Clinical decision support in a hospital electronic prescribing system informed by local data: experience at a tertiary New Zealand centre.

BACKGROUND: An electronic prescribing and administration (ePA) system has been progressively rolled out to Canterbury District Health Board (CDHB, Christchurch, New Zealand) public hospitals since 2014, and is currently used for around 1300 tertiary beds. ePA data can be used to monitor user behaviour, and to evaluate and inform the local customisation of clinical decision support (CDS) tools within the ePA system. AIMS: To describe retrospectively illustrative vignettes of CDHB ePA analyses that have been used for CDS. METHODS: Alerts were developed according to a set of common principles agreed upon by the CDHB CDS Working Group. Alerts were informed and evaluated by extracting and parsing data for various time periods during 2016 to 2018 from the CDHB ePA database. RESULTS: There was a median of 74 000 prescriptions a month. After examining 525 spironolactone prescriptions, the high dose alert threshold was set at 100 mg with an expected alert burden of 3%. The presence of a ceftriaxone shortage prescribing alert for 1 week was associated with a prescribing rate that was lower than 95% of the preceding 52 weeks. Following review of 367 fentanyl patch alerts, revision of the alert led to false positives falling from 43% to 3% (P < 0.0001). At the point of firing, 6% of antithrombotic drug interactions alerts led to immediate changes in prescriptions (94% overridden), and a further 22% were changed within 30 min after the alert. CONCLUSIONS: Local data extracts from ePA systems can inform iterative configuration of the software and monitor user behaviour.

Assessment of the relationship between methotrexate polyglutamates in red blood cells and clinical response in patients commencing methotrexate for rheumatoid arthritis.

BACKGROUND AND OBJECTIVES: Therapeutic drug monitoring in patients with rheumatoid arthritis (RA) receiving methotrexate (MTX, MTXGlu1) has not been established. In this study, we aim to explore the relationship between red blood cell (RBC) concentrations of MTX and its polyglutamate metabolites (MTXGlu(n); n = 2, 3, 4, 5) and clinical response in RA patients commencing MTX. METHODS: The binding activity of MTXGlu(n) to three putative enzymes involved in the MTX mechanism of action­dihydrofolate reductase, thymidylate synthase, and 5-aminoimidazole-4-carboxamide ribonucleotide transformylase­was simulated. RBC MTXGlu(n) concentrations that gave the highest inhibition activity across all three enzymes were linked with the disease activity score DAS28-3v (C-reactive protein [CRP]). A population pharmacokinetic-pharmacodynamic model was developed to describe the relationship between RBC MTX polyglutamate concentrations and clinical response in 12 RA patients commencing MTX. RESULTS: The highest inhibition activity was with RBC MTXGlu(3-5). These polyglutamates were further evaluated for their relationship with DAS28-3v (CRP). Three of the 12 patients had a high DAS28-3v (CRP) at baseline (mean = 6.1) and showed a delayed response to MTX treatment. The remaining nine patients with a lower DAS28-3v (CRP) baseline (mean = 3.6) showed an immediate response. The developed MTX pharmacokinetic-pharmacodynamic model provided an acceptable description of the observed DAS28-3v (CRP) across all patients. CONCLUSIONS: The developed model describes a longitudinal relationship between RBC MTXGlu(3-5) concentrations and DAS28-3v (CRP) in patients with RA commencing MTX. Further work is required to determine whether measurement of RBC MTX polyglutamates might be useful for dose individualisation in patients with RA.

Comparison of intracellular methotrexate kinetics in red blood cells with the kinetics in other cell types.

AIM: To assess the similarities in intracellular pharmacokinetics (PK) of methotrexate (MTX) in red blood cells (RBCs) and other cell lines. METHODS: Three previously published PK models for intracellular MTX and MTX polyglutamate (MTXGlu2-5 ) concentrations were used: (i) a model for the kinetics in RBCs, (ii) a model for the kinetics in human breast cancer cells (HBCCs) and (iii) a model for the kinetics in various white blood cell (WBC) lines. All three models were used to simulate the response in a typical individual receiving 10 mg oral MTX once weekly and the predicted steady-state concentrations (Css ) and time to Css (tss ) were compared. RESULTS: The HBCC model showed a lower Css for MTXGlu2 and 3 and higher Css for MTXGlu4 and 5 compared with the RBC PK model, while tss and overall intracellular MTX exposure appeared similar. The WBC PK model showed much lower Css for the parent MTXGlu1 and of tss for all MTXGlun , as well as a much lower cumulative Css for MTXGlu2-7 for the majority of the WBC cell lines. CONCLUSION: RBC kinetics of MTX differ from the kinetics in other cell types such as WBCs and HBCCs to a variable degree. It is possible that similarly diverse profiles may exist across other cell lines, including those on the causal path in rheumatoid arthritis. Hence, there may not necessarily be a clear link between RBC MTX concentrations and disease control in rheumatoid arthritis.

