A Systematic Review of Economic Evaluations of Pharmacogenetic Testing for Prevention of Adverse Drug Reactions

MOPDDI: Predicting Drug-Drug Interaction Events Based on Multimodal Mutual Orthogonal Projection and Intermodal Consistency Loss

Predicting accurately the mechanisms of drug-drug interaction (DDI) events is crucial in drug research and development. Existing methods used to predict these events are primarily based on deep learning and have achieved satisfactory results. However, they rarely consider the presence of redundant co-information between the multimodal data of a drug and the need for consistency in the predicted features of each drug modality. Herein, we propose a new method for drug interaction event prediction based on multimodal mutual orthogonal projection and intermodal consistency loss. Our method obtains the features of each modality through a multimodal mutual orthogonal projection module, which eliminates redundant common information with other modalities. In addition, we use the consistency loss between modalities and make the predicted features of each modality more similar. In comparative experiments, our proposed method achieves a prediction accuracy of 0.9500, and an area under the precision-recall (AUPR) curve is 0.9833 for known DDIs. This method outperforms existing methods. The results show that the proposed method is capable of accurately predicting DDIs.

Genetic polymorphisms influencing antihypertensive drug responses

Hypertension is a major contributor to cardiovascular disease and its associated morbidity and mortality. The low efficacy observed with some anti-hypertensive therapies has been attributed partly to inter-individual genetic variability. This paper reviews the major findings regarding these genetic variabilities that modulate responses to anti-hypertensive therapies such as angiotensin converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs), diuretics, calcium channel blockers (CCBs) and β-adrenoceptor blockers. The importance of studying these genetic polymorphisms stems from the goal to optimise anti-hypertensive therapy for each individual patient, aiming for the highest efficacy and lowest risk of adverse effects. It is important to recognise that environmental and epigenetic factors can contribute to the observed variations in drug responses. Owing to the multigenic and multifactorial nature of drug responses, further research is crucial for translating these findings into clinical practice and the establishment of reliable recommendations.

Suspected adverse drug reactions of rivaroxaban reported in the United States food and drug administration adverse event reporting system database: a pharmacovigilance study

Purpose

This study aimed to characterize the safety profiles of rivaroxaban-associated suspected adverse events by mining the Food and Drug Administration Adverse Event Reporting System (FAERS).

Methods

A disproportionality analysis of spontaneously reported suspected adverse drug reactions (ADRs) was conducted. The reports in FAERS from 2014 to 2024 were compiled. Frequentist and Bayesian statistics were both applied to calculate drug-AE combinations in system organ classes and preferred-term levels. Reporting odds ratio (ROR), proportional reporting ratio (PRR), the Medicines and Healthcare products Regulatory Agency (MHRA), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS) methods were analyzed and used to compare the suspected AEs.

Results

Of 77,384 ADR reports, 66,705 (86.20%) were serious rivaroxaban AE reports. The most common age group was above 65 years. The suspected adverse effects of rivaroxaban emerging for system organ classes (SOCs) primarily included "Gastrointestinal disorders"; "Injury, poisoning, and procedural complications", "Nervous system disorders" and "Vascular disorders". Ranked by EBGM, the top signal strength of suspected AE signals of rivaroxaban under ROR algorithm at the preferred-term (PT) level were "Haemorrhagic arteriovenous malformation" (N = 571, ROR = 756.520, PRR = 754.029, Information Component (IC) = 7.197, Empirical Bayesian Geometric Mean (EBGM) = 146.725), "Gastrointestinal vascular malformation haemorrhagic" (N = 197, ROR = 211.138, PRR = 210.950, IC = 6.614, EBGM = 97.923), and "Diverticulum intestinal haemorrhagic" (N = 722, ROR = 169.898, PRR = 169.210, IC = 6.458, EBGM = 97.920). Moreover, uncommon but significantly suspected AE signals, such as "Coagulation factor X level increased", "Basal ganglia haematoma", and "Proctitis haemorrhagic" were observed. Notably, "Gastrointestinal haemorrhage" (N = 13,436, ROR = 80.477, PRR = 74.460, IC = 5.729, EBGM = 53.042), "Upper gastrointestinal haemorrhage"(N = 2,872, ROR = 73.978, PRR = 72.797, IC = 5.706, EBGM = 52.198) and "Internal haemorrhage" (N = 2,368, ROR = 91.979, PRR = 80.899, IC = 5.813, EBGM = 56.212) exhibited relatively high occurrence rates and signal strengths. From 2014 to 2024, the IC values of rivaroxaban-associated suspected AEs for "Surgical and medical procedures" and "Cardiac disorders" showed an annual increasing trend in the time-span analysis. Based on the various visulization plots, a key discovery is that "Gastrointestinal hemorrhage" emerged as the most significant suspected AE across five algorithms. The exciting finding was that the MGPS algorithm revealed a higher risk of suspected AEs under the "Investigations" category. However, the results of the analyses of the other algorithms at the SOC level were not akin to this. Moreover, the results of signal mining for the three main types of indication populations with adverse drug reactions (ADRs), including Atrial fibrillation, Cerebrovascular accident prophylaxis, and Deep vein thrombosis were shown that "Gastrointestinal haemorrhage", "Epistaxis", "Haematuria", "Rectal haemorrhage", and "Upper gastrointestinal haemorrhage" were detected as the most common and significant signals of suspected adverse events.

