title: Chemogenomic analysis of safety profiling data.
abstract: Understanding the safety of newly developed compounds is a key task in each early drug discovery project. In early stages, pharmaceutical companies address this task by using so-called preclinical safety profiling, in which compounds are screened in inexpensive large-scale assays to understand possible liabilities. This process generates a large amount of binding data on various compounds against a panel of targets - usually thousands or tens of thousands of compounds profiled against approximately 100 different targets. This data matrix is highly valuable and elicits further analysis. After briefly introducing the nature of safety profiling data, we describe several computational methods used internally at Novartis to analyze it. We showcase protocols that can be used to understand compound promiscuity on a chemical structure level and protocols to evaluate the promiscuity of targets used in safety profiling. We also describe a method to quickly determine the chemical similarity of compounds active against different targets. Next, it is shown what protocols can be used to evaluate global chemical similarity of targets. The above approaches can be used either to optimize the composition of a panel of targets or to better understand certain toxicities. Finally, we will explain a simple method to elucidate hidden patterns in safety profiling data.
authors.affiliation: Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland.
authors.forename: Josef
authors.initials: J
authors.lastname: Scheiber
authors.forename: Jeremy L
authors.initials: JL
authors.lastname: Jenkins
mesh_terms: D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D055808:Drug Discovery / Q000706:statistics & numerical data*; D004353:Drug Evaluation, Preclinical / Q000706:statistics & numerical data; D064420:Drug-Related Side Effects and Adverse Reactions; D023281:Genomics / Q000706:statistics & numerical data; D015233:Models, Statistical; D008967:Molecular Biology / Q000706:statistics & numerical data; D025341:Principal Component Analysis