Daniel F. B. Wright

Interpreting population pharmacokinetic-pharmacodynamic analyses – a clinical viewpoint

The population analysis approach is an important tool for clinical pharmacology in aiding the dose individualization of medicines. However, due to their statistical complexity the clinical utility of population analyses is often overlooked. One of the key reasons to conduct a population analysis is to investigate the potential benefits of individualization of drug dosing based on patient characteristics (termed covariate identification). The purpose of this review is to provide a tool to interpret and extract information from publications that describe population analysis. The target audience is those readers who are aware of population analyses but have not conducted the technical aspects of an analysis themselves. Initially we introduce the general framework of population analysis and work through a simple example with visual plots. We then follow-up with specific details on how to interpret population analyses for the purpose of identifying covariates and how to interpret their likely importance for dose individualization.

A proposal for dose‐adjustment of dabigatran etexilate in atrial fibrillation guided by thrombin time

Dabigatran is an oral anticoagulant that is increasingly used for atrial fibrillation (AF). Presently, many authorities state that routine laboratory coagulation monitoring is not required. However, data have recently been published demonstrating that higher trough plasma dabigatran concentrations are associated with lower thromboembolic and higher haemorrhagic event rates. Using these data, we simulate a range of AF patients with varying risks for these events and derive a target range of trough plasma dabigatran concentrations (30-130 μg l(-1) ). Finally, we propose that a conventional screening coagulation assay, the thrombin time (TT), can be used to discern whether or not patients are within this range of dabigatran concentrations.

Coagulation assays and plasma fibrinogen concentrations in real-world patients with atrial fibrillation treated with dabigatran.

AIMS In patients with atrial fibrillation prescribed dabigatran, the aim was to examine the correlation between plasma dabigatran concentrations and the three screening coagulation assays [international normalized ratio (INR), activated partial thromboplastin time (aPTT) and thrombin time (TT)] as well as the dilute thrombin time (dTT) and to examine the contribution of plasma fibrinogen concentrations to the variability in TT results. METHODS Plasma from patients with atrial fibrillation on dabigatran were analysed for clotting times and concentrations of fibrinogen and dabigatran. Correlation plots (and associated r(2) values) were generated using these data. The variability in TT results explained by fibrinogen concentrations was quantified using linear regression. RESULTS Fifty-two patients (38-94 years old) contributed 120 samples, with plasma dabigatran concentrations ranging from 9 to 408 μg l(-1) . The r(2) values of INR, aPTT, TT and dTT against plasma dabigatran concentrations were 0.49, 0.54, 0.70 and 0.95, respectively. Plasma fibrinogen concentrations explained some of the residual variability in TT values after taking plasma dabigatran concentrations into account (r(2) = 0.12, P = 0.02). CONCLUSIONS Of the screening coagulation assays, the TT correlated best with plasma dabigatran concentrations. Variability in fibrinogen concentrations accounts for some of the variability in the TT.

Falsely elevated vancomycin plasma concentrations sampled from central venous implantable catheters (portacaths).

Vancomycin is a glycopeptide antibiotic used to treat infections caused by gram-positive pathogens, including methicillin-resistant Staphylococcus aureus (MRSA). The monitoring of steady-state vancomycin plasma concentrations is recommended to reduce the risk of ototoxicity and nephrotoxicity and to ensure that target therapeutic plasma concentrations are achieved [1–3]. Most current recommendations suggest that vancomycin doses should be adjusted to achieve trough plasma concentrations from 5–20 mg l−1, depending on the severity of the infection and the pathogen being treated [1, 3]. Clinicians therefore rely on accurate plasma concentrations to aid dose adjustments and to ensure optimal patient care. We present two cases of spurious vancomycin plasma concentrations drawn from central venous implantable catheters (portacaths). Whereas a previous report described spurious vancomycin plasma concentrations drawn from a central catheter [4], this problem does not appear to have been described for vancomycin sampled from portacaths.

