Development of physiologically‐based pharmacokinetic models for standard of care and newer tuberculosis drugs

Abstract

Tuberculosis (TB) remains a global health problem and there is an ongoing effort to develop more effective therapies and new combination regimes that can reduce duration of treatment. The purpose of this study was to demonstrate utility of a physiologically‐based pharmacokinetic modeling approach to predict plasma and lung concentrations of 11 compounds used or under development as TB therapies (bedaquiline [and N‐desmethyl bedaquiline], clofazimine, cycloserine, ethambutol, ethionamide, isoniazid, kanamycin, linezolid, pyrazinamide, rifampicin, and rifapentine). Model accuracy was assessed by comparison of simulated plasma pharmacokinetic parameters with healthy volunteer data for compounds administered alone or in combination. Eighty‐four percent (area under the curve [AUC]) and 91% (maximum concentration [Cmax]) of simulated mean values were within 1.5‐fold of the observed data and the simulated drug‐drug interaction ratios were within 1.5‐fold (AUC) and twofold (Cmax) of the observed data for nine (AUC) and eight (Cmax) of the 10 cases. Following satisfactory recovery of plasma concentrations in healthy volunteers, model accuracy was assessed further (where patients’ with TB data were available) by comparing clinical data with simulated lung concentrations (9 compounds) and simulated lung: plasma concentration ratios (7 compounds). The 5th–95th percentiles for the simulated lung concentration data recovered between 13% (isoniazid and pyrazinamide) and 88% (pyrazinamide) of the observed data points (Am J Respir Crit Care Med, 198, 2018, 1208; Nat Med, 21, 2015, 1223; PLoS Med, 16, 2019, e1002773). The impact of uncertain model parameters, such as the fraction of drug unbound in lung tissue mass (fumass), is discussed. Additionally, the variability associated with the patient lung concentration data, which was sparse and included extensive within‐subject, interlaboratory, and experimental variability (as well interindividual variability) is reviewed. All presented models are transparently documented and are available as open‐source to aid further research.

Predictions of Bedaquiline and Pretomanid Target Attainment in Lung Lesions of Tuberculosis Patients using Translational Minimal Physiologically Based Pharmacokinetic Modeling

“…Previously, standard compartmental models combined with effect compartment structural models or whole-body PBPK modeling combined with mechanistic lung models have been developed for mostly first-line TB antibiotics [ 8 , 9 , 34 ]. Middle-out approaches, such as mPBPK models, allow the balance between empirical compartmental models and rigorous whole-body PBPK models [ 35 ].…”

“…Following drug penetration to the lung tissue, the fraction of drug available to exert the effect depends on the tissue content and drug physicochemical properties, such as lipophilicity, solubility, tissue protein binding, and acidity [ 36 , 37 ]. Therefore, accounting for fraction unbound in tissue for predictions of the PK/PD relationship may be important [ 34 ]. In this work, we did not directly correct site-of-action exposures for protein or tissue binding processes as uncertainty exists in the overall impact of these parameters on the PK/PD relationship for bedaquiline and pretomanid.…”

Physiologically Based Pharmacokinetic Modeling to Determine the Impact of CYP2B6 Genotype on Efavirenz Exposure in Children, Mothers and Breastfeeding Infants

“…The PBPK model was validated previously in healthy adult White subjects 13 . As the nursing mothers from the clinical study 9 were from Nigeria, a South African population 18 was used in the simulations presented here, as it was more likely to reflect the characteristics of the subjects recruited into the clinical study 32 . In addition, a 0.74‐fold and 0.24‐fold reduction of CYP2B6 intrinsic clearance derived from an independent clinical study 28 was applied in simulations of mothers carrying CYP2B6 516GT and 516TT genetic polymorphisms, respectively, accounting for the reduced CYP2B6 activity/abundance in these subjects relative to that of subjects with the CYP2B6 516GG genotype.…”

PBPK modeling of ivermectin—Considerations for the purpose of developing alternative routes to optimize its safety profile

“…The clinical study in patients with onchocerciasis consisted of subjects living in South‐Eastern Nigeria, recruited from a community known to be hyperendemic for onchocerciasis 33 . Thus, a virtual population based on a Black South African population that was published previously was applied 34 . Details of this virtual population include data sources for age distribution, relationships between age, height and weight, and plasma protein levels.…”

“…33 Thus, a virtual population based on a Black South African population that was published previously was applied. 34 Details of this virtual population include data sources for age distribution, relationships between age, height and weight, and plasma protein levels. Simulations in African children were performed using the pediatric population, which includes extensive libraries on pediatric demography (age, height, weight, and body surface area), developmental physiology (liver size, renal function, and liver blood flow), and biochemistry (albumin and CYP ontogeny).…”