CLINICAL PHAI~ViACOLOGY & THERAPEUTICS VOLUME 73, NUMBER. 2 American Society for Clinical Pharmacology and Therapeutics P 5 1

PII-82 FIRST-ORDER VERSUS MICHAELIS-MENTEN PHARMA-

COKINETIC MODEL SELECTION USING A MIXTURE MODEL. S. P. Riley, PharmD, T. M. Lndden, PhD, GloboMax, LLC, Hanover, MD.

Population pharmacokinetic (PK) modeling involves fitting a model of specific compartmental form to data from many individuals to estimate PK parameters and their variability. The purpose of this study was to evaluate the utility of using a mixture model to permit data from different individuals to be described by different models. Simulated data sets (500 replications) containing various combina- tions of t-compartment (1CM) [clearance (CL) = 0.693, volume of distribution (Vc) = 1.0] and Michaelis-Menten (MM) [Vmax = 0.59, Km 0.35, Vc = 1.0] data were fitted (NONMEM V, First-Order Conditional Estimation with Interaction) with ICM and MM models, and a mixture (MIX) model that allowed for a choice between the two models. The ICM and MIX models provided accurate estimates of CL and Vc (82.2% to 113% of true values) in all cases where 1CM data were present in the dataset. When the data were analyzed using the MM model, Vma ~ and K m estimates became upwardly biased (12.9% to 603% relative prediction error (RPE)) and less precise (13.7% to 600% root mean squared error (RMSE)) as the 1CM fraction of the data increased from 20% to 80%. Parameter estimates from the MIX model remained comparatively unbiased (-2.19% to -0.730% RPE) and precise (2.72% to 7.46% RMSE). Individual model selection was correct for >96% of the individuals in the simulated datasets. Using a mixtnre model improved the accuracy and precision of pharmacokiuetic parameter estimates in the presence of a mixture of 1CM and MM data.

PII-84 USE OF THE MONTE-CARLO PARAMETRIC EXPECTA-

TION MAXIMIZATION (MC-PEM) ESTIMATION METHOD WITH IMPORTANT SAMPLING FOR VERY SPARSE DATA SETTINGS. S. Guz,/, PhD, B. Bauer, PhD, XOMA, Berkeley, CA.

We developed a new system for fitting procedures in population PK/PD. It is based on the exact expectation-maximization (EM) methodology with the incorporation of important sampling technique to more efficiently estimate the population parameters of interest.

The new method was tested on numerous simulated data sets characterized by different rich (10 sampling times per patient) vs sparse (1 sampling time per patient) ratios. Vague priors were used as initial estimates in order to assess robustness of the method. One, two and three compartment model as well as combined PK/PD models were used in the simulations. Using resampting methods (replicated datasets and bootstrap techniques), we showed the MC-PEM had no bias, was precise and extremely robust.These properties make MC- PEM very appealing as most of the common numerical instabilities were removed by using random generators rather than complex mathematical algorithms. NONMEM FOCE and FOCE with interac- tion was used on the same datasets but the data were so sparse that FOCE could not converge well at all. Only models running under FO terminated successfully.

In conclusion, we have demonstrated that with proper software implementation one can now analyze complex (with respect to both the model type and the sampling design) PK/PD modets reliably, accurately, and quickly, requiring only vague initial parameter input by the user and almost no human intervention during the fitting process.

PII-83 SIMULTANEOUS PHARMACOKINETIC-PHARMACODYN-

AMIC MODELING AND PHASE 2 CLINICAL TRIAL SIMULA- TION OF A REVERSIBLE PROTON PUMP INHIBITOR. K. Bae, MD, PhD, H. Lim, MD, K. Hong, MD, J. Chung, MD, S. Yi, MS, J. Cho, PhD, I. Jang, MD PhD, W. Hong, MD PhD, S. Shin, MD PhD, ASAN Medical Center, Seoul National University Hospital, Seou/National University College of Medicine, Seoul National Uni- versity College of Medicine and Hospital (SNUH), Seoul, Korea.

Simultaneous population pharmacokinetic-pharmacodynamic(PK- PD) modeling and phase 2 clinical trial simulation were conducted for a novel reversible proton pump inhibitor, YH-1885. lntragastric pH data with drug concentrations from phase 1 trial and reported relationship of intragastric pH to therapeutic success rate were used for the modeling and simulation.

Pharmacokinetic data were from 52 subjects of 5 different dose groups. Intragastric pH data for 48 - 72 hours were obtained in 46 of them. The simulated phase 2 clinical trial was designed to take drug before breakfast. NONMEM ® V 1.1 was used for the simulta- neous PK-PD modeling and Pharsight's Trial simulator ® 2.12 for phase 2 trial simulation.

Final PK model was 2-compartment model with absorption lag. Chosen PD model was inhibitory Emax model combined with a logiMike baseline physiology model. There was no need of link models like effect-compartment model or indirect response model. Phase 2 trial simulation showed that 200mg or 300rag of YH-1885 once daily before breakfast was expected to be sufficient in main- taining the intragastric pH above 3 for 19.7 -+ 4.4 or 21.3 +- 3.7 hours a day for each dose. This time is approximately corresponds to the therapeutic rate of 94.9 +- 3.1% and 97.0 +- 2.4 % for each dose, which is equivalent or superior to the current anti-ulcer treatments. In conclusion, this study showed increased availability of modeling and simulation in new drug development process for the support of decision.

PII-85 NEURAL NETWORKS PATTERN RECOGNITION TO PRE-

DICT ABCIX1MAB PHARMACODYNAMICS USING READILY AVAILABLE PATIENT CHARACTERISTICS. D. Abernethy, MD, PhD, M. Urquidi-MacDonald, PhD, M. Mascelli, PhD, B. Frederick, BS, J. Freedman, MD, D. Fitzgerald, MD, N. Kleiman, MD, National Institute on Aging, Pennsylvania State University, Centocor, Boston University School of Medicine, Royal College of Surgeons in Ireland, Baylor College of Medicine, Baltimore, MD.

Purpose: To explore the use of a Neural Network (NN) to predict abciximab (Ab)-induced inhibition of platelet aggregation from Ab dose and patient clinical history (CH).

Methods: A N N was designed to characterize the relationship of Ab dose, CH, and pharmacodynamics (PD) (inhibition of 201xM ADP-iuduced platelet aggregation). NN was trained in 8 patients undergoing angioptasty and 30 healthy subjects. Validity of NN was assessed by comparison of NN prediction to actual dose in 39 patients not previously seen by the NN and comparison of predicted Ab dose for desired PD to mean dose calculated using usual PK/PD for the same individuals.

Results: Ab dose predicted by NN for the clinical setting was 16.9 mg vs 18.9+-2 mg using usual PK/PD. Different CH (actual and hypothetical) were then modeled with the trained NN to compare NN prediction and PKIPD prediction. NN predictions were highly reli- able for using only CH components to link dose and PD.

Conclusions: In the case of Ab, NN methods using only CH may be useful for dose determination, For narrow therapeutic index drugs for which concentration and PD measures are not easily available for dose prediction, NN may be useful.