Predictive Modeling to Study the Treatment-Shortening Potential of Novel Tuberculosis Drug Regimens, Toward Bundling of Preclinical Data.

BACKGROUND: Given the persistently high global burden of tuberculosis, effective and shorter treatment options are needed. We explored the relationship between relapse and treatment length as well as interregimen differences for 2 novel antituberculosis drug regimens using a mouse model of tuberculosis infection and mathematical modeling. METHODS: Mycobacterium tuberculosis-infected mice were treated for up to 13 weeks with bedaquiline and pretomanid combined with moxifloxacin and pyrazinamide (BPaMZ) or linezolid (BPaL). Cure rates were evaluated 12 weeks after treatment completion. The standard regimen of isoniazid, rifampicin, pyrazinamide, and ethambutol (HRZE) was evaluated as a comparator. RESULTS: Six weeks of BPaMZ was sufficient to achieve cure in all mice. In contrast, 13 weeks of BPaL and 24 weeks of HRZE did not achieve 100% cure rates. Based on mathematical model predictions, 95% probability of cure was predicted to occur at 1.6, 4.3, and 7.9 months for BPaMZ, BPaL, and HRZE, respectively. CONCLUSION: This study provides additional evidence for the treatment-shortening capacity of BPaMZ over BPaL and HRZE. To optimally use preclinical data for predicting clinical outcomes, and to overcome the limitations that hamper such extrapolation, we advocate bundling of available published preclinical data into mathematical models.

Model-Based Interspecies Scaling for Predicting Human Pharmacokinetics of CB 4332, a Complement Factor I Protein.

Interspecies scaling of the pharmacokinetics (PK) of CB 4332, a 150 kDa recombinant complement factor I protein, was performed using traditional and model-based approaches to inform first-in-human dose selection. Plasma concentration versus time data from four preclinical PK studies of single intravenous and subcutaneous (SC) CB 4332 dosing in mice, rats and nonhuman primates (NHPs) were modeled simultaneously using naive pooling including allometric scaling. The human-equivalent dose was calculated using the preclinical no observed adverse effect level (NOAEL) as part of the dose-by-factor approach. Pharmacokinetic modeling of CB 4332 revealed species-specific differences in the elimination, which was accounted for by including an additional rat-specific clearance. Signs of anti-drug antibodies (ADA) formation in all rats and some NHPs were observed. Consequently, an additional ADA-induced clearance parameter was estimated including the time of onset. The traditional dose-by-factor approach calculated a maximum recommended starting SC dose of 0.9 mg/kg once weekly, which was predicted it to result in a trough steady-state concentration lower than the determined efficacy target range for CB 4332 in humans. Model simulations predicted the efficacy target range to be reached using 5 mg/kg once weekly SC dosing.

Combined quantitative tuberculosis biomarker model for time-to-positivity and colony forming unit to support tuberculosis drug development.

Biomarkers are quantifiable characteristics of biological processes. In Mycobacterium tuberculosis, common biomarkers used in clinical drug development are colony forming unit (CFU) and time-to-positivity (TTP) from sputum samples. This analysis aimed to develop a combined quantitative tuberculosis biomarker model for CFU and TTP biomarkers for assessing drug efficacy in early bactericidal activity studies. Daily CFU and TTP observations in 83 previously patients with uncomplicated pulmonary tuberculosis after 7 days of different rifampicin monotherapy treatments (10-40 mg/kg) from the HIGHRIF1 study were included in this analysis. The combined quantitative tuberculosis biomarker model employed the Multistate Tuberculosis Pharmacometric model linked to a rifampicin pharmacokinetic model in order to determine drug exposure-response relationships on three bacterial sub-states using both the CFU and TTP data simultaneously. CFU was predicted from the MTP model and TTP was predicted through a time-to-event approach from the TTP model, which was linked to the MTP model through the transfer of all bacterial sub-states in the MTP model to a one bacterial TTP model. The non-linear CFU-TTP relationship over time was well predicted by the final model. The combined quantitative tuberculosis biomarker model provides an efficient approach for assessing drug efficacy informed by both CFU and TTP data in early bactericidal activity studies and to describe the relationship between CFU and TTP over time.