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Published June 20, 2023 | Version v1
Dataset Open

Pharmacological dataset on drug-induced TdP risk assessment

Description

A manually collected dataset that investigates the proarrhythmic risk of 109 drugs using parameters obtained from biophysical models. The dataset defines 4 torsade de pointes (TdP) risk categories in the parameter “class”.  For each of 109 drugs, IC50 values and Hill coefficients (h) for INa, INaL, IKr, Ito, ICaL, IK1, and IKs and human effective free therapeutic plasma concentration (EFTPC) were obtained (Llopis-Lorente et al., 2020). Detailed description follows. A dash in the dataset means that no value was found publicly available, thus it was considered that the drug effect on that current was negligible. The dataset can be used (and is important) while studing causal discovery methods to infer ion channels selection for the drug-induced TdP risk

 

Summary: 

  • Size of dataset: 109 x 16
  • Task: Causal Discovery
  • Data Type: Mixed
  • Dataset Scope: Standalone
  • Ground Truth: Unknown
  • Temporal Structure: Static
  • License: TBD
  • Missing Values: Yes

 

Features:

  • Class - class of the drug according to the torsade de pointes (TdP) is a life-threatening arrhythmia characterised by a gradual change in the amplitude and twisting of the QRS complexes.  Class 1 (known risk of TdP), class 2 (possible risk of TdP), class 3 (conditional risk of TdP), class 4 (drugs with a lack of evidence of TdP).
  • EFTPC —effective free therapeutic plasma concentration, defined as the drug concentration in the plasma required to produce the desired therapeutic effect in the body.
  • IC50- value that represents the minimal concentration of a drug that is required for 50% inhibition in vitrovalues (given in nm).
  • hIKr, hINa,hINaL, hICaL, hIKs, hIK1, hIto - Hill coefficients of the currents.

 

Files: 

  • med_file.csv: dataset
  • medications_description.Rmd: R executable summary of med_file.csv
  • medications_description.html: html rendering of medications_description.Rmd

Files

med_file.csv

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Additional details

Related works

References
Journal article: 10.1021/acs.jcim.0c00201 (DOI)
Conference proceeding: 10.22489/cinc.2023.009 (DOI)

References

  • Llopis-Lorente, Jordi and Gomis-Tena, Julio and Cano, Jordi and Romero, Lucía and Saiz, Javier and Trenor, Beatriz. InSilico Classifiers for the Assessment of Drug Proarrhythmicity. Journal of Chemical Information and Modeling, 2020, 60(10), 5172--5187, https://doi.org/10.1021/acs.jcim.0c00201
  • Al-Ali,Safaa and Llopis-Lorente, Jordi and Mora, Maria Teresa and Sermesant, Maxime and Trénor, Beatriz and Balelli, Irene. A causal discovery approach for streamline ion channels selection to improve drug-induced TdP risk assessment. 2023.hal-04105144, https://doi.org/10.22489/cinc.2023.009