Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters
Irinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity, possibly leading to the delay or suspension of the cycle, with the consequent impact on the prognosis of survival. The main goal of this work is to predict the toxicities derived from CPT-11 using a...
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| Format: | info:eu-repo/semantics/article |
| Language: | eng |
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Elsevier BV
2021
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| Online Access: | https://hdl.handle.net/10171/61901 |
| _version_ | 1793400203554848768 |
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| author | Oyaga-Iriarte, E. (Esther) Insausti, A. (Asier) Sayar, O. (Onintza) Aldaz, A. (Azucena) |
| author_facet | Oyaga-Iriarte, E. (Esther) Insausti, A. (Asier) Sayar, O. (Onintza) Aldaz, A. (Azucena) |
| author_sort | Oyaga-Iriarte, E. (Esther) |
| collection | DSpace |
| description | Irinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity, possibly leading to the delay or suspension of the cycle, with the consequent impact on the prognosis of survival. The main goal of this work is to predict the toxicities derived from CPT-11 using artificial intelligence methods. The data for this study is conformed of 53 cycles of FOLFIRINOX, corresponding to patients with metastatic colorectal cancer. Supported by several demographic data, blood markers and pharmacokinetic parameters resulting from a non-compartmental pharmacokinetic study of CPT-11 and its metabolites (SN-38 and SN-38-G), we use machine learning techniques to predict high degrees of different toxicities (leukopenia, neutropenia and diarrhea) in new patients. We predict high degree of leukopenia with an accuracy of 76%, neutropenia with 75% and diarrhea with 91%. Among other variables, this study shows that the areas under the curve of CPT-11, SN-38 and SN-38-G play a relevant role in the prediction of the studied toxicities. The presented models allow to predict the degree of toxicity for each cycle of treatment according to the particularities of each patient |
| format | info:eu-repo/semantics/article |
| id | oai:dadun.unav.edu:10171-61901 |
| institution | Universidad de Navarra |
| language | eng |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | oai:dadun.unav.edu:10171-619012021-09-03T01:05:41Z Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters Oyaga-Iriarte, E. (Esther) Insausti, A. (Asier) Sayar, O. (Onintza) Aldaz, A. (Azucena) Materias Investigacion::Ciencias de la Salud::Química médica Colorectal cancer Irinotecan Machine learning Pharmacokinetics Toxicity Irinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity, possibly leading to the delay or suspension of the cycle, with the consequent impact on the prognosis of survival. The main goal of this work is to predict the toxicities derived from CPT-11 using artificial intelligence methods. The data for this study is conformed of 53 cycles of FOLFIRINOX, corresponding to patients with metastatic colorectal cancer. Supported by several demographic data, blood markers and pharmacokinetic parameters resulting from a non-compartmental pharmacokinetic study of CPT-11 and its metabolites (SN-38 and SN-38-G), we use machine learning techniques to predict high degrees of different toxicities (leukopenia, neutropenia and diarrhea) in new patients. We predict high degree of leukopenia with an accuracy of 76%, neutropenia with 75% and diarrhea with 91%. Among other variables, this study shows that the areas under the curve of CPT-11, SN-38 and SN-38-G play a relevant role in the prediction of the studied toxicities. The presented models allow to predict the degree of toxicity for each cycle of treatment according to the particularities of each patient 2021-09-02T07:03:27Z 2021-09-02T07:03:27Z 2019 info:eu-repo/semantics/article https://hdl.handle.net/10171/61901 eng 10.1016/j.jphs.2019.03.004 info:eu-repo/semantics/openAccess application/pdf Elsevier BV |
| spellingShingle | Materias Investigacion::Ciencias de la Salud::Química médica Colorectal cancer Irinotecan Machine learning Pharmacokinetics Toxicity Oyaga-Iriarte, E. (Esther) Insausti, A. (Asier) Sayar, O. (Onintza) Aldaz, A. (Azucena) Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters |
| title | Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters |
| title_full | Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters |
| title_fullStr | Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters |
| title_full_unstemmed | Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters |
| title_short | Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters |
| title_sort | prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters |
| topic | Materias Investigacion::Ciencias de la Salud::Química médica Colorectal cancer Irinotecan Machine learning Pharmacokinetics Toxicity |
| url | https://hdl.handle.net/10171/61901 |
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