MegaEye: Applying multiple machine learning approaches to identify oral compounds with ocular bioactivity

Abstract

The eye is a complex organ with the critical role of mediating the optical and initial signal processing steps of vision. As such, the eye has multiple physiological and dynamic barriers to protect ocular tissues and compartments. Oral administration of pharmacological agents to treat ocular diseases have often failed to demonstrate efficacy in clinical trials. The ability of a molecule to reach a specific target in the eye (e.g. cells in the anterior versus posterior segment) is largely determined by whether its physicochemical properties permit passage across the various ocular barriers (e.g. cornea, sclera, tear dilution, blood-retinal barrier, lymphatic outflow) that are relevant to the route of administration and the target location. The use of machine learning to predict ocular bioactivity of molecules is underexplored. We now describe the curation of several datasets, generated by a wide array of computational approaches, that are used to identify drugs predicted to reach the eye following oral delivery. These datasets included simple molecular properties (e.g. molecular weight), using the blood-brain barrier MPO score, and machine learning models as a proxy for the blood-retinal barrier using transporter and other relevant literature datasets. FDA approved drugs with reported ocular activity were used to validate the models’ ability to identify additional molecules not in the models. Finally, we used a large language model, to rank over 400,000 natural compounds by potential activity in the eye. In summary, we illustrate machine learning model applications that can be expanded for ocular applications in future to repurpose molecules.