Theory-based pharmacokinetics and pharmacodynamics of S- and R-warfarin and effects on international normalized ratio: influence of body size, composition and genotype in cardiac surgery patients

Leveraging QSP Models for MIPD: A Case Study for Warfarin/INR

Warfarin dosing remains challenging due to substantial inter-individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model-informed precision dosing (MIPD) can help individualize warfarin dosing, requiring the selection of a suitable model. For models developed from clinical data, the dependence on the study design and population raises questions about generalizability. Quantitative system pharmacology (QSP) models promise better extrapolation abilities; however, their complexity and lack of validation on clinical data raise questions about applicability in MIPD. We have previously derived a mechanistic warfarin/international normalized ratio (INR) model from a blood coagulation QSP model. In this article, we evaluated the predictive performance of the warfarin/INR model in the context of MIPD using an external dataset with INR data from patients starting warfarin treatment. We assessed the accuracy and precision of model predictions, benchmarked against an empirically based reference model. Additionally, we evaluated covariate contributions and assessed the predictive performance separately in the more challenging outpatient data. The warfarin/INR model performed comparably to the reference model across various measures despite not being calibrated with warfarin initiation data. Including CYP2C9 and/or VKORC1 genotypes as covariates improved the prediction quality of the warfarin/INR model, even after assimilating 4 days of INR data. The outpatient INR exhibited higher unexplained variability, and predictions slightly exceeded observed values, suggesting that model adjustments might be necessary when transitioning from an inpatient to an outpatient setting. Overall, this research underscores the potential of QSP-derived models for MIPD, offering a complementary approach to empirical model development.

Dynamics and implications of anti-drug antibodies against adalimumab using ultra-sensitive and highly drug-tolerant assays

Background

Adalimumab induces the production of anti-drug antibodies (ADA) that may lead to reduced drug concentration and loss-of-response, posing significant clinical challenges. However, traditional immunoassays have limitations in terms of sensitivity and drug-tolerance, hindering the insights of ADA response.

Methods

Herein, we developed an integrated immunoassay platform combining the electrochemiluminescence immunoassay with immunomagnetic separation strategy. A longitudinal cohort study involving 49 patients with ankylosing spondylitis was carried out to analyze the dynamic profiles of ADA and to investigate the impact of ADA on adalimumab pharmacokinetics using a population pharmacokinetic model. Additionally, cross-sectional data from 12 patients were collected to validate the correlation between ADA levels and disease relapse.

Results

The ADA assay demonstrated high sensitivity (0.4 ng/mL) and drug-tolerance (100 μg/mL), while the neutralizing antibodies (NAB) assay showed a sensitivity of 100 ng/mL and drug-tolerance of 20 μg/mL. Analysis of the longitudinal cohort revealed that a majority of patients (44/49, 90%) developed persistent ADA within the first 24 weeks of treatment. ADA levels tended to plateau over time after an initial increase during the early immune response phase. Further, nearly all of the tested patients (26/27, 96%) were classified as NAB positive, with a strong correlation between ADA levels and neutralization capacity (R2 = 0.83, P < 0.001). Population pharmacokinetic modeling revealed a significant positive association between model-estimated individual clearance and observed ADA levels. Higher ADA levels were associated with adalimumab clearance and disease relapse in a cross-sectional cohort, suggesting a promising ADA threshold of 10 for potential clinical application. Moreover, the IgG class was the primary contributor to ADA against adalimumab and the apparent affinity exhibited an increasing trend over time, indicating a T-cell dependent mechanism for ADA elicitation by adalimumab.

Conclusion

In summary, this integrated immunoassay platform shows promise for in-depth analysis of ADA against biologics, offering fresh insights into immunogenicity and its clinical implications.

Machine learning guided prediction of warfarin blood levels for personalized medicine based on clinical longitudinal data from cardiac surgery patients: a prospective observational study

BACKGROUND

Warfarin is a common oral anticoagulant, and its effects vary widely among individuals. Numerous dose-prediction algorithms have been reported based on cross-sectional data generated via multiple linear regression or machine learning. This study aimed to construct an information fusion perturbation theory and machine learning prediction model of warfarin blood levels based on clinical longitudinal data from cardiac surgery patients.

