A Bayesian Dose-Individualization Method for Warfarin

Construction of warfarin population pharmacokinetics and pharmacodynamics model in Han population based on Bayesian method

The purpose of this paper is to study the genetic polymorphisms of related gene loci (CYP2C9*3, VKORC1-1639G > A) based on demographic and clinical factors, and use the maximum a posterior Bayesian method to construct a warfarin individualized dose prediction model in line with the Chinese Han population. Finally, the built model is compared and analyzed with the widely used models at home and abroad. In this study, a total of 5467 INR measurements are collected from 646 eligible subjects in our hospital, and the maximum a posterior Bayesian method is used to construct a warfarin dose prediction that conforms to the Chinese Han population on the basis of the Hamberg model. The model is verified and compared with foreign models. This study finds that body weight and concomitant use of amiodarone have a significant effect on the anticoagulant effect of warfarin. The model can provide an effective basis for individualized and rational dosing of warfarin in Han population more accurately. In the performance of comparison with different warfarin dose prediction models, the new model has the highest prediction accuracy, and the prediction percentage is as high as 72.56%. The dose predicted by the Huang model is the closest to the actual dose of warfarin. The population pharmacokinetics and pharmacodynamics model established in this study can better reflect the distribution characteristics of INR values after warfarin administration in the Han population, and performs better than the models reported in the literature.

Application of a warfarin dosing calculator to guide individualized dosing versus empirical adjustment after fixed dosing: a pilot study

Background: Warfarin has a narrow therapeutic window and individual variation, and patients require regular follow-up and monitoring of the International Normalized Ratio (INR) for dose adjustment. The calculation method of Warfarin Dosing Calculator (WDC) software is based on the European and American populations, and its accuracy in the Chinese population is yet to be verified. Objective: This study was to evaluate the feasibility of applying Warfarin Dosing Calculator software intervention in a real-world clinical research setting in China. Methods: The pilot study divided the included patients after valve replacement into an experimental group and a control group, with 38 cases in each group. In the control group, the initial dose was fixed at 2.5 mg/d and the dose was adjusted empirically during the study period; in the experimental group, the Warfarin Dosing Calculator software was applied to guide the dosing, and patients in both groups were followed up for 3 months. Analysis of the incidence anticoagulation outcomes and excessive anticoagulation events in both groups. Kaplan-Meier survival curves were used to analyze the correlation between different dosing regimens and first International Normalized Ratio attainment, and Logrank tests were performed. Results: The mean time required for first International Normalized Ratio compliance in the experimental group was 4.38 days less than in the control group, and the mean number of tests was 1.43 less (p < 05). Time in therapeutic range (TTR) was significantly higher in the experimental group than in the control group (p < 05). Kaplan-Meier survival curve analysis showed that the first International Normalized Ratio attainment rate was significantly higher in the experimental group than in the control group (p = 01). No major bleeding events occurred in either group, but other excessive anticoagulation events (INR>3.5 and minor bleeding) were significantly reduced in the experimental group compared with the control group (p < 05). Conclusion: Application of Warfarin Dosing Calculator software to guide individualized warfarin dosing may be better than a fixed dose of 2.5 mg/d. It may be shorten the time to first International Normalized Ratio attainment, and the attainment rate in the same time, and can better improve the mean Time in therapeutic range level value and reduce excessive anticoagulation events, which improves the safety of warfarin anticoagulation therapy in clinical practice. Clinical Trial Registration: https://www.chictr.org.cn/showproj.html?proj=52793, ChiCTR2000032393.

Deriving mechanism-based pharmacodynamic models by reducing quantitative systems pharmacology models: An application to warfarin

Quantitative systems pharmacology (QSP) models integrate comprehensive qualitative and quantitative knowledge about pharmacologically relevant processes. We previously proposed a first approach to leverage the knowledge in QSP models to derive simpler, mechanism-based pharmacodynamic (PD) models. Their complexity, however, is typically still too large to be used in the population analysis of clinical data. Here, we extend the approach beyond state reduction to also include the simplification of reaction rates, elimination of reactions, and analytic solutions. We additionally ensure that the reduced model maintains a prespecified approximation quality not only for a reference individual but also for a diverse virtual population. We illustrate the extended approach for the warfarin effect on blood coagulation. Using the model-reduction approach, we derive a novel small-scale warfarin/international normalized ratio model and demonstrate its suitability for biomarker identification. Due to the systematic nature of the approach in comparison with empirical model building, the proposed model-reduction algorithm provides an improved rationale to build PD models also from QSP models in other applications.

