title: Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure.
abstract: Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.
authors.affiliation: Lead Finding Platform, Novartis Institutes for BioMedical Research Inc. 250 Massachusetts Ave., Cambridge, Massachusetts 02139, USA. andreas.bender@novartis.com
authors.forename: Andreas
authors.initials: A
authors.lastname: Bender
authors.forename: Josef
authors.initials: J
authors.lastname: Scheiber
authors.forename: Meir
authors.initials: M
authors.lastname: Glick
authors.forename: John W
authors.initials: JW
authors.lastname: Davies
authors.forename: Kamal
authors.initials: K
authors.lastname: Azzaoui
authors.forename: Jacques
authors.initials: J
authors.lastname: Hamon
authors.forename: Laszlo
authors.initials: L
authors.lastname: Urban
authors.forename: Steven
authors.initials: S
authors.lastname: Whitebread
authors.forename: Jeremy L
authors.initials: JL
authors.lastname: Jenkins
mesh_terms: D014150:Antipsychotic Agents / Q000009:adverse effects / Q000737:chemistry / Q000494:pharmacology / Q000627:therapeutic use; D001145:Arrhythmias, Cardiac / Q000139:chemically induced; D001544:Benperidol / Q000009:adverse effects / Q000737:chemistry / Q000494:pharmacology / Q000627:therapeutic use; D003198:Computer Simulation; D016208:Databases, Factual; D016503:Drug Delivery Systems; D015195:Drug Design; D004353:Drug Evaluation, Preclinical; D064420:Drug-Related Side Effects and Adverse Reactions; D008024:Ligands; D008956:Models, Chemical; D008958:Models, Molecular; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D011237:Predictive Value of Tests
chemical_list: D014150:Antipsychotic Agents; D008024:Ligands; D004364:Pharmaceutical Preparations; D001544:Benperidol