METHODS AND MATERIAL

The data of 246 patients were obtained from electronic medical records. Continuous variables were processed by calculating the distance of the raw data with the moving average (MA ∆vki(sj)), and categorical variables in different attribute groups were processed using Euclidean distance (ED ǁ∆vk(sj)ǁ). Regression and classification analyses were performed on the raw data, MA ∆vki(sj), and ED ǁ∆vk(sj)ǁ. Different machine-learning algorithms were chosen for the STATISTICA and WEKA software.

RESULTS

The random forest (RF) algorithm was the best for predicting continuous outputs using the raw data. The correlation coefficients of the RF algorithm were 0.978 and 0.595 for the training and validation sets, respectively, and the mean absolute errors were 0.135 and 0.362 for the training and validation sets, respectively. The proportion of ideal predictions of the RF algorithm was 59.0%. General discriminant analysis (GDA) was the best algorithm for predicting the categorical outputs using the MA ∆vki(sj) data. The GDA algorithm's total true positive rate (TPR) was 95.4% and 95.6% for the training and validation sets, respectively, with MA ∆vki(sj) data.

CONCLUSIONS

An information fusion perturbation theory and machine learning model for predicting warfarin blood levels was established. A model based on the RF algorithm could be used to predict the target international normalized ratio (INR), and a model based on the GDA algorithm could be used to predict the probability of being within the target INR range under different clinical scenarios.

Warfarin-A natural anticoagulant: A review of research trends for precision medication

BACKGROUND

Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability.

PURPOSE

The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage.

METHODS

A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature.

RESULTS

Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database.

CONCLUSION

Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.

A Randomized Trial Comparing Standard of Care to Bayesian Warfarin Dose Individualization

The quality of warfarin treatment may be improved if management is guided by the use of models based upon pharmacokinetic-pharmacodynamic theory. A prospective, two-armed, single-blind, randomized controlled trial compared management aided by a web-based dose calculator (NextDose) with standard clinical care. Participants were 240 adults receiving warfarin therapy following cardiac surgery, followed up until the first outpatient appointment at least 3 months after warfarin initiation. We compared the percentage of time spent in the international normalized ratio acceptable range (%TIR) during the first 28 days following warfarin initiation, and %TIR and count of bleeding events over the entire follow-up period. Two hundred thirty-four participants were followed up to day 28 (NextDose: 116 and standard of care: 118), and 228 participants (114 per arm) were followed up to the final study visit. Median %TIR tended to be higher for participants receiving NextDose guided warfarin management during the first 28 days (63 vs. 56%, P = 0.13) and over the entire follow-up period (74 vs. 71%, P = 0.04). The hazard of clinically relevant minor bleeding events was lower for participants in the NextDose arm (hazard ratio: 0.21, P = 0.041). In NextDose, there were 89.3% of proposed doses accepted by prescribers. NextDose guided dose management in cardiac surgery patients requiring warfarin was associated with an increase in %TIR across the full follow-up period and fewer hemorrhagic events. A theory-based, pharmacologically guided approach facilitates higher quality warfarin anticoagulation. An important practical benefit is a reduced requirement for clinical experience of warfarin management.

Covariate modeling in pharmacometrics: General points for consideration

Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.

A physiologically-based pharmacokinetic/pharmacodynamic modeling approach for drug-drug-gene interaction evaluation of S-warfarin with fluconazole

Warfarin is a widely used anticoagulant, and its S-enantiomer has higher potency compared to the R-enantiomer. S-warfarin is mainly metabolized by cytochrome P450 (CYP) 2C9, and its pharmacological target is vitamin K epoxide reductase complex subunit 1 (VKORC1). Both CYP2C9 and VKORC1 have genetic polymorphisms, leading to large variations in the pharmacokinetics (PKs) and pharmacodynamics (PDs) of warfarin in the population. This makes dosage management of warfarin difficult, especially in the case of drug-drug interactions (DDIs). This study provides a whole-body physiologically-based pharmacokinetic/PD (PBPK/PD) model of S-warfarin for predicting the effects of drug-drug-gene interactions on S-warfarin PKs and PDs. The PBPK/PD model of S-warfarin was developed in PK-Sim and MoBi. Drug-dependent parameters were obtained from the literature or optimized. Of the 34 S-warfarin plasma concentration-time profiles used, 96% predicted plasma concentrations within twofold range compared to observed data. For S-warfarin plasma concentration-time profiles with CYP2C9 genotype, 364 of 386 predicted plasma concentration values (~94%) fell within the twofold of the observed values. This model was tested in DDI predictions with fluconazole as CYP2C9 perpetrators, with all predicted DDI area under the plasma concentration-time curve to the last measurable timepoint (AUClast) ratio within twofold of the observed values. The anticoagulant effect of S-warfarin was described using an indirect response model, with all predicted international normalized ratio (INR) within twofold of the observed values. This model also incorporates a dose-adjustment method that can be used for dose adjustment and predict INR when warfarin is used in combination with CYP2C9 perpetrators.

