The experiences and perspectives of people with gout on urate self-monitoring.

INTRODUCTION: Gout management remains suboptimal despite safe and effective urate-lowering therapy. Self-monitoring of urate may improve gout management, however, the acceptability of urate self-monitoring by people with gout is unknown. The aim of this study was to explore the experiences of urate self-monitoring in people with gout. METHODS: Semistructured interviews were conducted with people taking urate-lowering therapy (N = 30) in a 12-month trial of urate self-monitoring in rural and urban Australia. Interviews covered the experience of monitoring and its effect on gout self-management. Deidentified transcripts were analysed thematically. RESULTS: Participants valued the ability to self-monitor and gain more understanding of urate control compared with the annual monitoring ordered by their doctors. Participants indicated that self-monitoring at home was easy, convenient and informed gout self-management behaviours such as dietary modifications, hydration, exercise and medication routines. Many participants self-monitored to understand urate concentration changes in response to feeling a gout flare was imminent or whether their behaviours, for example, alcohol intake, increased the risk of a gout flare. Urate concentrations were shared with doctors mainly when they were above target to seek management support, and this led to allopurinol dose increases in some cases. CONCLUSION: Urate self-monitoring was viewed by people with gout as convenient and useful for independent management of gout. They believed self-monitoring achieved better gout control with a less restricted lifestyle. Urate data was shared with doctors at the patient's discretion and helped inform clinical decisions, such as allopurinol dose changes. Further research on implementing urate self-monitoring in routine care would enable an evaluation of its impact on medication adherence and clinical outcomes, as well as inform gout management guidelines. PATIENT OR PUBLIC CONTRIBUTION: One person with gout, who was not a participant, was involved in the study design by providing feedback and pilot testing the semistructured interview guide. In response to their feedback, subsequent modifications to the interview guide were made to improve the understandability of the questions from a patient perspective. No additional questions were suggested.

The impact of peritoneal dialysis on oxypurinol and urate elimination in people with gout.

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.

Patient-Led Urate Self-Monitoring to Improve Clinical Outcomes in People With Gout: A Feasibility Study.

OBJECTIVE: Self-monitored point-of-care urate-measuring devices are an underexplored strategy to improve adherence to urate-lowering therapy and clinical outcomes in gout. This study observed patient-led urate self-monitoring practice and assessed its influence on allopurinol adherence, urate control, and health-related quality of life. METHODS: People with gout (n = 31) and prescribed allopurinol self-monitored their urate concentrations (HumaSens2.0plus) at baseline and thereafter monthly for 12 months (3 months per quarter). Adherence to allopurinol was measured using medication event monitoring technology (Medication Event Monitoring System cap). Time spent below the target urate concentration (<0.36 mmol/L) was determined. Health-related quality of life was measured using a survey (EuroQoL EQ-5D-5L). Gout flares were recorded. Two-tailed Spearman correlation and the Wilcoxon matched-pairs signed-rank test (P < 0.05) were used for statistical comparisons. RESULTS: Most participants were male (94%) and had urate concentrations below the target (74%) at baseline. Overall, seven participants demonstrated repeated periods of "missed doses" (two or fewer allopurinol doses missed consecutively) and "drug holidays" (three or more missed doses). Most participants (94%) persisted with allopurinol. Time spent within the target urate concentration increased 1.3-fold (from 79% to 100%; P = 0.346), and the incidence of gout flares decreased 1.6-fold (from 8 to 5; P = 0.25) in the final quarter compared to that in the first quarter of the study. Health-related quality of life was reduced for participants reporting at least one gout flare (median utility values 0.9309 vs 0.9563, P = 0.04). CONCLUSION: Patient-led urate self-monitoring may support the maintenance of allopurinol adherence and improve urate control, thus reducing the incidence of gout flares. Further research on patient-led urate self-monitoring in a randomized controlled study is warranted.

The development and evaluation of dose-prediction tools for allopurinol therapy (Easy-Allo tools).

AIMS: Dose escalation at the initiation of allopurinol therapy can be protracted and resource intensive. Tools to predict the allopurinol doses required to achieve target serum urate concentrations would facilitate the implementation of more efficient dose-escalation strategies. The aim of this research was to develop and externally evaluate allopurinol dosing tools, one for use when the pre-urate-lowering therapy serum urate is known (Easy-Allo1) and one for when it is not known (Easy-Allo2). METHODS: A revised population pharmacokinetic-pharmacodynamic model was developed using data from 653 people with gout. Maintenance doses to achieve the serum urate target of <0.36 mmol L-1 in >80% of individuals were simulated and evaluated against external data. The predicted and observed allopurinol doses were compared using the mean prediction error (MPE) and root mean square error (RMSE). The proportion of Easy-Allo predicted doses within 100 mg of the observed was quantified. RESULTS: Allopurinol doses were predicted by total body weight, baseline urate, ethnicity and creatinine clearance. Easy-Allo1 produced unbiased and suitably precise dose predictions (MPE 2 mg day-1 95% confidence interval [CI] -13-17, RMSE 91%, 90% within 100 mg of the observed dose). Easy-Allo2 was positively biased by about 70 mg day-1 and slightly less precise (MPE 70 mg day-1 95% CI 52-88, RMSE 131%, 71% within 100 mg of the observed dose). CONCLUSIONS: The Easy-Allo tools provide a guide to the allopurinol maintenance dose requirement to achieve the serum urate target of <0.36 mmol L-1 and will aid in the development of novel dose-escalation strategies for allopurinol therapy.

