title: Forecasting Accuracy of the Hollow Fiber Model of Tuberculosis for Clinical Therapeutic Outcomes.
abstract: BACKGROUND
  The hollow fiber system model of tuberculosis (HFS-TB), in tandem with Monte Carlo experiments, represents a drug development tool (DDT) with the potential for use to develop tuberculosis treatment regimens. However, the predictive accuracy of the HFS-TB, or any other nonclinical DDT such as an animal model, has yet to be robustly evaluated.
  
  
  METHODS
  To avoid hindsight bias, a literature search was performed to identify clinical studies published at least 6 months after HFS-TB experiments' quantitative predictions. Steps to minimize bias and for reporting systematic reviews were applied as outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Publications were scored for quality of evidence. Accuracy was calculated using the mean absolute percentage error, then summated with weighting assigned by sample size and quality-of-evidence score. Given the lack of a gold-standard tuberculosis DDT, the forecasting accuracy of a completely unreliable tool was also calculated from 1000 simulated experiments for a random or "total guesswork" model.
  
  
  RESULTS
  The quantitative forecasting accuracy (95% confidence interval [CI]) for the "total guesswork" model was 15.6% (95% CI, 8.7%-22.5%); bias was -0.1% (95% CI, -2.5% to 2.2%). Twenty clinical studies were published after HFS-TB experiments predicted optimal drug exposures and doses, susceptibility breakpoints, and optimal combination regimens. Based on these clinical studies, the predictive accuracy of the HFS-TB was 94.4% (95% CI, 84.3%-99.9%), and bias was 1.8% (95% CI, -13.7% to 6.2%).
  
  
  CONCLUSIONS
  The HFS-TB model is highly accurate at forecasting optimal drug exposures, doses, and dosing schedules for use in the clinic.
authors.affiliation: Center for Infectious Diseases Research and Experimental Therapeutics, Baylor Research Institute, Baylor University Medical Center, Dallas, Texas Department of Medicine, University of Cape Town, Observatory, South Africa.
authors.forename: Tawanda
authors.initials: T
authors.lastname: Gumbo
authors.affiliation: Center for Infectious Diseases Research and Experimental Therapeutics, Baylor Research Institute, Baylor University Medical Center, Dallas, Texas.
authors.forename: Jotam G
authors.initials: JG
authors.lastname: Pasipanodya
authors.affiliation: Critical Path Institute, Tucson, Arizona.
authors.forename: Klaus
authors.initials: K
authors.lastname: Romero
authors.affiliation: Critical Path Institute, Tucson, Arizona.
authors.forename: Debra
authors.initials: D
authors.lastname: Hanna
authors.affiliation: Center for Tuberculosis Research, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland.
authors.forename: Eric
authors.initials: E
authors.lastname: Nuermberger
mesh_terms: D000818:Animals; D000995:Antitubercular Agents / Q000592:standards / Q000627:therapeutic use*; D055808:Drug Discovery / Q000379:methods; D006801:Humans; D008954:Models, Biological; D009010:Monte Carlo Method; D009169:Mycobacterium tuberculosis / Q000187:drug effects*; D011237:Predictive Value of Tests; D014376:Tuberculosis / Q000188:drug therapy*; D014397:Tuberculosis, Pulmonary / Q000188:drug therapy
chemical_list: D000995:Antitubercular Agents
keywords: Monte Carlo experiments; drug development tool; hollow fiber system; predictive accuracy; tuberculosis