Exploring the complex relationship between vitamin K, gut microbiota, and warfarin variability in cardiac surgery patients

BACKGROUND AND OBJECTIVES

Due to the high individual variability of anticoagulant warfarin, this study aimed to investigate the effects of vitamin K concentration and gut microbiota on individual variability of warfarin in 246 cardiac surgery patients.

METHODS

The pharmacokinetics and pharmacodynamics (PKPD) model predicted international normalized ratio (INR) and warfarin concentration. Serum and fecal samples were collected to detect warfarin and vitamin K [VK1 and menaquinone-4 (MK4)] concentrations and gut microbiota diversity, respectively. In addition, the patient's medical records were reviewed for demographic characteristics, drug history, and CYP2C9, VKORC1, and CYP4F2 genotypes.

RESULTS

The PKPD model predicted ideal values of 62.7% for S-warfarin, 70.4% for R-warfarin, and 76.4% for INR. The normal VK1 level was 1.34±1.12 nmol/ml (95% CI: 0.33-4.08 nmol/ml), and the normal MK4 level was 0.22±0.18 nmol/ml (95% CI: 0.07-0.63 nmol/ml). The MK4 to total vitamin K ratio was 16.5±9.8% (95% CI: 4.3-41.5%). The S-warfarin concentration of producing 50% of maximum anticoagulation and the half-life of prothrombin complex activity tended to increase with vitamin K. Further, Prevotella and Eubacterium of gut microbiota identified as the main bacteria associated with individual variability of warfarin. The results suggest that an increase in vitamin K concentration can decrease anticoagulation, and gut microbiota may influence warfarin anticoagulation through vitamin K2 synthesis.

CONCLUSION

This study highlights the importance of considering vitamin K concentration and gut microbiota when prescribing warfarin. The findings may have significant implications for the personalized use of warfarin. Further research is needed to understand better the role of vitamin K and gut microbiota in warfarin anticoagulation.

Drug-microbiota interactions: an emerging priority for precision medicine

Individual variability in drug response (IVDR) can be a major cause of adverse drug reactions (ADRs) and prolonged therapy, resulting in a substantial health and economic burden. Despite extensive research in pharmacogenomics regarding the impact of individual genetic background on pharmacokinetics (PK) and pharmacodynamics (PD), genetic diversity explains only a limited proportion of IVDR. The role of gut microbiota, also known as the second genome, and its metabolites in modulating therapeutic outcomes in human diseases have been highlighted by recent studies. Consequently, the burgeoning field of pharmacomicrobiomics aims to explore the correlation between microbiota variation and IVDR or ADRs. This review presents an up-to-date overview of the intricate interactions between gut microbiota and classical therapeutic agents for human systemic diseases, including cancer, cardiovascular diseases (CVDs), endocrine diseases, and others. We summarise how microbiota, directly and indirectly, modify the absorption, distribution, metabolism, and excretion (ADME) of drugs. Conversely, drugs can also modulate the composition and function of gut microbiota, leading to changes in microbial metabolism and immune response. We also discuss the practical challenges, strategies, and opportunities in this field, emphasizing the critical need to develop an innovative approach to multi-omics, integrate various data types, including human and microbiota genomic data, as well as translate lab data into clinical practice. To sum up, pharmacomicrobiomics represents a promising avenue to address IVDR and improve patient outcomes, and further research in this field is imperative to unlock its full potential for precision medicine.

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.