Estimation of adherence to urate-lowering therapy in people living with gout using Australia's Pharmaceutical Benefits Scheme and patient-reported dosing.

AIMS: The aim of this study was to estimate adherence to urate-lowering therapy (ULT), predominately allopurinol, from Australia's Pharmaceutical Benefits Scheme (PBS) claims database in association with (1) patient-reported doses and (2) World Health Organization's (WHO) defined daily doses (DDD), namely, allopurinol (400 mg/day) or febuxostat (80 mg/day). METHODS: Proportion of days covered (PDC) was calculated in 108 Gout App (Gout APP) trial participants with at least two recorded ULT dispensings in an approximately 12-month period before provision of intervention or control apps. Adherence was defined as PDC ≥80%. We measured the correlation between the two methods of calculating PDC using a Wilcoxon signed rank test. Agreement between ULT-taking status (self-reports) and ULT-dispensed status (PBS records) was tested with Cohen's kappa (κ), and positive and negative percent agreement. RESULTS: Allopurinol was prescribed in 93.5% of participants taking ULT. Their self-reported mean daily dose (SD) was 291 (167) mg/day. Mean PDC (SD) for allopurinol was 83% (21%) calculated using self-reported dose, and 63% (24%) using WHO's DDD. Sixty-three percent of allopurinol users were identified as adherent (PDC ≥80%) using self-reported dose. There was good agreement between self-reported ULT use and PBS dispensing claims (κ = 0.708, P < .001; positive percent agreement = 90%, negative percent agreement = 82%). CONCLUSIONS: Participant-reported allopurinol daily doses, in addition to PBS dispensing claims, may enhance confidence in estimating PDC and adherence compared to using DDD. This approach improves adherence estimations from pharmaceutical claims datasets for medications where daily doses vary between individuals or where there is a wide therapeutic dose range.

A framework for understanding sources of bias in medication adherence research.

The sources of bias in medication adherence research have not been comprehensively explored. We aimed to identify biases expected to affect adherence research and to develop a framework for mapping these onto the phases of adherence (initiation, implementation and discontinuation). A literature search was conducted, key papers were reviewed and a Catalogue of Bias was consulted. The specific biases related to adherence measurement and metrics were mapped onto the phases of adherence using a tabular matrix. Twenty-three biases were identified, of which 11 were specifically relevant to adherence measures and metrics. The mapping framework showed differences in the numbers and types of biases associated with each measure and metric while highlighting those common to many adherence study designs (e.g., unacceptability bias and apprehension bias). The framework will inform the design of adherence studies and the development of risk of bias tools for adherence research.

The impact of genetic variability in urate transporters on oxypurinol pharmacokinetics.

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.

An allopurinol adherence tool using plasma oxypurinol concentrations.

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.

Clinical interventions to improve adherence to urate-lowering therapy in patients with gout: a systematic review.

The aim of this study was to systematically review and compare the quantitative effect of clinical interventions designed to improve adherence to urate-lowering therapy. MEDLINE, Embase, CINAHL, Scopus and Web of Science were searched for interventional studies reporting quantitative adherence to urate-lowering therapy information as an endpoint. Intervention details, quantitative adherence information, clinical outcome and cost-effectiveness data were extracted. Risk of bias was assessed. From 4721 records, 11 studies (3 randomised and 8 observational) met the inclusion criteria. Pharmacist- and nurse-led interventions were described, involving a mixture of patient education, telephone or mobile texting reminders, and medication blister packing. Quantitative adherence information was obtained using methods such as patient self-reporting and pharmacy-dispensing data. Most studies had a moderate-to-high risk of bias. Two of the three randomised studies reported improvement in adherence between the intervention and control groups, including a 13% increase in the mean proportion of days covered >0.8 [341/681 participants (50%) versus 289/782 participants (37%)] and an 88% increase in achieving a high Medicine Taking Behaviour questionnaire score [37/42 participants (88.1%) versus 0/40 participants (0%)]. Four of the eight observational studies reported improved adherence from baseline (ranging from 33% to 91% based on the longitudinal change in adherence metrics reported). A comparison of the different types of interventions was not feasible due to the heterogeneity between study designs and adherence metrics used. These findings support the need for more interventional studies to be conducted to aid adherence management.

Does the intact nephron hypothesis provide a reasonable model for metformin dosing in chronic kidney disease?

