Shaun S. Kumar

Metformin therapy in patients with chronic kidney disease.

Metformin therapy is limited in patients with chronic kidney disease (CKD) due to the potential risk of lactic acidosis. This open‐label observational study investigated metformin and lactate concentrations in patients with CKD (n = 22; creatinine clearances 15–40 ml/min) and in two dialysed patients. Patients were prescribed a range of metformin doses (250–2000 mg daily) and metformin concentrations were compared with data from healthy subjects (scaled to 1500 mg twice daily). A subset of patients (n = 7) was controlled on low doses of metformin (250 or 500 mg daily). No correlation between metformin and lactate concentrations was observed. Three patients had high lactate concentrations (>2.7 mmol/l) and two had high metformin concentrations (3–5 mg/l), but none had any symptoms of lactic acidosis. Reducing metformin dosage and monitoring metformin concentrations will allow the safe use of metformin in CKD, provided that renal function is stable.

Comparing dose prediction software used to manage gentamicin dosing

Current Australian guidelines recommend initiating directed therapy of gentamicin if administration exceeds 48 h. Directed doses of gentamicin require the monitoring of plasma concentrations of gentamicin to determine the 24‐h area under the time course of plasma gentamicin concentrations (AUC) and a dosage prediction program, for example TCIWorks or Aladdin. However, doses calculated by such programs have not been compared with an established program.

Does the availability of therapeutic drug monitoring, computerised dose recommendation and prescribing decision support services promote compliance with national gentamicin prescribing guidelines?

Gentamicin is an aminoglycoside antibiotic that is highly effective in treating Gram‐negative infections, but inappropriate use leads to toxicity. In 2010, the Australian Therapeutic Guidelines (Antibiotic) were revised to recommend the use of computerised methods to individualise dosing of gentamicin and optimise therapy, rather than traditional nomogram approaches.

Pharmacokinetics of Metformin in Patients Receiving Regular Hemodiafiltration

To the Editor: Metformin is first-line therapy for type 2 diabetes mellitus (T2DM). Concerns that metformin may accumulate and precipitate the severe adverse event lactic acidosis when kidney function is poor has led to its contraindication in patients with reduced kidney function. Most references discourage its use at a creatinine clearance , 30 mL/min. Recently, we showed that with appropriate dose reduction, metformin can be safely administered to patients with creatinine clearances as low as 15 mL/min without metformin or lactate concentrations increasing. One patient in this cohort was also on hemodiafiltration (HDF) and had no elevation of metformin or lactate concentrations. Previous studies investigating metformin in hemodialysis or HDF either lack detailed pharmacokinetic analysis of therapeutic doses or are in the context of metformin overdose and/or lactic acidosis. The aims of the present study were therefore to investigate the pharmacokinetics of metformin in diabetic patients receiving regular HDF and provide an initial examination of its safety in these patients. Four patients (HDF sessions thrice weekly) with T2DM were treated initially with metformin (500 mg, immediate-release formulation) after HDF (1,500 mg/wk). Pre-HDF blood samples were drawn on 6 occasions (end of weeks 1-4, 8, and 12) to measure biochemical parameters. Paired (before and after the hemodiafilter) serial blood samples (3-6 pairs) were collected over the course of each session to determine extracorporeal metformin

The pharmacokinetics of metformin and concentrations of haemoglobin A1C and lactate in Indigenous and non-Indigenous Australians with type 2 diabetes mellitus