Control-Relevant Adaptive Personalized Modeling From Limited Clinical Data for Precise Warfarin Management

Warfarin is a challenging drug to administer due to the narrow therapeutic index of the International Normalized Ratio (INR), the inter- and intra-variability of patients, limited clinical data, genetics, and the effects of other medications. Goal: To predict the optimal warfarin dosage in the presence of the aforementioned challenges, we present an adaptive individualized modeling framework based on model (In)validation and semi-blind robust system identification. The model (In)validation technique adapts the identified individualized patient model according to the change in the patient's status to ensure the model's suitability for prediction and controller design. Results: To implement the proposed adaptive modeling framework, the clinical data of warfarin-INR of forty-four patients has been collected at the Robley Rex Veterans Administration Medical Center, Louisville. The proposed algorithm is compared with recursive ARX and ARMAX model identification methods. The results of identified models using one-step-ahead prediction and minimum mean squared analysis (MMSE) show that the proposed framework effectively predicts the warfarin dosage to keep the INR values within the desired range and adapt the individualized patient model to exhibit the true status of the patient throughout treatment. Conclusion: This paper proposes an adaptive personalized patient modeling framework from limited patientspecific clinical data. It is shown by rigorous simulations that the proposed framework can accurately predict a patient's doseresponse characteristics and it can alert the clinician whenever identified models are no longer suitable for prediction and adapt the model to the current status of the patient to reduce the prediction error.

External Evaluation of a Bayesian Warfarin Dose Optimization Based on a Kinetic-Pharmacodynamic Model

Warfarin is a representative anticoagulant with large interindividual variability. The published kinetic-pharmacodynamic (K-PD) model allows the prediction of warfarin dose requirement in Swedish patients; however, its applicability in Japanese patients is not known. We evaluated the model's predictive performance in Japanese patients with various backgrounds and relationships using Bayesian parameter estimation and sampling times. A single-center retrospective observational study was conducted at Tokyo Women's Medical University, Medical Center East. The study population consisted of adult patients aged >20 years who commenced warfarin with a prothrombin time-international normalized ratio (PT-INR) from June 2015 to June 2019. The published K-PD model modified by Wright and Duffull was assessed using prediction-corrected visual predictive checks, focusing on clinical characteristics, including age, renal function, and individual prediction error. The external dataset included 232 patients who received an initial warfarin daily dose of 3.2 ± 1.28 mg with 2278 PT-INR points (median [range] follow-up period of 23 d [7-28]). Prediction-corrected visual predictive checks carried a propensity for underprediction. Additionally, age >60 years, body mass index ≤25 kg/m², and estimated glomerular filtration rate ≤60 mL/min/1.73 m² had a pronounced tendency to underpredict PT-INR. However, Bayesian prediction using four prior observations reduced underprediction. To improve the prediction performance of these special populations, further studies are required to construct a model to predict warfarin dose requirements in Japanese patients.

Adaptive Individualized Drug-Dose Response Modeling from a Limited Clinical Data: Case of Warfarin Management

Administration of drugs requires sophisticated methods to determine the drug quantity for optimal results, and it has been a challenging task for the number of diseases. To solve these challenges, in this paper, we present the semi-blind robust model identification technique to find individualized patient models using the minimum number of clinically acquired patient-specific data to determine optimal drug dosage. To ensure the usability of these models for dosage predictability and controller design, the model (In)validation technique is also investigated. As a case study, the patients treated with warfarin are studied to demonstrate the semi-blind robust identification and model (In)validation techniques. The performance of models is assessed by calculating minimum means squared error (MMSE).Clinical Relevance- This work establishes a general framework for adaptive individualized drug-dose response models from a limited number of clinical patient-specific data. This work will help clinicians in decision-making for improved drug dosing, patient care, and limiting patient exposure to agents with a narrow therapeutic range.

Evaluation of pharmacogenomics and hepatic nuclear imaging-related covariates by population pharmacokinetic models of irinotecan and its metabolites

BACKGROUND

Body surface area (BSA)-based dosing of irinotecan (IR) does not account for its pharmacokinetic (PK) and pharmacodynamic (PD) variabilities. Functional hepatic nuclear imaging (HNI) and excretory/metabolic/PD pharmacogenomics have shown correlations with IR disposition and toxicity/efficacy. This study reports the development of a nonlinear mixed-effect population model to identify pharmacogenomic and HNI-related covariates that impact on IR disposition to support dosage optimization.