What do we learn from repeated population analyses

Population analyses are performed on new and existing drugs. They play an important role in quantifying the time course of drug effects and provide a means of understanding the impact of variability between individuals on dosing requirements. For some drugs there have been several population analyses reported in the literature. It is important to understand how repeated population analyses can value add and what authors and readers can consider when reviewing such analyses. The purpose of this review is to explore what is learnt from repeated population analyses and provide an understanding of how the value‐added nature of these analyses can be considered.

The influence of dosing time, variable compliance and circadian low-density lipoprotein production on the effect of simvastatin: simulations from a pharmacokinetic-pharmacodynamic model.

The aim of this study was to explore the influence of simvastatin dosing time, variable compliance and circadian cholesterol production on the reduction of low-density lipoprotein (LDL). A published pharmacokinetic-pharmacodynamic (PKPD) model for simvastatin was identified and evaluated. A model for circadian LDL production was incorporated into the PKPD model. Reduction in LDL from baseline was simulated stochastically from the full model at dose levels of 10, 20, 40 and 80 mg daily for 30 days. Simulated dosing times for each data set were morning (8.00 a.m.), evening (22.00 p.m.), evening with reduced compliance and evening for a hypothetical bioequivalent generic. Differences in LDL reduction from baseline between evening (33-43%) and morning dosing (31-43%) were negligible across a range of doses. Any differences were negated when variable compliance was considered. In addition, differences in simvastatin effect between morning and evening dosing were found to be within the range of LDL concentrations that would be permissible for a bioequivalent generic (at the lower limit) and hence are not likely to be important clinically. The results of this study suggest that taking simvastatin in the evening is not superior to morning dosing.

Deterministic identifiability of population pharmacokinetic and pharmacokinetic–pharmacodynamic models

Identifiability is an important component of pharmacokinetic–pharmacodynamic (PKPD) model development. Structural identifiability is concerned with the uniqueness of the model parameters for a set of perfect input–output data and deterministic identifiability with the precision of parameter estimation given imperfect input–output data. We introduce two subcategories of deterministic identifiability, external and internal, and consider factors that distinguish between these forms. We define external deterministic identifiability as a function of externally controllable variables, i.e., the design, and internal deterministic identifiability as a function of the model and its parameter values. The concepts are explored using three common PK and PKPD models, and verified for their precision for the selected set of parameter values under optimal design.

Methods for Predicting Warfarin Dose Requirements.

Background: The aim of this study was to compare the predictive performance of different warfarin dosing methods. Methods: Data from 46 patients who were initiating warfarin therapy were available for analysis. Nine recently published dosing tools including 8 dose prediction algorithms and a Bayesian forecasting method were compared with each other in terms of their ability to predict the actual maintenance dose. The dosing tools included 4 algorithms that were based on patient characteristics (2 clinical and 2 genotype-driven algorithms), 4 algorithms based on international normalized ratio (INR) response feedback and patient characteristics (2 clinical and 2 genotype-driven algorithms), and a Bayesian forecasting method. Comparisons were conducted using measures of bias (mean prediction error) and imprecision [root mean square error (RMSE)]. Results: The 2 genotype-driven INR feedback algorithms by Horne et al and Lenzini et al produced more precise maintenance dose predictions (RMSE, 1.16 and 1.19 mg/d, respectively; P < 0.05) than the genotype-driven algorithms by Gage et al and Klein et al and the Bayesian method (RMSE, 1.60, 1.62, and 1.81 mg/d respectively). The dose predictions from clinical and genotype-driven algorithms by Gage et al, Klein et al, and Horne et al were all negatively biased. Only the INR feedback algorithms (clinical and genotype) by Lenzini et al produced unbiased dose predictions. The Bayesian method produced unbiased dose predictions overall (mean prediction error, +0.37 mg/d; 95% confidence interval, 0.89 to −0.15) but overpredicted doses in patients requiring >8 mg/d. Conclusions: Overall, warfarin dosing methods that included some measure of INR response (INR feedback algorithms and Bayesian methods) produced unbiased and more precise dose predictions. The Bayesian forecasting method produced positively biased dose predictions in patients who required doses >8 mg/d. Further research to assess differences in clinical endpoints when warfarin doses are predicted using Bayesian or INR-driven algorithms is warranted.

