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Big Data Approaches for Safe Therapeutics in Healthy Pregnancy – BOOST-HP

Funded by NICHD, this project is a joint effort between the University of Florida, Harvard Pilgrim Health Care, and Johns Hopkins University. BOOST-HP combines innovative data-mining techniques with formal assessments of prioritized safety signals to accelerate evidence on drug risk-benefit during pregnancy.

In the US, pregnant patients use 4 medications on average, and 70% use at least one. Yet, most drugs lack conclusive evidence about safety during pregnancy: of 290 new FDA labels approved between 2010 to 2019, 90% contain no human data on the risks and benefits for pregnant patients. With current evidence generation systems, the mean time for evidence development in pregnancy has been estimated at 27 years, which is too long. Current evidence generation relies largely on real-world data, typically prompted by signals from animal studies or extrapolation from known pharmacological pathways, which may miss pregnancy-specific context. Insufficient attention is also given to identifying causal mechanisms in vulnerable sub-populations at greatest risk. 

Specific aims

The BOOST-HP study uses a broad cross-section of privately and publicly insured pregnant patients and their offspring to implement a three-stage novel reverse translational framework to accelerate evidence generation: (1) Data-mining (“scans”) to identify new exposure-outcome associations; (2) expert triage of statistical signals from data-mining to assess plausibility of pharmacologic pathways; and (3) formal evaluation of signals considered to have the greatest clinical and public health importance.

BOOST-HP specific aims are:

(1) To scan for associations between (1a) pregnancy loss and antecedent prenatal exposures on the individual drug, chemical and therapeutic class level; and (1b) the 50 most prevalent drugs in pregnancy with incomplete information on teratogenic risk and a broad selection of neonatal adverse outcomes.

(2) To employ careful pharmacoepidemiologic designs to evaluate the two top prioritized signals.

Study team