Major Research Aim: To study novel molecular diagnostics and the pharmacokinetic variability among a spectrum of TB disease states, including severe forms of TB like disseminated TB, TB meningitis and drug resistant TB, among adults and children from multiple international sites.
Aim 1. Measure pharmacokinetics to anti-tuberculosis (TB) medications in severe TB syndromes (including multidrug-resistant TB, pediatric TB, TB sepsis and TB meningitis) from diverse geographies (including Tanzania, Uganda, Bangladesh, and Siberia) and correlate these findings to TB treatment outcome (TB treatment failure: death/ default/ relapse/ further acquired drug resistance). Aim 2. Decipher mechanisms of pharmacokinetic variability to TB drugs, particularly malabsorption due to concurrent gastrointestinal disease. Aim 3. Deployment of quantitative susceptibility testing (minimum inhibitory concentration-MIC) and rapid MIC-informed molecular methods (e.g., TaqMan Array Card-TAC) for M. tuberculosis. In addition to the stated aims, the primary elements of capacity building requisite for this project include the training in and deployment of the fieldable molecular diagnostic platforms, onsite pharmacokinetic monitoring, and a broad strengthening of longitudinal cohort management for clinical research.
Study Type
OBSERVATIONAL
Enrollment
478
Measure area under the concentration curve (AUC) to anti-tuberculosis (TB) medications relative to TB treatment outcome in severe TB syndromes
Severe TB syndromes include multidrug-resistant TB, pediatric TB, TB sepsis and TB meningitis from diverse geographies (including Tanzania, Uganda, Bangladesh, and Siberia). The parameter of most importance to cidal activity of anti-TB medications among the cohort is AUC. TB treatment outcome will be defined as death, microbiological failure, relapse or acquired drug resistance, and machine learning algorithms such as classification and regression tree analyses will be used to define AUC threshold for each anti-TB medication predictive of poor TB treatment outcome. Conventional logistic regression will then be used to determine the additive odds for a patient with one of more medications below an algorithm derived threshold being significantly more likely to have a poor TB treatment outcome.
Time frame: December 2019
Collect stool in patients undergoing pharmacokinetic testing to measure the environmental enteropathy index
Stool will be collected in patients with severe TB syndromes undergoing pharmacokinetic testing and assayed for stool biomarkers of malabsorption (environmental enteropathy index) and modeled as a determinant of those with AUC values of one or more anti-TB medications below thresholds predictive of TB treatment outcome.
Time frame: December 2019
Collect stool in patients undergoing pharmacokinetic testing to measure the quantitative burden and species distribution of enteric pathogens by the enteric TAC assay- 35 bacterial, viral, parasitic species)
Stool will be collected in patients with severe TB syndromes undergoing pharmacokinetic testing and assayed for detection of molecular targets of enteric pathogens by TaqMan Array Card (TAC) platform. Enteric pathogen burden (including the effect of multiple pathogens in a single sample) will be modeled as a determinant of those with AUC values of one or more anti-TB medications below thresholds predictive of TB treatment outcome.
Time frame: December 2019
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.