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Abstract Protein kinases play important role in the regulation of various biological processes, and their malfunction and dysregulation are intricately linked to the initiation and progression of cancer. The effectiveness of small-molecule kinase inhibitors (KIs) has been well-established in the therapeutic landscape, with the FDA approving over 70 KIs for the treatment of cancer and other diseases. However, the widespread utilization of KIs has been accompanied by reported adverse events, prominently featuring cardiac complications, alongside hepatic and renal function. While many studies have indicated on-target and off-target kinase specificity associated with organ damage, there is an urgent need for building predictive models that can pinpoint which specific kinases might be involved in different types of organ injuries. Such a model would not only facilitate mechanistic elucidation but also serve as a valuable tool for preemptively identifying potential adverse events associated with KI treatment. In this study, we performed polypharmacology-based screening of kinase inhibitors in organotypic tissue slices prepared from the normal heart, liver, and kidney of three distinct species—human, canine, and rodents. These tissue samples were treated individually using a curated selection of 32 KIs that are representative of a larger set of KIs and collectively target most of the 'kinome. ' Subsequently, we developed Elastic Net Regularization models by utilizing the kinase inhibition profiles of ~300 kinases. These models were used to predict changes in tissue viability, measured as a percentage change, following the treatment with the respective kinase inhibitors. Using these models, we predicted the toxicity of 428 kinase inhibitors, both as single agents and in pairwise combinations, against three organs—heart, liver, and kidney—in humans, rodents, or canines. Our data show that the models accurately predicted the renal and cardiac toxicity of many FDA-approved KIs that were previously known. For example, the inhibition of RET kinase was identified to be linked with renal toxicity. Further, we compared the toxicity landscape of kinase KIs among different species and identified distinct sets of kinases that were recognized as essential indicators of toxicity within the same organ across species. These disparities underscore the potential distinctions in organ metabolism. In summary, our data provide a comprehensive landscape of kinase inhibitor toxicity. Our models could serve as a valuable resource for predicting kinase inhibitor toxicity across species by leveraging inhibitor profile data. Citation Format: Yuqi Kang, Marina Chan, Songli Zhu, Taranjit S. Gujral. Integrating organotypic tissue slices and polypharmacology-based screening to map the toxicity landscape of kinase inhibitors abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 616.
Kang et al. (Fri,) studied this question.