Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches.
Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches.
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DOI：
10.2147/ijn.s344208
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发表时间：
2022
影响因子：
8
通讯作者：
Riviere JE
中科院分区：
文献类型：
--
作者：
Lin Z;Chou WC;Cheng YH;He C;Monteiro-Riviere NA;Riviere JE
纳米粒对肿瘤的低递送效率是肿瘤纳米药物研究领域的一个重要障碍。关于如何提高NP肿瘤递送效率的策略仍有待确定。这项研究使用多种机器学习和人工智能方法，分析了NP的物理化学性质、肿瘤模型和癌症类型在NP肿瘤传递效率中的作用，使用了最近发布的纳米肿瘤数据库的数据，该数据库包含376个数据集，由基于生理的药代动力学(PBPK)模型生成。深度神经网络模型充分预测了不同NPs对不同肿瘤的输送效率，其性能优于所有其他机器学习方法，包括随机森林、支持向量机、线性回归和袋装模型方法。在整个训练数据集中，最大传递效率(Demax)、24小时传递效率(DE24)、168 h传递效率(DE168)和最后采样时间(DETlast)的调整后决定系数(R2)分别为0.92、0.77、0.77和0.76。测试数据集中相应的R2值分别为0.70、0.46、0.33和0.63。此外，这项研究还表明，癌症类型是深度神经网络模型预测所有终点的肿瘤传递效率(19-29%)的重要决定因素。在所有的物理化学性质中，Zeta电位和核心材料比其他性质发挥了更大的作用，如类型、形状和靶向策略。本研究为改进肿瘤纳米药物的设计提供了一个定量模型，使其具有更高的肿瘤递送效率。这些结果有助于我们更好地理解NP肿瘤递送效率低的原因。这项研究论证了将人工智能与PBPK建模方法相结合来研究癌症纳米医学的可行性。
Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in the field of cancer nanomedicine. Strategies on how to improve NP tumor delivery efficiency remain to be determined. This study analyzed the roles of NP physicochemical properties, tumor models, and cancer types in NP tumor delivery efficiency using multiple machine learning and artificial intelligence methods, using data from a recently published Nano-Tumor Database that contains 376 datasets generated from a physiologically based pharmacokinetic (PBPK) model. The deep neural network model adequately predicted the delivery efficiency of different NPs to different tumors and it outperformed all other machine learning methods; including random forest, support vector machine, linear regression, and bagged model methods. The adjusted determination coefficients (R2) in the full training dataset were 0.92, 0.77, 0.77 and 0.76 for the maximum delivery efficiency (DEmax), delivery efficiency at 24 h (DE24), at 168 h (DE168), and at the last sampling time (DETlast). The corresponding R2 values in the test dataset were 0.70, 0.46, 0.33 and 0.63, respectively. Also, this study showed that cancer type was an important determinant for the deep neural network model in predicting the tumor delivery efficiency across all endpoints (19–29%). Among all physicochemical properties, the Zeta potential and core material played a greater role than other properties, such as the type, shape, and targeting strategy. This study provides a quantitative model to improve the design of cancer nanomedicine with greater tumor delivery efficiency. These results help to improve our understanding of the causes of low NP tumor delivery efficiency. This study demonstrates the feasibility of integrating artificial intelligence with PBPK modeling approaches to study cancer nanomedicine.