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Gout affects 15%–30% of individuals with advanced kidney disease. Allopurinol which is rapidly and extensively metabolised to an active metabolite, oxypurinol, is the most commonly prescribed urate‐lowering therapy. Oxypurinol is almost entirely eliminated by the kidneys (>95%) and has an elimination half‐life of 18–30 h in those with normal kidney function. However, oxypurinol pharmacokinetics are poorly understood in individuals with kidney failure on peritoneal dialysis. This study characterised the elimination of oxypurinol and urate in people with gout receiving peritoneal dialysis. Oxypurinol steady‐state oral clearance (CL/F), elimination half‐life as well as kidney (CLk) and peritoneal (CLpd) clearances for oxypurinol and urate were calculated from the plasma, urine and dialysate concentration data for each individual. Our results demonstrate that oxypurinol and urate are removed by peritoneal dialysis, accounting for more than 50% of oxypurinol and urate clearances. An allopurinol dose about 50%–60% lower than the usual dose used for a patient with normal kidney function will provide adequate urate‐lowering therapy. [ABSTRACT FROM AUTHOR]

Complex systems are typically difficult for students to understand and computer simulations offer a promising way forward. However, integrating such simulations into conventional classes presents numerous challenges. Framed within an educational design research, we studied the use of an in-house built simulation of the coagulation network in four pharmacy undergraduate classes. Drawing upon audio recordings of small group discussions, focus group interviews, and class observations, we identified implementation challenges related to: adaptation of simulation to align with student needs; compromises to learning design; and classroom infrastructure. These findings can serve to guide teachers and staff developers on the common challenges that are likely to arise from integrating computer simulations meaningfully into realistic contexts. (Contains 3 figures and 1 table.)

Aims: This study aimed to develop and evaluate an allopurinol adherence tool based on steady‐state oxypurinol plasma concentrations, allopurinol's active metabolite. Methods: Plasma oxypurinol concentrations were simulated stochastically from an oxypurinol pharmacokinetic model for allopurinol doses of 100‐800 mg daily, accounting for differences in renal function, diuretic use and ethnicity. For each scenario, the 20th percentile for peak and trough concentrations defined the adherence threshold, below which imperfect adherence was assumed. Predictive performance was evaluated using both simulated low adherence and against data from 146 individuals with paired oxypurinol plasma concentrations and adherence measures. Sensitivity and specificity (S&S), negative and positive predictive values (NPV, PPV) and receiver operating characteristic (ROC) area under the curve (AUC) were determined. The predictive performance of the tool was evaluated using adherence data from an external study (CKD‐FIX). Results: The allopurinol adherence tool produced S&S values for trough thresholds of 89‐98% and 76‐84%, respectively, and 90%‐98% and 76‐83% for peak thresholds. PPV and NPV were 79‐84% and 88‐94%, respectively, for trough and 80‐85% and 89‐98%, respectively, for peak concentrations. The ROC AUC values ranged from 0.84 to 0.88 and from 0.86 to 0.89 for trough and peak concentrations, respectively. S&S values for the external evaluation were found to be 75.8% and 86.5%, respectively, producing an ROC AUC of 0.8113. Conclusion: A tool to identify people with gout who require additional support to maintain adherence using plasma oxypurinol concentrations was developed and evaluated. The predictive performance of the tool is suitable for adherence screening in clinical trials and may have utility in some clinical practice settings. [ABSTRACT FROM AUTHOR]

The genetic determinants of the allopurinol dose‐concentration relationship have not been extensively studied. We aimed to clarify what factors, including genetic variation in urate transporters, influence oxypurinol pharmacokinetics (PKs). A population PK model for oxypurinol was developed with NONMEM (version 7.3). The influence of urate transporter genetic variants for ABCG2 (rs2231142 and rs10011796), SLC2A9/GLUT9 (rs11942223), SLC17A1/NPT1 (rs1183201), SLC22A12/URAT1 (rs3825018), SLC22A11/OAT4 (rs17300741), and ABCC4/MRP4 (rs4148500), as well as other participant factors on oxypurinol PKs was assessed. Data from 325 people with gout were available. The presence of the T allele for ABCG2 (rs2231142) and SLC17A1/NPT1 (rs1183201) was associated with a 24% and 22% increase in oxypurinol clearance, respectively, in univariate analysis. This effect was not significant in the multivariate analysis. In the final model, oxypurinol PKs were predicted by creatinine clearance, diuretic use, ethnicity, and body weight. We have found that genetic variability in the transporters examined does not appear to influence oxypurinol PKs. [ABSTRACT FROM AUTHOR]

7 HT ht 8 HT ht The pharmacodynamic effect can therefore be calculated: 9 HT ht Finally, we need to consider the system response. For many drugs, there may be more than one response system that contributes to variability (in responses), and hence, considering only one system will not capture the overall variability in drug response. [Extracted from the article]