Artificial intelligence–derived 3-Way Concentration-dependent Antagonism of Gatifloxacin, Pyrazinamide, and Rifampicin During Treatment of Pulmonary Tuberculosis. (28th November 2018)

• Record Type: Journal Article
• Authors: Pasipanodya, Jotam G; Smythe, Wynand; Merle, Corinne S; Olliaro, Piero L; Deshpande, Devyani; Magombedze, Gesham; McIlleron, Helen; Gumbo, Tawanda
• Is Part Of: Clinical infectious diseases. Volume 67(2018)Supplement 3
• Page Start: S284
• Page End: S292
• Publication Date: 2018-11-28
• Subjects: pharmacodynamic interactions -- artificial intelligence -- multivariate adaptive regression splines (MARS) -- stochastic gradient boosting -- hollow fiber system model; Communicable diseases -- Periodicals; 616.905
• DOI: 10.1093/cid/ciy610
• Languages: English
• ISSNs: 1058-4838

Abstract

Abstract: Background: In the experimental arm of the OFLOTUB trial, gatifloxacin replaced ethambutol in the standard 4-month regimen for drug-susceptible pulmonary tuberculosis. The study included a nested pharmacokinetic (PK) study. We sought to determine if PK variability played a role in patient outcomes. Methods: Patients recruited in the trial were followed for 24 months, and relapse ascertained using spoligotyping. Blood was drawn for drug concentrations on 2 separate days during the first 2 months of therapy, and compartmental PK analyses was performed. Failure to attain sustained sputum culture conversion at the end of treatment, relapse, or death during follow-up defined therapy failure. In addition to standard statistical analyses, we utilized an ensemble of machine-learning methods to identify patterns and predictors of therapy failure from among 27 clinical and laboratory features. Results: Of 126 patients, 95 (75%) had favorable outcomes and 19 (15%) failed therapy, relapsed, or died. Pyrazinamide and rifampicin peak concentrations and area under the concentration-time curves (AUCs) were ranked higher (more important) than gatifloxacin AUCs. The distribution of individual drug concentrations and their ranking varied significantly between South African and West African trial sites; however, drug concentrations still accounted for 31% and 75% of variance of outcomes, respectively. We identified a 3-way antagonistic interaction of pyrazinamide, gatifloxacin, and rifampicin concentrations. These negative interactions disappeared if rifampicin peak concentration was above 7 mg/L. Conclusions: Concentration-dependent antagonism contributed to death, relapse, and therapy failure but was abrogated by high rifampicin concentrations. Therefore, increasing both rifampin and gatifloxacin doses could improve outcomes. Clinical Trials Registration: NCT002216385.