Interaction of systemic drugs causing ocular toxicity with organic cation transporter: an artificial intelligence prediction.

Chronic disease patients (cancer, arthritis, cardiovascular diseases) undergo long-term systemic drug treatment. Membrane transporters in ocular barriers could falsely recognize these drugs and allow their trafficking into the eye from systemic circulation. Hence, despite their pharmacological activity, these drugs accumulate and cause toxicity at the non-target site, such as the eye. Since around 40% of clinically used drugs are organic cation in nature, it is essential to understand the role of organic cation transporter (OCT1) in ocular barriers to facilitate the entry of systemic drugs into the eye. We applied machine learning techniques and computer simulation models (molecular dynamics and metadynamics) in the current study to predict the potential OCT1 substrates. Artificial intelligence models were developed using a training dataset of a known substrates and non-substrates of OCT1 and predicted the potential OCT1 substrates from various systemic drugs causing ocular toxicity. Computer simulation studies was performed by developing the OCT1 homology model. Molecular dynamic simulations equilibrated the docked protein-ligand complex. And metadynamics revealed the movement of substrates across the transporter with minimum free energy near the binding pocket. The machine learning model showed an accuracy of about 80% and predicted the potential substrates for OCT1 among systemic drugs causing ocular toxicity - not known earlier, such as cyclophosphamide, bupivacaine, bortezomib, sulphanilamide, tosufloxacin, topiramate, and many more. However, further invitro and invivo studies are required to confirm these predictions.Communicated by Ramaswamy H. Sarma.