Considerations for Intravenous Anesthesia Dose in Obese Children: Understanding PKPD

The intravenous induction or loading dose in children is commonly prescribed per kilogram. That dose recognizes the linear relationship between volume of distribution and total body weight. Total body weight comprises both fat and fat-free mass. Fat mass influences the volume of distribution and the use of total body weight fails to recognize the impact of fat mass on pharmacokinetics in children. Size metrics alternative to total body mass (e.g., fat-free and normal fat mass, ideal body weight and lean body weight) have been proposed to scale pharmacokinetic parameters (clearance, volume of distribution) for size. Clearance is the key parameter used to calculate infusion rates or maintenance dosing at steady state. Dosing schedules recognize the curvilinear relationship, described using allometric theory, between clearance and size. Fat mass also has an indirect influence on clearance through both metabolic and renal function that is independent of its effects due to increased body mass. Fat-free mass, lean body mass and ideal body mass are not drug specific and fail to recognize the variable impact of fat mass contributing to body composition in children, both lean and obese. Normal fat mass, used in conjunction with allometry, may prove a useful size metric but computation by clinicians for the individual child is not facile. Dosing is further complicated by the need for multicompartment models to describe intravenous drug pharmacokinetics and the concentration effect relationship, both beneficial and adverse, is often poorly understood. Obesity is also associated with other morbidity that may also influence pharmacokinetics. Dose is best determined using pharmacokinetic-pharmacodynamic (PKPD) models that account for these varied factors. These models, along with covariates (age, weight, body composition), can be incorporated into programmable target-controlled infusion pumps. The use of target-controlled infusion pumps, assuming practitioners have a sound understanding of the PKPD within programs, provide the best available guide to intravenous dose in obese children.

A Physiological-Based Pharmacokinetic Model Embedded with a Target-Mediated Drug Disposition Mechanism Can Characterize Single-Dose Warfarin Pharmacokinetic Profiles in Subjects with Various CYP2C9 Genotypes under Different Cotreatments

Warfarin, a commonly prescribed oral anticoagulant medication, is highly effective in treating deep vein thrombosis and pulmonary embolism. However, the clinical dosing of warfarin is complicated by high interindividual variability in drug exposure and response and its narrow therapeutic index. CYP2C9 genetic polymorphism and drug-drug interactions (DDIs) are substantial contributors to this high variability of warfarin pharmacokinetics (PK), among numerous factors. Building a physiology-based pharmacokinetic (PBPK) model for warfarin is not only critical for a mechanistic characterization of warfarin PK but also useful for investigating the complicated dose-exposure relationship of warfarin. Thus, the objective of this study was to develop a PBPK model for warfarin that integrates information regarding CYP2C9 genetic polymorphisms and their impact on DDIs. Generic PBPK models for both S- and R-warfarin, the two enantiomers of warfarin, were constructed in R with the mrgsolve package. As expected, a generic PBPK model structure did not adequately characterize the warfarin PK profile collected up to 15 days following the administration of a single oral dose of warfarin, especially for S-warfarin. However, following the integration of an empirical target-mediated drug disposition (TMDD) component, the PBPK-TMDD model well characterized the PK profiles collected for both S- and R-warfarin in subjects with different CYP2C9 genotypes. Following the integration of enzyme inhibition and induction effects, the PBPK-TMDD model also characterized the PK profiles of both S- and R-warfarin in various DDI settings. The developed mathematic framework may be useful in building algorithms to better inform the clinical dosing of warfarin. SIGNIFICANCE STATEMENT: The present study found that a traditional physiology-based pharmacokinetic (PBPK) model cannot sufficiently characterize the pharmacokinetic profiles of warfarin enantiomers when warfarin is administered as a single dose, but a PBPK model with a target-mediated drug disposition mechanism can. After incorporating CYP2C9 genotypes and drug-drug interaction information, the developed model is anticipated to facilitate the understanding of warfarin disposition in subjects with different CYP2C9 genotypes in the absence and presence of both cytochrome P450 inhibitors and cytochrome P450 inducers.

Delayed concentration effect models for dabigatran anticoagulation

INTRODUCTION

Dabigatran is an anticoagulant with potential use during cardiopulmonary bypass in children and adults. The pharmacokinetic-pharmacodynamic relationship for dabigatran anticoagulation effect was investigated in an intact animal model using rabbits.

METHODS

Ten male New Zealand white rabbits were given a novel preparation of intravenous dabigatran 15 mg.kg^-1 . Blood samples were collected for activated clotting time, thromboelastometric reaction time, and drug assay at 5, 15, 30, 60, 120, 180, 300, and 420 min. Plasma dabigatran concentrations and coagulation measures were analyzed using an integrated pharmacokinetic-pharmacodynamic model using nonlinear mixed effects. Effects (activated clotting and thromboelastometric reaction times) were described using a sigmoidal E_MAX model. Pharmacokinetic parameters were scaled using allometry and standardized to a 70 kg size standard. Pharmacodynamics were investigated using both an effect compartment model and an indirect response (turnover) model.

