Mechanical, Aerospace and Biomedical Engineering Department, University of Tennessee, Knoxville, Tennessee, United States of America.
PLoS One. 2012;7(2):e31724. doi: 10.1371/journal.pone.0031724. Epub 2012 Feb 23.
In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.
在药物输送中，有效杀灭病原体与治疗相关的有害副作用之间常常存在权衡。由于难以在实验中测试每种给药方案，因此计算方法将有助于辅助预测有效的药物输送方法。在本文中，我们使用机器学习技术开发了一种数据驱动的预测系统，以在计算机上确定药物给药的有效性。该系统框架具有可扩展性、自主性、鲁棒性，并且能够预测当前药物治疗的效果以及随后的药物-病原体动态。该系统包括一个动态模型，将药物浓度和病原体种群纳入不同的状态。然后使用时间模型对这些状态进行分析，以描述随时间变化的药物-细胞相互作用。动态药物-细胞相互作用以自适应的方式进行学习，并用于对给药策略的有效性进行顺序预测。系统中还具有根据操作员为特定应用确定的阈值水平调整学习模型的灵敏度和特异性的能力。作为概念验证，该系统使用体外的病原体贾第虫和药物甲硝唑进行了实验验证。
