Pharmacokinetic/Pharmacodynamic Background and Methods and Scientific Evidence Base for Dosing of Second-line Tuberculosis Drugs

A World Health Organization workshop systematically examined the evidence base for dosing second-line tuberculosis drugs, identifying knowledge gaps. To fill these in, pharmacokinetics/pharmacodynamics, Monte Carlo experiments, and artificial intelligence algorithms were used in hollow-fiber model s...

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Published in:	Clinical infectious diseases Vol. 67; no. suppl_3; p. S267
Main Authors:	Gumbo, Tawanda, Alffenaar, Jan-Willem C
Format:	Journal Article
Language:	English
Published:	United States 28.11.2018
Subjects:	Algorithms Antitubercular Agents - pharmacokinetics Artificial Intelligence Drug Discovery Drug Evaluation, Preclinical Humans Monte Carlo Method Mycobacterium tuberculosis - drug effects Tuberculosis - drug therapy Tuberculosis - microbiology
ISSN:	1537-6591, 1537-6591
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Abstract	A World Health Organization workshop systematically examined the evidence base for dosing second-line tuberculosis drugs, identifying knowledge gaps. To fill these in, pharmacokinetics/pharmacodynamics, Monte Carlo experiments, and artificial intelligence algorithms were used in hollow-fiber model studies and clinical data analyses.
AbstractList	A World Health Organization workshop systematically examined the evidence base for dosing second-line tuberculosis drugs, identifying knowledge gaps. To fill these in, pharmacokinetics/pharmacodynamics, Monte Carlo experiments, and artificial intelligence algorithms were used in hollow-fiber model studies and clinical data analyses.A World Health Organization workshop systematically examined the evidence base for dosing second-line tuberculosis drugs, identifying knowledge gaps. To fill these in, pharmacokinetics/pharmacodynamics, Monte Carlo experiments, and artificial intelligence algorithms were used in hollow-fiber model studies and clinical data analyses. A World Health Organization workshop systematically examined the evidence base for dosing second-line tuberculosis drugs, identifying knowledge gaps. To fill these in, pharmacokinetics/pharmacodynamics, Monte Carlo experiments, and artificial intelligence algorithms were used in hollow-fiber model studies and clinical data analyses.
Author	Gumbo, Tawanda Alffenaar, Jan-Willem C
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SubjectTerms	Algorithms Antitubercular Agents - pharmacokinetics Artificial Intelligence Drug Discovery Drug Evaluation, Preclinical Humans Monte Carlo Method Mycobacterium tuberculosis - drug effects Tuberculosis - drug therapy Tuberculosis - microbiology
Title	Pharmacokinetic/Pharmacodynamic Background and Methods and Scientific Evidence Base for Dosing of Second-line Tuberculosis Drugs
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