Evaluation of Patient-Centric Sample Collection Technologies for Pharmacokinetic Assessment of Large and Small Molecules.

Low-volume sampling devices offer the promise of lower discomfort and greater convenience for patients, potentially reducing patient burden and enabling decentralized clinical trials. In this study, we determined whether low-volume sampling devices produce pharmacokinetic (PK) data comparable to conventional venipuncture for a diverse set of monoclonal antibodies (mAbs) and small molecules. We adopted an open-label, non-randomized, parallel-group, single-site study design, with four cohorts of 10 healthy subjects per arm. The study drugs, doses, and routes of administration included: crenezumab (15 mg/kg, intravenous infusion), etrolizumab (210 mg, subcutaneous), GDC-X (oral), and hydroxychloroquine (HCQ, 200 mg, oral). Samples were collected after administration of a single dose of each drug using conventional venipuncture and three low-volume capillary devices: TassoOne Plus for liquid blood, Tasso-M20 for dry blood, both applied to the arm, and Neoteryx Mitra® for dry blood obtained from fingertips. Serum/plasma concentrations from venipuncture and TassoOne Plus samples overlapped and PK parameters were comparable for all drugs, except HCQ. After applying a baseline hematocrit value, the dry blood concentrations and PK parameters for the two monoclonal antibodies were comparable to those obtained from venipuncture. For the two small molecules, two bridging strategies were evaluated for converting dry blood concentrations to equivalent plasma concentrations. A baseline hematocrit correction and/or linear regression-based correction was effective for GDC-X, but not for HCQ. Additionally, the study evaluated the bioanalytical data quality and comparability from the various collection methods, as well as patient preference for the devices.

A Modified Hybrid Wald's Approximation Method for Efficient Covariate Selection in Population Pharmacokinetic Analysis.

One important objective of population pharmacokinetic (PPK) analyses is to identify and quantify relationships between covariates and model parameters such as clearance and volume. To improve upon existing covariate model development methods including stepwise procedures and Wald's approximation method (WAM), this paper introduces an innovative method named the hybrid first-order conditional estimation (FOCE)/Monte-Carlo parametric expectation maximization (MCPEM)-based Wald's approximation method with backward elimination (BE), or H-WAM-BE. Compared with WAM, this new method uses MCPEM to obtain full covariance matrix after running FOCE to obtain full model parameter estimates, followed by BE to select the final covariate model. Two groups of datasets (simulation datasets and rituximab datasets) were used to compare the performance of H-WAM-BE with two other methods, likelihood ratio test (LRT)-based stepwise covariate method (SCM) and H-WAM with full subset approach (H-WAM-F) in NONMEM. Different scenarios with different sample sizes and sampling schemes were used for simulating datasets. The nominal model was used as the reference to evaluate the three methods for their ability to accurately identify parameter-covariate relationships. The methods were compared using the number of true and false positive covariates identified, number of times that they identified the reference model, computation times, and predictive performance. Best-performing H-WAM-BE methods (M2 and M4) showed comparable results with LRT-based SCM. H-WAM-BE required shorter or comparable computation times than LRT-based SCM and H-WAM-F regardless of the model structure, sample size, or sampling design used in this study.

Using medical claims database to develop a population disease progression model for leuprorelin-treated subjects with hormone-sensitive prostate cancer.

Androgen deprivation therapy (ADT) is a widely used treatment for patients with hormone-sensitive prostate cancer (PCa). However, duration of treatment response varies, and most patients eventually experience disease progression despite treatment. Leuprorelin is a luteinizing hormone-releasing hormone (LHRH) agonist, a commonly used form of ADT. Prostate-specific antigen (PSA) is a biomarker for monitoring disease progression and predicting treatment response and survival in PCa. However, time-dependent profile of tumor regression and growth in patients with hormone-sensitive PCa on ADT has never been fully characterized. In this analysis, nationwide medical claims database provided by Humana from 2007 to 2011 was used to construct a population-based disease progression model for patients with hormone-sensitive PCa on leuprorelin. Data were analyzed by nonlinear mixed effects modeling utilizing Monte Carlo Parametric Expectation Maximization (MCPEM) method in NONMEM. Covariate selection was performed using a modified Wald's approximation method with backward elimination (WAM-BE) proposed by our group. 1113 PSA observations from 264 subjects with malignant PCa were used for model development. PSA kinetics were well described by the final covariate model. Model parameters were well estimated, but large between-patient variability was observed. Hemoglobin significantly affected proportion of drug-resistant cells in the original tumor, while baseline PSA and antiandrogen use significantly affected treatment effect on drug-sensitive PCa cells (Ds). Population estimate of Ds was 3.78 x 10-2 day-1. Population estimates of growth rates for drug-sensitive (Gs) and drug-resistant PCa cells (GR) were 1.96 x 10-3 and 6.54 x 10-4 day-1, corresponding to a PSA doubling time of 354 and 1060 days, respectively. Proportion of the original PCa cells inherently resistant to treatment was estimated to be 1.94%. Application of population-based disease progression model to clinical data allowed characterization of tumor resistant patterns and growth/regression rates that enhances our understanding of how PCa responds to ADT.