Predicting tuberculosis drug efficacy in preclinical and clinical models from 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, V��ronique Dartois, Rada M Savic
Author Information
Janice J N Goh: Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
Anu Patel: Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
Bernard Ngara: Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
Rob C van Wijk: Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
Natasha Strydom: Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
Qianwen Wang: Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
Nhi Van: Department of Molecular Biology and Microbiology, Tufts University School of Medicine, and Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance Boston, Boston, MA, USA.
Tracy M Washington: Department of Molecular Biology and Microbiology, Tufts University School of Medicine, and Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance Boston, Boston, MA, USA.
Eric L Nuermberger: Center for Tuberculosis Research, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Bree B Aldridge: Department of Molecular Biology and Microbiology, Tufts University School of Medicine, and Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance Boston, Boston, MA, USA.
Christine Roubert: Evotec ID (LYON) SAS, Lyon, France.
Jansy Sarathy: Center for Discovery and Innovation, Hackensack Meridian School of Medicine, Hackensack Meridian Health, Nutley, NJ, USA.
V��ronique Dartois: Center for Discovery and Innovation, Hackensack Meridian School of Medicine, Hackensack Meridian Health, Nutley, NJ, USA.
Rada M Savic: Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
Multiple potency assays are used to evaluate compounds against , but a consensus on clinically relevant assays is lacking. We aimed to identify an assay signature that predicts preclinical efficacy and early clinical outcome. Thirty-one unique assays were compiled for 10 TB drugs. EC values were compared to pharmacokinetic-pharmacodynamic (PK-PD)-model-derived EC values from mice evaluated via multinomial regression. External validation of best-performing 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 caseum assay. Subsequent simulated mouse bacterial burden over time using predicted EC 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.
