Introduction: Transdermal drug delivery is gaining popularity as an alternative to traditional routes of administration. It can increase patient compliance because of its painless and noninvasive nature, aid compounds in bypassing presystemic metabolic effects, and reduce the likelihood of adverse effects through decreased systemic exposure. In silico physiological modeling is critical to predicting dermal exposure for a therapeutic and assessing the impact of different formulations on transdermal disposition.
Methods: The present study aimed at developing a physiologically based transdermal platform, “BIOiSIM”, that could be globally applied to a wide variety of compounds to predict their transdermal disposition. The platform integrates a 16-compartment model of compound pharmacokinetics and was used to simulate and predict drug exposure of three chemically and biologically distinct drug-like compounds. Machine learning optimization was composed of two components: exhaustive search algorithm (coarse-tuning) and descent (fine-tuning) integrated with the platform used to quantitatively determine parameters influencing pharmacokinetics (eg permeability, k_perm) of test compounds.
Results: The model successfully predicted drug exposure (AUC, C_max and T_max) following transdermal application of morphine, buprenorphine and nicotine in human subjects, mostly with less than two-fold absolute average fold error (AAFE). The model was further able to successfully characterize the relationship between observed systemic exposure and intended pharmacological effect. The predicted systemic concentration of morphine and plasma levels of endogenous pain biomarkers were used to estimate the effectiveness of a given therapeutic regimen.
Conclusion: BIOiSIM marks a novel approach to in silico prediction that will enable leveraging of machine learning technology in the pharmaceutical space. The approach to model development outlined results in scalable, accurate models and enables the generation of large parameter/coefficient datasets from in vivo clinical data that can be used in future work to train quantitative structure activity relationship (QSAR) models for predicting likelihood of compound utility as a transdermally administered therapeutic.

Keywords: transdermal absorption, computational modeling, machine learning, BIOiSIM


中文翻译：

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可扩展的透皮药物渗透性计算机模拟：BIOiSIM 平台的应用。

简介：透皮给药作为传统给药途径的替代方案越来越受欢迎。由于其无痛和无创的性质，它可以提高患者的依从性，帮助化合物绕过系统前代谢效应，并通过减少全身暴露来降低不良反应的可能性。计算机生理模型对于预测治疗剂的皮肤暴露和评估不同制剂对透皮分布的影响至关重要。
方法：本研究旨在开发一个基于生理学的透皮平台“BIOiSIM”，该平台可在全球范围内应用于多种化合物以预测其透皮分布。该平台集成了化合物药代动力学的 16 室模型，用于模拟和预测三种化学和生物学上不同的类药化合物的药物暴露。机器学习优化由两个部分组成：穷举搜索算法（粗调）和下降（微调），与用于定量确定影响测试化合物药代动力学参数（例如渗透性、k _{perm ）的平台集成。}
结果：该模型成功预测人类受试者经皮应用吗啡、丁丙诺啡和尼古丁后的药物暴露（AUC、C _max和 T _max），大部分绝对平均倍数误差 (AAFE) 小于两倍。该模型进一步能够成功地描述观察到的全身暴露与预期药理作用之间的关系。预测的吗啡全身浓度和内源性疼痛生物标志物的血浆水平用于估计给定治疗方案的有效性。
结论： BIOiSIM 标志着一种新的计算机预测方法，它将能够在制药领域利用机器学习技术。模型开发方法概述了可扩展、准确的模型，并能够从体内临床数据生成大型参数/系数数据集，这些数据集可在未来的工作中用于训练定量结构活性关系（QSAR）模型，以预测化合物效用的可能性作为透皮给药的治疗剂。

关键词：透皮吸收、计算建模、机器学习、BIOiSIM