Gobburu J V, Chen E P
Department of Pharmaceutical Sciences, North Dakota State University, Fargo 58105, USA.
J Pharm Sci. 1996 May;85(5):505-10. doi: 10.1021/js950433d.
A novel model-independent approach to analyze pharmacokinetic (PK)-pharmacodynamic (PD) data using artificial neural networks (ANNs) is presented. ANNs are versatile computational tools that possess the attributes of adaptive learning and self-organization. The emulative ability of neural networks is evaluated with simulated PK-PD data, and the power of ANNs to extrapolate the acquired knowledge is investigated. ANNs of one architecture are shown to be flexible enough to accurately predict PD profiles for a wide variety of PK-PD relationships (e.g., effect compartment linked to the central or peripheral compartment and indirect response models). Also, an example is given of the ability of ANNs to accurately predict PD profiles without requiring any information regarding the active metabolite. Because structural details are not required, ANNs exhibit a clear advantage over conventional model-dependent methods. ANNs are proved to be robust toward error in the data and perturbations in the initial estimates. Moreover, ANNs were shown to handle sparse data well. Neural networks are emerging as promising tools in the field of drug discovery and development.
本文提出了一种使用人工神经网络（ANN）分析药代动力学（PK）-药效动力学（PD）数据的新型非模型依赖方法。人工神经网络是具有自适应学习和自组织特性的通用计算工具。利用模拟的PK-PD数据评估神经网络的仿真能力，并研究人工神经网络外推所学知识的能力。结果表明，一种架构的人工神经网络具有足够的灵活性，能够准确预测各种PK-PD关系（例如，与中央或外周室相连的效应室和间接反应模型）的PD曲线。此外，还给出了一个例子，说明人工神经网络能够在不需要任何关于活性代谢物信息的情况下准确预测PD曲线。由于不需要结构细节，人工神经网络相对于传统的模型依赖方法具有明显优势。事实证明，人工神经网络对数据中的误差和初始估计中的扰动具有鲁棒性。此外，人工神经网络还被证明能够很好地处理稀疏数据。神经网络正在成为药物发现和开发领域中有前景的工具。
