Jaber Mutaz M, Yaman Burhaneddin, Sarafoglou Kyriakie, Brundage Richard C
Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455, USA.
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Pharmaceutics. 2021 May 26;13(6):797. doi: 10.3390/pharmaceutics13060797.
A specific model for drug absorption is necessarily assumed in pharmacokinetic (PK) analyses following extravascular dosing. Unfortunately, an inappropriate absorption model may force other model parameters to be poorly estimated. An added complexity arises in population PK analyses when different individuals appear to have different absorption patterns. The aim of this study is to demonstrate that a deep neural network (DNN) can be used to prescreen data and assign an individualized absorption model consistent with either a first-order, Erlang, or split-peak process. Ten thousand profiles were simulated for each of the three aforementioned shapes and used for training the DNN algorithm with a 30% hold-out validation set. During the training phase, a 99.7% accuracy was attained, with 99.4% accuracy during in the validation process. In testing the algorithm classification performance with external patient data, a 93.7% accuracy was reached. This algorithm was developed to prescreen individual data and assign a particular absorption model prior to a population PK analysis. We envision it being used as an efficient prescreening tool in other situations that involve a model component that appears to be variable across subjects. It has the potential to reduce the time needed to perform a manual visual assignment and eliminate inter-assessor variability and bias in assigning a sub-model.
在血管外给药后的药代动力学（PK）分析中，必然要假定一种特定的药物吸收模型。不幸的是，不合适的吸收模型可能会导致其他模型参数估计不佳。当不同个体似乎具有不同的吸收模式时，群体PK分析会出现额外的复杂性。本研究的目的是证明深度神经网络（DNN）可用于预先筛选数据，并分配与一级、埃尔朗或分裂峰过程一致的个性化吸收模型。针对上述三种形状中的每一种模拟了一万个曲线，并用于使用30%的留出验证集训练DNN算法。在训练阶段，准确率达到了99.7%，在验证过程中的准确率为99.4%。在用外部患者数据测试算法分类性能时，准确率达到了93.7%。开发该算法是为了在群体PK分析之前预先筛选个体数据并分配特定的吸收模型。我们设想它可在其他涉及模型组件在不同受试者之间似乎存在差异的情况下用作一种有效的预筛选工具。它有可能减少进行手动视觉分配所需的时间，并消除在分配子模型时评估者之间的变异性和偏差。
