Pharmacogenomics of anticoagulants: steps toward personal dosage

Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea

As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization. During this retrospective study, adult patients who were prescribed warfarin at Asan Medical Center (AMC) between January 1, 2018, and October 31, 2020, were recruited as a model development cohort (n = 3168). Additionally, we created an external validation dataset (n = 891) from a Medical Information Mart for Intensive Care III (MIMIC-III). Variables for a model prediction were selected based on the clinical rationale that turned out to be associated with warfarin dosage, such as bleeding. The discharge dosage of warfarin was used the study outcome, because we assumed that patients achieved target INR at discharge. In this study, four ML models that predicted the warfarin discharge dosage were developed. We evaluated the model performance using the mean absolute error (MAE) and prediction accuracy. Finally, we compared the accuracy of the predictions of our models and the predictions of physicians for 40 data point to verify a clinical relevance of the models. The MAEs obtained using the internal validation set were as follows: XGBoost, 0.9; artificial neural network, 0.9; random forest, 1.0; linear regression, 1.0; and physicians, 1.3. As a result, our models had better prediction accuracy than the physicians, who have difficulty determining the warfarin discharge dosage using clinical information obtained within 2 days of hospitalization. We not only conducted the internal validation but also external validation. In conclusion, our ML model could help physicians predict the warfarin discharge dosage as the initial warfarin dosage from Korean population. However, conducting a successfully external validation in a further work is required for the application of the models.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.

Disease-Specific Health Disparities: A Targeted Review Focusing on Race and Ethnicity

Background: Wide disparities in health status exist in the United States across race and ethnicity, broadly driven by social determinants of health—most notably race and ethnic group differences in income, education, and occupational status. However, disparities in disease frequency or severity remain underappreciated for many individual diseases whose distribution in the population varies. Such information is not readily accessible, nor emphasized in treatment guidelines or reviews used by practitioners. Specifically, a summary on disease-specific evidence of disparities from population-based studies is lacking. Our goal was to summarize the published evidence for specific disease disparities in the United States so that this knowledge becomes more widely available “at the bedside”. We hope this summary stimulates health equity research at the disease level so that these disparities can be addressed effectively. Methods: A targeted literature review of disorders in Pfizer’s current pipeline was conducted. The 38 diseases included metabolic disorders, cancers, inflammatory conditions, dermatologic disorders, rare diseases, and infectious targets of vaccines under development. Online searches in Ovid and Google were performed to identify sources focused on differences in disease rates and severity between non-Hispanic Whites and Black/African Americans, and between non-Hispanic Whites and Hispanics. As a model for how this might be accomplished for all disorders, disparities in disease rates and disease severity were scored to make the results of our review most readily accessible. After primary review of each condition by one author, another undertook an independent review. Differences between reviewers were resolved through discussion. Results: For Black/African Americans, 29 of the 38 disorders revealed a robust excess in incidence, prevalence, or severity. After sickle cell anemia, the largest excesses in frequency were identified for multiple myeloma and hidradenitis suppurativa. For Hispanics, there was evidence of disparity in 19 diseases. Most notable were metabolic disorders, including non-alcoholic steatohepatitis (NASH). Conclusions: This review summarized recent disease-specific evidence of disparities based on race and ethnicity across multiple diseases, to inform clinicians and health equity research. Our findings may be well known to researchers and specialists in their respective fields but may not be common knowledge to health care providers or public health and policy institutions. Our hope is that this effort spurs research into the causes of the many disease disparities that exist in the United States.

Two Strategies for the Dosage of Acenocoumarol Co-Administered with Rifampicin in Staphylococcal Prosthetic Valve Endocarditis

According to current European Society of Cardiology guidelines, for staphylococcal prosthetic valve endocarditis, rifampicin should be one of the drugs used. However, there is a concomitant need for vitamin K antagonists in patients with mechanical prostheses. It is widely known that rifampicin interacts with vitamin K antagonists (VKA), and this interaction makes it difficult to maintain the INR (international normalized ratio) value in the therapeutic range. We present two clinical cases of staphylococcal prosthetic valve endocarditis patients. Two different strategies for dealing with adverse drug interactions have been applied. In the first case, the dose of warfarin was up-titrated until the optimal INR value was obtained. In the second case, due to the history of labile INR values, a decision was made to modify the dosage of warfarin, taking into account pharmacological aspects of drug interactions.

