Machine learning for formulation of polymer microparticle-based long-acting injectables

Polymeric microparticles have become increasingly popular for drug delivery systems, particularly for long-acting injectables (LAI). One of the key features of these systems is their ability to achieve prolonged, controlled release of drugs over a period of weeks to months. This is particularly useful for compounds with poor water solubility, rapid clearance, or a short biological half-life. Importantly, by maintaining an optimal drug concentration for an extended time, LAI can improve drug efficacy while reducing the so-called dosage form index, the ratio between maximal and minimal drug concentrations achieved during a dose cycle. This can also minimize drug side effects by reducing peak dose. At the same time, the total amount of drug required can be lower, while maintaining—or even improving—efficacy, which can help reduce the cost of treatment. In this way, LAI are well-suited for delivering biologic therapies, such as protein-based drugs, or other drugs that need to be injected to avoid first-pass liver metabolism. Further, by eliminating the need for frequent dosing, LAI can minimize the risk of errors and reduce the burden of treatment on patients, thereby improving adherence to a therapeutic regime, even for drugs that could be delivered orally.

However, the development of microparticle drug formulations for use as LAI is particularly challenging. First, encapsulation can be difficult, because drug-polymer interactions based on the physico-chemical properties of both components drive compatibility. While there is a relatively limited range of Generally Recognized as Safe (GRAS) polymers available, each polymer actually has a range of variable properties, for example due to differences in molecular weight. When combined with the high diversity of drugs, the overall problem area is very large. Further, there are also many possible release mechanisms that are influenced by the properties of both polymer and drug. All of these factors combined result in a development process driven primarily by trial-and-error and requiring a great deal of experimental validation.

Conclusions and future directions

Overall, the work of Bannigan et al. (2023) shows that ML approaches hold promise for LAI development. With proper choice of framework, ML models can predict drug release profiles from polymer microparticle-based LAI and further SHAP analysis can identify key formulation parameters. Meanwhile, the study’s prospective experiment, where the authors used ML guidance to formulate two LAI for drugs that were not in the training data, demonstrated the potential for speeding up LAI development. However, the study also highlighted the crucial role played by the dataset used to train the ML models. Here, the hydrolytic degradation of the polymeric matrix was underestimated.

Nevertheless, the study illustrates the potential of ML to guide the development of LAI and provide a data-driven alternative to traditional trial-and-error formulation methods. To fulfil this promise, the continued development of ML frameworks needs to go hand-in-hand with the collection and curation of larger and more diverse datasets that better reflect drug release conditions. A key element of the work involved collecting data from the literature and then curating and collating it into a dataset that could then be made machine-readable. Limitations in this training dataset were reflected in limitations of the trained ML model. In the future, we hope that the BIOMATDB database can help provide easier access to biomaterial properties and data needed for improved training of ML models, resulting in better predictions.