Population Pharmacodynamic Analysis of Uric Acid-Lowering Effects of Febuxostat Based on Electronic Medical Records in Two Hospitals

Febuxostat dose requirement according to renal function in patients who achieve target serum urate levels: A retrospective cohort study

OBJECTIVES

To determine the febuxostat dose requirement according to renal function in patients who achieve target serum urate (SU) levels.

METHODS

Of 3153 gout patients who underwent febuxostat treatment, 873 patients with an initial SU level>6mg/dL were included and categorized by the estimated glomerular filtration rate: normal, chronic kidney disease (CKD) stage 3, and stages 4-5. Ninety-five patients with insufficient follow-up were further excluded. The dose of febuxostat in patients who achieved the SU target (< 6mg/dL) was defined as the average daily dosage at the time of SU target achievement.

RESULTS

The cohort of 778 gout patients had a median age of 52.0 years (IQR, 41.0-63.0) and comprised 711 (91.4%) men. The mean SU at febuxostat initiation was higher in the CKD 4-5 (9.6 [± 3.1] mg/dL) than in the other groups (CKD 3, 8.7 [± 1.7]; normal, 8.4 [± 1.7]; P<0.001). Patients achieved target SU at a median of 4.0 (1.9-9.6) months and in those who achieved target SU, the dose of febuxostat at the time of SU target achievement was significantly lower in the CKD 4-5 group (50.0 [± 16.5] mg) than in the other groups (vs. CKD stage 3, 60.0 [± 19.5] mg; P<0.01, vs. normal, 60.0 [± 19.8] mg; P<0.01). Furthermore, CKD stage 4-5 had a negative correlation with the febuxostat dose requirement (Beta: -2.334, P<0.05).

CONCLUSION

Among patients who achieved SU target, those with severely decreased renal function (CKD 4-5) required a lower febuxostat dose to achieve the target SU level compared to patients with normal or mild renal impairment.

Computational drug repurposing based on electronic health records: a scoping review

Computational drug repurposing methods adapt Artificial intelligence (AI) algorithms for the discovery of new applications of approved or investigational drugs. Among the heterogeneous datasets, electronic health records (EHRs) datasets provide rich longitudinal and pathophysiological data that facilitate the generation and validation of drug repurposing. Here, we present an appraisal of recently published research on computational drug repurposing utilizing the EHR. Thirty-three research articles, retrieved from Embase, Medline, Scopus, and Web of Science between January 2000 and January 2022, were included in the final review. Four themes, (1) publication venue, (2) data types and sources, (3) method for data processing and prediction, and (4) targeted disease, validation, and released tools were presented. The review summarized the contribution of EHR used in drug repurposing as well as revealed that the utilization is hindered by the validation, accessibility, and understanding of EHRs. These findings can support researchers in the utilization of medical data resources and the development of computational methods for drug repurposing.

Model-Based Approach to Improve Clinical Outcomes in Neonates With Opioid Withdrawal Syndrome Using Real-World Data

At least 60% of the neonates with opioid withdrawal syndrome (NOWS) require morphine to control withdrawal symptoms. Currently, the morphine dosing strategies are empiric, not optimal and associated with longer hospital stay. The aim of the study was to develop a quantitative, model-based, real-world data-driven approach to morphine dosing to improve clinical outcomes, such as reducing time on treatment. Longitudinal morphine dose, clinical response (Modified Finnegan Score (MFS)), and baseline risk factors were collected using a retrospective cohort design from the electronic medical records of neonates with NOWS (N = 177) admitted to the University of Maryland Medical Center. A dynamic linear mixed effects model was developed to describe the relationship between MFS and morphine dose adjusting for baseline risk factors using a split-sample data approach (70% training: 30% test). The training model was evaluated in the test dataset using a simulation based approach. Maternal methadone and benzodiazepine use, and race were significant predictors of the MFS response. Positive autocorrelations of 0.56 and 0.12 were estimated between consecutive MFS responses. On an average, for a 1,000 μg increase in the morphine dose, the MFS decreased by 0.3 units. The model evaluation showed that observed and predicted median time on treatment were similar (13.0 vs. 13.8 days). A model-based framework was developed to describe the MFS-morphine dose relationship using real-world data that could potentially be used to develop an adaptive, individualized morphine dosing strategy to improve clinical outcomes in infants with NOWS.