METHODS

Patients had advanced colorectal cancer treated with IR combination therapy. Baseline blood was analysed by Affymetrix DMET™ Plus Array and, for PD, single nucleotide polymorphisms (SNPs) by Sanger sequencing. For HNI, patients underwent ^99mTc-IDA hepatic imaging, and data was analysed for hepatic extraction/excretion parameters. Blood was taken for IR and metabolite (SN38, SN38G) analysis on day 1 cycle 1. Population modelling utilised NONMEM version 7.2.0, with structural PK models developed for each moiety. Covariates include patient demographics, HNI parameters and pharmacogenomic variants.

RESULTS

Analysis included (i) PK data: 32 patients; (ii) pharmacogenomic data: 31 patients: 750 DMET and 22 PD variants; and (iii) HNI data: 32 patients. On initial analysis, overall five SNPs were identified as significant covariates for CL_SN38. Only UGT1A3_c.31 T > C and ABCB1_c.3435C > T were included in the final model, whereby CL_SN38 reduced from 76.8 to 55.1%.

CONCLUSION

The identified UGT1A3_c.31 T > C and ABCB1_c.3435C > T variants, from wild type to homozygous, were included in the final model for SN38 clearance.

Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling

Patients requiring low-dose warfarin are more likely to suffer bleeding due to overdose. The goal of this work is to improve the feedforward neural network model's precision in predicting the low maintenance dose for Chinese in the aspect of training data construction. We built the model from a resampled dataset created by equal stratified sampling (maintaining the same sample number in three dose-groups with a total of 3639) and performed internal and external validations. Comparing to the model trained from the raw dataset of 19,060 eligible cases, we improved the low-dose group's ideal prediction percentage from 0.7 to 9.6% and maintained the overall performance (76.4% vs. 75.6%) in external validation. We further built neural network models on single-dose subsets to invest whether the subsets samples were sufficient and whether the selected factors were appropriate. The training set sizes were 1340 and 1478 for the low and high dose subsets; the corresponding ideal prediction percentages were 70.2% and 75.1%. The training set size for the intermediate dose varied and was 1553, 6214, and 12,429; the corresponding ideal prediction percentages were 95.6, 95.1%, and 95.3%. Our conclusion is that equal stratified sampling can be a considerable alternative approach in training data construction to build drug dosing models in the clinic.

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.

Effect of Protein Binding on Exposure of Unbound and Total Mycophenolic Acid: A Population Pharmacokinetic Analysis in Chinese Adult Kidney Transplant Recipients

OBJECTIVES

The population pharmacokinetic (popPK) characteristics of total mycophenolic acid (tMPA) have been investigated in various ethnic populations. However, investigations of popPK of unbound MPA (uMPA) are few. Thus, a popPK analysis was performed to: (1) characterize the PK of uMPA and tMPA and its 7-O-mycophenolic acid glucuronide (MPAG) metabolite in kidney transplant patients cotreated with cyclosporine (CsA), and (2) identify the clinically significant covariates that explain variability in the dose-exposure relationship.

METHODS

A total of 740 uMPA, 741 tMPA, and 734 total MPAG (tMPAG) concentration-time data from 58 Chinese kidney transplant patients receiving MPA in combination with CsA were analyzed using NONMEM® software with the stochastic approximation expectation maximization (SAEM) followed by the important sampling (IMP) method. The influence of covariates was tested using a stepwise procedure.

RESULTS

The PK of uMPA and unbound MPAG (uMPAG) were characterized by a two- and one-compartment model with first-order elimination, respectively. A linear protein binding model was used to link uMPA and tMPA. Apparent clearance (CL/F) and central volume of distribution (VC/F) of uMPA (CLuMPA/F and VCuMPA/F, respectively) and protein binding rate constant (k B) were estimated to be 851 L/h [relative standard error (RSE), 7.1%], 718 L (18.5%) and 53.4/h (2.3%), respectively. For uMPAG, the population values (RSE) of CL/F (CLuMPAG) and VC/F (VCuMPAG/F) were 5.71 L/h (4.4%) and 29.9 L (7.7%), respectively. Between-subject variability (BSVs) on CLuMPA/F, VCuMPA/F, CLuMPAG/F, and VCuMPAG/F were 51.0, 80.0, 31.8 and 48.4%, respectively, whereas residual unexplained variability (RUVs) for uMPA, tMPA, and uMPAG were 47.0, 45.9, and 22.0%, respectively. Significant relationships were found between k B and serum albumin (ALB) and between CLuMPAG/F and glomerular filtration rate (GFR). Additionally, model-based simulation showed that changes in ALB concentrations substantially affected tMPA but not uMPA exposure.

