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Supplementary Table from Reengineering Ponatinib to Minimize Cardiovascular Toxicity

Supplementary Figure from Reengineering Ponatinib to Minimize Cardiovascular Toxicity

Small molecule tyrosine kinase inhibitors (TKI) have revolutionized cancer treatment and greatly improved patient survival. However, life-threatening cardiotoxicity of many TKIs has become a major concern. Ponatinib (ICLUSIG) was developed as an inhibitor of the BCR-ABL oncogene and is among the most cardiotoxic of TKIs. Consequently, use of ponatinib is restricted to the treatment of tumors carrying T315I-mutated BCR-ABL, which occurs in chronic myeloid leukemia (CML) and confers resistance to first- and second-generation inhibitors such as imatinib and nilotinib. Through parallel screening of cardiovascular toxicity and antitumor efficacy assays, we engineered safer analogs of ponatinib that retained potency against T315I BCR-ABL kinase activity and suppressed T315I mutant CML tumor growth. The new compounds were substantially less toxic in human cardiac vasculogenesis and cardiomyocyte contractility assays in vitro. The compounds showed a larger therapeutic window in vivo, leading to regression of human T315I mutant CML xenografts without cardiotoxicity. Comparison of the kinase inhibition profiles of ponatinib and the new compounds suggested that ponatinib cardiotoxicity is mediated by a few kinases, some of which were previously unassociated with cardiovascular disease. Overall, the study develops an approach using complex phenotypic assays to reduce the high risk of cardiovascular toxicity that is prevalent among small molecule oncology therapeutics.Significance:Newly developed ponatinib analogs retain antitumor efficacy but elicit significantly decreased cardiotoxicity, representing a therapeutic opportunity for safer CML treatment.

Small molecule tyrosine kinase inhibitors (TKI) have revolutionized cancer treatment and greatly improved patient survival. However, life-threatening cardiotoxicity of many TKIs has become a major concern. Ponatinib (ICLUSIG) was developed as an inhibitor of the BCR-ABL oncogene and is among the most cardiotoxic of TKIs. Consequently, use of ponatinib is restricted to the treatment of tumors carrying T315I-mutated BCR-ABL, which occurs in chronic myeloid leukemia (CML) and confers resistance to first- and second-generation inhibitors such as imatinib and nilotinib. Through parallel screening of cardiovascular toxicity and antitumor efficacy assays, we engineered safer analogs of ponatinib that retained potency against T315I BCR-ABL kinase activity and suppressed T315I mutant CML tumor growth. The new compounds were substantially less toxic in human cardiac vasculogenesis and cardiomyocyte contractility assays in vitro. The compounds showed a larger therapeutic window in vivo, leading to regression of human T315I mutant CML xenografts without cardiotoxicity. Comparison of the kinase inhibition profiles of ponatinib and the new compounds suggested that ponatinib cardiotoxicity is mediated by a few kinases, some of which were previously unassociated with cardiovascular disease. Overall, the study develops an approach using complex phenotypic assays to reduce the high risk of cardiovascular toxicity that is prevalent among small molecule oncology therapeutics. Significance: Newly developed ponatinib analogs retain antitumor efficacy but elicit significantly decreased cardiotoxicity, representing a therapeutic opportunity for safer CML treatment.

Drug safety initiatives have endorsed human iPSC-derived cardiomyocytes (hiPSC-CMs) as an in vitro model for predicting drug-induced cardiac arrhythmia. However, the extent to which human-defined features of in vitro arrhythmia predict actual clinical risk has been much debated. Here, we trained a convolutional neural network classifier (CNN) to learn features of in vitro action potential recordings of hiPSC-CMs that are associated with lethal Torsade de Pointes arrhythmia. The CNN classifier accurately predicted the risk of drug-induced arrhythmia in people. The risk profile of the test drugs was similar across hiPSC-CMs derived from different healthy donors. In contrast, pathogenic mutations that cause arrhythmogenic cardiomyopathies in patients significantly increased the proarrhythmic propensity to certain intermediate and high-risk drugs in the hiPSC-CMs. Thus, deep learning can identify in vitro arrhythmic features that correlate with clinical arrhythmia and discern the influence of patient genetics on the risk of drug-induced arrhythmia.