A population pharmacokinetic model for low-dose methotrexate and its polyglutamated metabolites in red blood cells.

BACKGROUND: Measurement of intracellular concentrations of methotrexate (MTX) and its polyglutamated metabolites (MTXGlu(2-5)) in red blood cells (RBCs) has been suggested as a potential means of monitoring low-dose MTX treatment of rheumatoid arthritis (RA). However, a possible correlation between RBC MTX and MTXGlu2-5 concentrations and clinical outcomes of MTX treatment in RA is debated. A better understanding of the dose-concentration-time relationship of MTX and MTXGlu(2-5) in RBCs by population pharmacokinetic modelling is desirable and will facilitate assessing a potential RBC concentration-effect relationship in the future. AIM: The purpose of this analysis was to describe the pharmacokinetics of MTX and MTXGlu(2-5) in RBCs. Secondary objectives included investigation of deglutamation reactions and the loss of MTX and MTXGlu(2-5) from the RBC. METHODS: A model was developed using NONMEM(®) version 7.2 based on RBC data obtained from 48 patients with RA receiving once-weekly low-dose MTX treatment. This model was linked to a fixed two-compartment model that was used to describe the pharmacokinetics of MTX in the plasma. A series of five compartments were used to describe the intracellular pharmacokinetics of MTX and MTXGlu(2-5) in RBCs. Biologically plausible covariates were tested for a significant effect on MTX plasma clearance and the intracellular volume of distribution of all MTX species in RBCs ([Formula: see text]). The developed model was used to test hypotheses related to the enzymatic deglutamation of MTXGlu(2-5) and potential loss of MTXGlu(2-5) from RBCs. RESULTS: The final RBC pharmacokinetic model required the intracellular volumes of distribution for the parent and metabolites to be set to the value estimated for the parent drug MTX alone, and the rate constants describing the polyglutamation steps were fixed at literature values. Significant covariates included effect of body surface area-adjusted estimated glomerular filtration rate on renal plasma clearance and effect of allometrically scaled total body weight with a fixed exponent of 0.75 on non-renal plasma clearance of MTX. The only significant covariate with an effect on [Formula: see text] was mean corpuscular volume (MCV). The model supported single deglutamation steps and a single mechanism of MTX and MTXGlu(2-5) loss from RBCs. CONCLUSIONS: The developed model enabled acceptable description of the intracellular kinetics of MTX and MTXGlu(2-5) in RBCs. In the future it can form the basis of a full pharmacokinetic-pharmacodynamic model to assess the time-RBC concentration-effect relationship of low-dose MTX treatment in RA.

Pharmacokinetics of oral methotrexate in patients with rheumatoid arthritis.

OBJECTIVE: There is evidence supporting a therapeutic range for methotrexate polyglutamate (MTXGlu) concentrations in the treatment of rheumatoid arthritis (RA). Knowledge of the pharmacokinetics of MTXGlu1-5 is required for optimal timing of blood sampling. The aim of this study was to determine the time to steady state and the half-life of accumulation of red blood cell (RBC) MTXGlu1-5 in patients with RA commencing oral MTX, and the time for RBC MTXGlu1-5 to become undetectable and the half-life of elimination of RBC MTXGlu1-5 in patients ceasing treatment with oral MTX. METHODS: Ten patients beginning treatment and 10 patients stopping treatment with low-dose oral MTX were recruited. Blood samples were initially collected weekly, with gradual extension to monthly collection over the study period. RBC MTXGlu1-5 concentrations were assayed by high-performance liquid chromatography. Results were analyzed using a first-order exponential method. RESULTS: The median times to reach steady state in RBCs (defined as 90% of the maximum concentration) were 6.2, 10.6, 41.2, 149, and 139.8 weeks, respectively, for MTXGlu1, MTXGlu2, MTXGlu3, MTXGlu4, and MTXGlu5. The median half-life of accumulation for RBC MTXGlu1-5 ranged from 1.9 weeks to 45.2 weeks. The median times for MTXGlus to become undetectable in RBCs were 4.5, 5.5, 10, 6, and 4 weeks, respectively, for MTXGlu1, MTXGlu2, MTXGlu3, MTXGlu4, and MTXGlu5. The median half-life of elimination for RBC MTXGlu1-5 ranged from 1.2 weeks to 4.3 weeks. CONCLUSION: There is wide interpatient variability of RBC MTXGlu1-5 accumulation and elimination in adults with RA. These data also suggest that after a dose change, >6 months are required for RBC MTXGlu1-5 to reach steady state. Such delays in achieving steady state suggest that more rapid dose escalation or subcutaneous administration from the outset should be considered.