Conclusion

Rivaroxaban has risks of various suspected adverse reactions while providing therapeutic effects and being used widely. Our pharmacovigilance study may provide valuable hints that practitioners should closely monitor occurrences of "Gastrointestinal disorders", "Injury, poisoning, and procedural complications" and "Nervous system disorders", and other events in clinical applications. Consequently, it remains to persist in monitoring rivaroxaban, assessing the associated risks in the future.

Precision medicine in cardiovascular therapeutics: Evaluating the role of pharmacogenetic analysis prior to drug treatment

Pharmacogenomics is the examination of how genetic variation influences drug metabolism and response, in terms of both efficacy and safety. In cardiovascular disease, patient-specific diplotypes determine phenotypes, thereby influencing the efficacy and safety of drug treatments, including statins, antiarrhythmics, anticoagulants and antiplatelets. Notably, polymorphisms in key genes, such as CYP2C9, CYP2C19, VKORC1 and SLCO1B1, significantly impact the outcomes of treatment with clopidogrel, warfarin and simvastatin. Furthermore, the CYP2C19 polymorphism influences the pharmacokinetics and safety of the novel hypertrophic cardiomyopathy inhibitor, mavacamten. In this review, we critically assess the clinical application of pharmacogenomics in cardiovascular disease and delineate present and future utilization of pharmacogenomics. This includes insights into identifying missing heritability, the integration of whole genome sequencing and the application of polygenic risk scores to enhance the precision of personalized drug therapy. Our discussion encompasses health economic analyses that underscore the cost benefits associated with pre-emptive genotyping for warfarin and clopidogrel treatments, albeit acknowledging the need for further research in this area. In summary, we contend that cardiovascular pharmacogenomic analyses are underpinned by a wealth of evidence, and implementation is already occurring for some of these gene-drug pairs, but as with any area of medicine, we need to continually gather more information to optimize the use of pharmacogenomics in clinical practice.

Promoting advanced medical services in the framework of 3PM-a proof-of-concept by the "Centro" Region of Portugal

Multidisciplinary team from three universities based in the "Centro" Region of Portugal developed diverse approaches as parts of a project dedicated to enhancing and expanding Predictive, Preventive, and Personalized Medicine (3PM) in the Region. In a sense, outcomes acted as a proof-of-concept, in that they demonstrated the feasibility, but also the relevance of the approaches. The accomplishments comprise defining a new regional strategy for implementing 3PM within the Region, training of human resources in genomic sequencing, and generating good practices handbooks dedicated to diagnostic testing via next-generation sequencing, to legal and ethical concerns, and to knowledge transfer and entrepreneurship, aimed at increasing literacy on 3PM approaches. Further approaches also included support for entrepreneurship development and start-ups, and diverse and relevant initiatives aimed at increasing literacy relevant to 3PM. Efforts to enhance literacy encompassed citizens across the board, from patients and high school students to health professionals and health students. This focus on empowerment through literacy involved a variety of initiatives, including the creation of an illustrated book on genomics and the production of two theater plays centered on genetics. Additionally, authors stressed that genomic tools are relevant, but they are not the only resources 3PM is based on. Thus, they defend that other initiatives intended to enable citizens to take 3PM should include multi-omics and, having in mind the socio-economic burden of chronic diseases, suboptimal health status approaches in the 3PM framework should also be considered, in order to anticipate medical intervention in the subclinical phase.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00353-9.

