Using Human 'Experiments of Nature' to Predict Drug Safety Issues: An Example with PCSK9 Inhibitors.

INTRODUCTION: When a new drug enters the market, its full array of side effects remains to be defined. Current surveillance approaches targeting these effects remain largely reactive. There is a need for development of methods to predict specific safety events that should be sought for a given new drug during development and postmarketing activities. OBJECTIVE: We present here a safety signal identification approach applied to a new set of drug entities, inhibitors of the serine protease proprotein convertase subtilisin/kexin type 9 (PCSK9). METHODS: Using phenome-wide association study (PheWAS) methods, we analyzed available genotype and clinical data from 29,722 patients, leveraging the known effects of changes in PCSK9 to identify novel phenotypes in which this protein and its inhibitors may have impact. RESULTS: PheWAS revealed a significantly reduced risk of hypercholesterolemia (odds ratio [OR] 0.68, p = 7.6 × 10-4) in association with a known loss-of-function variant in PCSK9, R46L. Similarly, laboratory data indicated significantly reduced beta mean low-density lipoprotein cholesterol (- 14.47 mg/dL, p = 2.58 × 10-23) in individuals carrying the R46L variant. The R46L variant was also associated with an increased risk of spina bifida (OR 5.90, p = 2.7 × 10-4), suggesting that further investigation of potential connections between inhibition of PCSK9 and neural tube defects may be warranted. CONCLUSION: This novel methodology provides an opportunity to put in place new mechanisms to assess the safety and long-term tolerability of PCSK9 inhibitors specifically, and other new agents in general, as they move into human testing and expanded clinical use.

Classifying Clinical Trial Eligibility Criteria to Facilitate Phased Cohort Identification Using Clinical Data Repositories.

A major challenge in using electronic health record repositories for research is the difficulty matching subject eligibility criteria to query capabilities of the repositories. We propose categories for study criteria corresponding to the effort needed for querying those criteria: "easy" (supporting automated queries), mixed (initial automated querying with manual review), "hard" (fully manual record review), and "impossible" or "point of enrollment" (not typically in health repositories). We obtained a sample of 292 criteria from 20 studies from ClinicalTrials.gov. Six independent reviewers, three each from two academic research institutions, rated criteria according to our four types. We observed high interrater reliability both within and between institutions. The analysis demonstrated typical features of criteria that map with varying levels of difficulty to repositories. We propose using these features to improve enrollment workflow through more standardized study criteria, self-service repository queries, and analyst-mediated retrievals.

Classification of Clinical Research Study Eligibility Criteria to Support Multi-Stage Cohort Identification Using Clinical Data Repositories.

One of the challenges to using electronic health record (EHR) repositories for research is the difficulty mapping study subject eligibility criteria to the query capabilities of the repository. We sought to characterize criteria as "easy" (searchable in a typical repository), "hard" (requiring manual review of the record data), and "impossible" (not typically available in EHR repositories). We obtained 292 criteria from 20 studies available from Clinical Trials.gov and rated them according to our three types, plus a fourth "mixed" type. We had good agreement among three independent reviewers and chose 274 criteria that were characterized by single types for further analysis. The resulting analysis showed typical features of criteria that do and don't map to repositories. We propose that these features be used to guide researchers in specifying eligibility criteria to improve development of enrollment workflow, including the definition of EHR repository queries for self-service or analyst-mediated retrievals.