title: Comparing Measures of Promiscuity and Exploring Their Relationship to Toxicity.
abstract: Recent research has focused on algorithms to derive numerical measures of selectivity based on panels of in vitro pharmacology assays so that one molecule's activity profile may be compared easily with that of another. However, the questions concerning which method or algorithm is best to use, the optimal number of assays required to give an accurate measure of selectivity and the correlation of these measures to in vivo toxicity have remained largely unexplored. In this manuscript we describe a systematic approach to compare and contrast different calculation methods for promiscuity and determine the optimal number and constitution of a panel of assays to measure the selectivity/promiscuity of compounds across all targets. We then go on to examine their relationship to toxicity using a Pfizer proprietary compound set that has both selectivity profiles and exploratory toxicology study results. From this study we conclude that all five methods studied are useful in estimating compound selectivity; that a small panel of between 15 to 30 binding assays can be used as a surrogate for a broader panel enabling higher throughput with lower costs and this panel will most likely have the highest prediction power when correlating this measure to in vivo effects.
authors.affiliation: Compound Safety Prediction Group, Worldwide Medicinal Chemistry, MS 8118-B3, Pfizer Inc. 445 Eastern Point Road, Groton, CT 06340, USA.
authors.forename: Xiangyun
authors.initials: X
authors.lastname: Wang
authors.affiliation: Compound Safety Prediction Group, Worldwide Medicinal Chemistry, MS 8118-B3, Pfizer Inc. 445 Eastern Point Road, Groton, CT 06340, USA. nigel.greene@pfizer.com.
authors.forename: Nigel
authors.initials: N
authors.lastname: Greene
keywords: BioPrint; Compound selectivity; Gini coefficient; Hit rate; In vivo toxicity; Promiscuity; Rescaled geometric mean; Rescaled standard deviation; Thermodynamics partition index