Quantitative assessment of the activity of antituberculosis drugs and regimens

ABSTRACT

Introduction: Identification of optimal drug doses and drug combinations is crucial for optimized treatment of tuberculosis.

Areas covered: An unprecedented level of research activity involving multiple approaches is seeking to improve tuberculosis treatment. This report is a review of the quantitative methods currently used on clinical data sets to identify drug exposure targets and optimal drug combinations for tuberculosis treatment. A high-level summary of the methods, including the strengths and weaknesses of each method and potential methodological improvements is presented. Methods incorporating data generated from multiple sources such as in vitro and clinical studies, and their potential to provide better estimates of pharmacokinetic/pharmacodynamic (PK/PD) targets, are discussed. PK/PD relationships identified are compared between different studies and data analysis methods.

Expert opinion: The relationships between drug exposures and tuberculosis treatment outcomes are complex and require analytical methods capable of handling the multidimensional nature of the relationships. The choice of a method is guided by its complexity, interpretability of results, and type of data available.

KEYWORDS:

• Tuberculosis
• pharmacokinetic
• pharmacodynamic
• machine learning
• classification and regression trees
• random forests
• multivariate adaptive regression splines

Article highlights

• Pharmacokinetic differences within and between study populations together with lack of sensitive markers of tuberculosis treatment response pose a challenge to the optimization of drugs and regimens.

• Classical linear statistical models rely heavily on statistical assumptions underlying the distribution of the data and often perform poorly to describe the complex relationships between antituberculosis drug exposures and treatment outcomes.

• We describe machine learning and PK/PD modeling techniques currently used to identify drug exposure targets and optimal drug combinations for tuberculosis treatment.

• Drug exposure targets derived using machine learning techniques are sensitive to the training dataset but can be improved by applying ensemble methods.

• PK/PD modeling approach is a useful computational tool for tuberculosis treatment to identify new combination therapies, and to optimize existing drugs and regimens.

Declaration of interest

T Gumbo founded Praedicare LLC. The other authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

MT Chirehwa received support from the National Institute of Allergy and Infectious Diseases of the U.S National Institutes of Health (NIH), Bethesda, MD (R01 AI116155). GE Velásquez received support from the U.S. National Institutes of Health (National Institute of Allergy and Infectious Diseases grants K08 AI141740, L30 AI120170, P30 AI060354, and UM1 AI068636), the Ronda Stryker and William Johnston Fellowship in Global Health and Social Medicine and the Dr. Lynne Reid/Drs. Eleanor and Miles Shore Fellowship at Harvard Medical School, the Burke Global Health Fellowship at the Harvard Global Health Institute, the AIDS Clinical Trials Group Minority HIV Investigator Mentoring Program, and the Harvard University Center for AIDS Research (CFAR). T Gumbo is funded by the NIH/NIAID (R01 AI116155). H McIlleron is funded by the Wellcome Trust (206379/Z/17/Z).