Data-based regression models for predicting remifentanil pharmacokinetics.

Abstract

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.