Covariate model development
Demographics, clinical factors, concomitant medications, and genetic variants were evaluated for their influence on the parameters of PK and PD models. The selection of covariates for testing was based on previous significant findings 26,27,29,31,38 and biological plausibility.
Demographic covariates included gender, total body weight (TBW), adjusted body weight (AJBW), and fat-free mass (FFM).39 Renal function was tested as standardized CrCL, estimated from the Cockcroft–Gault equation then normalized to a standard CrCL of a 70 kg man (calculated as observed CrCL*70/ideal body weight). Concomitant medications were tested based on participants’ self-reported information. These included drugs that lower SU: losartan,40HMG-CoA reductase inhibitors (particularly, atorvastatin),41,42 and calcium channel blockers (CCBs)43; and drugs that increase SU: angiotensin converting enzyme inhibitors, angiotensin receptor blockers (ARBs, but not including losartan), beta-blockers, diuretics, and non-steroidal anti-inflammatory drugs (NSAIDs).43 In addition to testing the effect of each medication type, two categories were also tested: drugs that lower SU and drugs that increase SU.
Nine SNPs related to SU levels or risks of gout development (Supplementary Table S1 ) were tested. An additive genetic model was assumed for the effect of SNPs on the PKPD parameters.
A stepwise covariant modeling (SCM) approach using the PsN toolkit with the forward and backward thresholds at p < 0.05 andp < 0.01, respectively was used for selecting covariates that contributed to the CL/fm andV/fm for the PK model, andBLurate , Imax , andIC50 for the PD model. The significance of inclusion and elimination of each covariate was tested based on likelihood ratio test that follows the χ2distribution.