Building optimal 3-drug combination chemotherapy regimens

Objectives: Multi-drug therapy is often required. Examples include antiviral therapy, nosocomial infections, and, most commonly, anti-Mycobacterium tuberculosis (MTB) therapy. Our laboratory previously identified a mathematical approach to identify 2-drug regimens with synergistic or additive interaction using a full factorial study design. Our objective here was to generate a method to identify optimal 3-drug therapy. Methods: We studied MTB isolate H37Rv in Log-phase growth in flasks. Pretomanid and moxifloxacin were chosen as the base 2-drug regimen. Bedaquiline (plus M2 metabolite) was chosen as the third drug for evaluation. Total bacterial burden and bacterial burden less-susceptible to study drugs were enumerated. A large mathematical model was fit to all the data. This allowed extension to evaluation of the 3-drug regimen employing Monte Carlo simulation. Results: Pretomanid plus Moxifloxacin demonstrated excellent bacterial kill and suppressed amplification of less-susceptible pathogens. Total bacterial burden was driven to extinction in 3 weeks in 6 of 9 combination therapy evaluations. Only the lowest pretomanid/moxifloxacin exposures in combination did not extinguish the bacterial burden. No combination regimen allowed resistance amplification. Generation of 95% credible intervals about estimates of the interaction parameters α (αs, αr-p, αr-m) by bootstrapping showed the interaction was additive. Adding bedaquiline/M2 metabolite was evaluated by forming a 95% confidence interval about the decline in bacterial burden. Adding bedaquiline/M2 metabolite shortened time to eradication by 1 week and was significantly different. Conclusions: A model-based system approach to evaluating combinations of 3 agents shows promise to rapidly identify the most promising combinations that can then be trialed.