Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.
Biotechnol Genet Eng Rev. 2024 Nov;40(3):2180-2195. doi: 10.1080/02648725.2023.2199232. Epub 2023 Apr 5.
To solve the problem of inaccurate prediction caused by the lack of representativeness of samples due to the small sample size of the collected clinical data when using machine learning methods to predict drug concentration in plasma and describe the hysteresis phenomenon of drug effect lagging behind plasma drug concentration, this paper proposes a pharmacokinetic-pharmacodynamic (PK-PD) model based on the SSA-1DCNN-Attention network and the semicompartment method. First, a one-dimensional convolutional neural network (1DCNN) is established, and the attention mechanism is introduced to determine the importance of each physiological and biochemical parameter. The sparrow search algorithm (SSA) is used to optimize the parameters of the network to improve the prediction accuracy after data enhancement through the synthetic minority oversampling technique (SMOTE) method. After constructing the time-concentration relationship of the drug through the SSA-1DCNN-Attention network, the concentration-effect relationship of the drug is established by using the semicompartment method to synchronize the drug effect with the concentration. At last, the phenobarbital (PHB) combined with Cynanchum otophyllum saponins to treat epilepsy was taken as an example to validate the proposed method.
为了解决由于采集的临床数据样本量小，导致机器学习方法预测血浆药物浓度时样本代表性不足，从而导致预测不准确的问题，并描述药物效应滞后于血浆药物浓度的滞后现象，本文提出了一种基于麻雀搜索算法（SSA）-一维卷积神经网络（1DCNN）-注意力网络和半隔室法的药代动力学-药效学（PK-PD）模型。首先，建立了一个一维卷积神经网络（1DCNN），并引入注意力机制来确定每个生理生化参数的重要性。利用麻雀搜索算法（SSA）优化网络参数，通过合成少数过采样技术（SMOTE）方法对数据增强后提高预测精度。通过 SSA-1DCNN-注意力网络构建药物的时间-浓度关系后，再利用半隔室法建立药物的浓度-效应关系，使药物效应与浓度同步。最后，以苯巴比妥（PHB）联合杠柳苷元治疗癫痫为例验证了所提出的方法。
