Zhang Zilu, Xiang Yan, Laforet Joe, Spasojevic Ivan, Fan Ping, Heffernan Ava, Eyler Christine E, Wood Kris C, Hartman Zachary C, Reker Daniel
Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, United States.
Department of Medicine, Duke University School of Medicine, Durham, North Carolina 27710, United States.
ACS Nano. 2025 Sep 23;19(37):33288-33296. doi: 10.1021/acsnano.5c09066. Epub 2025 Sep 11.
Artificial intelligence (AI) has the potential to transform nanoparticle development for drug delivery; however, existing strategies typically optimize either material selection or component ratios in isolation. To enable simultaneous optimization of both, we integrated an automated liquid handling platform with machine learning to systematically explore the nanoparticle formulation space. A data set comprising 1275 distinct formulations (spanning drug molecules, excipients, and synthesis molar ratios) was generated, resulting in a 42.9% increase in successful nanoparticle formation through composition optimization. We developed a bespoke hybrid kernel machine that couples molecular feature learning with relative compositional inference, enhancing the modeling of formulation outcomes across chemical spaces. This hybrid kernel significantly improved prediction performance across three kernel-based algorithms, with a support vector machine (SVM) achieving superior performance when using our kernel compared to standard kernels and outperforming all other machine learning architectures, including transformer-based deep neural networks. Using SVM-guided predictions, we successfully formulated the difficult-to-encapsulate venetoclax with optimized taurocholic acid ratios, yielding enhanced efficacy against Kasumi-1 leukemia cells. In a second case study, our AI-guided platform reduced excipient usage by 75% in a trametinib formulation while preserving the efficacy and pharmacokinetics relative to the standard formulation. Taken together, this study establishes a generalizable framework that combines robotic experimentation, kernel machine learning, and experimental validation to accelerate nanoparticle composition optimization for drug delivery.
人工智能（AI）有潜力改变用于药物递送的纳米颗粒的研发；然而，现有的策略通常孤立地优化材料选择或组分比例。为了实现两者的同时优化，我们将自动化液体处理平台与机器学习相结合，系统地探索纳米颗粒配方空间。生成了一个包含1275种不同配方（涵盖药物分子、辅料和合成摩尔比）的数据集，通过成分优化使纳米颗粒成功形成的比例提高了42.9%。我们开发了一种定制的混合内核机器，它将分子特征学习与相对成分推断相结合，增强了跨化学空间的配方结果建模。这种混合内核在三种基于内核的算法中显著提高了预测性能，与标准内核相比，当使用我们的内核时，支持向量机（SVM）表现出卓越的性能，并且优于所有其他机器学习架构，包括基于Transformer的深度神经网络。使用支持向量机引导的预测，我们成功地用优化的牛磺胆酸比例配制了难以包封的维奈克拉，提高了对Kasumi-1白血病细胞的疗效。在第二个案例研究中，我们的人工智能引导平台在曲美替尼配方中减少了75%的辅料用量，同时保持了相对于标准配方的疗效和药代动力学。综上所述，本研究建立了一个可推广的框架，该框架结合了机器人实验、内核机器学习和实验验证，以加速用于药物递送的纳米颗粒成分优化。
