimental methods to identify and systematically characterize the kinase cross-pharmacology of representative HSP90 inhibitors. We demonstrate that the resorcinol clinical candidates ganetespib and, to a lesser extent, luminespib, display unique off-target kinase pharmacology as compared with other HSP90 inhibitors. We also demonstrate that polypharmacology evolved during the optimization to discover luminespib and that the hit, leads, and clinical candidate all have different polypharmacological profiles. We therefore recommend the computational and experimental characterization of polypharmacology earlier in drug discovery projects to unlock new multi-target drug design opportunities.

canSAR: update to the cancer translational research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is the largest, public, freely available, integrative translational research and drug discovery knowledgebase for oncology. canSAR integrates vast multidisciplinary data from across genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and more. It also provides unique data, curation and annotation and crucially, AI-informed target assessment for drug discovery. canSAR is widely used internationally by academia and industry. Here we describe significant developments and enhancements to the data, web interface and infrastructure of canSAR in the form of the new implementation of the system: canSARblack. We demonstrate new functionality in aiding translation hypothesis generation and experimental design, and show how canSAR can be adapted and utilised outside oncology.

The kinase polypharmacology landscape of clinical PARP inhibitors.

Polypharmacology plays an important role in defining response and adverse effects of drugs. For some mechanisms, experimentally mapping polypharmacology is commonplace, although this is typically done within the same protein class. Four PARP inhibitors have been approved by the FDA as cancer therapeutics, yet a precise mechanistic rationale to guide clinicians on which to choose for a particular patient is lacking. The four drugs have largely similar PARP family inhibition profiles, but several differences at the molecular and clinical level have been reported that remain poorly understood. Here, we report the first comprehensive characterization of the off-target kinase landscape of four FDA-approved PARP drugs. We demonstrate that all four PARP inhibitors have a unique polypharmacological profile across the kinome. Niraparib and rucaparib inhibit DYRK1s, CDK16 and PIM3 at clinically achievable, submicromolar concentrations. These kinases represent the most potently inhibited off-targets of PARP inhibitors identified to date and should be investigated further to clarify their potential implications for efficacy and safety in the clinic. Moreover, broad kinome profiling is recommended for the development of PARP inhibitors as PARP-kinase polypharmacology could potentially be exploited to modulate efficacy and side-effect profiles.

canSAR: update to the cancer translational research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is a public, freely available, integrative translational research and drug discovery knowlegebase. canSAR informs researchers to help solve key bottlenecks in cancer translation and drug discovery. It integrates genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and unique, comprehensive and orthogonal 'druggability' assessments. canSAR is widely used internationally by academia and industry. Here we describe major enhancements to canSAR including new and expanded data. We also describe the first components of canSARblack-an advanced, responsive, multi-device compatible redesign of canSAR with a question-led interface.

Polypharmacology in Precision Oncology: Current Applications and Future Prospects.

Over the past decade, a more comprehensive, large-scale approach to studying cancer genetics and biology has revealed the challenges of tumor heterogeneity, adaption, evolution and drug resistance, while systems-based pharmacology and chemical biology strategies have uncovered a much more complex interaction between drugs and the human proteome than was previously anticipated. In this mini-review we assess the progress and potential of drug polypharmacology in biomarker-driven precision oncology. Polypharmacology not only provides great opportunities for drug repurposing to exploit off-target effects in a new single-target indication but through simultaneous blockade of multiple targets or pathways offers exciting opportunities to slow, overcome or even prevent inherent or adaptive drug resistance. We highlight the many challenges associated with exploiting known or desired polypharmacology in drug design and development, and assess computational and experimental methods to uncover unknown polypharmacology. A comprehensive understanding of the intricate links between polypharmacology, efficacy and safety is urgently needed if we are to tackle the enduring challenge of cancer drug resistance and to fully exploit polypharmacology for the ultimate benefit of cancer patients.

canSAR: an updated cancer research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is a publicly available, multidisciplinary, cancer-focused knowledgebase developed to support cancer translational research and drug discovery. canSAR integrates genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and druggability data. canSAR is widely used to rapidly access information and help interpret experimental data in a translational and drug discovery context. Here we describe major enhancements to canSAR including new data, improved search and browsing capabilities, new disease and cancer cell line summaries and new and enhanced batch analysis tools.

