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Predicting tuberculosis drug efficacy in preclinical and clinical models from in vitro data

  • Janice J.N. Goh
  • , Anu Patel
  • , Bernard Ngara
  • , Rob C. van Wijk
  • , Natasha Strydom
  • , Qianwen Wang
  • , Nhi Van
  • , Tracy M. Washington
  • , Eric L. Nuermberger
  • , Bree B. Aldridge
  • , Christine Roubert
  • , Jansy Sarathy
  • , Véronique Dartois
  • , Rada M. Savic
  • University of California at San Francisco
  • Tufts University
  • Johns Hopkins University
  • Evotec (Lyon) SAS
  • Sanofi-Aventis

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Multiple in vitro potency assays are used to evaluate compounds against Mycobacterium tuberculosis, but a consensus on clinically relevant assays is lacking. We aimed to identify an in vitro assay signature that predicts preclinical efficacy and early clinical outcome. Thirty-one unique in vitro assays were compiled for 10 TB drugs. In vitro EC50 values were compared to pharmacokinetic-pharmacodynamic (PK-PD)-model-derived EC50 values from mice evaluated via multinomial regression. External validation of best-performing in vitro assay combinations was performed using five new TB drugs. Best-performing assay signatures for acute and subacute infections were described by assays that reproduce conditions found in macrophages and foamy macrophages and chronic infection by the ex vivo caseum assay. Subsequent simulated mouse bacterial burden over time using predicted in vivo EC50 was within 2-fold of observations. This study helps us identify clinically relevant assays and prioritize successful drug candidates, saving resources and accelerating clinical success.

Original languageEnglish
Article number111932
JournaliScience
Volume28
Issue number3
DOIs
StatePublished - 21 Mar 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • bioinformatics
  • biological sciences
  • natural sciences
  • pharmacoinformatics
  • pharmacology

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