A COMPARISON OF METHODS FOR LIMITED-SAMPLING STRATEGY DESIGN USING DATA FROM A PHASE-I TRIAL OF THE ANTHRAPYRAZOLE DUP-941

The pharmacokinetics of a drug in individual patients can be estimated using plasma samples collected at a limited number of time points. Ho wever, different methods for a limited-sampling strategy (LSS) design exist and the optimal method has not yet been defined. Plasma concentr ation data were available from 27 of 74 courses in a phase I study (do se range, 5-55 mg m(-2)) of the novel anthrapyrazole DuP-941. Three ap proaches to LSS development were compared, Firstly, forward stepwise r egression (FSR) was used to derive equations to predict the DuP-941 ar ea under the concentration-time curve (AUC) based on plasma concentrat ions measured at specified times. LSSs were developed using 14 randoml y chosen data sets and were validated using the remaining 13 data sets . Secondly, ''all subsets'' regression (ASR) was used to develop LSSs. A jack-knife technique was also used to allow model development utili sing 26 data sets and validation on the 27th data set. Thirdly, an LSS was developed using optimal sampling theory (OST), and the LSS was us ed in conjuction with a Bayesian algorithm. Selected sampling times fo r four-point LSSs were 10, 65, 185 and 485 min (FSR) and 10, 45, 200 a nd 480 min (OST), Ten candidate LSSs were developed using the ASR appr oach. ASR- and OST/Bayesian-derived four-point LSSs gave more precise (P < 0.05) estimates of AUC [mean absolute percentage of difference (M AD%) +/- SD: ASR, 6.4 +/- 3.7%; OST/Bayesian, 6.8 +/- 4.6%] than did F SR (MAD% = 15.1 +/- 9.9%), The OST/Bayesian approach is recommended be cause it allows estimation of all model parameters and is more flexibl e with regard to sample collection time and design variables.