Randomforest based assessment of the hERG channel inhibition potential for the early drug cardiotoxicity testing

• Autorzy: Wiśniowska B. , Mendyk A. , Polak M. , Szlęk J. , Polak S.
• Języki publikacji: EN

Abstrakty

EN

Acquired long QT syndrome (LQTS) can lead to fatal ventricular arrhythmia and one of the most common reasons for developing LQTS seen in clinical settings as Torsade de Pointes (TdP) are drugs. LQTS syndrome and TdP are principally caused by the inhibition of the potassium channels encoded by hERG (the human ether-a-go-go related gene). The potassium channels and ionic currents (lkr llo, lks and others) together with calcium and sodium channels and currents (lCaL, lNa respectively) are the key elements of the electrophysiological interplay in heart. Drugs affinity to hERG channels and life-threatening interferences in heart electrophysiology resulted in withdrawal of many substances from the pharmaceutical market and some other drugs were black-boxed as potentially dangerous. Aim of the study was to develop reliable and easy to use model for the drug affinity to the hERG channel inhibition. Database used for the modeling purposes contains 447 records which were utilized during the modeling and validation levels. Dataset is freely available from the CompTox project website (www.tox-portal. net). Three various validation modes were applied to the model performance assessment to ensure highest possible reliability of the finał model: standard 10-fold cross validation procedure (10-fold CV), enhanced 10-fold cross validation (whole drugs excluded from test sets) and validation on external test set of 62 records for both previously present (different in vitro models) and absent in native dataset drugs. Pre-processing included recalculation of the original output (IC50 value - concentration of a drug which causes 50% inhibition of the ionic current) derived from the in vitro experiments, with use of the scaling factors. Random Forest algorithm with either 10 or 50 or 100 generated trees and unlimited tree depth implemented in WEKA software was used. The input consisted of 1034 parameters describing in vitro setting (3), physico-chemical properties (7), and structure (so called Chemical fingerprint -1024). Output had a binary characteristic with IC50 equal to 1 ĘM concentration as the safety threshold value (encoded as 0-safe, 1- unsafe). The performance of the best model estimated in simple 10-fold CV was 85% (1-88%, 0-82%) with an average ROC accuracy of 0.92. Implementation of rigorous 10-fold CV procedurę resulted in decrease in total accuracy to 72% (1-72%, 0-72%) with ROC value equal to 0.791. Test on the external set consists of three measures: all 62 records (total - 73%, 1-62%, 0-81%), 33 enew f records describing previously unknown drugs (total - 73%, 1-62%, 0-81%) and eold f records describing previously present drugs (total - 83%, 1-78%, 091%).

Słowa kluczowe

EN

RandomForest; decision tree; hERG; lkr current inhibition; cardiotoxicity prediction; Torsade de Pointes

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2010
• Tom: Vol. 6, no. 12
• Strony: 131--136
• Opis fizyczny: Bibliogr. 25 poz., tab.

Twórcy

autor

Wiśniowska B.

• Unit of Pharmacoepidemiology and Pharmacoeconomics Faculty of Pharmacy Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland

autor

Mendyk A.

• Department of Pharmaceutical Technology and Biopharmaceutics Faculty of Pharmacy Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland

autor

Polak M.

• Unit of Pharmacoepidemiology and Pharmacoeconomics Faculty of Pharmacy Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland

autor

Szlęk J.

• Department of Pharmaceutical Technology and Biopharmaceutics Faculty of Pharmacy Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland

autor

Polak S.

• Unit of Pharmacoepidemiology and Pharmacoeconomics Faculty of Pharmacy Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland

