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Well-tempered MCMC simulations for population pharmacokinetic models.

ABSTRACT: A full Bayesian statistical treatment of complex pharmacokinetic or pharmacodynamic models, in particular in a population context, gives access to powerful inference, including on model structure. Markov Chain Monte Carlo (MCMC) samplers are typically used to estimate the joint posterior parameter distribution of interest. Among MCMC samplers, the simulated tempering algorithm (TMCMC) has a number of advantages: it can sample from sharp multi-modal posteriors; it provides insight into identifiability issues useful for model simplification; it can be used to compute accurate Bayes factors for model choice; the simulated Markov chains mix quickly and have assured convergence in certain conditions. The main challenge when implementing this approach is to find an adequate scale of auxiliary inverse temperatures (perks) and associated scaling constants. We solved that problem by adaptive stochastic optimization and describe our implementation of TMCMC sampling in the GNU MCSim software. Once a grid of perks is obtained, it is easy to perform posterior-tempered MCMC sampling or likelihood-tempered MCMC (thermodynamic integration, which bridges the joint prior and the posterior parameter distributions, with assured convergence of a single sampling chain). We compare TMCMC to other samplers and demonstrate its efficient sampling of multi-modal posteriors and calculation of Bayes factors in two stylized case-studies and two realistic population pharmacokinetic inference problems, one of them involving a large PBPK model.

SUBMITTER: Bois FY

PROVIDER: S-EPMC8082542 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRitlecitinib is an oral Janus kinase 3/tyrosine kinase expressed in hepatocellular carcinoma family inhibitor undergoing parallel clinical development for alopecia areata, vitiligo, ulcerative colitis, Crohn's disease, and rheumatoid arthritis.ObjectiveAs studies read out simultaneously, strategic planning of population pharmacokinetic model development and evaluation is required to ensure timely decisions.MethodsData from healthy participants and patients from 12 clinical trials between December 2014 and July 2021 were included: seven phase I studies in healthy participants and organ impairment, five phase II/III studies in patients with rheumatoid arthritis, ulcerative colitis, alopecia areata, and vitiligo. Population pharmacokinetic models consisted of stepwise procedures to accommodate data availability and the model's application to answering clinical development questions. At each iteration of the model update, parameters of the next model were re-estimated by leveraging previous information and new data.ResultsThree model development lifecycle iterations of the ritlecitinib population pharmacokinetic model were conducted to support alopecia areata, vitiligo, and ulcerative colitis study readouts. Initial structural modeling based on healthy participant data (and some rheumatoid arthritis and alopecia areata data) in iteration 1 provided a platform for comprehensive covariate testing during iteration 2, and model evaluation and implementation of the frequentist prior approach in iteration 3. The final model was a two-compartment model with first-order absorption and direct-response non-stationary clearance and bioavailability driven by concentrations in the peripheral compartment.ConclusionsThe present approach demonstrated the evolution of three population pharmacokinetic models with accumulating data, addressed clinical drug development questions related to systemic exposures of ritlecitinib, and informed the approved product label.Clinical trial registrationNCT02309827, NCT02684760, NCT02958865, NCT02969044, NCT03232905, NCT03732807, NCT04016077, NCT03715829, NCT04037865, NCT04004663, NCT04634565, NCT02974868.

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Well-tempered MCMC simulations for population pharmacokinetic models.

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