Dataset Information

Forecasting Clinical Dose-Response From Preclinical Studies in Tuberculosis Research: Translational Predictions With Rifampicin.

ABSTRACT: A crucial step for accelerating tuberculosis drug development is bridging the gap between preclinical and clinical trials. In this study, we developed a preclinical model-informed translational approach to predict drug effects across preclinical systems and early clinical trials using the in vitro-based Multistate Tuberculosis Pharmacometric (MTP) model using rifampicin as an example. The MTP model predicted rifampicin biomarker response observed in 1) a hollow-fiber infection model, 2) a murine study to determine pharmacokinetic/pharmacodynamic indices, and 3) several clinical phase IIa early bactericidal activity (EBA) studies. In addition, we predicted rifampicin biomarker response at high doses of up to 50?mg/kg, leading to an increased median EBA0-2 days (90% prediction interval) of 0.513 log CFU/mL/day (0.310; 0.701) compared to the standard dose of 10?mg/kg of 0.181 log/CFU/mL/day (0.076; 0.483). These results suggest that the translational approach could assist in the selection of drugs and doses in early-phase clinical tuberculosis trials.

SUBMITTER: Wicha SG

PROVIDER: S-EPMC6282494 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Forecasting Clinical Dose-Response From Preclinical Studies in Tuberculosis Research: Translational Predictions With Rifampicin.

Wicha Sebastian G SG Clewe Oskar O Svensson Robin J RJ Gillespie Stephen H SH Hu Yanmin Y Coates Anthony R M ARM Simonsson Ulrika S H USH

Clinical pharmacology and therapeutics 20180619 6

A crucial step for accelerating tuberculosis drug development is bridging the gap between preclinical and clinical trials. In this study, we developed a preclinical model-informed translational approach to predict drug effects across preclinical systems and early clinical trials using the in vitro-based Multistate Tuberculosis Pharmacometric (MTP) model using rifampicin as an example. The MTP model predicted rifampicin biomarker response observed in 1) a hollow-fiber infection model, 2) a murine ...[more]

PMID: 29700814

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Project description:There is a great deal of interest in personalized, individualized, or precision interventions for disease and health-risk mitigation. This is as true of nutrition-based intervention and prevention strategies as it is for pharmacotherapies and pharmaceutical-oriented prevention strategies. Essentially, technological breakthroughs have enabled researchers to probe an individual's unique genetic, biochemical, physiological, behavioral, and exposure profile, allowing them to identify very specific and often nuanced factors that an individual might possess, which may make it more or less likely that he or she responds favorably to a particular intervention (e.g., nutrient supplementation) or disease prevention strategy (e.g., specific diet). However, as compelling and intuitive as personalized nutrition might be in the current era in which data-intensive biomedical characterization of individuals is possible, appropriately and objectively vetting personalized nutrition strategies is not trivial and requires novel study designs and data analytical methods. These designs and methods must consider a very integrated use of the multiple contemporary biomedical assays and technologies that motivate them, which adds to their complexity. Single-subject or N-of-1 trials can be used to assess the utility of personalized interventions and, in addition, can be crafted in such a way as to accommodate the necessarily integrated use of many emerging biomedical technologies and assays. In this review, we consider the motivation, design, and implementation of N-of-1 trials in translational nutrition research that are meant to assess the utility of personalized nutritional strategies. We provide a number of example studies, discuss appropriate analytical methods given the complex data they generate and require, and consider how such studies could leverage integration of various biomarker assays and clinical end points. Importantly, we also consider the development of strategies and algorithms for matching nutritional needs to individual biomedical profiles and the issues surrounding them. Finally, we discuss the limitations of personalized nutrition studies, possible extensions of N-of-1 nutritional intervention studies, and areas of future research.

Dataset Information

Forecasting Clinical Dose-Response From Preclinical Studies in Tuberculosis Research: Translational Predictions With Rifampicin.

Publications

Forecasting Clinical Dose-Response From Preclinical Studies in Tuberculosis Research: Translational Predictions With Rifampicin.

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