Identification of a Kinase Profile that Predicts Chromosome Damage Induced by Small Molecule Kinase Inhibitors

Abstract

Kinases are heavily pursued pharmaceutical targets because of their mechanistic role in many diseases. Small molecule kinase inhibitors (SMKIs) are a compound class that includes marketed drugs and compounds in various stages of drug development. While effective, many SMKIs have been associated with toxicity including chromosomal damage. Screening for kinase-mediated toxicity as early as possible is crucial, as is a better understanding of how off-target kinase inhibition may give rise to chromosomal damage. To that end, we employed a competitive binding assay and an analytical method to predict the toxicity of SMKIs. Specifically, we developed a model based on the binding affinity of SMKIs to a panel of kinases to predict whether a compound tests positive for chromosome damage. As training data, we used the binding affinity of 113 SMKIs against a representative subset of all kinases (290 kinases), yielding a 113×290 data matrix. Additionally, these 113 SMKIs were tested for genotoxicity in an in vitro micronucleus test (MNT). Among a variety of models from our analytical toolbox, we selected using cross-validation a combination of feature selection and pattern recognition techniques: Kolmogorov-Smirnov/T-test hybrid as a univariate filter, followed by Random Forests for feature selection and Support Vector Machines (SVM) for pattern recognition. Feature selection identified 21 kinases predictive of MNT. Using the corresponding binding affinities, the SVM could accurately predict MNT results with 85% accuracy (68% sensitivity, 91% specificity). This indicates that kinase inhibition profiles are predictive of SMKI genotoxicity. While in vitro testing is required for regulatory review, our analysis identified a fast and cost-efficient method for screening out compounds earlier in drug development. Equally important, by identifying a panel of kinases predictive of genotoxicity, we provide medicinal chemists a set of kinases to avoid when designing compounds, thereby providing a basis for rational drug design away from genotoxicity.

Chemoproteomics-based kinome profiling and target deconvolution of clinical multi-kinase inhibitors in primary chronic lymphocytic leukemia cells

“…The inhibition of PCTAIRE and PFTAIRE kinases by small-molecule inhibitors has recently been linked to toxicity caused by chromosomal damage. 30 The kinobeads approach is not limited to the profiling of ATPcompetitive kinase inhibitors, but is also suitable to characterize agents that affect kinase abundance or activity in cells by other mechanisms, for example by inhibiting HSPs. The function of HSP90 is to maintain client proteins in a correctly folded conformation.…”

“…The BMS-387032 matrix captured a few kinases not identified with the kinobeads matrix, like the PCTAIRE and PFTAIRE kinases of the CDK family, which have been linked to genotoxicity. 30 We therefore decided to include BMS-387032 as a capturing ligand in the kinobeads for the subsequent inhibitor profiling experiments.…”

Dose Finding of Small-Molecule Oncology Drugs: Optimization throughout the Development Life Cycle

“…This approach already has been attempted by several groups, but the predictive capability of these approaches is severely compromised by the limited information available to each individual research entity (7,8). This approach also would be bolstered by robust data-sharing initiatives among academics, regulators, and drug developers in precompetitive spaces.…”

Nonclinical Evaluations of Small-Molecule Oncology Drugs: Integration into Clinical Dose Optimization and Toxicity Management

“…The pathways/networks in which the affected kinases participate and the range of biologic functions controlled via these networks determine the pattern of AE. In some cases, patterns of target activity may predict specific AEs (12). As more kinases are characterized, and the roles and interplay of the signaling networks in which they participate more completely understood, it should become possible to design KI drugs with improved efficacy and AE profiles.…”