Effects of coagulation factor VII polymorphisms on warfarin sensitivity and responsiveness in Jordanian cardiovascular patients during the initiation and maintenance phases of warfarin therapy

Purpose

This study aims to investigate the relationships between genetic polymorphisms of the coagulation factor VII (FVII) gene and warfarin responsiveness and sensitivity.

Patients and methods

The study population consisted of 417 subjects (207 Jordanian cardiovascular patients and 210 healthy individuals). Cardiovascular patients were classified into two groups: those sensitive to warfarin dosage (sensitive, moderate, and resistant) and those responsive to warfarin based on International Normalized Ratios (INRs; poor, good, and extensive responders). The HVR4 polymorphism of the FVII gene was genotyped.

Results

Our results showed that there are significant differences between patients and controls according to both genotypic and allelic frequencies (P<0.0001) in the genetic susceptibility study. Moreover, the pharmacogenetics study reported that HVR4 had no association with warfarin sensitivity or responsiveness during the initiation and maintenance phases of therapy, the only significant differences were in the INR outcome measured during the maintenance phase of therapy (P=0.012).

Conclusion

Our data suggests lacking of association between the HVR4 polymorphism in the FVII gene and warfarin sensitivity and responsiveness during the initiation and maintenance phases of therapy. It is possible that these patients carry additional mutations in genes involved in the coagulation pathway.

Personalized medicine: Genetic risk prediction of drug response

Pharmacogenomics (PGx), a substantial component of "personalized medicine", seeks to understand each individual's genetic composition to optimize drug therapy -- maximizing beneficial drug response, while minimizing adverse drug reactions (ADRs). Drug responses are highly variable because innumerable factors contribute to ultimate phenotypic outcomes. Recent genome-wide PGx studies have provided some insight into genetic basis of variability in drug response. These can be grouped into three categories. [a] Monogenic (Mendelian) traits include early examples mostly of inherited disorders, and some severe (idiosyncratic) ADRs typically influenced by single rare coding variants. [b] Predominantly oligogenic traits represent variation largely influenced by a small number of major pharmacokinetic or pharmacodynamic genes. [c] Complex PGx traits resemble most multifactorial quantitative traits -- influenced by numerous small-effect variants, together with epigenetic effects and environmental factors. Prediction of monogenic drug responses is relatively simple, involving detection of underlying mutations; due to rarity of these events and incomplete penetrance, however, prospective tests based on genotype will have high false-positive rates, plus pharmacoeconomics will require justification. Prediction of predominantly oligogenic traits is slowly improving. Although a substantial fraction of variation can be explained by limited numbers of large-effect genetic variants, uncertainty in successful predictions and overall cost-benefit ratios will make such tests elusive for everyday clinical use. Prediction of complex PGx traits is almost impossible in the foreseeable future. Genome-wide association studies of large cohorts will continue to discover relevant genetic variants; however, these small-effect variants, combined, explain only a small fraction of phenotypic variance -- thus having limited predictive power and clinical utility.

Pharmacokinetic and Pharmacodynamic Evaluation of the Drug-Drug Interaction Between Isavuconazole and Warfarin in Healthy Subjects

This phase 1 trial evaluated pharmacokinetic and pharmacodynamic interactions between the novel triazole antifungal agent isavuconazole and warfarin in healthy adults. Multiple doses of isavuconazole were administered as the oral prodrug, isavuconazonium sulfate (372 mg 3 times a day for 2 days loading dose, then 372 mg once daily thereafter; equivalent to isavuconazole 200 mg), in the presence and absence of single doses of oral warfarin sodium 20 mg. Coadministration with isavuconazole increased the mean area under the plasma concentration‐time curves from time 0 to infinity of S‐ and R‐warfarin by 11% and 20%, respectively, but decreased the mean maximum plasma concentrations of S‐ and R‐warfarin by 12% and 7%, respectively, relative to warfarin alone. Mean area under the international normalized ratio curve and maximum international normalized ratio were 4% lower in the presence vs absence of isavuconazole. Mean warfarin area under the prothrombin time curve and maximum prothrombin time were 3% lower in the presence vs absence of isavuconazole. There were no serious treatment‐emergent adverse events (TEAEs), and no subjects discontinued the study due to TEAEs. All TEAEs were mild in intensity. These findings indicate that coadministration with isavuconazole has no clinically relevant effects on warfarin pharmacokinetics or pharmacodynamics.