AIMS To compare the pharmacokinetics of metformin between diabetic Indigenous (Aboriginal and Torres Strait Islander) and non-Indigenous patients. METHODS An observational, cross-sectional study was conducted on type 2 diabetic Indigenous and non-Indigenous patients treated with metformin. Blood samples were collected to determine metformin, lactate, creatinine and vitamin B12 concentrations and glycosylated haemoglobin levels. A population model was used to determine the pharmacokinetic parameters. RESULTS The Indigenous patients (median age 55 years) were younger than the non-Indigenous patients (65 years), with a difference of 10 years (95% confidence interval 6-14 years, P < 0.001). The median glycosylated haemoglobin was higher in the Indigenous patients (8.5%) than in the non-Indigenous patients (7.2%), with a difference of 1.4% (0.8-2.2%, P < 0.001). Indigenous patients had a higher creatinine clearance (4.3 l h(-1) ) than the non-Indigenous patients (4.0 l h(-1) ), with a median difference of 0.3 l h(-1) (0.07-1.17 l h(-1) ; P < 0.05). The ratio of the apparent clearance of metformin to the creatinine clearance in Indigenous patients (13.1, 10.2-15.2; median, interquartile range) was comparable to that in non-Indigenous patients (12.6, 9.9-14.9). Median lactate concentrations were also similar [1.55 (1.20-1.88) vs. 1.60 (1.35-2.10) mmol l(-1) ] for Indigenous and non-Indigenous patients, respectively. The median vitamin B12 was 306 pmol l(-1) (range 105-920 pmol l(-1) ) for the Indigenous patients. CONCLUSIONS There were no significant differences in the pharmacokinetics of metformin or plasma concentrations of lactate between Indigenous and non-Indigenous patients with type 2 diabetes mellitus. Further studies are required in Indigenous patients with creatinine clearance <30 ml min(-1) .

Could metformin be used in patients with advanced chronic kidney disease

To the Editor: We read with great interest the review article by Chowdhury et al. Over the last 5 years there has been great interest in relaxing the contraindication of renal impairment for prescribing metformin. We have examined the use of metformin in patients with advanced chronic kidney disease (CKD). Chowdhury et al. referenced our publication of a population pharmacokinetic model, which we used to simulate possible dosing regimens for patients with all grades of renal function (down to creatinine clearances of 15 mL/min). The goal of this analysis was to ensure that the 95th percentile peak plasma concentrations of metformin did not exceed 5 mg/L. These dosing regimens have been assessed by MedSafe (the New Zealand medicines regulatory agency) and have been incorporated into their metformin product label. We encourage other regulatory bodies to consider making similar changes. Metformin is largely eliminated unchanged by the kidneys and, consequently, a major concern is that patients with renal impairment will accumulate metformin and this could lead to the development of lactic acidosis, a serious adverse effect of metformin. A putative metformin plasma concentration of 5 mg/L has been suggested as being indicative of significant risk of metformin-associated lactic acidosis (MALA). We and others have suggested that metformin at therapeutic dosages is not a causative agent, even at concentrations >5 mg/L, but rather is an innocent by-stander. Many patients on metformin have comorbidities that increase the risk of lactic acidosis. Indeed, a recent retrospective study showed that, while metformin was significantly associated with an increased risk of the need for acute dialysis, this risk was conditional upon concomitant “patient frailty.” This implies that secondary characteristics that predispose a patient to enhanced lactate concentrations, such as age, renal function and cardiac failure, work in conjunction with metformin concentrations to induce damage. Our metformin dosing regimen in acute kidney injury has also been supported by Hung et al. Their study showed the adverse effects of “over-dosing” of metformin in the setting of advanced chronic kidney disease, showing that metformin increased all-cause mortality in a dose-dependent manner in patients with CKD stage 5. The design of novel formulations of metformin that reduce systemic exposure to metformin, such as a delayed release form absorbed from the distal small intestine, could further increase the safety of metformin in renally compromised patients. In addition to monitoring metformin plasma concentrations and renal function, a practice we and others encourage, we agree with the authors’ guidance on counselling patients with advanced CKD for “sick day rules.” One of the common signs patients reference prior to the onset of MALA is severe gastrointestinal symptoms (vomiting, diarrhoea). This should be a red-light warning for patients to cease metformin use and seek medical attention. Chowdhury et al. conclude that more studies examining the safety and efficacy of metformin in patients with advanced CKD are required. We have recently completed a small pharmacokinetic study on metformin in patients on chronic haemofiltration over 12 weeks. Furthermore, we propose to study metformin in a larger haemofiltration patient cohort, for a longer duration, our aim being to determine the optimum metformin dosing regimen required in this high-risk patient group (www.anzctr.org.au, ACTRN12616000675426). We, too, believe that the cardiovascular benefits associated with metformin use far outweigh the risks of MALA, particularly if the patients are monitored carefully.

