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

Abstract

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.

Improving pharmacometrics analysis efficiency using DataCheQC: An interactive, Shiny‐based app for quality control of pharmacometrics datasets

“…As has been shown previously, a highly effective solution for improving the pharmacometrics workflow is to use programmatic tools 6 and interactive apps, such as those based on the Shiny R package. [7][8][9][10][11] The Shiny framework enables creation of web apps that are simple to use and customize, incorporating both frontend (visual design and interactivity) and back-end (calculations and database manipulation) capabilities. These apps enable the user to take advantage of the robust library of packages for data science and statistical analyses offered by the R language.…”

“…One way to ameliorate the QC process is to reduce the busywork required to check each new dataset by creating a system that is both interactive and data‐agnostic (i.e., can work with many different datasets without requiring additional configuration). As has been shown previously, a highly effective solution for improving the pharmacometrics workflow is to use programmatic tools 6 and interactive apps, such as those based on the Shiny R package 7–11 . The Shiny framework enables creation of web apps that are simple to use and customize, incorporating both front‐end (visual design and interactivity) and back‐end (calculations and database manipulation) capabilities.…”

Standards for model-based early bactericidal activity analysis and sample size determination in tuberculosis drug development

“…EBA is quantified by measurement of the viable mycobacterial load in overnight-collected sputum samples over time using colony forming units (CFU) of Mtb (measured in log10 CFU/mL sputum) and/or time-to-positivity (TTP) in liquid culture (measured in hours). The correlation between changes in TTP and CFU varies over time, which is due to that they likely reflect slightly different processes ( Diacon et al, 2012 ; Bowness et al, 2015 ; Ayoun Alsoud et al, 2022 ). CFU measures the quantity of viable mycobacteria regardless of the speed of growth, while TTP measures the consumption of critical ingredients in a closed liquid culture system, which is influenced by both the quantity and metabolic activity of the growing mycobacteria ( Alffenaar et al, 2022 ).…”

Power to identify exposure‐response relationships in phase IIa pulmonary tuberculosis trials with multi‐dimensional bacterial load modeling

“…10 A study on the combined analysis of CFU and TTP, using a semimechanistic pharmacometric model, showed that both metrics can be combined to evaluate the exposureresponse relationship in a phase IIa trial. 11 However, it is not known which culture method, or a combination of both methods, may provide the model with more power to identify an exposure-response relationship. Furthermore, we hypothesized that participants in extreme dose groups contribute more information on the exposure-response relationship compared to participants in middle dose groups, hence an uneven distribution of participants might improve the power to identify an exposure-response relationship over a balanced distribution.…”

“…Model‐based analysis of phase IIa clinical trial data has shown a significant increase in power to identify the activity of a drug compared to traditional statistical analyses 10 . A study on the combined analysis of CFU and TTP, using a semimechanistic pharmacometric model, showed that both metrics can be combined to evaluate the exposure‐response relationship in a phase IIa trial 11 . However, it is not known which culture method, or a combination of both methods, may provide the model with more power to identify an exposure‐response relationship.…”