CONCLUSIONS

The established model adequately described the popPK characteristics of the uMPA, tMPA, and MPAG. The estimated CLuMPA/F and unbound fraction of MPA (FUMPA) in Chinese kidney transplant recipients cotreated with CsA were comparable to those published previously in Caucasians. We recommend monitoring uMPA instead of tMPA to optimize mycophenolate mofetil (MMF) dosing for patients with lower ALB levels.

[Efficiency of pharmacogenetic approach to anticoagulant therapy in patients with prosthetic heart valves]

BACKGROUND

This study examined clinical, demographic, anthropometric, and inheritance factors that influence individual sensitivity to warfarin therapy after heart valve surgery. The clinical significance of the pharmacogenetic approach was assessed using the individual time frame and time spent in the INR therapeutic range.

AIMS

We determined the clinical outcome of the pharmacogenetic approach at the start of warfarin therapy in patients with prosthetic heart valves.

MATERIALS AND METHODS

The study included 915 patients, of which 512 women and 403 men (mean age 56±10 years), living in Western Siberia. Rheumatic heart disease was the main diagnosis that caused the acquired defect. Mechanical prostheses were used in 70% of cases of cardiac surgery. Real-time polymerase chain reaction used for molecular genetic testing.

RESULTS

The frequencies of the alleles and genotypes of CYP2C9 and VKORC1 in the study population of patients with heart valves prosthetic correspond to the distribution in Caucasoid populations. The use of pharmacogenetic testing results at the beginning of warfarin therapy reduced the time required for selecting a therapeutic dose of anticoagulant by 2 times and increased the duration of stay in the INR therapeutic range by 20.2%.

CONCLUSION

The use of the pharmacogenetic approach at the begin‑ ning of warfarin therapy contributes to the effectiveness and safety of anticoagulant therapy in this category of patients.

Characteristics of software used in self-management of vitamin K antagonist therapy: A systematic review

INTRODUCTION AND GOAL

Currently, 1-2% of the population in developed countries are under treatment with oral anticoagulants. An appropriate strategy to deal with this increase in demand of treatment with oral anticoagulants and to manage the costs is the transfer of part or all of the responsibility for managing treatment to the patients. The use of information technology, particularly electronic health software, can be an appropriate method to improve the quality of self-management of treatment with these drugs. Therefore, this systematic review investigated studies that discuss the characteristics of electronic health software in self-management of oral anticoagulation therapy.

METHOD

A systematic review based on PRISMA protocol was conducted. In this study, articles were investigated that were in English. Articles existing in Cochrane, EMBASE and PubMed databases were searched up to 14 May 2017. Then, articles searched through Google Scholar were added to this study.

FINDINGS

The common characteristics used in most software included 'encryption in exchanging information', having an 'instruction module' and 'being Android-based'. In terms of functionality, 'communication between the patient and healthcare team' existed in most of the software.

CONCLUSION

The results of the study showed that the accuracy of administration of the dose of the drug using computer to reach a target international normalized ratio level was not less than those administered with experienced medical staff. In addition, the results indicated that important characteristics of the software include encryption in exchanging information, instruction module and Android-based instruction module. The most important characteristic was the interaction between the patient and the healthcare team.

Evaluation of the predictive performance of Bayesian dosing for warfarin in Chinese patients

Aim: To evaluate the accuracy and predictive performance of Bayesian dosing for warfarin in Chinese patients. Materials & methods: Six multiple linear regression algorithms (Wei, Lou, Miao, Huang, Gage and IWPC) and a Bayesian method implemented in Warfarin Dose Calculator were compared with each other. Results: Six multiple linear regression warfarin dosing algorithms had similar predictive ability, except Miao and Lou. The mean prediction error of Bayesian priori and posteriori method were 0.01 mg/day (95% CI: -0.18 to 0.19) and 0.17 mg/day (95% CI: -0.05 to 0.29), respectively, and Bayesian posteriori method demonstrated better performance in all dose ranges. Conclusion: The Bayesian method showed a good potential for warfarin maintenance dose prediction in Chinese patients requiring less than 6 mg/day.

A factor VII-based method for the prediction of anticoagulant response to warfarin

Warfarin dosing methods based on existing models for warfarin and the international normalised ratio (INR) give biased maintenance dose predictions at the upper and lower quantiles of dose requirements. The aim of this work is to propose a conceptually different approach to predict INR after warfarin dosing. Factor VII concentration was proposed as the principal driving force for the INR. The time to steady-state INR (t_SS,INR) was determined based on the INR response to changes in factor VII concentrations following warfarin initiation, and from this the steady-state INR (INR_SS) was derived. The proposed method requires timed, paired blood samples of INR and factor VII. At different simulated warfarin dose rates, the prediction error associated with the proposed method was shown to be within clinically acceptable limits for both the t_SS,INR (±2 days) and INR_SS (±0.2). The use of the method was demonstrated in two patients who were initiated with 5 mg of warfarin daily. The difference in predicted versus actual steady-state INR were 0.0 and −0.4. The proposed method represents a unique approach to predict the INR. It considers factor VII as the main driver for INR and provides valuable information about the time to steady state INR.