RESULTS

A two-compartment model described dabigatran pharmacokinetics with a clearance (CL 0.135 L.min^-1 .70 kg^-1 ), intercompartment clearance (Q 0.33 L.min^-1 .70 kg^-1 ), central volume of distribution (V1 12.3 L.70 kg^-1 ), and peripheral volume of distribution (V2 30.1 L.70 kg^-1 ). The effect compartment model estimates for a sigmoid E_MAX model with activated clotting time had an effect site concentration (Ce₅₀ 20.1 mg.L^-1 ) eliciting half of the maximal effect (E_MAX 899 s) and a Hill coefficient (N 0.66). The equilibration half time (T_1/2 keo) was 1.4 min. Results for the reaction time were plasma concentration (Cp₅₀ 65.3 mg.L^-1 ), E_MAX 34 min, N 0.80 with a baseline thromboelastometric reaction time of 0.4 min. The equilibration half time (T_1/2 keo) was 2.04 min.

CONCLUSIONS

Dabigatran reversibly binds to the active site on the thrombin molecule, preventing thrombin-mediated activation of coagulation factors. The effect compartment model performed slightly better than the turnover model and was able to adequately capture pharmacodynamics for both activated clotting and thromboelastometric reaction times. The equilibration half time was short (<2 min). These data can be used to inform future animal preclinical studies for those undergoing cardiopulmonary bypass. These preclinical data also demonstrate the magnitude of parameter values for a delayed effect compartment model that are applicable to humans.

A novel, rapid and simple UHPLC-MS/MS method for quantification of warfarin in dried blood spots

Warfarin is a common first line anticoagulant with a narrow therapeutic window. Because of the large blood volume needed, previous warfarin determination methods were not applicable to small animals, such as mice. To reduce the number of small animals used needed, we developed and validated a sensitive rapid assay for the simultaneous detection of warfarin enantiomers in mouse dried blood spot (DBS) samples. Analytes were extracted by tert-butyl methyl ether and then separated by a chiral Cellulose-1 column with a mobile phase of 75% acetonitrile (containing 0.1% formic acid). The total chromatographic run time was 3 min. Negative mode electrospray ionization was used for MS/MS detection, where the monitored ion transitions were m/z 307.1 → 161.0 and 341.1 → 284.0 for warfarin and coumachlor (internal standard) respectively. The calibration curves were linear with a correlation coefficient of ≥0.994 for both enantiomers over a concentration range of 10-1000 ng/mL. The satisfactory accuracy and adequate reproducibility of both warfarin enantiomers were validated in terms of intra- and interday precision with mouse DBS cards. The samples were stable at room temperature for at least 14 days. The validated method was applied to a pharmacokinetic study in mice.

Drug Response Diversity: A Hidden Bacterium?

Interindividual heterogeneity in response to treatment is a real public health problem. It is a factor that can be responsible not only for ineffectiveness or fatal toxicity but also for hospitalization due to iatrogenic effects, thus increasing the cost of patient care. Several research teams have been interested in what may be at the origin of these phenomena, particularly at the genetic level and the basal activity of organs dedicated to the inactivation and elimination of drug molecules. Today, a new branch is being set up, explaining the enigmatic part that could not be explained before. Pharmacomicrobiomics attempts to investigate the interactions between bacteria, especially those in the gut, and drug response. In this review, we provide a state of the art on what this field has brought as new information and discuss the challenges that lie ahead to see the real application in clinical practice.