Orally Administered Drugs and Their Complicated Relationship with Our Gastrointestinal Tract

Orally administered compounds represent the great majority of all pharmaceutical compounds produced for human use and are the most popular among patients since they are practical and easy to self-administer. Following ingestion, orally administered drugs begin a "perilous" journey down the gastrointestinal tract and their bioavailability is modulated by numerous factors. The gastrointestinal (GI) tract anatomy can modulate drug bioavailability and accounts for interpatient drug response heterogeneity. Furthermore, host genetics is a contributor to drug bioavailability modulation. Importantly, a component of the GI tract that has been gaining notoriety with regard to drug treatment interactions is the gut microbiota, which shares a two-way interaction with pharmaceutical compounds in that they can be influenced by and are able to influence administered drugs. Overall, orally administered drugs are a patient-friendly treatment option. However, during their journey down the GI tract, there are numerous host factors that can modulate drug bioavailability in a patient-specific manner.

Risk-conferring HLA variants in an epilepsy cohort: benefits of multifaceted use of whole genome sequencing in clinical practice

BACKGROUND

Whole genome sequencing is increasingly used in healthcare, particularly for diagnostics. However, its clinically multifaceted potential for individually customised diagnostic and therapeutic care remains largely unexploited. We used existing whole genome sequencing data to screen for pharmacogenomic risk factors related to antiseizure medication-induced cutaneous adverse drug reactions (cADRs), such as human leucocyte antigen HLA-B*15:02, HLA-A*31:01 variants.

METHODS

Genotyping results, generated from the Genomics England UK 100 000 Genomes Project primarily for identification of disease-causing variants, were used to additionally screen for relevant HLA variants and other pharmacogenomic variants. Medical records were retrospectively reviewed for clinical and cADR phenotypes for HLA variant carriers. Descriptive statistics and the χ2 test were used to analyse phenotype/genotype data for HLA carriers and compare frequencies of additional pharmacogenomic variants between HLA carriers with and without cADRs, respectively.

RESULTS

1043 people with epilepsy were included. Four HLA-B*15:02 and 86 HLA-A*31:01 carriers were identified. One out of the four identified HLA-B*15:02 carriers had suffered antiseizure medication-induced cADRs; the point prevalence of cADRs was 16.9% for HLA-A*31:01 carriers of European origin (n=46) and 14.4% for HLA-A*31:01 carriers irrespective of ancestry (n=83).

CONCLUSIONS

Comprehensive utilisation of genetic data spreads beyond the search for causal variants alone and can be extended to additional clinical benefits such as identifying pharmacogenomic biomarkers, which can guide pharmacotherapy for genetically-susceptible individuals.

Pharmacogenetic Approach for the Prevention of Rivaroxaban's ADRs: A Systematic Review and Meta-Analysis

Introduction

Pharmacogenetics is a potential approach that can be applied to decline the burden of rivaroxaban's ADRs. The current systematic review and meta-analysis aim to identify genetic variants correlated with rivaroxaban exposure and evaluate their importance.

Methods

We systematically searched PubMed, Web of Science, and Scopus databases for all observational and interventional studies. The fixed effect method was used to pool the data when the Q-test's p value was higher than 0.1. We used random models when the p value was less than 0.1.

Results

Data from ten studies (4721 participants) were analyzed in the current review. Qualitative synthesis from included studies found that two variants of ABCB1 (rs1045642 and rs2032582) and one variant of APOB (rs13306198) are potential contributors to rivaroxaban concentrations. Both wild homozygotes (AA) and heterozygotes (AC) of rs1045642 have significantly lower rivaroxaban concentrations compared to mutated homozygotes (CC) (SMD = 0.516, 95% CI: 0.115 to 0.917; SMD = 0.772, 95% CI: 0.088 to 1.455, respectively). Nevertheless, pooling unadjusted odds ratios did not yield a statistically significant correlation between rivaroxaban ADRs and genetic mutations.

Conclusion

This study revealed that being an AC or CC for rs1045642 is attributed to a considerably higher rivaroxaban level in participants using rivaroxaban. That is to say, rs1045642 is a remarkable predictor of rivaroxaban metabolism. We concluded that identifying rs1045642 before drug administration might decrease ADRs although further studies adjusted for potential confounders are strongly suggested.

