Prospectively predicting BPaMZ Phase IIb outcomes using a translational preclinical mouse to human platform

Abstract Despite known treatments, tuberculosis (TB) remains the world’s top infectious killer, highlighting the pressing need for new drug regimens. To prioritize the most efficacious drugs for clinical testing, we previously developed a PK-PD translational platform with bacterial dynamics that reliably predicted short-term monotherapy outcomes in Phase IIa trials from preclinical mouse studies. In this study, we extended our platform to include PK-PD models that account for drug-drug interactions in combination regimens and bacterial regrowth in our bacterial dynamics model to predict cure at end of treatment and relapse 6 months post-treatment. The Phase III trial STAND, testing new regimen pretomanid (Pa), moxifloxacin (M), and pyrazinamide (Z) (PaMZ), predicted to shorten treatment duration by 2 months was put on hold after a separate ongoing trial showed adding bedaquiline (B) to the PaMZ regimen (SimpliciTB) suggested superior efficacy. To forecast if the addition of B would indeed benefit the PaMZ regimen, we applied an extended translational platform to both regimens. We predicted currently available short- and long-term clinical data well for drug combinations related to BPaMZ. We predict the addition of B to PaMZ will shorten treatment duration by 2 months and be non-inferior compared to control HRZE, both at the end of treatment for treatment efficacy and 6 months after treatment has ended in relapse prevention. Using BPaMZ as a case study, we have demonstrated our translational platform can predict Phase II and III outcomes prior to actual trials, allowing us to better prioritize the regimens most likely to succeed.