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.