How Genomics Is Personalizing Drug Discovery

Regeneron Genetics Center’s Luca Lotta explains how population-scale genetic evidence can reveal more promising drug targets and open new possibilities for disease prevention

By
Mouj Hijazi
October 20, 2026
8 min

The Medicine Maker

Overview

Regeneron Genetics Center (RGC) uses de-identified genomic and health data from more than three million people to identify disease-associated and protective genes and pathways. RGC Vice President Luca A. Lotta reports that historical drug-development targets supported by human genetics were two to three times more likely to succeed and be approved; a study of more than one million DNA samples linked rare FNIP1 mutations with favorable metabolic measures and approximately 60% lower cardiometabolic disease risk.

Background

Drug development can take 10 to 15 years, and more than 90% of development efforts fail, according to Lotta. Human genetic variation can provide evidence about the effects of activating or inactivating a biological pathway, offering a way to investigate potential therapeutic targets in people. RGC collaborates with academic centers, hospital systems, and foundations to combine genomic data with de-identified health information. The interview describes personalized medicine as an emerging application of improved understanding of genetic variation, while noting that current applications remain relatively narrow.

Data Highlights

Measure or finding	Reported data	Source context
RGC genomic and health data	More than 3 million people	Context provided for the interview
Drug-development timeline	10 to 15 years	Estimate cited by Lotta
Drug-development failure rate	More than 90%	Estimate cited by Lotta
Genetic support for drug targets	Two to three times greater likelihood of success and eventual approval	Retrospective historical drug-development analyses, as described in the interview
FNIP1 study sample	More than 1 million DNA samples	Study conducted with international collaborators
FNIP1 mutations and cardiometabolic disease	Approximately 60% lower risk	Rare mutations were also associated with lower liver fat, more favorable body fat distribution, and lower glucose levels

Key Findings

Lotta describes RGC’s work as large-scale human genetics research intended to generate insights that may support development of new medicines.
In historical analyses cited in the interview, drug targets supported by human genetics were two to three times more likely to succeed and eventually be approved.
Natural genetic variants that activate or inactivate pathways can be studied as proxies for modifying those pathways with medicines.
In a study involving more than one million DNA samples, rare FNIP1 mutations were associated with lower liver fat, more favorable body fat distribution, lower glucose levels, and approximately 60% lower risk of cardiometabolic disease, including myocardial infarction, metabolic liver disease, and type 2 diabetes.
Lotta says therapeutic prioritization depends on unmet need and whether a pathway can be specifically targeted with an available or developable modality.
The related article “To Be Precise” notes that some precision approaches are already used in healthcare, including blood and organ typing and consideration of genetic variation in clinical decisions; it identifies pharmacogenomics as an area of research.

Clinical Implications

The interview presents human genetic evidence as a means of informing target selection and understanding the potential consequences of modifying biological pathways. It also describes genome-based assessment of disease predisposition as a possible future application, while noting that personalized medicine is at an early stage and current applications are relatively narrow.

Conclusion

Population-scale genomics can identify disease-associated and protective pathways and provide evidence relevant to therapeutic development. The interview frames broader personalized medicine as a developing possibility dependent on deeper understanding of genomic variation.