Month 2 culture status and treatment duration as predictors of recurrence in pulmonary tuberculosis: model validation and update.

New regimens capable of shortening tuberculosis treatment without increasing the risk of recurrence are urgently needed. A 2013 meta-regression analysis, using data from trials published from 1973 to 1997 involving 7793 patients, identified 2-month sputum culture status and treatment duration as independent predictors of recurrence. The resulting model predicted that if a new 4-month regimen […]

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Model-based development of gemcabene, a new lipid-altering agent

The purpose of this study was to evaluate the value of model-based, quantitative decision making during the development of gemcabene, a novel lipid-altering agent. The decisions were driven by a model of the likely clinical profile of gemcabene in comparison with its competitors, such as 3-hydroxymethylglutaryl coenzyme A reductase inhibitors (statins), the cholesterol absorption inhibitor […]

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Certara’s Best of Blogs 2015

A selection of short essays from our blog, written to empower our customers with biosimulation and regulatory writing solutions in order to help them solve the toughest drug development problems. Certara staff contributions range in topic from pharmacometrics to systems biology to the growing importance of regulatory writing.

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Development of a Multicompartment Permeability-Limited Lung PBPK Model and Its Application in Predicting Pulmonary Pharmacokinetics of Antituberculosis Drugs

Achieving sufficient concentrations of antituberculosis (TB) drugs in pulmonary tissue at the optimum time is still a challenge in developing therapeutic regimens for TB. A physiologically based pharmacokinetic model incorporating a multi-compartment permeability-limited lung model was developed and used to simulate plasma and pulmonary concentrations of seven drugs. Passive permeability of drugs within the lung was predicted using an in vitro-in vivo extrapolation approach. Simulated epithelial lining fluid (ELF):plasma concentration ratios showed reasonable […]

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A Clearer Crystal Ball: A Meta-Regression Model Predicts TB Relapse

David Hermann

What would you guess is the world’s most neglected disease? I bet that you wouldn’t pick tuberculosis (TB)— a disease that causes an estimated 9 million new cases and 1.3 million deaths annually. This infectious disease is caused by the bacterium Mycobacterium tuberculosis. TB usually attacks the lungs, but can attack any part of the body.

Patients infected with TB are typically treated with a standard six-month course of multiple antimicrobial drugs. It is quite difficult to get patients to adhere to this long course of treatment. Often, patients will fail to complete the entire drug course. This increases the likelihood of relapse and antibiotic resistance developing. Thus, there is an urgent need for shorter treatment regimens that minimize the risk of relapse. In this blog post, I’ll discuss how meta-regression modeling of relapse can inform TB clinical trial design.

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Topics: Clinical Trial Design
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