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Open AccessData Descriptor

An Open-Access Dataset of Thorough QT Studies Results

1
Pharmacoepidemiology and Farmacoeconomics Unit, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, str., 30-688 Krakow, Poland
2
Certara UK Ltd. (Simcyp Division), 1 Concourse Way, Sheffield S1 2BJ, UK
*
Author to whom correspondence should be addressed.
Received: 18 December 2019 / Revised: 14 January 2020 / Accepted: 16 January 2020 / Published: 25 January 2020
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.
Keywords: Dataset: 10.17632/47nknnw666.1 Dataset: 10.17632/47nknnw666.1
MDPI and ACS Style

Wiśniowska, B.; Tylutki, Z.; Polak, S. An Open-Access Dataset of Thorough QT Studies Results. Data 2020, 5, 10.

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