Toward the Future: A Race-Agnostic, Bayesian Approach to Defining Glomerular Filtration for Personalized Drug Dosing

Accurate assessment of renal function is fundamental to dosing renally eliminated drugs. For decades, serum creatinine-based estimating equations have been used to approximate glomerular filtration rate (GFR) for this purpose. While these equations perform reasonably well at the population level, they exhibit substantial individual-level imprecision and inaccuracy. Simultaneously, recent efforts to remove race from estimating equations addressed important ethical concerns but did not resolve the core limitation of population-based estimation for individual drug dosing. In this perspective, we argue that personalized dosing requires a shift away from demographic surrogates toward direct measurement of renal clearance and clarify when incremental therapeutic complexity is warranted. We review the limitations of endogenous biomarkers, discuss the role of cystatin C, and propose exogenously measured GFR using iohexol integrated within a Bayesian pharmacometric framework as a clinically actionable approach to individualized drug dosing. Practically, this framework can be implemented by administering a small iohexol dose, collecting sparse timed samples, estimating absolute mGFR and its uncertainty with Bayesian software, and using that posterior estimate to refine renal dose selection.