Impact of Pharmacometric Analyses on New Drug Approval and Labelling Decisions

Abstract

Pharmacometric analyses have become an increasingly important component of New Drug Application (NDA) and Biological License Application (BLA) submissions to the US FDA to support drug approval, labelling and trial design decisions. Pharmacometrics is defined as a science that quantifies drug, disease and trial information to aid drug development, therapeutic decisions and/or regulatory decisions. In this report, we present the results of a survey evaluating the impact of pharmacometric analyses on regulatory decisions for 198 submissions during the period from 2000 to 2008. Pharmacometric review of NDAs included independent, quantitative analyses by FDA pharmacometricians, even when such analysis was not conducted by the sponsor, as well as evaluation of the sponsor's report. During 2000-2008, the number of reviews with pharmacometric analyses increased dramatically and the number of reviews with an impact on approval and labelling also increased in a similar fashion. We also present the impact of pharmacometric analyses on selection of paediatric dosing regimens, approval of regimens that had not been directly studied in clinical trials and provision of evidence of effectiveness to support a single pivotal trial. Case studies are presented to better illustrate the role of pharmacometric analyses in regulatory decision making.

Parameter estimation for sigmoid Emax models in exposure-response relationship

“…The approximate values of the T max (the time at which the maximum concentration was observed) for single-and multiple-dose study were 3.15 and 2.78 h, respectively and the half-life was around 13.9 h. The blood sampling time points for dense sampling schedules were 0 (predose), 0.25, 0.5, 0.75, 1,2,3,4,5,6,8,12,24, and 48 h after drug administration. The samples at 24 and 48 h were not included for multiple-dose scenarios.…”

“…[1] The number of regulatory decisions, including new drug approval and labeling, that were effected by pharmacometric analysis increased from 45 submissions between 2000 and 2004 to 87 submissions between 2007 and 2008. [2,3] The population approach or pharmacometric analysis based on computational methods is being applied to bridging studies, proof of concept studies for go/no-go decision, simulation for dose selection or study design, and extension to other indications (drug repositioning), among many others. [1] PK/PD modeling and simulation is also used for individualized pharmacotherapy based on relevant demographic factors including race, age, sex, weight, height and genotype.…”

The implications of model-informed drug discovery and development for tuberculosis

“…In particular, attention is given to opportunities for improved evidence generation as well as evidence synthesis for the evaluation of new, more effective combinations of treatments. PKPD-disease modelling and simulation have been established as powerful tools for the characterisation of efficacy and safety in other therapeutic areas [12,13]. Their impacts on therapeutics and drug development have been reviewed extensively elsewhere [14].…”

“…By contrast, Phase II studies have often ignored PK variability and other sources of variation in treatment response in the target patient population, which need to be accounted for when exploring the dose-exposure-response relation. The impact of CTS during clinical development by means of providing stronger support for regulatory approval and labelling has been A c c e p t e d M a n u s c r i p t established in other therapeutic areas [12,25] and acknowledged by regulatory agencies [26]. Given that only a limited number of combinations can be tested in humans, it is crucial to harness methods that facilitate more robust study design and dose-range selection before the start of the trial.…”

Pharmacotherapy in pediatric epilepsy: from trial and error to rational drug and dose selection – a long way to go

“…The incorporation of covariates into a PKPD or disease model has an important advantage in that it enhances the prediction of response for specific groups of patients [94][95][96]. In conjunction with clinical trial simulations, model-based techniques offer an excellent opportunity for the evaluation of novel therapies [97] as well as personalization of the dosing regimen for children [98].…”