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

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

Investigation of Engine Distortion Interaction

Inflow distortions in the compression system of a jet engine are becoming increasingly important for research focus. The investigation of the emergence of a distortion, its interaction with the rotor and the resulting impact on the rotor flow is challenging. In this work a separation in the inflow of a transonic compressor was created and the impact on stage aerodynamics investigated. The separation resulted in a total pressure distortion close to the casing within a sector of 120°. Effects were studied both numerically and experimentally in a joint collaboration project. The numerical model consisted of the full rotor-stator compressor stage, the inlet duct and the distortion generator upstream of the stage. This enables both an accurate validation of the numerical results and contributes to a deeper understanding of the flow. The results of both the numerical and experimental studies were in good agreement. The rotor is locally throttled by the inlet separation, resulting in the formation of an additional loss core at the stability limit due to a local aerodynamic overload. Considering classic distortion descriptors like the DC60, it is shown that they are not able to adequately assess the impact of a strong, but small distortion close to the tip of the rotor. The data can be considered as test case for future numerical models as well as for the validation of new analytical models. Furthermore, the results of this study reveal effects in both experimental and numerical studies that would not be realized if only a model of the separation was analyzed.</jats:p