Can ad hoc analyses of clinical trials help personalize treatment decisions

Division of Clinical Pharmacology, Department of Paediatrics, University of Utah, School of Medicine, Salt Lake City, Utah, USA, Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, Utah, USA, Program in Personalized Health, University of Utah, Salt Lake City, Utah, USA, and Department of Pathology, University of Utah, School of Medicine, Salt Lake City, Utah, USA

State-of-the-Art Review on Physiologically Based Pharmacokinetic Modeling in Pediatric Drug Development

Physiologically based pharmacokinetic modeling and simulation is an important tool for predicting the pharmacokinetics, pharmacodynamics, and safety of drugs in pediatrics. Physiologically based pharmacokinetic modeling is applied in pediatric drug development for first-time-in-pediatric dose selection, simulation-based trial design, correlation with target organ toxicities, risk assessment by investigating possible drug–drug interactions, real-time assessment of pharmacokinetic–safety relationships, and assessment of non-systemic biodistribution targets. This review summarizes the details of a physiologically based pharmacokinetic modeling approach in pediatric drug research, emphasizing reports on pediatric physiologically based pharmacokinetic models of individual drugs. We also compare and contrast the strategies employed by various researchers in pediatric physiologically based pharmacokinetic modeling and provide a comprehensive overview of physiologically based pharmacokinetic modeling strategies and approaches in pediatrics. We discuss the impact of physiologically based pharmacokinetic models on regulatory reviews and product labels in the field of pediatric pharmacotherapy. Additionally, we examine in detail the current limitations and future directions of physiologically based pharmacokinetic modeling in pediatrics with regard to the ability to predict plasma concentrations and pharmacokinetic parameters. Despite the skepticism and concern in the pediatric community about the reliability of physiologically based pharmacokinetic models, there is substantial evidence that pediatric physiologically based pharmacokinetic models have been used successfully to predict differences in pharmacokinetics between adults and children for several drugs. It is obvious that the use of physiologically based pharmacokinetic modeling to support various stages of pediatric drug development is highly attractive and will rapidly increase, provided the robustness and reliability of these techniques are well established.

The clinical utility of pharmacometric models

Division of Clinical Pharmacology, Department of Pediatrics, University of Utah, School of Medicine, Salt Lake City, UT, USA, Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA, and Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, Computational and Data Enabled Science and Engineering Program, State University of New York, Buffalo, NY, USA

Predicting tacrolimus concentrations in children receiving a heart transplant using a population pharmacokinetic model

Objective Immunosuppressant therapy plays a pivotal role in transplant success and longevity. Tacrolimus, a primary immunosuppressive agent, is well known to exhibit significant pharmacological interpatient and intrapatient variability. This variability necessitates the collection of serial trough concentrations to ensure that the drug remains within therapeutic range. The objective of this study was to build a population pharmacokinetic (PK) model and use it to determine the minimum number of trough samples needed to guide the prediction of an individual’s future concentrations. Design, setting and patients Retrospective data from 48 children who received tacrolimus as inpatients at Primary Children’s Hospital in Salt Lake City, Utah were included in the study. Data were collected within the first 6 weeks after heart transplant. Outcome measures Data analysis used population PK modelling techniques in NONMEM. Predictive ability of the model was determined using median prediction error (MPE, a measure of bias) and median absolute prediction error (MAPE, a measure of accuracy). Of the 48 children in the study, 30 were used in the model building dataset, and 18 in the model validation dataset. Results Concentrations ranged between 1.5 and 37.7 µg/L across all collected data, with only 40% of those concentrations falling within the targeted concentration range (12 to 16 µg/L). The final population PK model contained the impact of age (on volume), creatinine clearance (on elimination rate) and fluconazole use (on elimination rate) as covariates. Our analysis demonstrated that as few as three concentrations could be used to predict future concentrations, with negligible bias (MPE (95% CI)=0.10% (−2.9% to 3.7%)) and good accuracy (MAPE (95% CI)=24.1% (19.7% to 27.7%)). Conclusions The use of PK in dose guidance has the potential to provide significant benefits to clinical care, including dose optimisation during the early stages of therapy, and the potential to limit the need for frequent drug monitoring.