A physiologically based pharmacokinetic/pharmacodynamic modeling approach for drug-drug interaction evaluation of warfarin enantiomers with sorafenib

Sorafenib was suggested to cause drug-drug interaction (DDI) with the common anticoagulant, warfarin based on published studies. The inhibition on CYP2C9 enzyme was thought to be the mechanism, but further studies are warranted. Thus, a mechanistic PBPK/PD model for warfarin enantiomers was developed to predict DDI potential with sorafenib, aiming at providing reference for the rational use of both drugs. PBPK models of warfarin enantiomers were constructed by Simcyp software. A mechanistic PK/PD model was built in NONMEM software. PBPK model of sorafenib was fitted via a top-down method. The final PBPK/PD model of warfarin enantiomers was verified and validated by different dosing regimens, ethnicities and genetic polymorphisms, and used to perform DDI simulations between warfarin racemate and sorafenib among general populations and sub-populations with various CYP2C9 and VKORC1 genotypes. Results suggested low DDI risk between warfarin and sorafenib for general populations. Potentially serious consequence was seen for those carrying both CYP2C9 ∗2 and ∗3 and VKORC1 A/A genotypes. This PBPK/PD modeling approach for warfarin enantiomers enabled DDI evaluation with sorafenib. Close monitoring and warfarin dosage adjustment were recommended for patients carrying mutant genotypes. The novel model could be applied to investigate other drugs that may interact with warfarin.

Comparison of the prediction performance of different warfarin dosing algorithms based on Chinese patients

Aim: To compare the prediction performance of different warfarin dosing algorithms based on Chinese patients. Materials & methods: A total of 18 algorithms were tested in 325 patients. The predictive efficacy of selected algorithms was evaluated by calculating the percentage of patients whose predicted dose fell within ±20% of their actual stable warfarin dose and the mean absolute error. Results: The percentage within ± 20% and the mean absolute error of the algorithms ranged from 11.9 to 41.2% and -0.20 (-0.29 to -0.11) mg/d to -1.63 (-1.75 to -1.50) mg/d. The algorithms established by Miao et al. and Wei et al. had optimal predictive performance. Conclusion: Algorithms based on geographical populations might be more suitable for the prediction of stable warfarin doses in local patients.

TDM is dead. Long live TCI!

Twenty years ago, target concentration intervention (TCI) was distinguished from therapeutic drug monitoring (TDM). It was proposed that TCI would bring more clinical benefit because of the precision of the approach and the ability to link TCI to principles of pharmacokinetics and pharmacodynamics to predict the dose required by an individual (1). We examine the theory and clinical trial evidence supporting the benefits of TCI over TDM and conclude that in the digital age TDM should be abandoned and replaced by TCI.

The gut microbes, Enterococcus and Escherichia-Shigella, affect the responses of heart valve replacement patients to the anticoagulant warfarin

Numerous algorithms based on patient genetic variants have been established with the aim of reducing the risk of GI bleeding and thromboembolism during warfarin administration. However, approximately 35 % of individual warfarin sensitivity still remains unexplained. Few of warfarin algorithms take into account gut microbiota profiles. The identification of certain microbiome will provide new targets and new strategies for reducing the risk of bleeding and thromboembolism during warfarin administration. In this study, we collected plasma and stool samples from 200 inpatients undergoing heart valve replacement (HVR), which were classified as low responder (LR), high responder (HR) and normal responder (NR). Significant differences were observed in the diversity and relative abundance of the gut microbiota among the three groups. The genus Escherichia-Shigella was enriched significantly in the LRs (P = 3.189e^-11), while the genus Enterococcus was enriched significantly in the HRs (P = 1.249e^-11). The amount of VK2 synthesized by gut microbiota in LR group was much higher than that in HR group (P = 0.005). Whole genome shotgun sequencing indicated that the relative abundance of enzymes and modules associated with VK biosynthesis was significantly higher in LRs than in HRs or NRs. The 12 microbial markers were identified through tenfold cross-validation with a random forest model. The results provided a new microbial diagnostic model that can be used to inform modulation of warfarin dosage on the basis of patient intestinal flora composition.

Warfarin dose requirement in patients having severe thrombosis or thrombophilia

AIMS

Warfarin dose requirement varies significantly. We compared the clinically established doses based on international normalized ratio (INR) among patients with severe thrombosis and/or thrombophilia with estimates from genetic dosing algorithms.

METHODS

Fifty patients with severe thrombosis and/or thrombophilia requiring permanent anticoagulation, referred to the Helsinki University Hospital Coagulation Center, were screened for thrombophilias and genotyped for CYP2C9*2 (c.430C>T, rs1799853), CYP2C9*3 (c.1075A>C, rs1057910) and VKORC1 c.-1639G>A (rs9923231) variants. The warfarin maintenance doses (target INR 2.0-3.0 in 94%, 2.5-3.5 in 6%) were estimated by the Gage and the International Warfarin Pharmacogenetics Consortium (IWPC) algorithms. The individual warfarin maintenance dose was tailored, supplementing estimates with comprehensive clinical evaluation and INR data.

