Diagnostics and Pharmacotherapy for Severe Forms of TB (DMID 15-0100)

Tuberculosis Clinical Trial

Official title:

Diagnostics and Pharmacotherapy for Severe Forms of TB

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03559582
• Other study ID #: 18452
• Secondary ID: U01AI115594
• Status: Completed
• Start date: April 28, 2016
• Est. completion date: January 31, 2021

Study information

• Verified date: March 2022
• Source: University of Virginia
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Description:

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 478
• Est. completion date: January 31, 2021
• Est. primary completion date: July 31, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

Patients admitted to one of the study site hospitals with at least ONE of the following:

1. Clinical suspicion for TB in a child, as defined by NIH Consensus Case Definitions for TB research in children, and started on TB treatment

2. Clinical suspicion for TB meningitis, as defined by the International TB Meningitis Workshop Consensus Case Definitions for TB Meningitis

3. Clinical suspicion for TB sepsis, as defined by the Uganda/PRISM-U definitions

4. Microbiologic evidence of MDR-TB from a respiratory specimen within the past 6 months

Exclusion Criteria:

1. Pregnant women-self reported

2. Patient unable per treating physician discretion to undergo sample collection

3. Patient or representative/guardian unable to sign written informed consent

4. Patient unable to return for follow-up or be contacted by phone for follow-up

Related Conditions & MeSH terms

• Tuberculosis

Locations

Country	Name	City	State
n/a

Sponsors (8)

Lead Sponsor	Collaborator
University of Virginia	Haydom Lutheran Hospital, International Centre for Diarrhoeal Disease Research, Bangladesh, Kilimanjaro Christian Medical Centre, Tanzania, Mbarara University of Science and Technology, National Institute of Allergy and Infectious Diseases (NIAID), Scientific Center for Family Health and Human Reproduction Problems, Russia, University of Florida

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	December 2019
Secondary	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.	December 2019
Secondary	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.	December 2019