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- W2912373572 abstract "In silico computer modeling to assess drug proarrhythmia risk is a key component of the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative. The recently-published qNet metric shows promise for correctly determining drug-induced Torsades-de-Pointes (TdP) risk (high/intermediate/low) of drugs using an in silico model of the human ventricular action potential (AP). The model combines simulations of multiple ionic channels (including O’Hara Rudy dynamic (ORd) drug-binding hERG model) and in vitro pharmacology data. Using this model, TdP risk can be determined via qNet for the 12 CiPA training and 16 validation compounds. The FDA made an open source R script of this model available (github). This program can be used to calculate both the qNet metric and its uncertainty quantification. Simulating the AP and determining the uncertainty quantification is a computationally-intensive task, not well-suited for R. We translated the FDA R script to C and parallelized the code using both OpenMP (shared memory parallelism) and MPI (distributed memory parallelism). The serial C code produces the same results as the R script, but with a five-fold increase in efficiency. Running the OpenMP C code on a PC with an eight core processor results in ∼25-fold increase in efficiency vs. R script. The MPI version of the code can be run on a high performance computing resource and results in an even greater increase, depending on the number of processors employed. Using the R script to generate a simulation of the effect of a single drug on the AP using 500 samples takes 16.5 days using R script. With the parallel C code, it takes just 15.3 Hours. This reduction in computational time required will significantly reduce the time taken to calculate the arrhythmogenic profile of any compound." @default.
- W2912373572 created "2019-02-21" @default.
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- W2912373572 date "2019-02-01" @default.
- W2912373572 modified "2023-09-29" @default.
- W2912373572 title "Implementation of the FDA CiPA Qnet Model for Drug Safety Screening Which Increases Efficiency 25 Fold" @default.
- W2912373572 doi "https://doi.org/10.1016/j.bpj.2018.11.3028" @default.
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