Designing Personalized Formulations with Machine Learning

Duke University, Durham NC

Investigators

Linked publications & trials

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. 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. 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.