In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities.

Large efforts dedicated to detect somatic alterations across tumor genomes/exomes are expected to produce significant improvements in precision cancer medicine. However, high inter-tumor heterogeneity is a major obstacle to developing and applying therapeutic targeted agents to treat most cancer patients. Here, we offer a comprehensive assessment of the scope of targeted therapeutic agents in a large pan-cancer cohort. We developed an in silico prescription strategy based on identification of the driver alterations in each tumor and their druggability options. Although relatively few tumors are tractable by approved agents following clinical guidelines (5.9%), up to 40.2% could benefit from different repurposing options, and up to 73.3% considering treatments currently under clinical investigation. We also identified 80 therapeutically targetable cancer genes.

Distant polypharmacology among MLP chemical probes.

Small molecules are essential tool compounds to probe the role of proteins in biology and advance toward more efficient therapeutics. However, they are used without a complete knowledge of their selectivity across the entire proteome, at risk of confounding their effects due to unknown off-target interactions. Current state-of-the-art computational approaches to predicting the affinity profile of small molecules offer a means to anticipate potential nonobvious selectivity liabilities of chemical probes. The application of in silico target profiling on the full set of chemical probes from the NIH Molecular Libraries Program (MLP) resulted in the identification of biologically relevant in vitro affinities for proteins distantly related to the primary targets of ML006, ML123, ML141, and ML204, helping to lower the risk of their further use in chemical biology.

Linking off-target kinase pharmacology to the differential cellular effects observed among PARP inhibitors.

PARP inhibitors hold promise as a novel class of targeted anticancer drugs. However, their true mechanism of action is still not well understood following recent reports that show marked differences in cellular effects. Here, we demonstrate that three PARP drug candidates, namely, rucaparib, veliparib, and olaparib, have a clearly different in vitro affinity profile across a panel of diverse kinases selected using a computational approach that relates proteins by ligand similarity. In this respect, rucaparib inhibits nine kinases with micromolar affinity, including PIM1, PIM2, PRKD2, DYRK1A, CDK1, CDK9, HIPK2, CK2, and ALK. In contrast, olaparib does not inhibit any of the sixteen kinases tested. In between, veliparib inhibits only two, namely, PIM1 and CDK9. The differential kinase pharmacology observed among PARP inhibitors provides a plausible explanation to their different cellular effects and offers unexplored opportunities for this drug class, but alerts also on the risk associated to transferring directly both preclinical and clinical outcomes from one PARP drug candidate to another.

Exploring the effect of PARP-1 flexibility in docking studies.

Poly(ADP-ribose)polymerase-1 (PARP-1) is an enzyme belonging to the ADP-ribosyltransferase family. A large body of works has validated PARP-1 as an attractive drug target for different therapeutic areas, including cancers and ischemia. Accordingly, sampling the conformational space of the enzyme is pivotal to understand its functions and improve structure-based drug discovery approaches. In the first part of this study we apply replica exchange molecular dynamic (REMD) simulations to sample the conformational space of the catalytic domain of PARP-1 in the ligand-bound and unbound forms. In the second part, we assess how and to what extend the emerging enzyme flexibility affects the performance of docking experiments of a library of PARP-1 inhibitors. This study pinpoints a putative key role of conformational shifts of Leu324, Tyr325 and Lys242 in opening an additional binding site pocket that affects the binding of ligands to the catalytic cleft of PARP-1. Furthermore, it highlights the improvement of the enrichment factor of active ligands obtained in docking experiments when using conformations generated with REMD simulations of ligand-bound PARP-1.

Identification of pim kinases as novel targets for PJ34 with confounding effects in PARP biology.

Small molecules are widely used in chemical biology without complete knowledge of their target profile, at risk of deriving conclusions that ignore potential confounding effects from unknown off-target interactions. The prediction and further experimental confirmation of novel affinities for PJ34 on Pim1 (IC(50) = 3.7 µM) and Pim2 (IC(50) = 16 µM) serine/threonine kinases, together with their involvement in many of the processes relevant to PARP biology, questions the appropriateness of using PJ34 as a chemical tool to probe the biological role of PARP1 and PARP2 at the high micromolar concentrations applied in most studies.