Bibliografia

• 1. Valerio L.G., Jr. (2009), In silico toxicology for the pharmaceutical sciences. Toxicology and Applied Pharmacology 241(3), 356-370.
• 2. Merlot C. (2010), Computational toxicology - a tool for early safety evaluation. Drug Discovery Today 15(1-2), 16-22.
• 3. Langham J.J., Jain A.N. (2008), Accurate and interpretable computational modeling of chemical mutagenicity. Journal of Chemical Information and Modeling 48(9), 1833-1839.
• 4. Ursem C.J., Kruhlak N.L., Contrera J.F. et al. (2009), Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans. Part A: use of FDA post-market reports to create a database of hepatobiliary and urinary tract toxicities. Regulatory toxicology and phar­macology 54(1), 1-22.
• 5. Matthews E.J., Ursem C.J., Kruhlak NI. et al. (2009), Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans. Part B: Use of (Q) SAR systems for early detection of drug-induced hepatobiliary and urinary tract toxicities. Regulatory toxicology and pharmacology 54(1), 23-42.
• 6. Matthews E.J., Kruhlak N.L., Benz R.D. et al. (2009), Iden­tification of structure-activity relationships for adverse effects of pharmaceuticals in humans. Part C: use of QSAR and an expert system for the estimation of the mechanism of action of drug-induced hepatobiliary and urinary tract toxicities. Regulatory toxicology and pharmacology 54(1), 43-65.
• 7. Cohen Hubal E.A., Richard A.M., Shah I. et al. (2010), Exposure science and the U.S. EPA National Center for Computational Toxicology. Journal of Exposure Science & Environmental Epidemiology 20(3), 231-236.
• 8. Mohan C.G., Gandhi T., Garg D., Shinde R. (2007), Computer-assisted Methods in Chemical Toxicity Prediction. Mini Reviews in Medicinal Chemistry 7(5), 499-508.
• 9. Witten I.H., Frank E. (2005), Data Mining: Practical ma­chine learning tools and techniques. 2nd Edition, Morgan Kaufmann, San Francisco.
• 10. Polak S., Wiśniowska B., Brandys J. (2009), Collation, assessment and analysis of literature in vitro data on hERG receptor blocking potency for subsequent modeling of drugs' cardiotoxic properties. Journal of Applied Toxicology 29(3), 183-206.
• 11. http://www.tox-portal.net; lastaccessed 15.05.2010.
• 12. http://www.ich.org; lastaccessed 15.05.2010.
• 13. Talete srl, DRAGON for Windows (Software for Molecular Descriptor Calculations). Version 5.5 - 2007: http://www.talete.mi.it; lastaccessed 15.05.2010.
• 14. Frid A.A., Matthews EJ. (2010), Prediction of drug-related cardiac adverse effects in humans: B: Use of QSAR programs for early detection of drug-induced cardiac toxicities. Regulatory toxicology and pharmacology 56(3), 276-289.
• 15. FDSCritical PathWhite Paper: www.fda.gov; lastaccessed 15.05.2010.
• 16. Cheng C.S., Alderman D., Kwash J., Dessaint J., Patel R., Lescoe M.K., Kinrade M.B., Yu W. (2002), A high-through-put HERG potassium channel function assay: An old assay with a new look. Drug. Dev. Ind. Pharm. 28(2), 177-191.
• 17. Gili S., Gili J., Lee S.S., Hesketh C, Fedida D., Rezazadeh S., Stankovich L, Liang D. (2003), Flux assays in high throughput screening of ion channels in drug discovery. Assay Drug Dev. Technol. 1(5), 709-717.
• 18. Finlayson K., Pennington A.J., Kelly J.S. (2001), [3H] Dofetilide binding in SHSY5Y and HEK293 cells expressing a HERG-like K+ channel? Eur. J. Pharmacol. 412,203-212.
• 19. Wang J., Delia Penna K., Wang H., Karczewski J., Connolly T„ Koblan K., Bennett P., Sałata J. (2003), Functional and pharmacological properties of canine ERG potas­sium channels. Am. J. Physiol. Heart Circ. Physiol. 284(1), H256-H267.
• 20. Witchel H., Milnes J., Mitcheson J., Hancox J. (2002), Troubleshooting problems with in vitro screening of drugs for QT interval prolongation using HERG K+ channels expressed in mammalian celi lines and Xenopus oocytes. J. Pharmacol. Toxicol. Methods 48(2), 65-80.
• 21. Baxter D.F., Kirk M., Garcia A.F., Raimondi A., Holmqvist M.H., Flint K.K., Bojanic D., Distefano P.S., Curtis R., Xie Y. (2002), A novel membrane potential-sensitive fluorescent dye improves cell-based assays for ion channels. J. Biomol. Screen. 7(1), 79-85.
• 22. Dom A., Hermann F., Ebneth A., Bothmann H., Trube G., Christensen K., Apfel C. (2005), Evaluation of a high-throughput fluorescence assay method for hERG potas­sium channel inhibition. J. Biom. Screen. 10(4), 339-347.
• 23. Gonzalez J.E., Oades K., Leychkis Y, Harootunian A., Negulescu P.A. (1999), Cell-based assays and instrumentation for screening ion-channel targets. Drug. Discov. Ther. 4(9), 431-439.
• 24. Wiśniowska B., Polak S. (2009), HERG in vitro interchange factors development and verification hERG in vitro inter change factors. Toxicology Mechanisms and Methods 19(4), 278-284.
• 25. http://cdelib.sourceforge.net/doc/fingerprints.html; last accessed 15.05.2010.