Evaluating the performance of machine‐learning regression models for pharmacokinetic drug–drug interactions
Abstract Combination therapy or concomitant drug administration can be associated with pharmacokinetic drug–drug interactions, increasing the risk of adverse drug events and reduced drug efficacy. Thus far, machine‐learning models have been developed that can classify drug–drug interactions. However...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | CPT: Pharmacometrics & Systems Pharmacology |
| Online Access: | https://doi.org/10.1002/psp4.12884 |
| _version_ | 1797955033653837824 |
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| author | Jaidip Gill Marie Moullet Anton Martinsson Filip Miljković Beth Williamson Rosalinda H. Arends Venkatesh Pilla Reddy |
| author_facet | Jaidip Gill Marie Moullet Anton Martinsson Filip Miljković Beth Williamson Rosalinda H. Arends Venkatesh Pilla Reddy |
| author_sort | Jaidip Gill |
| collection | DOAJ |
| description | Abstract Combination therapy or concomitant drug administration can be associated with pharmacokinetic drug–drug interactions, increasing the risk of adverse drug events and reduced drug efficacy. Thus far, machine‐learning models have been developed that can classify drug–drug interactions. However, to enable quantification of the pharmacokinetic effects of a drug–drug interaction, regression‐based machine learning should be explored. Therefore, this study investigated the use of regression‐based machine learning to predict changes in drug exposure caused by pharmacokinetic drug–drug interactions. Fold changes in exposure relative to substrate drug monotherapy were collected from 120 clinical drug–drug interaction studies extracted from the Washington Drug Interaction Database and SimCYP compound library files. Drug characteristics (features) were collected such as structure, physicochemical properties, in vitro pharmacokinetic properties, cytochrome P450 metabolic activity, and population characteristics. Three different regression‐based supervised machine‐learning models were then applied to the prediction task: random forest, elastic net, and support vector regressor. Model performance was evaluated using fivefold cross‐validation. Strongest performance was observed with support vector regression, with 78% of predictions within twofold of the observed exposure changes. The results show that changes in drug exposure can be predicted with reasonable accuracy using regression‐based machine‐learning models trained on data available early in drug discovery. This has potential applications in enabling earlier drug–drug interaction risk assessment for new drug candidates. |
| first_indexed | 2024-04-10T23:26:55Z |
| format | Article |
| id | doaj.art-f96ea62fd0d24a9babc7f8349e98ad16 |
| institution | Directory Open Access Journal |
| issn | 2163-8306 |
| language | English |
| last_indexed | 2024-04-10T23:26:55Z |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | CPT: Pharmacometrics & Systems Pharmacology |
| spelling | doaj.art-f96ea62fd0d24a9babc7f8349e98ad162023-01-12T12:01:56ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062023-01-0112112213410.1002/psp4.12884Evaluating the performance of machine‐learning regression models for pharmacokinetic drug–drug interactionsJaidip Gill0Marie Moullet1Anton Martinsson2Filip Miljković3Beth Williamson4Rosalinda H. Arends5Venkatesh Pilla Reddy6Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences, Biopharmaceuticals Research & Development, AstraZeneca Cambridge UKClinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences, Biopharmaceuticals Research & Development, AstraZeneca Cambridge UKImaging and Data Analytics Clinical Pharmacology & Safety Sciences, Research & Development, AstraZeneca Gothenburg SwedenImaging and Data Analytics Clinical Pharmacology & Safety Sciences, Research & Development, AstraZeneca Gothenburg SwedenOncology Drug Metabolism and Pharmacokinetics, Research & Development, AstraZeneca Cambridge UKClinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences, Biopharmaceuticals, Research & Development, AstraZeneca Gaithersburg Maryland USAClinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences, Biopharmaceuticals Research & Development, AstraZeneca Cambridge UKAbstract Combination therapy or concomitant drug administration can be associated with pharmacokinetic drug–drug interactions, increasing the risk of adverse drug events and reduced drug efficacy. Thus far, machine‐learning models have been developed that can classify drug–drug interactions. However, to enable quantification of the pharmacokinetic effects of a drug–drug interaction, regression‐based machine learning should be explored. Therefore, this study investigated the use of regression‐based machine learning to predict changes in drug exposure caused by pharmacokinetic drug–drug interactions. Fold changes in exposure relative to substrate drug monotherapy were collected from 120 clinical drug–drug interaction studies extracted from the Washington Drug Interaction Database and SimCYP compound library files. Drug characteristics (features) were collected such as structure, physicochemical properties, in vitro pharmacokinetic properties, cytochrome P450 metabolic activity, and population characteristics. Three different regression‐based supervised machine‐learning models were then applied to the prediction task: random forest, elastic net, and support vector regressor. Model performance was evaluated using fivefold cross‐validation. Strongest performance was observed with support vector regression, with 78% of predictions within twofold of the observed exposure changes. The results show that changes in drug exposure can be predicted with reasonable accuracy using regression‐based machine‐learning models trained on data available early in drug discovery. This has potential applications in enabling earlier drug–drug interaction risk assessment for new drug candidates.https://doi.org/10.1002/psp4.12884 |
| spellingShingle | Jaidip Gill Marie Moullet Anton Martinsson Filip Miljković Beth Williamson Rosalinda H. Arends Venkatesh Pilla Reddy Evaluating the performance of machine‐learning regression models for pharmacokinetic drug–drug interactions CPT: Pharmacometrics & Systems Pharmacology |
| title | Evaluating the performance of machine‐learning regression models for pharmacokinetic drug–drug interactions |
| title_full | Evaluating the performance of machine‐learning regression models for pharmacokinetic drug–drug interactions |
| title_fullStr | Evaluating the performance of machine‐learning regression models for pharmacokinetic drug–drug interactions |
| title_full_unstemmed | Evaluating the performance of machine‐learning regression models for pharmacokinetic drug–drug interactions |
| title_short | Evaluating the performance of machine‐learning regression models for pharmacokinetic drug–drug interactions |
| title_sort | evaluating the performance of machine learning regression models for pharmacokinetic drug drug interactions |
| url | https://doi.org/10.1002/psp4.12884 |
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