Pharmacodynamic genome-wide association study identifies new responsive loci for glucocorticoid intervention in asthma

Asthma is a chronic lung disease that has a high prevalence. The therapeutic intervention of this disease can be made more effective if genetic variability in patients' response to medications is implemented. However, a clear picture of the genetic architecture of asthma intervention response remains elusive. We conducted a genome-wide association study (GWAS) to identify drug response-associated genes for asthma, in which 909 622 SNPs were genotyped for 120 randomized participants who inhaled multiple doses of glucocorticoids. By integrating pharmacodynamic properties of drug reactions, we implemented a mechanistic model to analyze the GWAS data, enhancing the scope of inference about the genetic architecture of asthma intervention. Our pharmacodynamic model observed associations of genome-wide significance between dose-dependent response to inhaled glucocorticoids (measured as %FEV1) and five loci (P=5.315 × 10(-7) to 3.924 × 10(-9)), many of which map to metabolic genes related to lung function and asthma risk. All significant SNPs detected indicate a recessive effect, at which the homozygotes for the mutant alleles drive variability in %FEV1. Significant associations were well replicated in three additional independent GWAS studies. Pooled together over these three trials, two SNPs, chr6 rs6924808 and chr11 rs1353649, display an increased significance level (P=6.661 × 10(-16) and 5.670 × 10(-11)). Our study reveals a general picture of pharmacogenomic control for asthma intervention. The results obtained help to tailor an optimal dose for individual patients to treat asthma based on their genetic makeup.

Warfarin dose requirement in Turkish patients: the influences of patient characteristics and polymorphisms in CYP2C9, VKORC1 and factor VII

BACKGROUND

To determine the contribution of cytochrome P4502C9 (CYP2C9), vitamin K epoxide reductase (VKORC1) and factor VII genotypes, age, body mass index (BMI), international normalized ratio (INR) and other individual patient characteristics on warfarin dose requirements in an adult Turkish population.

METHODS

Blood samples were collected from 101 Turkish patients. Genetic analyses for CYP2C9*2 and *3, VKORC1 -1639 G>A and factor VII -401 G>T polymorphisms were performed. Age, INR, BMI values and other individual patient characteristics were also recorded.

RESULTS

The mean daily warfarin dosage was significantly higher in patients with the CYP2C9*1/*1 genotype than in the CYP2C9*2/*2 and CYP2C9*1/*3 groups (p ≤ 0.05). With respect to the VKORC1 -1639 G>A polymorphism, the mean warfarin daily dose requirement was higher in the wild type group compared to the heterozygous group (p≤0.001). The mean daily dose requirement for patients with the GG form of factor VII was significantly higher than that of patients with the TT genotype (p ≤ 0.05). Age, gender, BMI, INR had no statistically significant correlation with warfarin dose (p ≥ 0.05).

CONCLUSIONS

Polymorphisms in CYP2C9, VKORC1 and factor VII did partially affect daily warfarin dose requirements, while age, gender, BMI and INR do not. However, further case-control studies with a larger study size and different genetic loci are needed to confirm our study.

Stochastic modeling of systems mapping in pharmacogenomics

As a basis of personalized medicine, pharmacogenetics and pharmacogenomics that aim to study the genetic architecture of drug response critically rely on dynamic modeling of how a drug is absorbed and transported to target tissues where the drug interacts with body molecules to produce drug effects. Systems mapping provides a general framework for integrating systems pharmacology and pharmacogenomics through robust ordinary differential equations. In this chapter, we extend systems mapping to more complex and more heterogeneous structure of drug response by implementing stochastic differential equations (SDE). We argue that SDE-implemented systems mapping provides a computational tool for pharmacogenetic or pharmacogenomic research towards personalized medicine.

