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- Title
Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens.
- Authors
Budak, Maral; Cicchese, Joseph M.; Maiello, Pauline; Borish, H. Jacob; White, Alexander G.; Chishti, Harris B.; Tomko, Jaime; Frye, L. James; Fillmore, Daniel; Kracinovsky, Kara; Sakal, Jennifer; Scanga, Charles A.; Lin, Philana Ling; Dartois, Véronique; Linderman, Jennifer J.; Flynn, JoAnne L.; Kirschner, Denise E.
- Abstract
Tuberculosis (TB) continues to be one of the deadliest infectious diseases in the world, causing ~1.5 million deaths every year. The World Health Organization initiated an End TB Strategy that aims to reduce TB-related deaths in 2035 by 95%. Recent research goals have focused on discovering more effective and more patient-friendly antibiotic drug regimens to increase patient compliance and decrease emergence of resistant TB. Moxifloxacin is one promising antibiotic that may improve the current standard regimen by shortening treatment time. Clinical trials and in vivo mouse studies suggest that regimens containing moxifloxacin have better bactericidal activity. However, testing every possible combination regimen with moxifloxacin either in vivo or clinically is not feasible due to experimental and clinical limitations. To identify better regimens more systematically, we simulated pharmacokinetics/pharmacodynamics of various regimens (with and without moxifloxacin) to evaluate efficacies, and then compared our predictions to both clinical trials and nonhuman primate studies performed herein. We used GranSim, our well-established hybrid agent-based model that simulates granuloma formation and antibiotic treatment, for this task. In addition, we established a multiple-objective optimization pipeline using GranSim to discover optimized regimens based on treatment objectives of interest, i.e., minimizing total drug dosage and lowering time needed to sterilize granulomas. Our approach can efficiently test many regimens and successfully identify optimal regimens to inform pre-clinical studies or clinical trials and ultimately accelerate the TB regimen discovery process. Author summary: Tuberculosis (TB) is a top global health concern and the WHO has made END TB a goal for 2050. Treatment for TB requires multiple antibiotics taken for long periods of time, which is challenging for TB patients due to side effects and compliance issues. Therefore, identifying regimens that are more effective and more patient-friendly than the currently used 4-drug standard regimen treatment is urgently needed. It is also known that non-compliance leads to the development of drug resistant TB. In this work, we first apply our next-generation computational model that captures the immune response to infection in lungs with M. tuberculosis via the formation of granulomas to predict new regimens for the treatment of TB. These include regimens that have been recently tried in clinical trials with controversial results. Our goal is to identify regimens that optimize how fast bacteria are cleared using minimal dosages. We then pair our studies with the best experimental system for TB, namely, validating our predictions using a non-human primate model. Our findings suggest new regimens and additionally that systems pharmacological modeling should be employed as a method to narrow the design space for drug regimens for tuberculosis and other diseases as well prior to clinical trials.
- Subjects
WORLD Health Organization; TREATMENT effectiveness; TUBERCULOSIS; PATIENT compliance; COMMUNICABLE diseases; DRUG dosage
- Publication
PLoS Computational Biology, 2023, Vol 19, Issue 6, p1
- ISSN
1553-734X
- Publication type
Article
- DOI
10.1371/journal.pcbi.1010823