title: Design and analysis of in vitro antitumor pharmacodynamic studies.
abstract: The relationship between drug concentration (C), exposure time (t), and the resulting effect (h) for a chemotherapeutic agent is expressed as Cn x t = h. The value of n, derived from curve fitting of the C versus t plot, indicates the relative importance of concentration and exposure time. The selection of concentrations and exposure times in a pharmacodynamic experiment may affect the precision and accuracy of parameter estimation. The use of optimal designs is even more critical when the numbers of experimental conditions are limited by tumor availability (e.g., small size of surgical specimens from patients). The present study used computer-simulated data to define the most efficient in vitro pharmacodynamic experimental designs and the optimal method of pharmacodynamic data analysis. All studies used Monte Carlo simulations to compare designs with varying numbers of drug concentrations, exposure times, and replications. For each selected design, 50-100 error-containing data sets were created by addition of experience-based random errors to expected concentration-response profiles. To compare methods of data analysis, the same 1250 simulated data sets were analyzed by two methods (i.e., surface response method and traditional method). The results showed that simultaneous fitting of drug effect at all concentrations and all exposure times by the surface response method yielded n estimates that had greater precision and accuracy than a traditional method that required sequential determination of the effective inhibitory concentration (e.g., IC50) and then the n value using the IC50 at different exposure times. Subsequent studies were analyzed using the surface response method. To evaluate the effect of selection of concentrations and exposure times on the precision of n estimation, between 1100 and 2200 simulated data sets, with 400 observations per data set, were generated using different exposure times and drug concentrations. Because the number of observations was limited to 400, the number of replications at each condition varied depending on the total number of selected conditions.(ABSTRACT TRUNCATED AT 400 WORDS)
authors.affiliation: Division of Pharmaceutics and Pharmaceutical Chemistry, College of Pharmacy, Ohio State University, Columbus 43210, USA.
authors.forename: J E
authors.initials: JE
authors.lastname: Kalns
authors.forename: N J
authors.initials: NJ
authors.lastname: Millenbaugh
authors.forename: M G
authors.initials: MG
authors.lastname: Wientjes
authors.forename: J L
authors.initials: JL
authors.lastname: Au
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004305:Dose-Response Relationship, Drug; D006801:Humans; D008954:Models, Biological; D013997:Time Factors
chemical_list: D000970:Antineoplastic Agents