# MegaTrans – human transporter machine learning models

> **NIH NIH R42** · COLLABORATIONS PHARMACEUTICALS, INC. · 2022 · $864,767

## Abstract

Summary
Being able to predict interactions with important human transporters would be of value to new drug design to
avoid compounds that interact with them and cause undesirable side effects. Conversely, some drug transporters
can be used for targeting molecules to specific organs and this may have considerable utility. Understanding the
interactions of novel drugs, natural products and environmental toxicants and their interactions with an array of
such transporters is, therefore, important for several industries, as well as from a regulatory perspective (e.g.
FDA, EPA and EMA). Being able to predict such interactions in a fast and reliable manner effectively requires
using computational approaches and learning from in vitro data, the latter a resource that is rapidly growing.
Over the past 20 years, we have been at the forefront of applying different machine learning approaches to
modeling drug transporters and, in many cases, developing datasets for transporters for which there was scant
available data. We now propose doing this for several transporters that may be important for drug discovery. In
Phase I we focused on OATP1B1 (SLCO1B1), which is an uptake transporter largely restricted to the sinusoidal
aspect of hepatocytes where it mediates transport of a variety of structurally unrelated compounds, including
members of several clinically important drug families (incl. statins, sartans and angiotensin converting enzyme
(ACE) inhibitors). We tested 476 drugs against one substrate in vitro. We then curated these data and built
machine learning models using multiple machine learning methods as well as model evaluation metrics. This
enabled us to develop models for integration in a web-based software tool called MegaTrans® that enables the
user to input their own compound structures and generate predictions for interactions with transporter/s of
interest, as well as visualize the similarity to the training set of each model using several different visualization
methods. In addition, during Phase I we also performed preliminary data curation, model building and validation
for two equilibrative nucleoside transporters (ENTs), ENT1 and ENT2, that are present at the blood testes barrier
(BTB), where they can facilitate drug disposition (e.g. for antivirals, thereby potentially eliminating a sanctuary
site for viruses detectable in semen). We generated Bayesian and pharmacophore models and used these to
predict numerous compounds that were then tested in vitro against ENTs. We used these ENT models to predict
(i) the antivirals used in treating COVID-19, remdesivir and molnupiravir, inhibit ENT activity, and that (ii)
remdesivir is an ENT substrate, as well as validating these predictions. In Phase II we plan on building on the
foundation of Phase I and propose greatly expanding the ENT1 and ENT2 models through in vitro testing (at the
University of Arizona) of >2000 approved drugs, natural products, and environmental toxicants as inhibitors of
ENT...

## Key facts

- **NIH application ID:** 10546264
- **Project number:** 2R42GM131433-02
- **Recipient organization:** COLLABORATIONS PHARMACEUTICALS, INC.
- **Principal Investigator:** Nathan J Cherrington
- **Activity code:** R42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $864,767
- **Award type:** 2
- **Project period:** 2019-04-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10546264

## Citation

> US National Institutes of Health, RePORTER application 10546264, MegaTrans – human transporter machine learning models (2R42GM131433-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10546264. Licensed CC0.

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