One difficulty in validating the model is that the "known" teratogenicity it's being tested against often isn't known. Between Class A and Class X are Classes B, C and D, with increasing amounts of risk, but the boundaries between them are based on minimal data. Teratogenic effects may be difficult to spot, since most drugs are taken relatively rarely in pregnancy, some may be taken along with other drugs, and any effects tend to be rare or too subtle to be noted in medical records. Moreover, data from animal testing doesn't necessarily apply to humans.
"A lot of drugs in the middle of the spectrum, and maybe even some in Class A, may cause subtle defects that we haven't detected," says Schachter. "We can't provide a yes/no answer, but we found a pattern that can predict which are riskier."
Given the degree of uncertainty, Schachter and Kohane believe their model may be of interest to drug developers and prescribing physicians, and might provide useful information to incorporate in drug labeling.
"We can now say to patients, 'This drug targets a ton of genes that are involved in developmental processes,'" says Schachter.
Or, conversely, if a young pregnant woman has a heart condition and needs to be treated, physicians may be reassured by a cardiac drug's profile, he adds. "Instead of saying, 'we don't know,' we can now say that the drug is more likely to be safe in pregnancy."
"We have here a prismatic example of the utility of a big-picture, macrobiological approach," says Kohane, director of CHIP. "By combining a comprehensive database of protein targets of drugs and a database of birth defects associated with drugs, we find a promising predictive model of drug risk for birth defects."
|Contact: Keri Stedman|
Children's Hospital Boston