Dataset Information

Mechanistic Model-Informed Proarrhythmic Risk Assessment of Drugs: Review of the "CiPA" Initiative and Design of a Prospective Clinical Validation Study.

ABSTRACT: The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative is developing and validating a mechanistic-based assessment of the proarrhythmic risk of drugs. CiPA proposes to assess a drug's effect on multiple ion channels and integrate the effects in a computer model of the human cardiomyocyte to predict proarrhythmic risk. Unanticipated or missed effects will be assessed with human stem cell-derived cardiomyocytes and electrocardiogram (ECG) analysis in early phase I clinical trials. This article provides an overview of CiPA and the rationale and design of the CiPA phase I ECG validation clinical trial, which involves assessing an additional ECG biomarker (J-Tpeak) for QT prolonging drugs. If successful, CiPA will 1) create a pathway for drugs with hERG block / QT prolongation to advance without intensive ECG monitoring in phase III trials if they have low proarrhythmic risk; and 2) enable updating drug labels to be more informative about proarrhythmic risk, not just QT prolongation.

SUBMITTER: Vicente J

PROVIDER: S-EPMC5765372 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Mechanistic Model-Informed Proarrhythmic Risk Assessment of Drugs: Review of the "CiPA" Initiative and Design of a Prospective Clinical Validation Study.

Vicente Jose J Zusterzeel Robbert R Johannesen Lars L Mason Jay J Sager Philip P Patel Vikram V Matta Murali K MK Li Zhihua Z Liu Jiang J Garnett Christine C Stockbridge Norman N Zineh Issam I Strauss David G DG

Clinical pharmacology and therapeutics 20171116 1

The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative is developing and validating a mechanistic-based assessment of the proarrhythmic risk of drugs. CiPA proposes to assess a drug's effect on multiple ion channels and integrate the effects in a computer model of the human cardiomyocyte to predict proarrhythmic risk. Unanticipated or missed effects will be assessed with human stem cell-derived cardiomyocytes and electrocardiogram (ECG) analysis in early phase I clinical trials. This a ...[more]

PMID: 28986934

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Project description:Many researchers have suggested evaluation methods and Torsades de Pointes (TdP) metrics to assess the proarrhythmic risk of a drug based on the in silico simulation, as part of the Comprehensive in-vitro Proarrhythmia Assay (CiPA) project. In the previous study, we validated the robustness of 12 in silico features using the ordinal logistic regression (OLR) model by comparing the classification performances of metrics according to the in-vitro experimental datasets used; however, the OLR model using 12 in silico features did not provide desirable results. This study proposed a convolutional neural network (CNN) model using the variability of promising in silico TdP metrics hypothesizing that the variability of in silico features based on beats has more information than the single value of in silico features. We performed the action potential (AP) simulation using a human ventricular myocyte model to calculate seven in silico features representing the electrophysiological cell states of drug effects over 1,000 beats: qNet, qInward, intracellular calcium duration at returning to 50% baseline (CaD50) and 90% baseline (CaD90), AP duration at 50% repolarization (APD50) and 90% repolarization (APD90), and dVm/dtMax_repol. The proposed CNN classifier was trained using 12 train drugs and tested using 16 test drugs among CiPA drugs. The torsadogenic risk of drugs was classified as high, intermediate, and low risks. We determined the CNN classifier by comparing the classification performance according to the variabilities of seven in silico biomarkers computed from the in silico drug simulation using the Chantest dataset. The proposed CNN classifier performed the best when using qInward variability to classify the TdP-risk drugs with 0.94 AUC for high risk and 0.93 AUC for low risk. In addition, the final CNN classifier was validated using the qInward variability obtained after merging three in-vitro datasets, but the model performance decreased to a moderate level of 0.75 and 0.78 AUC. These results suggest the need for the proposed CNN model to be trained and tested using various types of drugs.

Project description:BackgroundMechanistic data is increasingly used in hazard identification of chemicals. However, the volume of data is large, challenging the efficient identification and clustering of relevant data.ObjectivesWe investigated whether evidence identification for hazard assessment can become more efficient and informed through an automated approach that combines machine reading of publications with network visualization tools.MethodsWe chose 13 chemicals that were evaluated by the International Agency for Research on Cancer (IARC) Monographs program incorporating the key characteristics of carcinogens (KCCs) approach. Using established literature search terms for KCCs, we retrieved and analyzed literature using Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA combines large-scale literature processing with pathway databases and extracts relationships between biomolecules, bioprocesses, and chemicals into statements (e.g., "benzene activates DNA damage"). These statements were subsequently assembled into networks and compared with the KCC evaluation by the IARC, to evaluate the informativeness of our approach.ResultsWe found, in general, larger networks for those chemicals which the IARC has evaluated the evidence to be strong for KCC induction. Larger networks were not directly linked to publication count, given that we retrieved small networks for several chemicals with little support for KCC activation according to the IARC, despite the significant volume of literature for these specific chemicals. In addition, interpreting networks for genotoxicity and DNA repair showed concordance with the IARC KCC evaluation.DiscussionOur method is an automated approach to condense mechanistic literature into searchable and interpretable networks based on an a priori ontology. The approach is no replacement of expert evaluation but, instead, provides an informed structure for experts to quickly identify which statements are made in which papers and how these could connect. We focused on the KCCs because these are supported by well-described search terms. The method needs to be tested in other frameworks as well to demonstrate its generalizability. https://doi.org/10.1289/EHP9112.

Dataset Information

Mechanistic Model-Informed Proarrhythmic Risk Assessment of Drugs: Review of the "CiPA" Initiative and Design of a Prospective Clinical Validation Study.

Publications

Mechanistic Model-Informed Proarrhythmic Risk Assessment of Drugs: Review of the "CiPA" Initiative and Design of a Prospective Clinical Validation Study.

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