EHR data, alongside other sources of clinical information, can be effective in determining different drug interactions that can help improve lifespan in breast cancer patients, according to a recent study from Stanford Medicine.
The study, published last week in the Journal of the American Medical Informatics Association, investigated the use of EHR and biomolecular data to detect which, if any, non-cancer drugs can produce positive outcomes for patients.
“We looked at all the non-cancer drugs that breast cancer patients were on,” said Nigam Shah, MBBS, PhD, senior author of the study and associate professor of medicine and biomedical science at Stanford Medicine, in a press release.
“People have other things going on in life. They might have hypertension, they might have high cholesterol or diabetes. They would be taking drugs for those as well. So the question we were asking was, do any of the drugs they are taking associate with better outcomes for breast cancer?”
Using EHR data in Stanford’s Oncoshare cancer database, the researchers looked at de-identified information for nearly 10,000 adult women diagnosed with breast cancer between 2000 and 2013. From that data, the research team examined interactions for 294 non-cancer drugs in more than 43,000 pairs.
Overall, the researchers found three drug combinations that resulted in prolonged lifespan by up to five years: anti-inflammatories and lipid modifiers, and anti-inflammatories and anti-cancer hormone antagonists.
The research team confirmed their findings using gene-expression data. This data identified any molecular composition that might make these drugs predisposed to prolonging breast cancer patient lifespans.
In an independent analysis of gene-expression data, the research team identified the same drug groups – anti-inflammatories and lipid modifiers, and anti-inflammatories and anti-cancer hormone antagonists – as beneficial in prolonging lifespan for breast cancer patients.
This study is about more than the drugs that can benefit a breast cancer patient’s life, the researchers said. Rather, it highlights the importance of EHR use and data analysis in identifying different medication and drug patterns.
“This study demonstrates the novel use of both EHRs and molecular data to discover and validate pairs of drugs whose combined therapeutic effect on mortality among breast cancer patients appears to be greater than that of the individual drugs alone,” the researchers said.
“Our approach for eliciting beneficial pairs of drugs is a first step toward discovering more complex multidrug combinations that can optimize the use of existing drugs.”
“This is a holistic look at the data — EHR, gene expression, protein targets of drugs — all in one analysis,” Shah continued in the press release.
According to Shah and his team, this drug-drug interaction research is a part of Stanford Medicine’s push for precision medicine. Uncovering the benefits of EHR use in precision medicine is significant, especially given some skepticism from industry players.
Late last summer, for example, a group of healthcare experts published an op-ed in the Journal of the American Medical Association stating that EHR use may be inhibiting the progress of precision medicine.
Lacking interoperability, poor technical quality, and data accuracy have all contributed to a slow down for precision medicine research, wrote authors Michael J. Joyner, MD, Nigel Paneth, MD, MPH, and John P. A. Ioannidis, MD, DSC.
“These features make the use of EHRs for research into the origins of disease, as proposed in the Precision Medicine Initiative, highly problematic,” Joyner, Paneth, and Ioannidis wrote. “No clearly specified targets for either improved outcomes or reduced costs have been developed to assess the performance efficiency of EHRs.”
According to the researchers from Stanford, these claims against EHR use in precision medicine may not be true.
“This study further demonstrates the translational potential of existing data sources such as real-world patient EHRs and gene expression databases,” the researchers concluded.
“The multidrug combinations uncovered can be computationally prioritized to help direct preclinical research and, if promising, undergo clinical trial validation, repurposing, and optimizing of existing drugs for maximum therapeutic benefit.”