107 Pathway to Independence Award (K99/R00) Funding Trends and Prediction of Future NIH Research Project Funding

OBJECTIVES/GOALS: The objective of this study was to use NIH RePORTER (Research Portfolio Online Reporting Tools) to analyze K99 funding trends and determine if R00 to R01 or R21 achievement time correlates with the future success of an early-stage NIH-funded investigator. METHODS/STUDY POPULATION: All award data were collected from NIH RePORTER. All K99 awards and funding data in this study were limited to All Clinical Departments (ACD). All researchers (n = 1,148) and awards (n = 2,022) were identified through a K99 search from FY 2007 to FY 2022 across ACD. Historic trends in K99 awards and funding from NIH Fiscal Year (FY) 2007 to FY 2022 were investigated. An R00 dataset was generated from NIH RePORTER. The K99 to R00 achievement statistics from FY 2007 to FY 2022 was investigated. NIH annual datafiles for FY 2007 to FY 2021 were aggregated to generate a master datafile of all R01 (n = 395,505) and R21 awards (n = 61,766). R01 and R21 award data were linked to the researcher previously identified through the K99 search. The connection between K99/R00 awardees and subsequent R01 or R21 awards was focused on. RESULTS/ANTICIPATED RESULTS: From FY 2008 to FY 2022, the number of K99 awards per year increased 123.4%, from 94 to 210. Over the same period, after correcting for inflation, the NIH K99 budget increased 127.0% while the NIH program level budget increased 17.3%. For researchers who achieved their first R01 or R21 0–3 years versus 3–6 years after the start of their R00, their average funding per year since the start of the R00 phase was $467,425 versus $290,604, respectively (p < 0.001). In summary, NIH investment in the K99 award pathway has substantially outpaced the NIH program level budget increase, and there is a strong relationship between average funding per year since the start of the R00 phase and time from R00 to R01 or R21. DISCUSSION/SIGNIFICANCE: Our study offers additional evidence of the Matthew effect in science, where previous success generates future success. This analysis may be useful to clinical departments as they evaluate selecting new and retaining current biomedical scientists for independent research positions.