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| ID | Type | Description | Link |
|---|---|---|---|
| U01AI115594 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute of Allergy and Infectious Diseases (NIAID) | NIH |
| Scientific Center for Family Health and Human Reproduction Problems, Russia | OTHER_GOV |
| Kilimanjaro Christian Medical Centre, Tanzania |
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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.
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| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
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Inclusion Criteria:
Patients admitted to one of the study site hospitals with at least ONE of the following:
Exclusion Criteria:
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Subjects will be recruited by medical officer review of new admissions to the TB hospitals at the study sites- Kibong'oto National TB Hospital (Tanzania), Haydom Lutheran Hospital (Tanzania), Irkutsk Regional Clinical Tuberculosis Hospital (Siberia/Russian Federation), National Institute of Diseases of the Chest Hospital (Bangladesh), ICDDRB Hospital (Bangladesh), Mbarara Regional Referral Hospital (Uganda).
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| Name | Affiliation | Role |
|---|---|---|
| Scott K Heysell, MD | University of Virginia | Principal Investigator |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 35731948 | Derived | Heysell SK, Mpagama SG, Ogarkov OB, Conaway M, Ahmed S, Zhdanova S, Pholwat S, Alshaer MH, Chongolo AM, Mujaga B, Sariko M, Saba S, Rahman SMM, Uddin MKM, Suzdalnitsky A, Moiseeva E, Zorkaltseva E, Koshcheyev M, Vitko S, Mmbaga BT, Kibiki GS, Pasipanodya JG, Peloquin CA, Banu S, Houpt ER. Pharmacokinetic-Pharmacodynamic Determinants of Clinical Outcomes for Rifampin-Resistant Tuberculosis: A Multisite Prospective Cohort Study. Clin Infect Dis. 2023 Feb 8;76(3):497-505. doi: 10.1093/cid/ciac511. |
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| ID | Term |
|---|---|
| D014376 | Tuberculosis |
| ID | Term |
|---|---|
| D009164 | Mycobacterium Infections |
| D000193 | Actinomycetales Infections |
| D016908 | Gram-Positive Bacterial Infections |
| D001424 | Bacterial Infections |
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| Haydom Lutheran Hospital | OTHER |
| Mbarara University of Science and Technology | OTHER |
| International Centre for Diarrhoeal Disease Research, Bangladesh | OTHER |
| University of Florida | OTHER |
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This study will use clinical and related phenotypic data and saliva samples to identify and characterize genetic and molecular biological markers that will enrich our understanding of the biological basis of an individual's response to anti-TB drugs.
| 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. | December 2019 |
| D001423 | Bacterial Infections and Mycoses |
| D007239 | Infections |