Kao Corporation, Safety Science Research Laboratories.
Faculty of Pharmacy and Pharmaceutical Sciences, Josai University.
J Toxicol Sci. 2024;49(5):219-230. doi: 10.2131/jts.49.219.
Quantitative structure permeation relationship (QSPR) models have gained prominence in recent years owing to their capacity to elucidate the influence of physicochemical properties on the dermal absorption of chemicals. These models facilitate the prediction of permeation coefficient (Kp) values, indicating the skin permeability of a chemical under infinite dose conditions. Conversely, obtaining dermal absorption rates (DAs) under finite dose conditions, which are crucial for skin product safety evaluation, remains a challenge when relying solely on Kp predictions from QSPR models. One proposed resolution involves using Kroes' methodology, categorizing DAs based on Kp values; however, refinement becomes necessary owing to discreteness in the obtained values. We previously developed a mathematical model using Kp values obtained from in vitro dermal absorption tests to predict DAs. The present study introduces a new methodology, Integrating Mathematical Approaches (IMAS), which combines QSPR models and our mathematical model to predict DAs for risk assessments without conducting in vitro dermal absorption tests. Regarding 40 chemicals (76.1 ≤ MW ≤ 220; -1.4 ≤ Log K ≤ 3.1), IMAS showed that 65.0% (26/40) predictions of DA values were accurate to within twofold of the observed values in finite dose experiments. Compared to Kroes' methodology, IMAS notably mitigated overestimation, particularly for hydrophilic chemicals with water solubility exceeding 57.0 mg/cm. These findings highlight the value of IMAS as a tool for skin product risk assessments, particularly for hydrophilic compounds.
定量构效关系 (QSPR) 模型近年来备受关注,因为它们能够阐明物理化学性质对化学品经皮吸收的影响。这些模型有助于预测渗透系数 (Kp) 值,表明化学物质在无限剂量条件下的皮肤渗透性。相反,在仅依赖 QSPR 模型的 Kp 预测来获得有限剂量条件下的经皮吸收率 (DA) 时,仍然存在挑战,因为这对于皮肤产品的安全评估至关重要。一种提出的解决方案是使用 Kroes 方法,根据 Kp 值对 DAs 进行分类;然而,由于获得的值存在离散性,需要进行细化。我们之前使用从体外皮肤吸收试验中获得的 Kp 值开发了一种数学模型,用于预测 DAs。本研究引入了一种新的方法,即整合数学方法 (IMAS),它结合了 QSPR 模型和我们的数学模型,无需进行体外皮肤吸收试验即可预测风险评估中的 DAs。对于 40 种化学物质(76.1 ≤ MW ≤ 220;-1.4 ≤ Log K ≤ 3.1),IMAS 表明,65.0%(26/40)的 DA 值预测与有限剂量实验中观察到的值相差两倍以内。与 Kroes 方法相比,IMAS 显著减少了高估,特别是对于水溶性超过 57.0mg/cm 的亲水化学物质。这些发现强调了 IMAS 作为皮肤产品风险评估工具的价值,特别是对于亲水化合物。