Translational and clinical applications of salivary diagnostics

There have been significant advances in techniques for the detection of biomarker signals in the oral cavity (e.g., ELISAs for proteins, PCR for RNA and DNA) as well as the engineering and development of microfluidic approaches to make oral-based point-of-care (POC) methods for the diagnosis for both local and systemic conditions a reality. In this section, we focus on three such approaches, namely, periodontal disease management, early markers for systemic diseases, and salivary markers useful for pharmacogenomic studies. Novel approaches using non-invasive, salivary samples and user-friendly devices offer results that are as sensitive and specific as laboratory-based analyses using blood or urine.

Absence of novel CYP4F2 and VKORC1 coding region DNA variants in patients requiring high warfarin doses

OBJECTIVE

Warfarin is an FDA-approved oral anticoagulant for long-term prevention of thromboembolism. Substantial inter-individual variation in dosing requirements and the narrow therapeutic index of this widely-prescribed drug make safe initiation and dose stabilization challenging. Single nucleotide polymorphisms (SNPs) occurring in CYP2C9, VKORC1, and CYP4F2 genes are known to impact dose, and VKORC1 and CYP4F2 polymorphisms are associated with higher therapeutic dose requirements in our cohort. However, the most advanced regression models using personal, clinical, and genetic factors to predict individual stable dose account for only 50% to 60% of the observed variability in stable therapeutic dose in Caucasians.

DESIGN AND METHODS

In this study, we used DNA sequence analysis to determine whether additional variants in CYP4F2 and VKORC1 gene coding regions contribute to variable dosing requirements among individuals for whom the actual dose was the highest relative to regression model- predicted dose.

RESULTS AND CONCLUSIONS

No novel DNA variants in the coding regions of these genes were identified among subjects requiring high warfarin doses, suggesting that other factors yet to be defined contribute to variability in warfarin dose requirements in this subset of our cohort.

A conceptual framework for pharmacodynamic genome-wide association studies in pharmacogenomics

Genome-wide association studies (GWAS) have emerged as a powerful tool to identify loci that affect drug response or susceptibility to adverse drug reactions. However, current GWAS based on a simple analysis of associations between genotype and phenotype ignores the biochemical reactions of drug response, thus limiting the scope of inference about its genetic architecture. To facilitate the inference of GWAS in pharmacogenomics, we sought to undertake the mathematical integration of the pharmacodynamic (PD) process of drug reactions through computational models. By estimating and testing the genetic control of PD and pharmacokinetic (PK) parameters, this mechanistic approach does not only enhance the biological and clinical relevance of significant genetic associations, but also improve the statistical power and robustness of gene detection. This report discusses the general principle and development of PDs-based GWAS, highlights the practical use of this approach in addressing various pharmacogenomic problems, and suggests that this approach will be an important method to study the genetic architecture of drug responses or reactions.

Genome-wide association studies in pharmacogenomics

Genome-wide association (GWA) studies for pharmacogenomics-related traits are increasingly being performed to identify loci that affect either drug response or susceptibility to adverse drug reactions. Until now, only the largest effects have been detected, partly because of the challenges of obtaining large numbers of cases for pharmacogenomic studies. Since 2007, a range of pharmacogenomics GWA studies have been published that have identified several interesting and novel associations between drug responses or reactions and clinically relevant loci, showing the value of this approach.

Delineating the Hemostaseome as an aid to individualize the analysis of the hereditary basis of thrombotic and bleeding disorders

