SU Researchers Working on Tool to Determine Drug Risks During Pregnancy

 SU Researchers Working on Tool to Determine Drug Risks During Pregnancy

June 3, 2020

Collecting accurate data showing whether or not any pharmaceutical drug could be harmful to unborn children is very difficult. Without clear embryotoxicity data, doctors often have to balance risks to the health of an expectant mother against the health of her baby and hope a drug does not have any negative side effects.

“There are tons of drugs on the market that have not been evaluated yet,” said biomedical and chemical engineering Professor Zhen Ma. “We want to think about how we can re-evaluate everything”

Ma and his Syracuse University research team developed an in vitro 3D tissue model of a human heart based on human induced pluripotent stem cells (hiPSC). A model existing outside the body makes it possible to test drugs often prescribed during pregnancy and learn how they influence hiPSC growth, cardiac differentiation, and early heart formation in a fetus.

“The goal of this project is to use stem cell technology as a tool for screening embryotoxicity for pharmaceutical compounds,” said Ma. “We will be using this to create a model so we can classify the potential risk of future drugs.”

The National Institutes of Health (NIH) sees pediatric pharmacology as an area of need. Ma, in collaboration with a Syracuse University Falk College of Public Health professor and a professor from SUNY Upstate received funding for five years through an NIH Research Grant Award (RO1) to use the cardiac organoid model to improve traditional pharmaceutical screening.

“This funding will take us to another level on these embryotoxicity studies.” Said Ma. “What we really propose for this finding is we can develop a risk classification system for the drugs.”

To begin achieving their goal, the team is running optimizations and exposing the cardiac organoid model to drugs with known embryotoxicity levels to calibrate drug response. By introducing data analytics technologies into their research, the team has begun establishing a new biostatistical model to classify risk and with the predictive model in place, Ma and his collaborators are evaluating the embryotoxic potentials of psychotropic drugs. The research could enable expectant mothers struggling with mental health issues to continue receiving treatment through pregnancy without added concern for what the impact is on the unborn child.

“With this model we built, we can tell which drugs will maybe control the syndrome and choose the one that is safer for fetal development,” said Ma.

Additional breakthroughs could come in the area of drug discovery. Ma foresees the potential to evaluate new drugs during pre-clinical trials and to build in safeguards against embryotoxicity.

“Using this data from the drugs we already know have an embryotoxicity issue or don’t have embryotoxicity issue, we can create a database and use that database to create a statistical model,” said Ma. “The idea is in the future if a pharmaceutical company develops a new drug, let’s say a new drug for COVID-19, we can put this drug in our model and feed it the data so we can classify how risky this drug could be in terms of embryotoxicity.”

An embryotoxicity risk classification system would be a pioneering breakthrough because it could allow for a more precise assessment drug effects on early embryonic development, leading to safer pregnancies. The model also has the potential to become a critical part of the standard for pharmaceutical development. It would provide developers with a human based system for testing to compliment research done with rodents.

“Our model can be run in parallel,” said Ma.