Abstract

Along with the current interest in changes of cardiovascular risk assessment strategy and inclusion of in silico modelling into the applicable paradigm, the need for data has increased, both for model generation and testing. Data collection is often time-consuming but an inevitable step in the modelling process, requiring extensive literature searches and other identification of alternative resources providing complementary results. The next step, namely data extraction, can also be challenging. Here we present a collection of thorough QT/QTc (TQT) study results with detailed descriptions of study design, pharmacokinetics, and pharmacodynamic endpoints. The presented dataset provides information that can be further utilized to assess the predictive performance of different preclinical biomarkers for QT prolongation effects with the use of various modelling approaches. As the exposure levels and population description are included, the study design and characteristics of the study population can be recovered precisely in the simulation. Another possible application of the TQT dataset is the analysis of drug characteristic/QT prolongation/TdP (torsade de pointes) relationship after the integration of provided information with other databases and tools. This includes drug cardiac safety classifications (e.g., CredibleMeds), Comprehensive in vitro Proarrhythmia Assay (CiPA) compounds classification, as well as those containing information on physico-chemical properties or absorption, distribution, metabolism, excretion (ADME) data like PubChem or DrugBank.

Highlights

  • Data 2020, 5, 10 the document, almost all new drugs, or those for which a new dose or route of administration is under development, have to be tested in a trial dedicated to the assessment of their potential to delay cardiac repolarization, called a “Thorough QT/QTc (TQT) study”

  • The clinical endpoint corresponding to the changes in the time of ventricular repolarization is QT interval prolongation on the surface electrocardiogram (ECG)

  • Because QT interval length is related to the heart rate, it is recommended that applications should contain, apart from the raw QT data, RR interval data and QT interval data corrected for the heart rate (QTc)

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Summary

Summary

Cardiovascular toxicity has been one of the leading causes of concern throughout the drug development process as well as a major contributor to drug withdrawals [1]. As the exposure levels and population description are included, the study design and characteristics of the study population can be recovered precisely in the simulation Another possible application of the TQT dataset is the analysis of the drug characteristic/QT prolongation/TdP relationship after the integration of provided information with other databases and tools. This includes drug cardiac safety classifications (e.g., CredibleMeds [8]), CiPA compound classification (TdP risk level), as well as those containing information on physico-chemical properties or ADME (absorption, distribution, metabolism, excretion) data like PubChem (substance structure files and physico-chemical properties) or DrugBank

Data Description
Data Source Identification
Extracting the Data
Study design
User Notes
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