EHRs with Machine Learning Deciphering Drug Effects In Pregnant Women

By Deb Borfitz, Senior Science Writer
Researchers at Vanderbilt University Medical Center are using a novel, data-driven “target trial” framework to investigate the efficacy and safety of medicines in pregnant women, who are underrepresented in randomized controlled trials (RCTs). The approach leverages observational data in electronic health records (EHRs) to spot connections between real-world drug exposures during pregnancy and adverse outcomes in the women’s offspring—an exercise that can be expedited with commonly used AI machine learning models like logistic regression.
So says Vanderbilt undergraduate student Anup Challa, founding director of the investigative collaboration MADRE (Modeling Adverse Drug Reactions in Embryos). The group works in the emerging field of PregOMICS that applies systems biology and bioinformatics to study the efficacy and safety of drugs in treating a rising tide of obstetrical diseases. Partnering institutions are Northwestern University, the National Institutes of Health, and the Harvard T.H. Chan School of Public Health.
The concept of target trials was first mentioned in epidemiology literature a decade ago, says Challa, and is only starting to gain traction. “Target trials really hinge on retrospective analysis of existing data using machine learning methods or other kinds of inferential statistics.”
Anup Challa, founding director, MADRE (Modeling Adverse Drug Reactions in Embryos) A study coming out of Vanderbilt a few years ago looked at the effects of pregnant patients’ genomics on outcomes in their neonates and found harmful single-nucleotide mutations on key maternal genes that mimicked patients taking inhibitory drugs, says Challa. Specifically, the research team conducted a target trial to learn that these mutations on the gene PCSK9 , which controls cholesterol levels, led mothers to deliver babies with spina bifida.
That was a signal that mothers ought not to be taking PCSK9 inhibitors, Challa continues, which are “becoming of increasing interest to physicians for treating hypercholesterolemia.” It also meant common genetic variants could serve as a proxy for drug exposures in target trials when insufficient prescription data exist in pregnant people’s records.
A probability value generated by a machine learning algorithm would not be “sufficiently indicative” of a drug safety signal to warrant immediate interrogation in humans, says David Aronoff, M.D., director of the division of infectious diseases at the Vanderbilt University Medical Center. But, as he and his MADRE colleagues argued in a recent paper published in Nature Medicine ( DOI: 10.1038/s41591-020-0925-1 ), target trials are a viable and potentially more definitive alternative to fetal safety than animal models or cellular response to a drug in a dish.
The ultimate goal with target trials is to simulate the level of safety and efficacy testing done in RCTs with...

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