利用对消费者生理测量数据和口腔接触水平的实时监测,模拟电子烟的尼古丁药代动力学特征。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2024-07-17 DOI:10.1186/s13040-024-00375-z
Krishna Prasad, Allen Griffiths, Kavya Agrawal, Michael McEwan, Flavio Macci, Marco Ghisoni, Matthew Stopher, Matthew Napleton, Joel Strickland, David Keating, Thomas Whitehead, Gareth Conduit, Stacey Murray, Lauren Edward
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引用次数: 0

摘要

药代动力学(PK)研究可以提供有关尼古丁和烟草产品滥用责任的重要信息,但具有侵入性,必须在临床环境中进行。这项研究的目的是探索在使用电子烟之前、期间和之后,利用可穿戴设备对生理参数和口腔尼古丁暴露水平(MLE)进行实时监测,是否可以预测使用电子烟后血浆尼古丁水平的变化。建立这种预测模型可能有助于纵向收集在正常环境中使用尼古丁产品的消费者的产品使用和尼古丁表达数据,从而减少对侵入性临床研究的需求,同时生成与真实世界中产品使用相关的 PK 数据。我们开发了一个探索性的机器学习模型,以预测使用电子烟后血浆尼古丁水平的变化;该模型来自对电子烟吸食前、吸食中和吸食后的生理参数和尼古丁 MLE 的实时监测。这项初步研究确定了一些关键参数,如心率(HR)、心率变异性(HRV)和生理压力(PS),这些参数可作为个人血浆尼古丁反应(PK 曲线)的预测因子。相对于基线测量值(每位参与者),含有尼古丁的电子烟的心率显著增加,并且在不同疗程中(参与者内部)保持一致。对所有数据进行缺失值补偿和模型训练后,原始 "学习 "数据提高了 57%,中位验证 R2 为 0.70。该研究目前处于探索阶段,其局限性包括样本量小且不多样化,以及依赖于单一电子烟产品的数据。这些发现需要进一步的研究来验证,并增强模型在现实环境中的普遍性和适用性。这项研究为开发尼古丁产品使用的非侵入式 PK 模型迈出了基础性的一步。
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Modelling the nicotine pharmacokinetic profile for e-cigarettes using real time monitoring of consumers' physiological measurements and mouth level exposure.

Pharmacokinetic (PK) studies can provide essential information on abuse liability of nicotine and tobacco products but are intrusive and must be conducted in a clinical environment. The objective of the study was to explore whether changes in plasma nicotine levels following use of an e-cigarette can be predicted from real time monitoring of physiological parameters and mouth level exposure (MLE) to nicotine before, during, and after e-cigarette vaping, using wearable devices. Such an approach would allow an -effective pre-screening process, reducing the number of clinical studies, reducing the number of products to be tested and the number of blood draws required in a clinical PK study Establishing such a prediction model might facilitate the longitudinal collection of data on product use and nicotine expression among consumers using nicotine products in their normal environments, thereby reducing the need for intrusive clinical studies while generating PK data related to product use in the real world.An exploratory machine learning model was developed to predict changes in plasma nicotine levels following the use of an e-cigarette; from real time monitoring of physiological parameters and MLE to nicotine before, during, and after e-cigarette vaping. This preliminary study identified key parameters, such as heart rate (HR), heart rate variability (HRV), and physiological stress (PS) that may act as predictors for an individual's plasma nicotine response (PK curve). Relative to baseline measurements (per participant), HR showed a significant increase for nicotine containing e-liquids and was consistent across sessions (intra-participant). Imputing missing values and training the model on all data resulted in 57% improvement from the original'learning' data and achieved a median validation R2 of 0.70.The study is in its exploratory phase, with limitations including a small and non-diverse sample size and reliance on data from a single e-cigarette product. These findings necessitate further research for validation and to enhance the model's generalisability and applicability in real-world settings. This study serves as a foundational step towards developing non-intrusive PK models for nicotine product use.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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