Bayesian inference for integrated pharmacokinetic modelling of mitragynine and 7-hydroxymitragynine.

Abstract

Background and purpose: Mitragynine is an active compound in kratom that is metabolized to the pharmacologically active 7-hydroxymitragynine, requiring an integrated pharmacokinetic approach to maintain plasma concentrations of both within the optimal range. This study aims to develop an integrated pharmacokinetic model of mitragynine and 7-hydroxymitragynine using Bayesian inference.

Experimental approach: A secondary dataset of mitragynine and 7-hydroxymitragynine in healthy human plasma was extracted and used to construct a two-compartment pharmacokinetic model upon oral administration. Initial parameter estimation was performed using a deterministic model fit to determine prior parameters. Bayesian inference was performed using Hamiltonian Monte Carlo across four independent chains, each with 2,000 iterations.

Key results: The prior distribution estimation indicated that the Markov Chain Monte Carlo chain had converged and attained stationarity, yielding many independent effective samples. In general, no correlation between pharmacokinetic parameters was found due to modelling errors. The posterior predictive check plot confirmed a good fit between the model and the data. Pharmacokinetic simulations of repeated administration have been successfully developed and used to predict essential parameters in repeated administration, such as accumulation factors, maximum plasma concentration, time to maximum concentration, minimum plasma concentration, and area under the curve.

Conclusion: The pharmacokinetics of mitragynine and 7-hydroxymitragynine were successfully modelled simultaneously with two compartments and proportional residuals using Bayesian inference with high accuracy.