This research explored the intact nephron hypothesis (INH) as a model for metformin dosing in patients with chronic kidney disease (CKD). The INH assumes that glomerular filtration rate (GFR) will account for all kidney drug handling even for drugs eliminated by tubular secretion like metformin. We conducted two studies: (1) a regression analysis to explore the relationship between metformin clearance and eGFR metrics, and (2) a joint population pharmacokinetic analysis to test the relationship between metformin renal clearance and gentamicin clearance. The relationship between metformin renal clearance and eGFR metrics and gentamicin clearance was found to be linear, suggesting that a proportional dose reduction based on GFR in patients with CKD is reasonable.

Metformin doses to ensure efficacy and safety in patients with reduced kidney function.

We aimed to develop a metformin dosing strategy to optimise efficacy and safety in patients with reduced kidney function. Metformin data from two studies stratified by kidney function were analysed. The relationship between metformin clearance and kidney function estimates was explored using a regression analysis. The maintenance dose range was predicted at different bands of kidney function to achieve an efficacy target of 1 mg/L for steady-state plasma concentrations. The dosing strategy was evaluated using simulations from a published metformin pharmacokinetic model to determine the probability of concentrations exceeding those associated with lactic acidosis risk, i.e. a steady-state average concentration of 3 mg/L and a maximum (peak) concentration of 5 mg/L. A strong relationship between metformin clearance and estimated kidney function using the Cockcroft and Gault (r2 = 0.699), MDRD (r2 = 0.717) and CKD-Epi (r2 = 0.735) equations was found. The probability of exceeding the safety targets for plasma metformin concentration was <5% for most doses and kidney function levels. The lower dose of 500 mg daily was required to maintain concentrations below the safety limits for patients with an eGFR of 15-29 mL/min. Our analysis suggests that a maximum daily dose of 2250, 1700, 1250, 1000, and 500 in patients with normal kidney function, CKD stage 2, 3a, 3b and 4, respectively, will provide a reasonable probability of achieving efficacy and safety. Our results support the cautious of use metformin at appropriate doses in patients with impaired kidney function.

Therapeutic decision-making in primary care pharmacy practice.

BACKGROUND: Therapeutic decision-making is a core element of pharmacy practice, however, little has been documented about how it is enacted in practice and how it can be theorised. OBJECTIVE(S): This study aims to contribute to pharmacy education and practice theory by investigating the correspondence between explanations from primary care pharmacists in clinical practice roles about how they make decisions related to medicines therapy and a theoretical model of therapeutic decision-making. METHODS: In this qualitative study, interview data from 10 pharmacists in primary care settings were analysed using a general inductive approach. The emergent themes were compared to a theoretical model of therapeutic decision-making. RESULTS: Eight themes were identified from the explanations of how participants were making therapeutic decisions in practice. The themes were found to correspond to at least one of the four steps of therapeutic decision-making in the model. Themes corresponding to the information gathering step were described most vividly, whereas, the themes corresponding to the reasoning, judgement, and decision steps were less well-articulated. CONCLUSIONS: These findings suggest that the theoretical model can be useful to interpret empirical data about therapeutic decision-making in practice. These findings might provide a means for pharmacists to adopt language to better describe the steps in their therapeutic decision-making process to others, and especially, their colleagues and patients. Findings can be used by pharmacy educators to design learning opportunities for students about therapeutic decision-making.

Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy.

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.

Evaluation of designs for renal drug studies based on the European Medicines Agency and Food and Drug Administration guidelines for drugs that are predominantly secreted.

AIMS: Dose adjustment for drugs eliminated by the kidneys generally assume a linear relationship between renal drug clearance (CLR ) and glomerular filtration rate (GFR). This assumption may not hold for drugs that undergo extensive tubular secretion where nonlinearity in drug handling is expected. The aim of this study is to determine if renal drug study designs recommended by the European Medicines Agency (EMA) and Food and Drug Administration (FDA) could distinguish linear from nonlinear renal drug handling. METHODS: In this simulation and estimation study, the study designs based on the EMA and FDA guidelines for Phase I renal drug studies were evaluated for their ability to discriminate a linear from a nonlinear relationship between CLR and GFR. The number of subjects for each simulated study ranged from 4 to 960. Power, relative standard error and bias were calculated. RESULTS: Study designs under the EMA and FDA guidelines required ≥8 and ≥48 subjects, respectively, to achieve ≥80% power to discriminate a linear from nonlinear relationship between CLR and GFR. The relative standard error of estimated parameters were 13-37 and 17-44% for the designs with 24 subjects under the EMA and FDA guidelines, respectively. The bias in parameter estimates under the EMA designs were not evident, however, they were biased (13-21%) under the FDA designs. CONCLUSION: The EMA design was found to require fewer subjects (n = 8) compared to the FDA (n = 48) to discriminate linear from nonlinear drug renal handling at ≥80% study power while both the designs perform poorly for the parameter precision.