Model-Based Meta-Analysis of Relapsing Mouse Model Studies from the Critical Path to Tuberculosis Drug Regimens Initiative Database

Abstract

Tuberculosis (TB), the disease caused by Mycobacterium tuberculosis ( Mtb ), remains a leading infectious disease-related cause of death worldwide, necessitating the development of new and improved treatment regimens. Non-clinical evaluation of candidate drug combinations via the relapsing mouse model (RMM) is an important step in regimen development, through which candidate regimens that provide the greatest decrease in probability of relapse following treatment in mice may be identified for further development. Although RMM studies are a critical tool to evaluate regimen efficacy, making comprehensive “apples to apples” comparisons of regimen performance in the RMM has been a challenge, in large part due to the need to evaluate and adjust for variability across studies arising from differences in design and execution. To address this knowledge gap, we performed a model-based meta-analysis on data for 17 unique regimens obtained from a total of 1592 mice across 28 RMM studies. Specifically, a mixed-effects logistic regression model was developed that described the treatment duration-dependent probability of relapse for each regimen and identified relevant covariates contributing to inter-study variability. Using the model, covariate-normalized metrics of interest, namely treatment duration required to reach 50% and 10% relapse probability, were derived and used to compare relative regimen performance. Overall, the model-based meta-analysis approach presented herein enables cross-study comparison of efficacy in the RMM, and provides a framework whereby data from emerging studies may be analyzed in the context of historical data to aid in selecting candidate drug combinations for clinical evaluation as TB drug regimens.

Design principles to assemble drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements

“…These binned annotations were consistent with the combination treatment improvement estimated in an interstudy comparison using a mixed-effects logistic regression model approach to normalize the differences in study methodologies. 16 Principal-component (PC) analysis (PCA) revealed partial separation of +C1 and −C1 combinations along the first PC, indicating a strong predictive signal in pairwise data and suggesting that linear combinations of in vitro pairwise drug responses may be sufficient to distinguish drug combinations with different in vivo outcomes, even in the absence of trained supervised learning models. Notably, the signal was robust to the number of drugs involved in a combination, as we observed separation between 3-drug and 4+-drug combinations along the second PC, which was orthogonal to the first ( Figure S2 A).…”

“…BPaL is established as better than the SOC for both decreasing disease relapse and shortening treatment time of mice infected with drug-sensitive M. tuberculosis . 6 , 16 , 17 In addition, the use of BPaL has dramatically shortened the treatment time of MDR-TB in the clinic. 5 We, therefore, chose BPaL as a benchmark for further treatment improvement over the SOC in the RMM, despite not yet knowing the outcome of BPaL over the SOC in clinical trials for drug-sensitive TB (DS TB) treatment.…”

Eradication of Drug-Tolerant Mycobacterium tuberculosis 2022: Where We Stand

“…All the data on drug activity against drug-tolerant Mtb, ranging from the most studied in vitro Wayne dormancy model [ 40 ] to the papers on the sterilization of non caseum- [ 83 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 ] and caseum-forming [ 97 , 102 , 103 ] mice, shed a new light on TB biology [ 129 , 130 ]. These and other preclinical efficacy studies have shown that the greatest difficulties in curing TB do not depend only on intracellular AR bacilli and of their mutants but, above all, on extracellular NR drug-tolerant bacilli living in the caseous tuberculomas [ 4 ], the hallmark of TB.…”

“…Overall, these studies confirmed that the successful treatment of active TB requires the use of drug combinations to eradicate the diverse populations of bacteria present in the infected host [ 129 ]. The proportion of non caseum- and caseum-forming mice not exhibiting relapses following different treatments appeared to be useful in selecting candidate drug combinations for clinical evaluation as TB drug regimens [ 130 ].…”

Spectinamide MBX-4888A exhibits favorable lesion and tissue distribution and promotes treatment shortening in advanced murine models of tuberculosis

“…One strategy successfully used to identify effective partner agents capable of contributing to treatment shortening, is to evaluate bactericidal and sterilizing activity of drug combinations in both the presence and absence of the new partner agent (for examples see, (10)(11)(12)(13)). Regimens can then be rank ordered based on performance by one or both treatment outcomes as quantitative measures of the contribution of the new partner agent (14).…”