AI‐Enabled Parabolic Response Surface Approach Identifies Ultra Short‐Course Near‐Universal TB Drug Regimens

Abstract

Tuberculosis (TB) is a major health problem that causes more deaths worldwide than any other single infectious disease. Current multidrug therapy for tuberculosis is exceedingly lengthy, leading to poor drug adherence, and consequently the emergence of drug resistance. Hence, much more rapid treatments are needed. Experimentally identifying the most synergistic drug combinations among available drugs is complicated by the astronomical number of possible drug-dose combinations. This problem is dealt with by the use of an artificial-intelligence-enabled parabolic response surface platform in conjunction with an in vitro Mycobacterium tuberculosis-infected macrophage cell culture assay amenable to high-throughput screening. This strategy allows rapid identification of the most effective drug-dose combinations by testing only a small fraction of the total drug-dose efficacy response surface. The same platform is then used to optimize the in vivo doses of each drug in the most potent regimens. Thus, regimens are identified that are dramatically more effective than the Standard Regimen in treating TB in a mouse model-a model broadly predictive of drug efficacy in humans. The most effective regimens reported herein shorten the duration of treatment required to achieve relapse-free cure by 80% and are suitable for treating both drug-sensitive and most drug-resistant cases of tuberculosis.

Orthogonal array composite designs for drug combination experiments with applications for tuberculosis

“…To reduce drug resistance and toxicity, various drug combinations are being used. One challenge in identifying an optimal combination of drugs is that the number of possible combinations increases exponentially as the number of drugs increases 3 . In the search to find the best drug‐dose combinations, Silva et al 4 developed a second generation feedback system control (FSC.II) technology that dramatically reduces the number of iterations required for drug‐dose optimization.…”

“…Section 6 illustrates the construction and analysis of an OACD for a TB drug combination study. This study was selected from the various applications of OACDs in the TB literature 2‐4,25 . Additionally, in the context of the TB application, the built‐in ability to perform cross‐validation and the robustness to missing observations from either a factorial or additional point are shown.…”

“…This study was selected from the various applications of OACDs in the TB literature. [2][3][4]25 Additionally, in the context of the TB application, the built-in ability to perform cross-validation and the robustness to missing observations from either a factorial or additional point are shown. The article concludes with some remarks in Section 7.…”