Designing Personalized Formulations with Machine Learning

摘要 药物制剂的设计是药物开发的重要组成部分，以实现安全和有效的治疗。 有效的药物输送。不幸的是，配方优化目前是使用试错法进行的 在这方面，可以采取“一刀切”的做法，或坚持已经确立的制定战略。这 已经产生了简单的配方，并且仅确保适当的物理性质， 和解放复杂的靶向制剂可以提高药物的安全性和有效性，但这种制剂可能会导致药物的毒性。 系统的设计、制造和管理都很昂贵，限制了它们更广泛的部署。这里我们 描述我们的目标，以扩大和加强我们在开发创新机器学习方法方面的努力， 将其与实验工作流程相结合，以设计新型靶向药物制剂。我们将 特别关注（1）防止微生物组的功能性赋形剂的机器学习指导设计 在一些实施方案中，所述前体药物包括（1）靶向自组装纳米颗粒，（2）靶向自组装纳米颗粒，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，否则必要的试错实验，预测 最有希望的实验验证候选人。这使我们能够系统地探索新药 为特定药物和患者提供更好的解决方案。我们专注于 功能性赋形剂、自组装纳米颗粒和前药将提供更容易递送的递送解决方案， 大规模生产和部署，从而增强先进药物输送系统的影响， 让医疗更公平。我们的体外和体内实验将验证我们的预测，并提供 为创新药物输送解决方案提供临床前数据，以便进一步翻译。我们希望我们 该平台将使先进的药物输送解决方案的快速和有效的设计，创造更安全， 对每一个病人都有效。

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.