Creation of a Knowledge Management System for QT Analyses

• Published in: Journal of clinical pharmacology 2011-07, Vol.51 (7), p.1035-1042
• Main Authors: Tornøe, Christoffer W., Garnett, Christine E., Wang, Yaning, Florian, Jeffry, Li, Michael, Gobburu, Jogarao V.
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Aged; Anti-Bacterial Agents - administration & dosage; Anti-Bacterial Agents - adverse effects; Anti-Bacterial Agents - blood; Anti-Bacterial Agents - pharmacokinetics; Arrhythmias, Cardiac - chemically induced; Arrhythmias, Cardiac - diagnosis; automated analysis; Automatic Data Processing; Automation; Aza Compounds - administration & dosage; Aza Compounds - adverse effects; Aza Compounds - blood; Aza Compounds - pharmacokinetics; central tendency analysis; concentration-QTc analysis; Cross-Over Studies; Diagnosis, Computer-Assisted - methods; Dose-Response Relationship, Drug; Drugs, Investigational - administration & dosage; Drugs, Investigational - adverse effects; Drugs, Investigational - analysis; Drugs, Investigational - pharmacokinetics; Electrocardiography; Female; Fluoroquinolones; Humans; Knowledge Management; Male; Middle Aged; pharmacometrics; QT correction; QT/QTc; Quinolines - administration & dosage; Quinolines - adverse effects; Quinolines - blood; Quinolines - pharmacokinetics; Risk Assessment; Studies; United States; United States Food and Drug Administration; Young Adult
• ISSN: 0091-2700; 1552-4604
• DOI: 10.1177/0091270010378408

An increasing number of thorough QT (TQT) reports are being submitted to the US Food and Drug Administration's interdisciplinary review team for QT (IRT‐QT), requiring time‐intensive quantitative analyses by a multidisciplinary review team within 45 days. This calls for systematic learning to guide future trials and policies by standardizing and automating the QT analyses to improve review efficiency, provide consistent advice, and enable pooled data analyses to answer key regulatory questions. The QT interval represents the time from initiation of ventricular depolarization to completion of ventricular repolarization recorded by electrocardiograph (ECG) and is used in the proarrhythmic risk assessment. The developed QT knowledge management system is implemented in the R package “QT.” Data from 11 crossover TQT studies including time‐matched ECGs and pharmacokinetic measurements following single doses of 400 to 1200 mg moxifloxacin were used for the QT analysis example. The automated workflow was divided into 3 components (data management, analysis, and archival). The generated data sets, scripts, tables, and graphs are automatically stored in a queryable repository and summarized in an analysis report. More than 100 TQT studies have been analyzed using the system since 2007. This has dramatically reduced the time needed to review TQT studies and has made the IRT‐QT reviews consistent across reviewers. Furthermore, the system enables leveraging prior knowledge through pooled data analyses to answer policy‐related questions and to understand the various effects that influence study results.