Pharmacogenomics in clinical trials: an overview

With the trend towards promoting personalised medicine (PM), the application of pharmacogenetics and pharmacogenomics (PGx) is of growing importance. For the purposes of clinical trials, the inclusion of PGx is an additional tool that should be considered for improving our knowledge about the effectiveness and safety of new drugs. A search of available clinical trials containing pharmacogenetic and PGx information was conducted on ClinicalTrials.gov. The results show there has been an increase in the number of trials containing PGx information since the 2000 s, with particular relevance in the areas of Oncology (28.43%) and Mental Health (10.66%). Most of the clinical trials focus on treatment as their primary purpose. In those clinical trials entries where the specific genes considered for study are detailed, the most frequently explored genes are CYP2D6 (especially in Mental Health and Pain), CYP2C9 (in Hematology), CYP2C19 (in Cardiology and Mental Health) and ABCB1 and CYP3A5 (particularly prominent in Transplantation and Cardiology), among others. Researchers and clinicans should be trained in pharmacogenetics and PGx in order to be able to make a proper interpretation of this data, contributing to better prescribing decisions and an improvement in patients' care, which would lead to the performance of PM.

Identification of adverse drug reactions that may be related to pharmacogenetics in a public hospital in the South of Brazil

BACKGROUND

Adverse drug reactions (ADRs) are of great concern in clinical practice. Pharmacogenetics can identify individuals and groups at increased risk of developing ADRs, enabling treatment adjustments to improve outcomes. The study aimed to determine the prevalence of ADRs related to drugs with pharmacogenetic evidence level 1A in a public hospital in Southern Brazil.

RESEARCH DESIGN AND METHODS

ADR information was collected from the pharmaceutical registries from 2017 to 2019. Drugs that have pharmacogenetic evidence level 1A were selected. Public genomic databases were used to estimate the genotypes/phenotypes frequency.

RESULTS

During the period, 585 ADRs were spontaneously notified. Most were moderate (76.3%), whereas severe reactions accounted for 33.8%. Additionally, 109 ADRs caused by 41 drugs presented pharmacogenetic evidence level 1A, representing 18.6% of all notified reactions. Depending on the drug-gene pair, up to 35% of individuals from Southern Brazil could be at risk of developing ADRs.

CONCLUSIONS

Relevant amount of ADRs were related to drugs with pharmacogenetic recommendations on drug labels and/or guidelines. Genetic information could guide and improve clinical outcomes, decreasing ADR incidence and reducing treatment costs.

Genetic variants and enzyme activity in citidin deaminase: Relationship with capecitabine toxicity and recommendation for dose adjustment

OBJECTIVE

Capecitabine, an antineoplastic drug used in the treatment of breast and colon cancer, can cause severe, even fatal toxicity in some patients. The interindividual variability of this toxicity is largely due to genetic variations in target genes and enzymes of metabolism of this drug, such as thymidylate synthase and dihydropyrimidine dehydrogenase. The enzyme cytidine deaminase (CDA), involved in the activation of capecitabine, also has several variants associated with an increased risk of toxicity to treatment, although its role as a biomarker is not yet clearly defined. Therefore, our main objective is to study the association between the presence of genetic variants in CDA gen, CDA enzymatic activity and the development of severe toxicity in patients treated with capecitabine whose initial dose was adjusted based on the genetic profile of the dihydropyrimidine dehydrogenase gen (DPYD).

METHOD

Prospective multicenter observational cohort study, focused on the analysis of the genotype-phenotype association of the CDA enzyme. After the experimental phase, an algorithm will be developed to determine the dose adjustment needed to reduce the risk of treatment toxicity according to CDA genotype, developing a clinical guide for capecitabine dosing according to genetic variants in DPYD and CDA. Based on this guide, a Bioinformatics Tool will be created to generate the pharmacotherapeutic report automatically, facilitating the implementation of pharmacogenetic advice in clinical practice. This tool will be a great support in making pharmacotherapeutic decisions based on the patient's genetic profile, incorporating precision medicine into clinical routine. Once the usefulness of this tool has been validated, it will be offered free of charge to facilitate the implementation of pharmacogenetics in hospital centers and equitably benefit all patients on capecitabine treatment.

[Translated article] Genetic variants and enzyme activity in citidin deaminase: Relationship with capecitabine toxicity and recommendation for dose adjustment

OBJECTIVE

Capecitabine, an antineoplastic drug used in the treatment of breast and colon cancer, can cause severe, even fatal toxicity in some patients. The interindividual variability of this toxicity is largely due to genetic variations in target genes and enzymes of metabolism of this drug, such as Thymidylate Synthase (TS) and Dihydropyrimidine Dehydrogenase (DPD). The enzyme Cytidine Deaminase (CDA), involved in the activation of capecitabine, also has several variants associated with an increased risk of toxicity to treatment, although its role as a biomarker is not yet clearly defined. Therefore, our main objective is to study the association between the presence of genetic variants in CDA gen, CDA enzymatic activity and the development of severe toxicity in patients treated with capecitabine whose initial dose was adjusted based on the genetic profile of the DPD gen (DPYD).

