Designing Personalized Formulations with Machine Learning

摘要 药物配方的设计是药物开发的重要组成部分，以使安全和 有效的药物输送。不幸的是，目前使用试错法进行配方优化 方法或坚持已经确立的制定战略，遵循一刀切的心态。这 产生了简单的配方，并且只确保适当的物理性能，如保质期 和解放。复杂的靶向制剂可以增加药物的安全性和有效性，但这样 系统的设计、制造和管理成本高昂，限制了其更广泛的部署。在这里，我们 描述我们的目标是扩大和加强我们在开发创新的机器学习方法和 将它们与实验工作流程相结合，以设计新的、有针对性的药物配方。我们会 特别关注机器学习指导的(1)防止微生物组的功能辅料的设计 新陈代谢，(2)靶向自组装纳米粒，和(3)组织选择性前药。我们的机器学习 模型将使我们能够绕过数十亿个原本必要的反复试验，通过预测 最有希望进行实验验证的候选人。这使我们能够系统地探索新药 提供系统，以确定对特定药物和患者最有效的更好的解决方案。我们的重点是 功能性辅料、自组装纳米颗粒和前药将提供更容易的给药方案 更大规模的生产和部署，从而增强先进药物输送系统和 让医疗更加公平。我们的体外和体内实验将验证我们的预测，并提供 创新的药物输送解决方案的临床前数据，可供进一步翻译。我们希望我们的 平台将使先进的药物输送解决方案能够快速有效地设计，创造更安全、更多的 为每个患者提供有效的治疗方法。

ABSTRACT The design of drug formulations is an essential part of pharmaceutical development to enable the safe and effective delivery of medications. Unfortunately, formulation optimization is currently done using a trial-and-error approach or by adhering to already established formulation strategies following a one-size-fits-all mindset. This has resulted in formulations that are simple and only ensure appropriate physical properties such as shelf life and liberation. Complex, targeted formulations can increase the safety and efficacy of medications, but such systems are expensive to design, manufacture, and administer – limiting their broader deployment. Here, we describe our goals to expand and augment our efforts in developing innovative machine learning methods and integrate them with experimental workflows for the design of novel, targeted drug formulations. We will specifically focus on the machine learning-guided design of (1) functional excipients that prevent microbiome metabolism, (2) targeted self-assembling nanoparticles, and (3) tissue-selective prodrugs. Our machine learning models will enable us to circumvent billions of otherwise necessary trial-and-error experiments by predicting the most promising candidates for experimental validation. This allows us to systematically explore novel drug delivery systems to identify better solutions that work best for specific medications and patients. Our focus on functional excipients, self-assembled nanoparticles, and prodrugs will provide delivery solutions that are easier to produce and deploy on a larger scale, thereby enhancing the impact of advanced drug delivery systems and making medicine more equitable. Our in vitro and in vivo experiments will validate our predictions and provide pre-clinical data for innovative drug delivery solutions positioned for further translation. We expect that our platform will enable the rapid and effective design of advanced drug delivery solutions to create safer and more efficacious therapeutics for every patient.