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.

A Credibility Assessment Plan for an In Silico Model that Predicts the Dose–Response Relationship of New Tuberculosis Treatments

“…Considering the definitions presented in References 17 , 20 , and 21 where the concept of regulatory impact is related to how regulators weigh the importance of models compared to alternative methods to address the final regulatory question, the regulatory impact of the developed modelling framework for the proposed CoU is low. An iterative and stepwise approach process will be adopted for the qualification advice request to the regulatory agency.…”

“…Specifically for TB, the most commonly used computational approach relies on mechanistic and physiologically based pharmacokinetic/pharmacodynamic (PK/PD) models. 4 , 16 , 20 In Reference 20 , a computational framework that combines a PK-PD model and a multi-objective optimization approach was presented to identify a set of trade-off optimal dosage regimens for pulmonary TB. Boonpeng et al .…”

Profiling Pretomanid as a Therapeutic Option for TB Infection: Evidence to Date

“…Recent modeling and simulation studies using data from these early phase clinical trials (eg, NC-002, NC-005, NC-006, Nix-TB) suggest that the approved pretomanid dosing regimen (200 mg daily with food) provides optimal exposure (eg, %T>MIC) compared with alternate dosing schemes to meet efficacy endpoints even after assuming wide inter-individual variability in drug disposition. 57 , 58 …”

Deterministic Bi-Criteria Model for Solving Stochastic Mixed Vector Variational Inequality Problems

“…In fact, solving the weakly efficient solution of problem ( 4) is equivalent to solving MVVIP (2) (the proof of which can be found in Theorem 1), which indicates that, for a sufficiently large penalty parameter, the solution of MVVIP can be used to approximate the efficient solution of problem (3). Multiobjective optimization problems can be applied to artificial intelligence, engineering, electrical engineering, and medicine [3][4][5][6]. In addition, multiobjective optimization problems also have wide applications in fields such as design optimization, manufacturing, structural health monitoring [7], and chemical engineering [8].…”

“…Due to the existence of many uncertain factors, such as price, output, and sales, in actual production and life, in this paper, we mainly consider MVVIP with uncertain factors, that is, stochastic mixed vector variational inequality problems (SMVVIP): finding x * ∈ D satisfies (y − x * ) T F(x * , ξ(ω)) + g(y, ξ(ω)) − g(x * , ξ(ω)) / ∈ −intR m + , ∀y ∈ D, a.s. ξ(ω) ∈ Ξ, (5) where ξ : Ω → Ξ ⊂ R b is a stochastic vector defined on the probability space (Ω, F , P ) and Ξ is a support set of the probability space. Under the given probability measure, "a.s." stands the abbreviation for "almost surely".…”