A Joint Model for Vitamin K-Dependent Clotting Factors and Anticoagulation Proteins

BACKGROUND

Warfarin acts by inhibiting the reduction of vitamin K (VK) to its active form, thereby decreasing the production of VK-dependent coagulation proteins. The aim of this research is to develop a joint model for the VK-dependent clotting factors II, VII, IX and X, and the anticoagulation proteins, proteins C and S, during warfarin initiation.

METHODS

Data from 18 patients with atrial fibrillation who had warfarin therapy initiated were available for analysis. Nine blood samples were collected from each subject at baseline, and at 1-5, 8, 15 and 29 days after warfarin initiation and assayed for factors II, VII, IX and X, and proteins C and S. Warfarin concentration-time data were not available. The coagulation proteins data were modelled in a stepwise manner using NONMEM^® Version 7.2. In the first stage, each of the coagulation proteins was modelled independently using a kinetic-pharmacodynamic model. In the subsequent step, the six kinetic-pharmacodynamic models were combined into a single joint model.

RESULTS

One patient was administered VK and was excluded from the analysis. Each kinetic-pharmacodynamic model consisted of two parts: (1) a common one-compartment pharmacokinetic model with first-order absorption and elimination for warfarin; and (2) an inhibitory E _max model linked to a turnover model for coagulation proteins. In the joint model, an unexpected pharmacodynamic lag was identified and the estimated degradation half-life of VK-dependent coagulation proteins were in agreement with previously published values. The model provided an adequate fit to the observed data.

CONCLUSION

The joint model represents the first work to quantify the influence of warfarin on all six VK-dependent coagulation proteins simultaneously. Future work will expand the model to predict the influence of exogenously administered VK on the time course of clotting factor concentrations after warfarin overdose and during perioperative warfarin reversal procedures.

Influence of Genotype on Warfarin Maintenance Dose Predictions Produced Using a Bayesian Dose Individualization Tool

BACKGROUND

A previously established Bayesian dosing tool for warfarin was found to produce biased maintenance dose predictions. In this study, we aimed (1) to determine whether the biased warfarin dose predictions previously observed could be replicated in a new cohort of patients from 2 different clinical settings, (2) to explore the influence of CYP2C9 and VKORC1 genotype on predictive performance of the Bayesian dosing tool, and (3) to determine whether the previous population used to develop the kinetic-pharmacodynamic model underpinning the Bayesian dosing tool was sufficiently different from the test (posterior) population to account for the biased dose predictions.

METHODS

The warfarin maintenance doses for 140 patients were predicted using the dosing tool and compared with the observed maintenance dose. The impact of genotype was assessed by predicting maintenance doses with prior parameter values known to be altered by genetic variability (eg, EC50 for VKORC1 genotype). The prior population was evaluated by fitting the published kinetic-pharmacodynamic model, which underpins the Bayesian tool, to the observed data using NONMEM and comparing the model parameter estimates with published values.

RESULTS

The Bayesian tool produced positively biased dose predictions in the new cohort of patients (mean prediction error [95% confidence interval]; 0.32 mg/d [0.14-0.5]). The bias was only observed in patients requiring ≥7 mg/d. The direction and magnitude of the observed bias was not influenced by genotype. The prior model provided a good fit to our data, which suggests that the bias was not caused by different prior and posterior populations.

CONCLUSIONS

Maintenance doses for patients requiring ≥7 mg/d were overpredicted. The bias was not due to the influence of genotype nor was it related to differences between the prior and posterior populations. There is a need for a more mechanistic model that captures warfarin dose-response relationship at higher warfarin doses.

Evaluation of Concomitant Antiretrovirals and CYP2C9/CYP2C19 Polymorphisms on the Pharmacokinetics of Etravirine

BACKGROUND

Etravirine is a non-nucleoside reverse transcriptase inhibitor indicated in combination with other antiretrovirals for treatment-experienced HIV patients ≥6 years of age. Etravirine is primarily metabolized by cytochrome P450 (CYP) 2C9, CYP2C19, and CYP3A. This analysis determined the impact of concomitant antiretrovirals and CYP2C9/CYP2C19 phenotype on the pharmacokinetics of etravirine.

