Dataset Information

New Paradigm for Translational Modeling to Predict Long-term Tuberculosis Treatment Response.

ABSTRACT: Disappointing results of recent tuberculosis chemotherapy trials suggest that knowledge gained from preclinical investigations was not utilized to maximal effect. A mouse-to-human translational pharmacokinetics (PKs) - pharmacodynamics (PDs) model built on a rich mouse database may improve clinical trial outcome predictions. The model included Mycobacterium tuberculosis growth function in mice, adaptive immune response effect on bacterial growth, relationships among moxifloxacin, rifapentine, and rifampin concentrations accelerating bacterial death, clinical PK data, species-specific protein binding, drug-drug interactions, and patient-specific pathology. Simulations of recent trials testing 4-month regimens predicted 65% (95% confidence interval [CI], 55-74) relapse-free patients vs. 80% observed in the REMox-TB trial, and 79% (95% CI, 72-87) vs. 82% observed in the Rifaquin trial. Simulation of 6-month regimens predicted 97% (95% CI, 93-99) vs. 92% and 95% observed in 2RHZE/4RH control arms, and 100% predicted and observed in the 35 mg/kg rifampin arm of PanACEA MAMS. These results suggest that the model can inform regimen optimization and predict outcomes of ongoing trials.

SUBMITTER: Bartelink IH

PROVIDER: S-EPMC5593171 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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New Paradigm for Translational Modeling to Predict Long-term Tuberculosis Treatment Response.

Bartelink I H IH Zhang N N Keizer R J RJ Strydom N N Converse P J PJ Dooley K E KE Nuermberger E L EL Savic R M RM

Clinical and translational science 20170531 5

Disappointing results of recent tuberculosis chemotherapy trials suggest that knowledge gained from preclinical investigations was not utilized to maximal effect. A mouse-to-human translational pharmacokinetics (PKs) - pharmacodynamics (PDs) model built on a rich mouse database may improve clinical trial outcome predictions. The model included Mycobacterium tuberculosis growth function in mice, adaptive immune response effect on bacterial growth, relationships among moxifloxacin, rifapentine, an ...[more]

PMID: 28561946

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Project description:Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR's were within the historical N rate error range (40-50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.

Project description:IntroductionLong-term mortality among TB survivors appears to be higher than control populations without TB in many settings. However, data are limited among persons with HIV (PWH). We assessed the association between cured TB and long-term mortality among persons with PWH in Haiti.MethodsA prospective cohort of PWH from the CIPRA HT-001 trial was followed from study enrolment (August 2005 to July 2008) to study closure (December 2018) to compare mortality between participants with and without TB. The index date for the survival analysis was defined as 240 days after TB diagnosis or randomization date. Time to death was described using Kaplan-Meier curves, and log-rank tests were used to compare time to death between the TB and no-TB cohorts. The association between TB and long-term mortality was estimated with multivariable Cox models.ResultsOf the 816 participants in the CIPRA HT-001 trial, 77 were excluded for a history of TB prior to study enrolment and 31 were excluded due to death or attrition prior to the index date, leaving 574 in the no-TB and 134 in the TB cohort. Twenty-four (17.9%) participants in the TB and 48 (8.4%) in the no-TB cohort died during follow-up. Five and 10-year mortality rates were 14.2% and 17.9% respectively, in the TB cohort, and 6.1% and 8.4% in the no-TB cohort. In Kaplan-Meier analysis, participants in the TB cohort had a significantly shorter time to death (log-rank p < 0.001). In multivariable analysis, TB treatment was the only predictor of mortality (HR: 2.78; 95% CI: 1.61, 4.79). Sensitivity analyses, which included only baseline TB cases, an index date of two years after TB diagnosis, and study enrolment and case-control matching yielded results that were consistent with primary analyses.ConclusionsPWH who are successfully treated for TB have higher long-term mortality than those who are never diagnosed with TB, even after accounting for acute TB-related mortality. A better understanding of the underlying mechanisms associated with TB sequelae is critically needed to guide specific interventions. Until then, more aggressive measures for health promotion and disease prevention are essential to improve long-term survival for PWH after TB treatment.

Dataset Information

New Paradigm for Translational Modeling to Predict Long-term Tuberculosis Treatment Response.

Publications

New Paradigm for Translational Modeling to Predict Long-term Tuberculosis Treatment Response.

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