Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis

Abstract

Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.

Keywords: antituberculosis agents; drug development; modeling; pharmacokinetics-pharmacodynamics; simulation; translational science; tuberculosis.

Figure 1

Examples of empirical and mechanistic models. (a) Empirical models use a top-down approach to link predictive variables to outcomes. Clemens et al. (64) use in vivo dose-response data and a parabolic response surface to empirically predict effective drug-dose combinations. (b) Mechanistic models use a bottom-up or middle-out approach to recapitulate biological processes. Pienaar et al. (80) simulate host-drug-bacteria dynamics at a molecular level to compare efficacies of fluoroquinolones. Figure adapted with permission from References and .

Figure 2

Pathway to translating nonclinical to clinical outcomes for TB. Different pathways exist to translate preclinical to clinical outcomes. Two methods reviewed here include a PK approach and a systems approach. The PK approach can be used to predict equivalent drug exposure between nonclinical models and humans. Here, the assumption is that the outcomes will be comparable between species if drug exposure is matched. However, with the complexity of TB disease, a more systems-like approach may be necessary, as species differences in disease pathology do not capture the full spectrum of outcomes in patients. This approach therefore incorporates host-specific differences and models host-bacteria-drug dynamics at the site of action to make more accurate predictions in patients. Abbreviations: MIC, minimum inhibitory concentration; PK, pharmacokinetics; PD, pharmacodynamics; TB, tuberculosis.

Figure 3

Modeling tools important for a translational platform. Each modeling tool listed is an essential component for predicting clinical outcomes. (①) Plasma pharmacokinetics (PK) studies are a cornerstone to translation. Efficacy, safety, and tissue distribution parameters can be linked to plasma concentration, and plasma PK can inform first-in-human dosing. (②) Lesion PK studies can determine whether a new drug is likely to reach the site of action, including heterogeneous lesions. Mechanistic PK models can simulate predicted levels in patient lesions. (③) Comparing and modeling responses in immune-competent and immune-compromised animal models can predict anticipated differences in host immune response and natural disease progression. (④, ⑤) Monotherapy and combination pharmacokinetics-pharmacodynamics (PK-PD) models link drug concentration to drug effect and are a cornerstone of defining optimal drug combinations. (⑥) Resistance models incorporate mechanisms related to the emergence of resistance to therapy over time. (⑦) Biomarkers of disease progression and treatment response in different species should be aligned using statistical models to link nonclinical and clinical readouts that determine outcome.