Duration of pretomanid/moxifloxacin/pyrazinamide therapy compared with standard therapy based on time-to-extinction mathematics

Abstract

Objectives Animal models have suggested that the combination of pretomanid with pyrazinamide and moxifloxacin (PaMZ) may shorten TB therapy duration to 3–4 months. Here, we tested that in the hollow-fibre system model of TB (HFS-TB). Methods A series of HFS-TB experiments were performed to compare the kill rates of the PaMZ regimen with the standard three-drug combination therapy. HFS-TB experiments were performed with bacilli in log-phase growth treated for 28 days, intracellular bacilli treated daily for 28 days and semi-dormant Mycobacterium tuberculosis treated with daily therapy for 56 days for sterilizing effect. Next, time-to-extinction equations were employed, followed by morphism transformation and Latin hypercube sampling, to determine the proportion of patients who achieved a time to extinction of 3, 4 or 6 months with each regimen. Results Using linear regression, the HFS-TB sterilizing effect rates of the PaMZ regimen versus the standard-therapy regimen during the 56 days were 0.18 (95% credible interval=0.13–0.23) versus 0.15 (95% credible interval=0.08–0.21) log10 cfu/mL/day, compared with 0.16 (95% credible interval=0.13–0.18) versus 0.11 (95% credible interval=0.09–0.13) log10 cfu/mL/day in the Phase II clinical trial, respectively. Using time-to-extinction and Latin hypercube sampling modelling, the expected percentages of patients in which the PaMZ regimen would achieve sterilization were 40.37% (95% credible interval=39.1–41.34) and 72.30% (95% credible interval=71.41–73.17) at 3 and 4 months duration of therapy, respectively, versus 93.67% (95% credible interval=93.18–94.13) at 6 months for standard therapy. Conclusions The kill rates of the PaMZ regimen were predicted to be insufficient to achieve cure in less than 6 months in most patients.

Novel Short-Course Therapy and Morphism Mapping for Clinical Pulmonary Mycobacterium kansasii

“…Given this mathematical property, we could use morphisms to calculate a multistep transformation function to map the time to extinction from the HFS-TB to patients. In TB, this approach correctly predicted cure rates versus duration of therapy in several clinical trials of novel TB regimens ( 11 , 16 ). Here, we used the same ordinary differential equations and category theory with serial sputum M. kansasii TTP in a cohort of Taiwanese patients with pulmonary disease on treatment with IDSA recommended therapy and from HFS of pulmonary M. kansasii (HFS- Mkn ) on standard therapy ( 17 , 18 ).…”

Improving the Drug Development Pipeline for Mycobacteria: Modelling Antibiotic Exposure in the Hollow Fibre Infection Model

“…The HFIM’s strength is in testing the effects of combination therapy as it can mimic human PK profiles of different drugs accurately. HFIM experiments on the PaMZ drug regimen in the STAND trial [ 8 ] revealed that the time-to-sterilisation for the above was identical to that of standard therapy, thereby indicating that the regimen being investigated would likely not shorten treatment duration [ 64 ]. HFIM experiments in combination with modelling tools can be used to predict the duration of treatment required for a particular combination therapy.…”

“…HFIM experiments in combination with modelling tools can be used to predict the duration of treatment required for a particular combination therapy. Srivastava et al hypothesise that to shorten treatment time to 2 months, the trajectory of bacterial sterilisation would need to be three times steeper than that of standard therapy [ 64 ]. This could serve as a guide for future trials investigating the potential of drug combinations in reduction in treatment time.…”

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

“…A mechanism-based PK-PD model by Hollow-fiber systems for TB has the advantage of being able to mimic dynamic PK in comparison to more traditional static time-kill experiments. A semi-mechanistic PK-PD model can be derived using HTS data [117][118][119]. Khan et al describes susceptible, resting, and non-colony-forming bacterial populations [120].…”