METHODS

We used 4728 plasma concentrations from 817 adult subjects collected from four clinical studies to develop the population pharmacokinetic model. The presence of atazanavir/ritonavir, lopinavir/ritonavir, darunavir/ritonavir, tenofovir disoproxil fumarate, or enfuvirtide together with the CYP2C9 and CYP2C19 phenotype and other demographics were evaluated.

RESULTS

A one-compartment model with first-order input and a lag-time best described the data. Estimates of apparent total clearance (CL/F), apparent central volume of distribution (V _c/F), first-order absorption rate constant (k _a), and absorption lag-time were 41.7 L/h, 972 L, 1.16 h, and 1.32 h, respectively. Estimates of between-subject variability on CL/F, V _c/F, and relative bioavailability (F) were 39.4 %CV (percentage coefficient of variation), 35.9 %CV and 35.5 %CV, respectively. Between-occasion variability on F was estimated to be 30.0 %CV. CL/F increased non-linearly with body weight and creatinine clearance (CL_CR), and also varied based on CYP2C9/CYP2C19 phenotype.

CONCLUSIONS

In this analysis, body weight, CL_CR, and CYP2C9/CYP2C19 phenotype were found to describe some of the variability in CL/F. It was not possible to show an impact of concomitant antiretrovirals on the pharmacokinetics of etravirine for adults predominantly taking coadministered boosted protease inhibitors as a background antiretroviral regimen.

A Nonparametric Method to Optimize Initial Drug Dosing and Attainment of a Target Exposure Interval: Concepts and Application to Busulfan in Pediatrics

The traditional approach for model-based initial dosing is based on the use of a single vector of typical population parameters for targeting a specific exposure. This approach is theoretically ill-suited for targeting a range of exposure. The objective of this work was to develop a general approach for optimal (OPT) targeting of a drug exposure interval. After methodological purposes, we applied our method to the busulfan case. We used a nonparametric population pharmacokinetic model of intravenous busulfan to estimate the individual pharmacokinetic parameters of 163 bone marrow-transplanted children. Then, an array of 151 doses of busulfan ranging from 0.5 to 2 mg/kg was simulated a priori in each patient. For each dose, 29 possible busulfan plasma concentration profiles, corresponding to the nonparametric prior, each associated with a probability, were obtained. The multiple-model-based, OPT dose was identified as the dose maximizing the a priori probability of achieving the busulfan target area under the concentration-time curve (AUC). Two AUC targets were considered: 900-1500 (conventional) or <1500 µM min^-1. Finally, the OPT dose was individually simulated in each patient. We compared the ability of this method to achieve the target exposure interval with that of three other traditional model-based methods and one based on the non-parametric approach. When targeting the busulfan conventional AUC range, the OPT dose provided better attainment than the best of the three other methods after one dose (82.2 vs. 41.7 %, p < 0.005), two doses (79.1 vs. 65.0 %, p < 0.005), and at the end of therapy (80.4 vs. 76.7 %, p < 0.42). The approach provided a balanced distribution between under- (10.4 %) and overexposure (9.2 %), while other approaches showed higher rates of underexposure (≥19 %). When targeting an AUC <1500 µM min, the OPT dose was successful in minimizing overexposure as 0 % of children showed simulated AUC >1500 µM min^-1. Our approach has been designed to optimize the targeting of an exposure interval. When applied to busulfan in children, it outperformed the traditional model-based dosing approach, with earlier and better achievement of busulfan target AUC. The approach can be applied for OPT dosing of many drugs, when the target objective is an interval.

Warfarin Dosing Algorithms Underpredict Dose Requirements in Patients Requiring ≥7 mg Daily: A Systematic Review and Meta-analysis

There is preliminary evidence to suggest that some published warfarin dosing algorithms produce biased maintenance dose predictions in patients who require higher than average doses. We conducted a meta-analysis of warfarin dosing algorithms to determine if there exists a systematic under- or overprediction of dose requirements for patients requiring ≥7 mg/day across published algorithms. Medline and Embase databases were searched up to September 2015. We quantified the proportion of over- and underpredicted doses in patients whose observed maintenance dose was ≥7 mg/day. The meta-analysis included 47 evaluations of 22 different warfarin dosing algorithms from 16 studies. The meta-analysis included data from 1,492 patients who required warfarin doses of ≥7 mg/day. All 22 algorithms were found to underpredict warfarin dosing requirements in patients who required ≥7 mg/day by an average of 2.3 mg/day with a pooled estimate of underpredicted doses of 92.3% (95% confidence interval 90.3-94.1, I² = 24%).

