Predictive values of hCG clearance for risk of methotrexate resistance in low-risk gestational trophoblastic neoplasias.

BACKGROUND: Early identification of patients at high risk for chemoresistance among those treated with methotrexate (MTX) for low-risk gestational trophoblastic neoplasia (GTN) is needed. We modeled human chorionic gonadotropin (hCG) decline during MTX therapy using a kinetic population approach to calculate individual hCG clearance (CL(hCG)) and assessed the predictive value of CL(hCG) for MTX resistance. PATIENTS AND METHODS: A total of 154 patients with low-risk GTN treated with 8-day MTX regimen were retrospectively studied. NONMEM was used to model hCG decrease equations between day 0 and day 40 of chemotherapy. Receiver operating characteristic curve analysis defined the best CL(hCG) threshold. Univariate/multivariate survival analyses determined the predictive value of CL(hCG) and compared it with published predictive factors. RESULTS: A monoexponential equation best modeled hCG decrease: hCG(t) = 3900 x e(-0.149 x t). Median CL(hCG) was 0.57 l/day (quartiles: 0.37-0.74). Only choriocarcinoma pathology [yes versus no: hazard ratio (HR) = 6.01; 95% confidence interval (CI) 2.2-16.6; P < 0.001] and unfavorable CL(hCG) quartile (< or =0.37 versus >0.37 l/day: HR = 6.75; 95% CI 2.7-16.8; P < 0.001) were significant independent predictive factors of MTX resistance risk. CONCLUSION: In the second largest cohort of low-risk GTN patients reported to date, choriocarcinoma pathology and CL(hCG) < or =0.37 l/day were major independent predictive factors for MTX resistance risk.

Modeling interindividual variability in physiologically based pharmacokinetics and its link to mechanistic covariate modeling.

Covariate modeling is a key step in the analysis of clinical data and is essential for establishing dosing recommendations for specific populations, e.g., in obese individuals and children. So far, no systematic approach exists to leverage the knowledge inherent in physiologically based pharmacokinetic (PBPK) models in this context. We introduce (i) a novel approach to model interindividual variability in PBPK models based on lean body weight (LBW); and (ii) a systematic approach to translate interindividual variability into the design of mechanistic covariate models. We derive a new covariate relation for the volume of distribution at steady state (Vss) that seamlessly integrates body weight and LBW as covariates, with a weighting factor depending on the physicochemical properties of the drug. We further show that for children, PBPK-based extrapolation and allometric scaling result in very similar predictions for Vss and blood clearance.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e4; doi:10.1038/psp.2012.3; advance online publication 26 September 2012.