Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tumor DE can help improve the design of nanomedicines. Multiple machine learning and artificial intelligence models, including linear regression, support vector machine, random forest, gradient boosting, and deep neural networks (DNN), were trained and validated to predict tissue distribution and tumor delivery based on NPs' physicochemical properties and tumor therapeutic strategies with the dataset from Nano-Tumor Database. Compared to other machine learning models, the DNN model had superior predictions of DE to tumors and major tissues. The determination coefficients (R2) for the test datasets were 0.41, 0.42, 0.45, 0.79, 0.87, and 0.83 for DE in tumor, heart, liver, spleen, lung, and kidney, respectively. All the R2 and root mean squared error (RMSE) results of the test datasets were similar to the 5-fold cross validation results. Feature importance analysis showed that the core material of NPs played an important role in output predictions among all physicochemical properties. Furthermore, multiple NP formulations with greater DE to the tumor were determined by the DNN model. To facilitate model applications, the final model was converted to a web dashboard. This model could serve as a high-throughput pre-screening tool to support the design of new and efficient nanomedicines with greater tumor DE and serve as an alternative tool to reduce, refine, and partially replace animal experimentation in cancer nanomedicine research.

中文翻译：

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使用机器学习模型预测小鼠体内纳米粒子的组织分布和肿瘤递送


纳米颗粒（NP）可设计用于癌症纳米医学中的靶向递送，但挑战在于肿瘤部位的递送效率（DE）较低。了解纳米粒子的理化特性对靶组织分布和肿瘤 DE 的影响有助于改进纳米药物的设计。多种机器学习和人工智能模型，包括线性回归、支持向量机、随机森林、梯度提升和深度神经网络（DNN），经过训练和验证，可根据纳米颗粒的理化特性和肿瘤治疗来预测组织分布和肿瘤递送使用纳米肿瘤数据库的数据集的策略。与其他机器学习模型相比，DNN 模型对肿瘤和主要组织的 DE 预测效果更好。测试数据集的肿瘤​​、心脏、肝脏、脾脏、肺脏和肾脏的 DE 判定系数 (R2) 分别为 0.41、0.42、0.45、0.79、0.87 和 0.83。测试数据集的所有 R2 和均方根误差 (RMSE) 结果与 5 倍交叉验证结果相似。特征重要性分析表明，纳米粒子的核心材料在所有物理化学性质中的产量预测中发挥着重要作用。此外，通过 DNN 模型确定了对肿瘤具有更大 DE 的多种 NP 制剂。为了方便模型应用，最终模型被转换为Web仪表板。该模型可以作为高通量预筛选工具，支持设计具有更大肿瘤DE的新型高效纳米药物，并作为减少、完善和部分替代癌症纳米药物研究中动物实验的替代工具。