Clinical Decision Support Tools: The Evolution of a Revolution

Dashboard systems for clinical decision support integrate data from multiple sources. These systems, the newest in a long line of dose calculators and other decision support tools, utilize Bayesian approaches to fully individualize dosing using information gathered through therapeutic drug monitoring. In the treatment of inflammatory bowel disease patients with infliximab, dashboards may reduce therapeutic failures and treatment costs. The history and future development of modern Bayesian dashboard systems is described.

A rolling-horizon pharmacokinetic pharmacodynamic model for warfarin inpatients in transient clinical states

Aim: To design a pharmacokinetic pharmacodynamic model to make individualized and adaptive international normalized ratio (INR) predictions for warfarin inpatients in changing clinical status. Methods: We tested a new model on 60 inpatients at Columbia University. The model personalizes four submodels and minimizes the number of parameters to be estimated. Prediction accuracy was assessed by prediction error, absolute prediction error and percentage absolute prediction error. Results: The INRs were accurately predicted 5 days into the future. Median prediction error: 0.01–0.12; median absolute prediction error: 0.17–0.5 and median percentage absolute prediction error: 9.85–26.06%. Conclusion: Patients exhibit interindividual and intertemporal variability. The model captures the variability and provides accurate and personalized INR predictions.

Effects of VKORC1 Genetic Polymorphisms on Warfarin Maintenance Dose Requirement in a Chinese Han Population

Background

VKORC1 is reported to be capable of treating several diseases with thrombotic risk, such as cardiac valve replacement. Some single-nucleotide polymorphisms (SNPs) in VKORC1 are documented to be associated with clinical differences in warfarin maintenance dose. This study explored the correlations of VKORC1–1639 G/A, 1173 C/T and 497 T/G genetic polymorphisms with warfarin maintenance dose requirement in patients undergoing cardiac valve replacement.

Material/Methods

A total of 298 patients undergoing cardiac valve replacement were recruited. During follow-up, clinical data were recorded. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was applied to detect VKORC1–1639 G/A, 1173 C/T and 497 T/G polymorphisms, and genotypes were analyzed.

Results

Correlations between warfarin maintenance dose and baseline characteristics revealed statistical significances of age, gender and operation methods with warfarin maintenance dose (all P<0.05). Warfarin maintenance dose in VKORC1–1639 G/A AG + GG carriers was obviously higher than in AA carriers (P<0.001). As compared with patients with TT genotype in VKORC1 1173 C/T, warfarin maintenance dose was apparently higher in patients with CT genotype (P<0.001). Linear regression analysis revealed that gender, operation method, method for heart valve replacement, as well as VKORC1–1639 G/A and 1173 C/T gene polymorphisms were significantly related to warfarin maintenance dose (all P<0.05).

Conclusions

VKORC1 gene polymorphisms are key genetic factors to affect individual differences in warfarin maintenance dose in patients undergoing cardiac valve replacement; meanwhile, gender, operation method and method for heart valve replacement might also be correlate with warfarin maintenance dose.

Optimal sampling of antipsychotic medicines: a pharmacometric approach for clinical practice

AIM

To determine optimal sampling strategies to allow the calculation of clinical pharmacokinetic parameters for selected antipsychotic medicines using a pharmacometric approach.

METHODS

This study utilized previous population pharmacokinetic parameters of the antipsychotic medicines aripiprazole, clozapine, olanzapine, perphenazine, quetiapine, risperidone (including 9-OH risperidone) and ziprasidone. d-optimality was utilized to identify time points which accurately predicted the pharmacokinetic parameters (and expected error) of each drug at steady-state. A standard two stage population approach (STS) with MAP-Bayesian estimation was used to compare area under the concentration-time curves (AUC) generated from sparse optimal time points and rich extensive data. Monte Carlo Simulation (MCS) was used to simulate 1000 patients with population variability in pharmacokinetic parameters. Forward stepwise regression analysis was used to determine the most predictive time points of the AUC for each drug at steady-state.

RESULTS

Three optimal sampling times were identified for each antipsychotic medicine. For aripiprazole, clozapine, olanzapine, perphenazine, risperidone, 9-OH risperidone, quetiapine and ziprasidone the CV% of the apparent clearance using optimal sampling strategies were 19.5, 8.6, 9.5, 13.5, 12.9, 10.0, 16.0 and 10.7, respectively. Using the MCS and linear regression approach to predict AUC, the recommended sampling windows were 16.5-17.5 h, 10-11 h, 23-24 h, 19-20 h, 16.5-17.5 h, 22.5-23.5 h, 5-6 h and 5.5-6.5 h, respectively.

