Machine Learning Predictions of Drug Release from Polymeric Long Acting Injectables

03 September 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study explores the application of machine learning to address a critical challenge in pharmaceutical formulation development: the prediction of drug release profiles from polymer-based long-acting injectables. Long acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it near to impossible to predict the performance of these systems a priori. This results in a need to develop and characterize a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. In this study, various neural network architectures are constructed and trained, resulting in accurate predictions of drug release profiles that agree with experimental data. The simplicity with which these broadly applicable machine learning models are identified, using a limited amount of training data, is evidence of the promising potential of data-driven approaches in advanced pharmaceutical formulation development.


Machine learning
Drug delivery
Materials science
Long acting injectables
Controlled-release system

Supplementary materials

Supplementary Information for Machine Learning Predictions of Drug Release from Polymeric Long Acting Injectables
File contains; (1) Materials and methods section describing dataset construction and a description of machine learning model architectures. (2) Additional results section which summarizes the results of the machine learning model training and of the pseudo-prospective study.


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.