Data-based regression models for predicting remifentanil pharmacokinetics
Background and Aims: Remifentanil is a powerful synthetic opioid drug with a short initiation and period of action, making it an ultra-short-acting opioid. It is delivered as an intravenous infusion during surgical procedures for pain management. However, deciding on a suitable dosage depends on var...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer Medknow Publications
2024-12-01
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| Series: | Indian Journal of Anaesthesia |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.4103/ija.ija_549_24 |
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| _version_ | 1841556874441261056 |
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| author | Prathvi Shenoy Mahadev Rao Shreesha Chokkadi Sushma Bhatnagar Naveen Salins |
| author_facet | Prathvi Shenoy Mahadev Rao Shreesha Chokkadi Sushma Bhatnagar Naveen Salins |
| author_sort | Prathvi Shenoy |
| collection | DOAJ |
| description | Background and Aims:
Remifentanil is a powerful synthetic opioid drug with a short initiation and period of action, making it an ultra-short-acting opioid. It is delivered as an intravenous infusion during surgical procedures for pain management. However, deciding on a suitable dosage depends on various aspects specific to each individual.
Methods:
Conventional pharmacokinetic and pharmacodynamic (PK-PD) models mainly rely on manually choosing the parameters. Target-controlled drug delivery systems need precise predictions of the drug’s analgesic effects. This work investigates various supervised machine learning (ML) methods to analyse the pharmacokinetic characteristics of remifentanil, imitating the measured data. From the Kaggle database, features such as age, gender, infusion rate, body surface area, and lean body mass are extracted to determine the drug concentration at a specific instant of time.
Results:
The characteristics show that the prediction algorithms perform better over traditional PK-PD models with greater accuracy and minimum mean squared error (MSE). By optimising the hyperparameters with Bayesian methods, the performance of these models is significantly improved, attaining the minimum MSE value.
Conclusion:
Applying ML algorithms in drug delivery can significantly reduce resource costs and the time and effort essential for laboratory experiments in the pharmaceutical industry. |
| format | Article |
| id | doaj-art-84914eda6a02493ba9fd6e0c6ffbd94e |
| institution | Kabale University |
| issn | 0019-5049 0976-2817 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Indian Journal of Anaesthesia |
| spelling | doaj-art-84914eda6a02493ba9fd6e0c6ffbd94e2025-01-07T05:46:28ZengWolters Kluwer Medknow PublicationsIndian Journal of Anaesthesia0019-50490976-28172024-12-0168121081109110.4103/ija.ija_549_24Data-based regression models for predicting remifentanil pharmacokineticsPrathvi ShenoyMahadev RaoShreesha ChokkadiSushma BhatnagarNaveen SalinsBackground and Aims: Remifentanil is a powerful synthetic opioid drug with a short initiation and period of action, making it an ultra-short-acting opioid. It is delivered as an intravenous infusion during surgical procedures for pain management. However, deciding on a suitable dosage depends on various aspects specific to each individual. Methods: Conventional pharmacokinetic and pharmacodynamic (PK-PD) models mainly rely on manually choosing the parameters. Target-controlled drug delivery systems need precise predictions of the drug’s analgesic effects. This work investigates various supervised machine learning (ML) methods to analyse the pharmacokinetic characteristics of remifentanil, imitating the measured data. From the Kaggle database, features such as age, gender, infusion rate, body surface area, and lean body mass are extracted to determine the drug concentration at a specific instant of time. Results: The characteristics show that the prediction algorithms perform better over traditional PK-PD models with greater accuracy and minimum mean squared error (MSE). By optimising the hyperparameters with Bayesian methods, the performance of these models is significantly improved, attaining the minimum MSE value. Conclusion: Applying ML algorithms in drug delivery can significantly reduce resource costs and the time and effort essential for laboratory experiments in the pharmaceutical industry.https://journals.lww.com/10.4103/ija.ija_549_24analgesiaartificial intelligencemachine learningmathematical modelpainpalliative carepharmacodynamicpharmacokineticremifentanil |
| spellingShingle | Prathvi Shenoy Mahadev Rao Shreesha Chokkadi Sushma Bhatnagar Naveen Salins Data-based regression models for predicting remifentanil pharmacokinetics Indian Journal of Anaesthesia analgesia artificial intelligence machine learning mathematical model pain palliative care pharmacodynamic pharmacokinetic remifentanil |
| title | Data-based regression models for predicting remifentanil pharmacokinetics |
| title_full | Data-based regression models for predicting remifentanil pharmacokinetics |
| title_fullStr | Data-based regression models for predicting remifentanil pharmacokinetics |
| title_full_unstemmed | Data-based regression models for predicting remifentanil pharmacokinetics |
| title_short | Data-based regression models for predicting remifentanil pharmacokinetics |
| title_sort | data based regression models for predicting remifentanil pharmacokinetics |
| topic | analgesia artificial intelligence machine learning mathematical model pain palliative care pharmacodynamic pharmacokinetic remifentanil |
| url | https://journals.lww.com/10.4103/ija.ija_549_24 |
| work_keys_str_mv | AT prathvishenoy databasedregressionmodelsforpredictingremifentanilpharmacokinetics AT mahadevrao databasedregressionmodelsforpredictingremifentanilpharmacokinetics AT shreeshachokkadi databasedregressionmodelsforpredictingremifentanilpharmacokinetics AT sushmabhatnagar databasedregressionmodelsforpredictingremifentanilpharmacokinetics AT naveensalins databasedregressionmodelsforpredictingremifentanilpharmacokinetics |