Is the dose of dabigatran really more predictable than warfarin

We read with interest a recent commentary on the dosing of dabigatran ‘Optimizing the dose of dabigatran etexilate’ [1]. The author argues that warfarin fails to meet the required target profile for an oral anticoagulant because of large inter- and intra-individual variability in dosage requirements [1]. He notes that this variability can be attributed to the influence of patient characteristics, such as age, body weight, genetic polymorphisms, diet, organ dysfunction and drug interactions, on warfarin pharmacokinetics. By contrast, the author contends that dabigatran pharmacokinetics exhibit low to moderate variability overall and that differences between patients in terms of measured Cmax and AUC can largely be explained by differences in renal function [1]. We interpret the message to be that dabigatran is a step towards the required target profile for an oral anticoagulant and would be expected to have a ‘readily predictable dosage’. However, we wonder whether dabigatran will ultimately prove to be all that different from warfarin in terms of the need to individualize dosage and to monitor anticoagulation. The suggestion that dabigatran has less variable pharmacokinetics than warfarin cannot be substantiated with evidence from published population analyses. The unexplained variability in dabigatran clearance (CL) has been found to be approximately 50% (CV) [2], a value that reflects the variability remaining after differences in renal function between patients had been taken into account. By contrast, the unexplained variability in S-warfarin CL after accounting for important covariates (e.g. CYP2C9 genotype) has been reported to be 30–40% (CV) [3, 4]. Indeed a level of variability of the order of 30–60% (CV) has been shown to be typical for the CL of all the new anticoagulants [5]. In addition, dabigatran etexilate (the prodrug) is poorly soluble at pH > 3.0 and is a substrate for the efflux transporter P-glycoprotein (P-gp) in the gut wall resulting in a low fraction absorbed (6–7%) [6]. Pharmacokinetic interactions between dabigatran and proton pump inhibitors and P-gp inhibitors or inducers would be expected to provide additional sources of pharmacokinetic variability. Consequently, the factors that make warfarin a less than ideal oral anticoagulant may also apply to dabigatran, e.g. drug interactions, organ dysfunction, genetic polymorphisms (e.g. P-gp), age and body size. In short, according to available evidence the pharmacokinetics of dabigatran are not less variable than warfarin and hence are not more predictable. In addition, we question whether it is mechanistically possible to produce a drug that will fulfil the requirements of an ideal anticoagulant. There will always be a fine line between therapeutic anticoagulation and the risk of bleeding, regardless of the anticoagulant. This is due to the innate variability and sensitivity of the coagulation network which means that small perturbations in the network can result in significant influences on the overall effect. We contend that any drug that perturbs this system will inevitably have a narrow therapeutic range and will be likely to require monitoring of anticoagulation to ensure safe and effective prescribing [7]. Furthermore, we speculate that there may be less natural dampening of the coagulation system with anticoagulants that act close to the final stage of clot formation (i.e. an anticoagulant that inactivates thrombin) which means variability in drug concentration may well result in more profound variability in coagulation response. In summary, all oral anticoagulants seem to have similar pharmacokinetic variability and will be subject to a narrow therapeutic range. We suggest that new anticoagulants, such as dabigatran, will require monitoring of anticoagulation to determine the dose that best meets the needs of each patient.

A philosophical framework for pharmacy in the 21st century guided by ethical principles

&NA; Pharmacy has a long history of providing products and services for healthcare. In the last century, these roles have taken a strong focus on clinical care with the provision of medicines review, medicines optimisation, and prescribing services being at the forefront. The profession, however, is diverse. Pharmacists operate across a wide range of healthcare practices that often embrace both historic and contemporary roles simultaneously. The purpose of this article is to provide an overarching philosophical framework for pharmacy that encompasses roles that the modern pharmacist may assume. In doing this, we explore how pharmacy services align with healthcare and how different services require different approaches to clinical decision making.