Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug

Abstract

Due to its cost-effectiveness, convenience, and high patient adherence, oral drug administration often remains the preferred approach. Yet, the effective delivery of hydrophobic drugs via the oral route is often hindered by their limited water solubility and first-pass metabolism. To mitigate these challenges, advanced delivery systems such as solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been developed to encapsulate hydrophobic drugs and enhance their bioavailability. However, traditional design methodologies for these complex formulations often present intricate challenges because they are restricted to a relatively narrow design space. Here, we present a data-driven approach for the accelerated design of SLNs/NLCs encapsulating a model hydrophobic drug, cannabidiol, that combines experimental automation and machine learning. A small subset of formulations, comprising 10% of all formulations in the design space, was prepared in-house, leveraging miniaturized experimental automation to improve throughput and decrease the quantity of drug and materials required. Machine learning models were then trained on the data generated from these formulations and used to predict properties of all SLNs/NLCs within this design space (i.e., estimated to be more than 1200 formulations).Notably, formulations predicted to be high-performers via this approach were confirmed to significantly enhance the solubility of the drug by up to 3000-fold and prevent drug degradation. Moreover, our high-performance formulations significantly enhanced the oral bioavailability of the drug compared to both its free form and an over-the-counter version. Furthermore, this bioavailability matched that of a formulation equivalent in composition to the FDA-approved product, Epidiolex®.

Exploring the Promise and Challenges of Artificial Intelligence in Biomedical Research and Clinical Practice

“…AI can also be used in the development of advanced delivery systems like nanostructured lipid carriers and solid lipid nanoparticles, which are used to enhance the bioavailability of hydrophobic drugs. 19 The use of PROteolysis TArgeting Chimeras (PROTACs) is a new approach in pharmacology designed to target proteins that are “undruggable" or difficult to target with chemical molecules. 20 This approach is showing great promise for treatment of cancer and other diseases.…”

Machine learning-guided high throughput nanoparticle design

“…We selected 29 formulations (cycle 0) using a Design of Experiments (DoE) methodology. A mixture-process variable design allowed us to pick formulations that were distributed homogeneously within the design space (Figure 3a), yielding a starting set with diverse compositions spread over the whole design space 39 . These formulations were produced and characterized for their cell uptake.…”

“…3a), yielding a starting set with diverse compositions spread over the whole design space. 39 These formulations were produced and characterized for their cell uptake. The nanoparticle uptake ranged from 0.40 to 4.77-fold with respect to the uptake standard, with an average uptake of 2.03 ± 1.28-fold (Fig.…”