RESULTS

Mean patient age was 47 years (range 20-76), and BMI 27 (SD 6), 68% being women. Forty-six (92%) had previous venous or arterial thrombosis, and 26 (52%) had a thrombophilia, with 22% having concurrent aspirin. A total of 40% carried the CYP2C9*2 or *3 allele and 54% carried the VKORC1-1639A allele. The daily mean maintenance dose of warfarin estimated by the Gage algorithm was 5.4 mg (95% CI 4.9-5.9 mg), and by the IWPC algorithm was 5.2 mg (95% CI 4.7-5.7 mg). The daily warfarin maintenance dose after clinical visits and follow-up was higher than the estimates, mean 6.9 mg (95% CI 5.6-8.2 mg, P < 0.006), with highest dose in patients having multiple thrombophilic factors (P < 0.03).

CONCLUSIONS

In severe thrombosis and/or thrombophilia, variation in thrombin generation and pharmacodynamics influences warfarin response. Pharmacogenetic dosing algorithms seem to underestimate dose requirement.

A simple, sensitive, and high-throughput LC-APCI-MS/MS method for simultaneous determination of vitamin K1, vitamin K1 2,3-epoxide in human plasma and its application to a clinical pharmacodynamic study of warfarin

Warfarin exerts its anticoagulation activity by blocking the vitamin K-epoxide cycle. A quantitative understanding of how warfarin and related genes interact with the vitamin K-epoxide cycle and the associated change of coagulation activity in the human body may help study the pharmacodynamics of warfarin. The plasma concentration of vitamin K₁ (VK₁) and vitamin K₁ 2,3-epoxide (VK₁O) could reflect the status of vitamin K-epoxide cycle. However, their determination is a challenging task due to their extremely low concentrations in human plasma and the severe interferences caused by co-extracted lipids. In this study, we developed an LC-APCI-MS/MS method for the simultaneous determination of VK₁ and VK₁O in human plasma using stable deuterium-labeled vitamin K₁ (vitamin K₁-d7) as the internal standard (IS). Plasma samples were prepared through protein denaturation followed by one-step liquid extraction with cyclohexane. Chromatographic separation of analytes from isobaric interferences and endogenous ion suppressor was performed on a Synergi Hydro-RP column (150 mm × 4.6 mm, 4 μm) under the reversed-phase condition with isocratic elution. The selective reaction monitoring (SRM) transitions were chosen as m/z = 451.5→187.3 for VK₁, m/z = 467.5→161.2 for VK₁O, and m/z = 458.6→194.3 for IS in APCI positive mode. The assay was linear in the range of 100-10,000 pg/mL for the two analytes and achieved considerable extraction recoveries (87.8-93.3%, 91.0-96.9%, and 92.0% for VK₁, VK₁O, and IS, respectively), negligible matrix effects (93.6-96.0%, 96.3-100.1%, and 95.5%), and high selectivity with a small sample volume requirement (0.2 mL) and short run time (15 min). The validated method was successfully applied in a clinical pharmacodynamic study of warfarin, and the clotting activity was found to be negatively correlated with the plasma concentration ratio of VK₁O to VK₁.

The pharmacokinetics of intravenous ketorolac in children aged 2 months to 16 years: A population analysis

BACKGROUND

Intravenous ketorolac is commonly administered to children for the control of postoperative pain. An effect site EC₅₀ for analgesia of 0.37 mg^. L^-1 is described in adults.

AIMS

The aim of this study was to review age- and weight-related effects on ketorolac pharmacokinetic parameters in children and current dosing schedules.

METHODS

Pooled intravenous ketorolac (0.5 mg^. kg^-1 ) concentration-time data in children aged 2 months to 16 years were analyzed using nonlinear mixed-effects models. Allometry was used to scale to a 70 kg person.

RESULTS

There were 64 children aged 2 months to 16 years (641 plasma concentrations) available for analysis. A two-compartment mammillary model was used to describe pharmacokinetics. Clearance was 2.53 (CV 45.9%) L^. h^-1. 70 kg^-1 and intercompartment clearance was 4.43 (CV 95.6%) L^. h^-1. 70 kg^-1 . Both central (V1) and peripheral (V2) volumes of distribution decreased with age over the first few years of postnatal life to reach V1 6.89 (CV 30.3%) L^. 70 kg^-1 and V2 5.53 (CV 47.6%) L^. 70 kg^-1 .

