Biology subjects

Clary, J.

Publications and source records attributed to Clary, J..

3 recordsLinked to original sources

bioRxiv2025

A modeling-based framework to evaluate forgiveness of TB drug combinations in a BALB/c relapsing mouse model.

Tuberculosis (TB) remains a leading cause of death due to an infectious agent. Adherence to long and complex TB treatments is supported by methods including directly observed therapy. The negative impact of missed drug doses on clinical outcomes is well-established, highlighting both the importance of adherence support and methods to quantify the ability of a regimen to continue exerting a biologic effect, during gaps in dosing known as "forgiveness" property. To explore the value of the BALB/c Relapsing Mouse Model of TB in evaluating treatment forgiveness, we assessed the impact of weekend dose holidays on the bactericidal, including RS ratio(R), and sterilizing efficacy of RHZE/RH and BPaMZ in perspective of each drug exposure. The cure/relapse data from this study plus multiple historical studies were used to identify a nonlinear mixed-effects Emax model that was used to estimate time to cure 50% and derive time to cure 90% mice (T90). Expected time-dependent bactericidal activity and reductions in RS ratio were observed for both treatments, with more rapid decreases for the BPaMZ groups. The weekend dosing holiday significantly decreased reductions in lung CFU and RS ratio earlier in RHZE/RH treatment, but no such effect was observed for BPaMZ. Similarly, the predicted T90 was significantly greater for RHZE/RH (but not BPaMZ), with weekend doses omitted. No major drug exposure difference was observed between the 2 dosing schedules. Our results suggest BPaMZ is more forgiving of missed doses than RHZE/RH and suggests utility of this methodology to support evaluation of TB treatment forgiveness.

pharmacology and toxicology↗

bioRxiv2025

A Stochastic Simulation-Based Approach to Inform Relapsing Mouse Model (RMM) Study Design for Non-Clinical Assessment of Tuberculosis

The development of new regimens to treat tuberculosis (TB), the disease caused by Mycobacterium tuberculosis (Mtb), is critical to improving patient outcomes and decreasing global infectious disease mortality. Early evaluation of candidate regimens in non-clinical models of TB, such as the relapsing mouse model (RMM), remains an important step in prioritizing the most efficacious regimens for further clinical evaluation. Although RMM studies may be informative, they are also animal-, labor-, and time-intensive to complete and represent significant investment in time and resources during non-clinical development. Given the strong pipeline of regimens in development, identification of "leaner" RMM studies may have a significant impact on resource utilization, and hence we compared alternative study designs with the goal of identifying study attributes that can be modified to improve resource use, particularly animal use. By simulating relapse outcomes from "virtual" studies (i.e., groups mice treated for selected durations with control and hypothetical anti-TB regimens) followed by model-based analysis of the simulated data, we were able to compare the "true" (input) values with model estimates of time to 95% cure probability (T95) and assess bias and precision of competing designs. Using this approach, we demonstrated that 28% fewer mice could be used in RMM studies while maintaining low bias and a precision for T95 estimation within +/- 1-2 weeks for most regimens. Therefore, it is expected that RMM studies based upon the alternative designs evaluated herein may be employed to promote improved animal stewardship while generating informative data for decision making.

pharmacology and toxicology↗

bioRxiv2021

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

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.

pharmacology and toxicology↗