Pretomanid dose selection for pulmonary tuberculosis: An application of multi-objective optimization to dosage regimen design

Abstract

Clinical development of combination chemotherapies for tuberculosis (TB) is complicated by partial or restricted phase II dose-finding. Barriers include a propensity for drug resistance with monotherapy, practical limits on numbers of treatment arms for component dose combinations, and limited application of current dose selection methods to multidrug regimens. A multi-objective optimization approach to dose selection was developed as a conceptual and computational framework for currently evolving approaches to clinical testing of novel TB regimens. Pharmacokinetic-pharmacodynamic (PK-PD) modeling was combined with an evolutionary algorithm to identify dosage regimens that yield optimal trade-offs between multiple conflicting therapeutic objectives. The phase IIa studies for pretomanid, a newly approved nitroimidazole for specific cases of highly drug-resistant pulmonary TB, were used to demonstrate the approach with Pareto optimized dosing that best minimized sputum bacillary load and the probability of drug-related adverse events. Results include a population-typical characterization of the recommended 200 mg once daily dosage, the optimality of time-dependent dosing, examples of individualized therapy, and the determination of optimal loading doses. The approach generalizes conventional PK-PD target attainment to a design problem that scales to drug combinations, and provides a benefit-risk context for clinical testing of complex drug regimens.

Figure 1

Once‐daily dosing to maximize early bactericidal activity (EBA) colony forming unit (CFU) and minimize probability of a drug‐related adverse event (Pr(AE)). Pareto fronts and cluster exemplars (a), and benefit‐risk profiles (b), for population‐typical, male‐typical, and female‐typical dose‐response. Monte‐Carlo simulations of end‐of‐treatment outcomes for EBA(CFU) (c) and Pr(AE) (d) for population‐typical Pareto front cluster exemplars. Plots show median (points) and 90th percentile range (error bars) with simulation sample size equal to 10,000. Plasma concentration‐time profiles and day‐14 outcomes for EBA(CFU) and Pr(AE) with once‐daily dosing for the first 3 participants in the CL‐007 200 mg dose group (e). Each row shows model simulations for the administered (Adm) 200 mg doses and optimized (Opt) doses of 230, 100, and 380 mg for participants ID1, ID2, and ID3; respectively

Figure 2

Bactericidal effect. Cluster exemplars and dosage parameter sampling space (a), Pareto front (b), and benefit‐risk curve (c), for variable dose and frequency of administration to maximize early bactericidal activity (EBA) colony forming unit (CFU) and minimize probability of a drug‐related adverse event (Pr(AE))

Figure 3

Loading dose. Cluster exemplars and dosage parameter sampling space (a), Pareto front (b), and benefit‐risk curve (c) for two variable doses and day of dose change to maximize early bactericidal activity (EBA) colony forming unit (CFU) and minimize probability of a drug‐related adverse event (Pr(AE))