title: How experimental errors influence drug metabolism and pharmacokinetic QSAR/QSPR models.
abstract: We consider the impact of gross, systematic, and random experimental errors in relation to their impact on the predictive ability of QSAR/QSPR DMPK models used within early drug discovery. Models whose training sets contain fewer but repeatedly measured data points, with a defined threshold for the random error, resulted in prediction improvements ranging from 3.3% to 23.0% for an external test set, compared to models built from training sets in which the molecules were defined by single measurements. Similarly, models built on data with low experimental uncertainty, compared to those built on data with higher experimental uncertainty, gave prediction improvements ranging from 3.3% to 27.5%.
authors.affiliation: Drug Safety & Metabolism, AstraZeneca R&D Alderley Park , Macclesfield, Cheshire, SK10 4TF, U.K.
authors.forename: Mark C
authors.initials: MC
authors.lastname: Wenlock
authors.forename: Lars A
authors.initials: LA
authors.lastname: Carlsson
mesh_terms: D000818:Animals; D055808:Drug Discovery; D004353:Drug Evaluation, Preclinical / Q000379:methods; D006801:Humans; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D010599:Pharmacokinetics; D021281:Quantitative Structure-Activity Relationship; D012107:Research Design
chemical_list: D004364:Pharmaceutical Preparations