title: Comparing probabilistic and descriptive analyses of time-dose-toxicity relationship for determining no-observed-adverse-effect level in drug development.
abstract: The no-observed-adverse-effect level (NOAEL) of a drug defined from animal studies is important for inferring a maximal safe dose in human. However, several issues are associated with its concept, determination and application. It is confined to the actual doses used in the study; becomes lower with increasing sample size or dose levels; and reflects the risk level seen in the experiment rather than what may be relevant for human. We explored a pharmacometric approach in an attempt to address these issues. We first used simulation to examine the behaviour of the NOAEL values as determined by current common practice; and then fitted the probability of toxicity as a function of treatment duration and dose to data collected from all applicable toxicology studies of a test compound. Our investigation was in the context of an irreversible toxicity that is detected at the end of the study. Simulations illustrated NOAEL's dependency on experimental factors such as dose and sample size, as well as the underlying uncertainty. Modelling the probability as a continuous function of treatment duration and dose simultaneously to data from multiple studies allowed the estimation of the dose, along with its confidence interval, for a maximal risk level that might be deemed as acceptable for human. The model-based data integration also reconciled between-study inconsistency and explicitly provided maximised estimation confidence. Such alternative NOAEL determination method should be explored for its more efficient data use, more quantifiable insight to toxic doses, and the potential for more relevant animal-to-human translation.
authors.affiliation: Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, 1 Iron Bridge Road, Uxbridge, UB11 1BT London, UK.
authors.forename: Anaïs
authors.initials: A
authors.lastname: Glatard
authors.affiliation: Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, 1 Iron Bridge Road, Uxbridge, UB11 1BT London, UK.
authors.forename: Aliénor
authors.initials: A
authors.lastname: Berges
authors.affiliation: Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, 1 Iron Bridge Road, Uxbridge, UB11 1BT London, UK.
authors.forename: Tarjinder
authors.initials: T
authors.lastname: Sahota
authors.affiliation: Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, 1 Iron Bridge Road, Uxbridge, UB11 1BT London, UK.
authors.forename: Claire
authors.initials: C
authors.lastname: Ambery
authors.affiliation: Non-Clinical Safety Projects, GlaxoSmithKline, Ware, UK.
authors.forename: Jan
authors.initials: J
authors.lastname: Osborne
authors.affiliation: Computational Toxicology, GlaxoSmithKline, Upper Merion, USA.
authors.forename: Randall
authors.initials: R
authors.lastname: Smith
authors.affiliation: UMR 5558 Laboratoire Biométrie et de Biologie Evolutive, Equipe EMET (Evaluation et Modélisation des Effets Thérapeutiques), Université Claude Bernard Lyon1; Service de Pharmacologie Clinique et Essais Thérapeutiques, Hospices Civils de Lyon, Lyon, France.
authors.forename: Emilie
authors.initials: E
authors.lastname: Hénin
authors.affiliation: Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, 1 Iron Bridge Road, Uxbridge, UB11 1BT London, UK. Electronic address: chao.c.chen@gsk.com.
authors.forename: Chao
authors.initials: C
authors.lastname: Chen
mesh_terms: D000818:Animals; D001522:Behavior, Animal / Q000187:drug effects*; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D019055:No-Observed-Adverse-Effect Level; D011336:Probability; D051381:Rats; D018570:Risk Assessment; D013045:Species Specificity; D013737:Testis / Q000187:drug effects* / Q000473:pathology; D013997:Time Factors; D018675:Toxicity Tests / Q000379:methods*
keywords: No-observed-adverse-effect level; Pharmacometrics; Probabilistic modelling; Time–dose–toxicity relationship