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Review Article

机器学习技术在ADME相关药代动力学参数预测中的应用

卷 30, 期 17, 2023

发表于: 07 October, 2022

页: [1945 - 1962] 页: 18

弟呕挨: 10.2174/0929867329666220819122205

价格: $65

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摘要

背景：作为药物发现的重要决定因素，药物动力学参数的准确分析和获取对于药物的临床应用非常重要。目前，新药的研发主要通过数据分析、生理模型构建等方法获取其药代动力学参数，但结果往往与实际情况大相径庭，需要更多的人力物力。 目的：我们主要讨论机器学习技术在药物动力学参数预测中的应用，这些参数主要与药物在人体内的吸收、分布、代谢和排泄的定量研究有关，如生物利用度、清除率、表观分布体积等。 方法：本文首先介绍了药物动力学参数、定量构效关系模型与机器学习之间的关系，然后讨论了机器学习技术在不同预测模型中的应用，预测药代动力学参数的机器学习模型的前景和未来发展。 结果：与传统的药代动力学分析不同，机器学习技术可以使用计算机和算法在不同程度上加快药代动力学参数的获取。它为加快和缩短药物开发周期提供了新思路，并已成功应用于药物设计和开发。结论：机器学习技术在预测药代动力学参数方面具有巨大潜力。它也为未来临床药物的设计和开发提供了更多的选择和机会。

关键词: 药物发现、机器学习、药代动力学参数、吸收、分布、代谢、排泄、定量构效关系。

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