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A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts <i>in vivo</i> interactions of tuberculosis drugs

2020-09-04

Abstract excerpt

Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using dru...

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Literature Corpus work
fd388a4a-51e4-5677-9da5-451837f26618
DOI
10.1101/2020.09.03.281550
Open publication

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A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts <i>in vivo</i> interactions of tuberculosis drugsDOI 10.1101/2020.09.03.281550
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