title: Role of pharmacokinetic-pharmacodynamic principles in rational and cost-effective drug development.
abstract: An important goal of drug development is to define dose and concentration-response relationships for new drugs and biologics. Such critical information from controlled clinical trials can provide primary evidence of efficacy and safety and an informative database for devising dosing instructions for clinical use. This article describes applications of pharmacologic principles [pharmacokinetic-pharmacodynamic (PK-PD)] and modeling methods for drugs in which the evaluation process is guided by and/or identifies significant PK and/or PD variability in drug response. In the case of the recently registered immunosuppressive agent, tacrolimus, preclinical PK-PD in model systems can be used to rationally design safe and effective immunomodulatory dosing regimens for phase 1 clinical studies. Furthermore, a study design based on concentration control guided by a novel artificial intelligence modeling system (AIMS) can be efficiently applied to conduct randomized clinical trials in auto-immunity and to implement cost-effective therapeutic drug monitoring of tacrolimus and cyclosporine in clinical transplantation. In the case of a cardioselective beta-adrenergic blocking agent, betaxolol, marketed for essential hypertension, population PD modeling can be shown to be a more efficient method for estimating dose response compared with standard statistical tests. Using a sigmoid Emax PD model, only a fraction (40 of 300) of the randomized patients was needed to demonstrate dose response. Therefore, two methods, i.e., PD modeling of dose response and AIMS-guided dosing, can achieve significant cost benefits for drug developers, patient care, and the health care system.
authors.affiliation: Center for Drug Evaluation and Research, Food and Drug Administration, Rockville, Maryland 20857, USA.
authors.forename: R
authors.initials: R
authors.lastname: Lieberman
authors.forename: J
authors.initials: J
authors.lastname: McMichael
mesh_terms: D001185:Artificial Intelligence; D004305:Dose-Response Relationship, Drug; D016903:Drug Monitoring / Q000379:methods*; D006801:Humans