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Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors.

ABSTRACT: Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to the mechanism-independent effects of these drugs. Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing. Here, we develop iDTPnd (integrated Drug Target Predictor with negative dataset), a computational approach for large-scale discovery of novel targets for known drugs. For a given drug, we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding sites. To facilitate assessment of unintended targets, iDTPnd also provides a docking-based interaction score and its statistical significance. We confirm the interactions of sorafenib, imatinib, dasatinib, sunitinib, and pazopanib with their known targets at a sensitivity of 52% and a specificity of 55%. We also validate 10 predicted novel targets by using in vitro experiments. Our results suggest that proteins other than kinases, such as nuclear receptors, cytochrome P450, and MHC class I molecules, can also be physiologically relevant targets of kinase inhibitors. Our method is general and broadly applicable for the identification of protein-small molecule interactions, when sufficient drug-target 3D data are available. The code for constructing the structural signatures is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.

SUBMITTER: Naveed H

PROVIDER: S-EPMC9403029 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors.

Naveed Hammad H Reglin Corinna C Schubert Thomas T Gao Xin X Arold Stefan T ST Maitland Michael L ML

Genomics, proteomics & bioinformatics 20210329 6

Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to the mechanism-independent effects of these drugs. Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing. Here, we develop iDTPnd (integrated Drug Target Predictor with negative dataset), a computational approach for large-scale discovery of novel targets for known drugs. For a given drug, we construct a positive structural signature as well as a neg ...[more]

PMID: 33794377

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Project description:BackgroundEndometriosis (EM) is a chronic painful condition that predominantly affects women of reproductive age. Currently, surgery or medication can only provide limited symptom relief. This study used a comprehensive genetic analytical approach to explore potential drug targets for EM in the plasma proteome.MethodsIn this study, 2,923 plasma proteins were selected as exposure and EM as outcome for two-sample Mendelian randomization (MR) analyses. The plasma proteomic data were derived from the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP), while the EM dataset from the FinnGen consortium R10 release data. Several sensitivity analyses were performed, including summary-data-based MR (SMR) analyses, heterogeneity in dependent instruments (HEIDI) test, reverse MR analyses, steiger detection test, and bayesian co-localization analyses. Furthermore, proteome-wide association study (PWAS) and single-cell transcriptomic analyses were also conducted to validate the findings.ResultsSix significant (p < 3.06 × 10-5) plasma protein-EM pairs were identified by MR analyses. These included EPHB4 (OR = 1.40, 95% CI: 1.20 - 1.63), FSHB (OR = 3.91, 95% CI: 3.13 - 4.87), RSPO3 (OR = 1.60, 95% CI: 1.38 - 1.86), SEZ6L2 (OR = 1.44, 95% CI: 1.23 - 1.68) and WASHC3 (OR = 2.00, 95% CI: 1.54 - 2.59) were identified as risk factors, whereas KDR (OR = 0.80, 95% CI: 0.75 - 0.90) was found to be a protective factor. All six plasma proteins passed the SMR test (P < 8.33 × 10-3), but only four plasma proteins passed the HEIDI heterogeneity test (PHEIDI > 0.05), namely FSHB, RSPO3, SEZ6L2 and EPHB4. These four proteins showed strong evidence of co-localization (PPH4 > 0.7). In particular, RSPO3 and EPHB4 were replicated in the validated PWAS. Single-cell analyses revealed high expression of SEZ6L2 and EPHB4 in stromal and epithelial cells within EM lesions, while RSPO3 exhibited elevated expression in stromal cells and fibroblasts.ConclusionOur study identified FSHB, RSPO3, SEZ6L2, and EPHB4 as potential drug targets for EM and highlighted the critical role of stromal and epithelial cells in disease development. These findings provide new insights into the diagnosis and treatment of EM.

Project description:Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications.

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Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors.

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Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors.

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