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Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms
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| Zeitschriftentitel: | Clinical Pharmacology & Therapeutics |
|---|---|
| Personen und Körperschaften: | , |
| In: | Clinical Pharmacology & Therapeutics, 100, 2016, 4, S. 371-379 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Wiley
|
| Schlagwörter: |
| author_facet |
Lancaster, M Cummins Sobie, EA Lancaster, M Cummins Sobie, EA |
|---|---|
| author |
Lancaster, M Cummins Sobie, EA |
| spellingShingle |
Lancaster, M Cummins Sobie, EA Clinical Pharmacology & Therapeutics Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms Pharmacology (medical) Pharmacology |
| author_sort |
lancaster, m cummins |
| spelling |
Lancaster, M Cummins Sobie, EA 0009-9236 1532-6535 Wiley Pharmacology (medical) Pharmacology http://dx.doi.org/10.1002/cpt.367 <jats:p>The ventricular arrhythmia Torsades de Pointes (TdP) is a common form of drug‐induced cardiotoxicity, but prediction of this arrhythmia remains an unresolved issue in drug development. Current assays to evaluate arrhythmia risk are limited by poor specificity and a lack of mechanistic insight. We addressed this important unresolved issue through a novel computational approach that combined simulations of drug effects on dynamics with statistical analysis and machine‐learning. Drugs that blocked multiple ion channels were simulated in ventricular myocyte models, and metrics computed from the action potential and intracellular (Ca<jats:sup>2+</jats:sup>) waveform were used to construct classifiers that distinguished between arrhythmogenic and nonarrhythmogenic drugs. We found that: (1) these classifiers provide superior risk prediction; (2) drug‐induced changes to both the action potential and intracellular (Ca<jats:sup>2+</jats:sup>) influence risk; and (3) cardiac ion channels not typically assessed may significantly affect risk. Our algorithm demonstrates the value of systematic simulations in predicting pharmacological toxicity.</jats:p> Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms Clinical Pharmacology & Therapeutics |
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| title |
Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms |
| title_unstemmed |
Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms |
| title_full |
Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms |
| title_fullStr |
Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms |
| title_full_unstemmed |
Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms |
| title_short |
Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms |
| title_sort |
improved prediction of drug‐induced torsades de pointes through simulations of dynamics and machine learning algorithms |
| topic |
Pharmacology (medical) Pharmacology |
| url |
http://dx.doi.org/10.1002/cpt.367 |
| publishDate |
2016 |
| physical |
371-379 |
| description |
<jats:p>The ventricular arrhythmia Torsades de Pointes (TdP) is a common form of drug‐induced cardiotoxicity, but prediction of this arrhythmia remains an unresolved issue in drug development. Current assays to evaluate arrhythmia risk are limited by poor specificity and a lack of mechanistic insight. We addressed this important unresolved issue through a novel computational approach that combined simulations of drug effects on dynamics with statistical analysis and machine‐learning. Drugs that blocked multiple ion channels were simulated in ventricular myocyte models, and metrics computed from the action potential and intracellular (Ca<jats:sup>2+</jats:sup>) waveform were used to construct classifiers that distinguished between arrhythmogenic and nonarrhythmogenic drugs. We found that: (1) these classifiers provide superior risk prediction; (2) drug‐induced changes to both the action potential and intracellular (Ca<jats:sup>2+</jats:sup>) influence risk; and (3) cardiac ion channels not typically assessed may significantly affect risk. Our algorithm demonstrates the value of systematic simulations in predicting pharmacological toxicity.</jats:p> |
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| author | Lancaster, M Cummins, Sobie, EA |
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| description | <jats:p>The ventricular arrhythmia Torsades de Pointes (TdP) is a common form of drug‐induced cardiotoxicity, but prediction of this arrhythmia remains an unresolved issue in drug development. Current assays to evaluate arrhythmia risk are limited by poor specificity and a lack of mechanistic insight. We addressed this important unresolved issue through a novel computational approach that combined simulations of drug effects on dynamics with statistical analysis and machine‐learning. Drugs that blocked multiple ion channels were simulated in ventricular myocyte models, and metrics computed from the action potential and intracellular (Ca<jats:sup>2+</jats:sup>) waveform were used to construct classifiers that distinguished between arrhythmogenic and nonarrhythmogenic drugs. We found that: (1) these classifiers provide superior risk prediction; (2) drug‐induced changes to both the action potential and intracellular (Ca<jats:sup>2+</jats:sup>) influence risk; and (3) cardiac ion channels not typically assessed may significantly affect risk. Our algorithm demonstrates the value of systematic simulations in predicting pharmacological toxicity.</jats:p> |
| doi_str_mv | 10.1002/cpt.367 |
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| physical | 371-379 |
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| series | Clinical Pharmacology & Therapeutics |
| source_id | 49 |
| spelling | Lancaster, M Cummins Sobie, EA 0009-9236 1532-6535 Wiley Pharmacology (medical) Pharmacology http://dx.doi.org/10.1002/cpt.367 <jats:p>The ventricular arrhythmia Torsades de Pointes (TdP) is a common form of drug‐induced cardiotoxicity, but prediction of this arrhythmia remains an unresolved issue in drug development. Current assays to evaluate arrhythmia risk are limited by poor specificity and a lack of mechanistic insight. We addressed this important unresolved issue through a novel computational approach that combined simulations of drug effects on dynamics with statistical analysis and machine‐learning. Drugs that blocked multiple ion channels were simulated in ventricular myocyte models, and metrics computed from the action potential and intracellular (Ca<jats:sup>2+</jats:sup>) waveform were used to construct classifiers that distinguished between arrhythmogenic and nonarrhythmogenic drugs. We found that: (1) these classifiers provide superior risk prediction; (2) drug‐induced changes to both the action potential and intracellular (Ca<jats:sup>2+</jats:sup>) influence risk; and (3) cardiac ion channels not typically assessed may significantly affect risk. Our algorithm demonstrates the value of systematic simulations in predicting pharmacological toxicity.</jats:p> Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms Clinical Pharmacology & Therapeutics |
| spellingShingle | Lancaster, M Cummins, Sobie, EA, Clinical Pharmacology & Therapeutics, Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms, Pharmacology (medical), Pharmacology |
| title | Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms |
| title_full | Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms |
| title_fullStr | Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms |
| title_full_unstemmed | Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms |
| title_short | Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms |
| title_sort | improved prediction of drug‐induced torsades de pointes through simulations of dynamics and machine learning algorithms |
| title_unstemmed | Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms |
| topic | Pharmacology (medical), Pharmacology |
| url | http://dx.doi.org/10.1002/cpt.367 |