Transformation Morphisms and Time-to-Extinction Analysis That Map Therapy Duration From Preclinical Models to Patients With Tuberculosis: Translating From Apples to Oranges.

Abstract

A major challenge in medicine is translation of preclinical model findings to humans, especially therapy duration. One major example is recent shorter-duration therapy regimen failures in tuberculosis. We used set theory mapping to develop a computational/modeling framework to map the time it takes to extinguish the Mycobacterium tuberculosis population on chemotherapy from multiple hollow fiber system model of tuberculosis (HFS-TB) experiments to that observed in patients. The predictive accuracy of the derived translation transformations was then tested using data from 108 HFS-TB Rapid Evaluation of Moxifloxacin in Tuberculosis (REMoxTB) units, including 756 colony-forming units (CFU)/mL. Derived transformations, and Latin hypercube sampling-guided simulations were used to predict cure and relapse after 4 and 6 months of therapy. Outcomes were compared to observations, in 1932 patients in the REMoxTB clinical trial. HFS-TB serial bacillary burden and serial sputum data in the derivation dataset formed a structure-preserving map. Bactericidal effect was mapped with a single step transformation, while the sterilizing effect was mapped with a 3-step transformation function. Using the HFS-TB REMoxTB data, we accurately predicted the proportion of patients cured in the 4-month REMoxTB clinical trial. Model-predicted vs clinical trial observations were (i) the ethambutol arm (77.0% [95% confidence interval {CI}, 74.4%-79.6%] vs 77.7% [95% CI, 74.3%-80.9%]) and (ii) the isoniazid arm (76.4% [95% CI, 73.9%-79.0%] vs 79.5% [95% CI, 76.1%-82.5%]). We developed a method to translate duration of therapy outcomes from preclinical models to tuberculosis patients.