A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis

Abstract

Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework. Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b. Results: The model has been successfully tested and validated against 11 anti-TB compounds-rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices. Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.

Keywords: antituberculosis agents; mPBPK; minimal PBPK model; model informed drug development; modeling; simulation; tuberculosis.

FIGURE 1

A visual representation of the minimal PBPK model. The model consists of nine compartments, eight of which describing: arterial and venous blood, gut, splenic, liver, lung, kidney, and the lumped compartment “other”. In addition, there is a compartment to account for the oral dose disposition. Black lines represent exchange between the compartments. Grey lines represent the first-order clearance.

FIGURE 2

(A) Visual predictive check of the mPBPK model for six drugs in plasma and lung. The figure shows the performance in training and validation for the mPBPK model in plasma and lung. All figures show the median of the simulated VP (solid line) and the five and ninety-five percentiles (shaded area); dots represent the experimental data at each time point (or their mean if multiple measurements were available). Red refers to the training sets in plasma, blue to the training sets in lungs, and grey are the validation sets. INH—isoniazid (training 25 mg/kg, validation 5 mg/kg); RPT—rifapentine (training 15 mg/kg, validation 20 mg/kg); PZA—pyrazinamide (training 150 mg/kg, validation 150 mg/kg); MOX—moxifloxacin (training 100 mg/kg, validation 100 mg/kg); BDQ—bedaquiline (training 25 mg/kg, validation 25 mg/kg); PRE—pretomanid (training 25 mg/kg, validation 100 mg/kg). (B) Correlation plots between observed and best-fit predicted AUCs and Cmax, color-coded for drugs and shape-coded for compartments. Solid lines are the theoretical perfect agreement reference lines (bisector), while the dashed lines mark the 1.5- and two-fold from reference. Observed AUCs are computed via the trapezoidal rule.

FIGURE 3

Sensitivity analysis indexes color-coded by relevant PK metrics with row-aligned jittered dots representing the 11 drugs. Drug labels are omitted to provide a unified analysis and improve the visualization.

FIGURE 4

Number of compounds and percentage of time above the different efficacy targets in plasma and lungs for the regimens HRZE, BPaMZ, and HPMZ. MIC50 stands for the minimum inhibitory concentration to reduce bacterial growth by 50%, while MBC90, MacroIC90, and WCC90 refer to the minimum bactericidal concentration to kill 90% viable bacteria under aerobic, nutrient-rich conditions, within the macrophage, and under hypoxic, nutrient-rich condition, respectively.