Next-generation sequencing and genome-wide association studies represent powerful tools to identify genetic variants that confer disease risk within populations. On their own, however, they cannot provide insight into how these variants contribute to individual risk for diseases that exhibit complex inheritance, or alternatively confer health in a given individual. Even in the case of well-characterized variants that confer a significant disease risk, more healthy individuals carry the variant, with no apparent ill effect, than those who manifest disease. Access to low-cost genome sequence data promises to provide an unprecedentedly detailed view of the nature of the hereditary component of complex diseases, but requires the large-scale comparison of sequence data from individuals with and without disease to deliver a clinical calibration. The provision of informatics support remains problematic as there are currently no means to interpret the data generated. Here, we initiate this process, a prerequisite for such a study, by narrowing the focus from an entire genome to that of a single biological system. To this end, we examine the 'Hemostaseome,' and more specifically focus on DNA sequence changes pertaining to those human genes known to impact upon hemostasis and thrombosis that can be analyzed coordinately, and on an individual basis, to interrogate how specific combinations of variants act to confer disease predisposition. As a first step, we delineate known members of the Hemostaseome and explore the nature of the genetic variants that may cause disease in individuals whose hemostatic balance has become shifted toward either a prothrombotic or anticoagulant phenotype.

Pharmacogenetics and human genetic polymorphisms

The term pharmacogenetics was first used in the late 1950s and can be defined as the study of genetic factors affecting drug response. Prior to formal use of this term, there was already clinical data available in relation to variable patient responses to the drugs isoniazid, primaquine and succinylcholine. The subject area developed rapidly, particularly with regard to genetic factors affecting drug disposition. There is now comprehensive understanding of the molecular basis for variable drug metabolism by the cytochromes P450 and also for variable glucuronidation, acetylation and methylation of certain drugs. Some of this knowledge has already been translated to the clinic. The molecular basis of variation in drug targets, such as receptors and enzymes, is generally less well understood, although there is consistent evidence that polymorphisms in the genes encoding the β-adrenergic receptors and the enzyme vitamin K epoxide reductase is of clinical importance. The genetic basis of rare idiosyncratic adverse drug reactions had also been examined. Susceptibility to reactions affecting skin and liver appears to be determined in part by the HLA (human leucocyte antigen) genotype, whereas reactions affecting the heart and muscle may be determined by polymorphisms in genes encoding ion channels and transporters respectively. Genome-wide association studies are increasingly being used to study drug response and susceptibility to adverse drug reactions, resulting in identification of some novel pharmacogenetic associations.

A novel, single algorithm approach to predict acenocoumarol dose based on CYP2C9 and VKORC1 allele variants

The identification of CYP2C9 and VKORC1 genes has strongly stimulated the research on pharmacogenetics of coumarins in the last decade. We assessed the combined influence of CYP2C9 *2 and *3, and VKORC1 c.-1639G>A, 497C>G, and 1173C>T variants, on acenocoumarol dosage using a novel algorithm approach, in 193 outpatients who had achieved stable anticoagulation. We constructed an "acenocoumarol-dose genotype score" (AGS, maximum score = 100) based on the number of alleles associated with higher acenocoumarol dosage carried by each subject for each polymorphism. The mean AGS was higher in the high-dose (> 28 mg/week) compared with the low-dose (< 7 mg/week) group (mean(SEM) of 84.1+/-3.4 vs. 62.2+/-4.8, P = 0.008). An AGS > 70 was associated with an increased odds ratio (OR) of requiring high acenocoumarol dosage (OR: 3.347; 95%CI: 1.112-10.075; P = 0.032). In summary, although more research is necessary in other patient cohorts, and this algorithm should be replicated in an independent sample, our data suggest that the AGS algorithm could be used to help discriminating patients requiring high acenocoumarol doses to achieve stable anti-coagulation.

Genomics of cardiovascular disease

As the leading cause of death worldwide and a major cause of disability, cardiovascular disease remains a central focus of basic research, pharmacological treatment, surgical interventions, and long-term care. Inherited, monogenic syndromes have provided insight into pathophysiological mechanisms across the range of cardiovascular diseases. With the advent of post-Human Genome Project resources and technology, there has been a flood of research aimed at genome-wide predisposition markers, pharmacogenetics, and genomic signatures in complex cardiovascular disorders. Genomic research has both further elucidated the impact of genes previously identified in cardiovascular disease development and progression and discovered genomic regions as yet unknown to be associated with cardiovascular outcomes. The promise of personalized medicine lies in combining this genetic information with other biomarkers to tailor preventive and therapeutic strategies to individual patients for effective management, fewer adverse events, and preventive care.