CONCLUSION

This analysis provides important sampling information for future population pharmacokinetic studies and clinical studies investigating the pharmacokinetics of antipsychotic medicines.

Optimizing azole antifungal therapy in the prophylaxis and treatment of fungal infections

PURPOSE OF REVIEW

Azole antifungals are widely used in the prophylaxis and treatment of fungal infections, but are associated with a range of pharmacokinetic challenges and safety issues that necessitate individualized therapy to achieve optimal clinical outcomes. Recent advances in our knowledge of azole exposure-response relationships, therapeutic drug monitoring and individualized dosing strategies are reviewed as follows.

RECENT FINDINGS

Recent studies have significantly improved the understanding of exposure-response relationships for efficacy and toxicity, increasing confidence in target exposure ranges for azole antifungal agents. Population pharmacokinetic modelling of voriconazole has led to studies demonstrating the feasibility of model-guided dose individualization strategies with the drug, which holds significant promise for optimizing therapy. The recent approval of a solid oral tablet formulation of posaconazole with improved bioavailability and once-daily dosing has significantly improved the clinical utility of this agent. Further clinical experience with the investigational azole isavuconazole is needed to determine the role of individualized therapy.

SUMMARY

The coordination of CYP2C19 pharmacogenomic testing with model-guided dose individualization holds significant promise for optimizing therapy with voriconazole. Pharmacokinetic challenges with itraconazole, voriconazole and posaconazole oral suspension continue to require therapeutic drug monitoring to individualize therapy and optimize treatment outcomes.

How to manage warfarin therapy

Long-term treatment with warfarin is recommended for patients with atrial fibrillation at risk of stroke and those with recurrent venous thrombosis or prosthetic heart valves. Patient education before commencing warfarin - regarding signs and symptoms of bleeding, the impact of diet, potential drug interactions and the actions to take if a dose is missed - is pivotal to successful use. Scoring systems such as the CHADS2 score are used to determine if patients with atrial fibrillation are suitable for warfarin treatment. To rapidly achieve stable anticoagulation, use an age-adjusted protocol for starting warfarin. Regular monitoring of the anticoagulant effect is required. Evidence suggests that patients who self-monitor using point-of-care testing have better outcomes than other patients.

Quantification of the Forgiveness of Drugs to Imperfect Adherence

The circumstance of how sensitive therapeutic success is under imperfect adherence is driven by the property known as forgiveness. To date, no studies have considered variability in the pharmacokinetic-pharmacodynamic process in conjunction with imperfect adherence patterns in order to develop a comparative criterion to determine the forgiveness of a drug. In this study, we have proposed a criterion to quantify forgiveness; illustrated the criterion for a theoretical example and evaluated the forgiveness of a motivating example, namely warfarin. A forgiveness criterion, relative forgiveness, is defined as the number of times more likely that a target is successfully attained under perfect adherence compared to imperfect adherence; or when comparing two drugs under a standard setting of imperfect adherence. The relative forgiveness criterion may have important implications for both drug development and clinical practice since the choice of drug can account for the likely influence of its forgiveness.

A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children

BACKGROUND

Warfarin is the most widely prescribed anticoagulant for the prevention and treatment of thromboembolic events. Although highly effective, the use of warfarin is limited by a narrow therapeutic range combined with a more than ten-fold difference in the dose required for adequate anticoagulation in adults. An optimal dose that leads to a favourable balance between the wanted antithrombotic effect and the risk of bleeding as measured by the prothrombin time International Normalised Ratio (INR) must be found for each patient. A model describing the time-course of the INR response can be used to aid dose selection before starting therapy (a priori dose prediction) and after therapy has been initiated (a posteriori dose revision).

RESULTS

In this paper we describe a warfarin decision support tool. It was transferred from a population PKPD-model for warfarin developed in NONMEM to a platform independent tool written in Java. The tool proved capable of solving a system of differential equations that represent the pharmacokinetics and pharmacodynamics of warfarin with a performance comparable to NONMEM. To estimate an a priori dose the user enters information on body weight, age, baseline and target INR, and optionally CYP2C9 and VKORC1 genotype. By adding information about previous doses and INR observations, the tool will suggest a new dose a posteriori through Bayesian forecasting. Results are displayed as the predicted dose per day and per week, and graphically as the predicted INR curve. The tool can also be used to predict INR following any given dose regimen, e.g. a fixed or an individualized loading-dose regimen.

CONCLUSIONS

We believe that this type of mechanism-based decision support tool could be useful for initiating and maintaining warfarin therapy in the clinic. It will ensure more consistent dose adjustment practices between prescribers, and provide efficient and truly individualized warfarin dosing in both children and adults.