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Updated: Jul 26, 2025
Toxicity Screens in Human Retinal Organoids for Pharmaceutical Discovery
Published on: March 4, 2021
Interaction of systemic drugs causing ocular toxicity with organic cation transporter: an artificial intelligence
Manisha Malani1, Manthan S Hiremath1, Surbhi Sharma2
1Translational Pharmaceutics Research Laboratory, Birla Institute of Technology and Science-Pilani, Hyderabad, Telangana, India.
Abstract:
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.
Insights
Machine learning and computer simulations identified new systemic drugs that may enter the eye via organic cation transporter 1 (OCT1), potentially causing ocular toxicity in chronic disease patients.
Area of Science:
- Ocular pharmacology
- Computational chemistry
- Biomedical engineering
Background:
- Systemic drugs treating chronic diseases can accumulate in the eye, causing toxicity.
- Ocular barriers possess membrane transporters that may misdirect systemically administered drugs into the eye.
- Organic cation transporter 1 (OCT1) is crucial for transporting organic cation drugs, which constitute ~40% of pharmaceuticals.
Purpose of the Study:
- To predict potential substrates of organic cation transporter 1 (OCT1) in ocular barriers.
- To identify systemic drugs that may cause ocular toxicity due to OCT1-mediated entry.
- To leverage artificial intelligence and computational modeling for drug safety assessment in the eye.
Main Methods:
- Developed machine learning models trained on known OCT1 substrates and non-substrates.
- Utilized molecular dynamics and metadynamics simulations with an OCT1 homology model.
- Predicted potential OCT1 substrates among systemic drugs linked to ocular toxicity.
Main Results:
- The machine learning model achieved approximately 80% accuracy in predicting OCT1 substrates.
- Identified several systemic drugs, including cyclophosphamide, bupivacaine, and bortezomib, as potential OCT1 substrates.
- Computational simulations revealed substrate movement across the OCT1 transporter near the binding pocket.
Conclusions:
- This study successfully predicted novel systemic drugs that may be transported into the eye by OCT1.
- Findings highlight the potential for unintended ocular drug accumulation and toxicity from common systemic treatments.
- Further in vitro and in vivo validation is necessary to confirm the predicted OCT1 substrates and their ocular effects.
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