CONCLUSION

Clearance, expressed as L^. h^-1. kg^-1 , decreased with age from infancy. A dosing regimen of 0.5 mg^. kg^-1 every 6 hours maintains a trough concentration larger than 0.37 mg^. L^-1 in children 9 months to 16 years of age. This dosing regimen is consistent with current recommendations.

Pharmacokinetic variability due to environmental differences

This tutorial describes sources of pharmacokinetic variability that are not obviously linked to genetic differences. The sources of variability are therefore described as environmental. The major quantitative sources of environmental variability are body size (including body composition), maturation and organ function. Size should be considered in all patients. Maturation is mainly relevant to neonates and infants less than 2 years of age. Renal function is the most important predictable source of variability due to differences in organ function.

Assessing the relative potency of (S)- and (R)-warfarin with a new PK-PD model, in relation to VKORC1 genotypes

PURPOSE

The purpose of this study is to develop a new pharmacokinetic-pharmacodynamic (PK-PD) model to characterise the contribution of (S)- and (R)-warfarin to the anticoagulant effect on patients in treatment with rac-warfarin.

METHODS

Fifty-seven patients starting warfarin (W) therapy were studied, from the first dose and during chronic treatment at INR stabilization. Plasma concentrations of (S)- and (R)-W and INRs were measured 12, 36 and 60 h after the first dose and at steady state 12-14 h after dosing. Patients were also genotyped for the G>A VKORC1 polymorphism. The PK-PD model assumed a linear relationship between W enantiomer concentration and INR and included a scaling factor k to account for a different potency of (R)-W. Two parallel compartment chains with different transit times (MTT₁ and MTT₂) were used to model the delay in the W effect. PD parameters were estimated with the maximum likelihood approach.

RESULTS

The model satisfactorily described the mean time-course of INR, both after the initial dose and during long-term treatment. (R)-W contributed to the rac-W anticoagulant effect with a potency of about 27% that of (S)-W. This effect was independent of VKORC1 genotype. As expected, the slope of the PK/PD linear correlation increased stepwise from GG to GA and from GA to AA VKORC1 genotype (0.71, 0.90 and 1.49, respectively).

CONCLUSIONS

Our PK-PD linear model can quantify the partial pharmacodynamic activity of (R)-W in patients contemporaneously exposed to therapeutic (S)-W plasma levels. This concept may be useful in improving the performance of future algorithms aiming at identifying the most appropriate W maintenance dose.

Theory-based pharmacokinetics and pharmacodynamics of S- and R-warfarin and effects on international normalized ratio: influence of body size, composition and genotype in cardiac surgery patients

AIMS

The aims of this study are to apply a theory-based mechanistic model to describe the pharmacokinetics (PK) and pharmacodynamics (PD) of S- and R-warfarin.

METHODS

Clinical data were obtained from 264 patients. Total concentrations for S- and R-warfarin were measured by ultra-high performance liquid tandem mass spectrometry. Genotypes were measured using pyrosequencing. A sequential population PK parameter with data method was used to describe the international normalized ratio (INR) time course. Data were analyzed with NONMEM. Model evaluation was based on parameter plausibility and prediction-corrected visual predictive checks.

RESULTS

Warfarin PK was described using a one-compartment model. CYP2C9 *1/*3 genotype had reduced clearance for S-warfarin, but increased clearance for R-warfarin. The in vitro parameters for the relationship between prothrombin complex activity (PCA) and INR were markedly different (A = 0.560, B = 0.386) from the theory-based values (A = 1, B = 0). There was a small difference between healthy subjects and patients. A sigmoid E_max PD model inhibiting PCA synthesis as a function of S-warfarin concentration predicted INR. Small R-warfarin effects was described by competitive antagonism of S-warfarin inhibition. Patients with VKORC1 AA and CYP4F2 CC or CT genotypes had lower C50 for S-warfarin.

CONCLUSION

A theory-based PKPD model describes warfarin concentrations and clinical response. Expected PK and PD genotype effects were confirmed. The role of predicted fat free mass with theory-based allometric scaling of PK parameters was identified. R-warfarin had a minor effect compared with S-warfarin on PCA synthesis. INR is predictable from 1/PCA in vivo.