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.