METHOD

Prospective multicenter observational cohort study, focused on the analysis of the genotype-phenotype association of the CDA enzyme. After the experimental phase, an algorithm will be developed to determine the dose adjustment needed to reduce the risk of treatment toxicity according to CDA genotype, developing a Clinical Guide for capecitabine dosing according to genetic variants in DPYD and CDA. Based on this guide, a Bioinformatics Tool will be created to generate the pharmacotherapeutic report automatically, facilitating the implementation of pharmacogenetic advice in clinical practice. This tool will be a great support in making pharmacotherapeutic decisions based on the patient's genetic profile, incorporating precision medicine into clinical routine. Once the usefulness of this tool has been validated, it will be offered free of charge to facilitate the implementation of pharmacogenetics in hospital centers and equitably benefit all patients on capecitabine treatment.

Multi-view feature representation and fusion for drug-drug interactions prediction

BACKGROUND

Drug-drug interactions (DDIs) prediction is vital for pharmacology and clinical application to avoid adverse drug reactions on patients. It is challenging because DDIs are related to multiple factors, such as genes, drug molecular structure, diseases, biological processes, side effects, etc. It is a crucial technology for Knowledge graph to present multi-relation among entities. Recently some existing graph-based computation models have been proposed for DDIs prediction and get good performance. However, there are still some challenges in the knowledge graph representation, which can extract rich latent features from drug knowledge graph (KG).

RESULTS

In this work, we propose a novel multi-view feature representation and fusion (MuFRF) architecture to realize DDIs prediction. It consists of two views of feature representation and a multi-level latent feature fusion. For the feature representation from the graph view and KG view, we use graph isomorphism network to map drug molecular structures and use RotatE to implement the vector representation on bio-medical knowledge graph, respectively. We design concatenate-level and scalar-level strategies in the multi-level latent feature fusion to capture latent features from drug molecular structure information and semantic features from bio-medical KG. And the multi-head attention mechanism achieves the optimization of features on binary and multi-class classification tasks. We evaluate our proposed method based on two open datasets in the experiments. Experiments indicate that MuFRF outperforms the classic and state-of-the-art models.

CONCLUSIONS

Our proposed model can fully exploit and integrate the latent feature from the drug molecular structure graph (graph view) and rich bio-medical knowledge graph (KG view). We find that a multi-view feature representation and fusion model can accurately predict DDIs. It may contribute to providing with some guidance for research and validation for discovering novel DDIs.

Implementation of pharmacogenomics into inpatient general medicine

Pharmacogenomics is a crucial piece of personalized medicine. Preemptive pharmacogenomic testing is only used sparsely in the inpatient setting and there are few models to date for fostering the adoption of pharmacogenomic treatment in the inpatient setting. We created a multi-institutional project in Chicago to enable the translation of pharmacogenomics into inpatient practice. We are reporting our implementation process and barriers we encountered with solutions. This study, 'Implementation of Point-of-Care Pharmacogenomic Decision Support Accounting for Minority Disparities', sought to implement pharmacogenomics into inpatient practice at three sites: The University of Chicago, Northwestern Memorial Hospital, and the University of Illinois at Chicago. This study involved enrolling African American adult patients for preemptive genotyping across a panel of actionable germline variants predicting drug response or toxicity risk. We report our approach to implementation and the barriers we encountered engaging hospitalists and general medical providers in the inpatient pharmacogenomic intervention. Our strategies included: a streamlined delivery system for pharmacogenomic information, attendance at hospital medicine section meetings, use of physician and pharmacist champions, focus on hospitalists' care and optimizing system function to fit their workflow, hand-offs, and dealing with hospitalists turnover. Our work provides insights into strategies for the initial engagement of inpatient general medicine providers that we hope will benefit other institutions seeking to implement pharmacogenomics in the inpatient setting.

Pharmacogenomics: current status and future perspectives

Inter-individual variability in drug response, be it efficacy or safety, is common and likely to become an increasing problem globally given the growing elderly population requiring treatment. Reasons for this inter-individual variability include genomic factors, an area of study called pharmacogenomics. With genotyping technologies now widely available and decreasing in cost, implementing pharmacogenomics into clinical practice - widely regarded as one of the initial steps in mainstreaming genomic medicine - is currently a focus in many countries worldwide. However, major challenges of implementation lie at the point of delivery into health-care systems, including the modification of current clinical pathways coupled with a massive knowledge gap in pharmacogenomics in the health-care workforce. Pharmacogenomics can also be used in a broader sense for drug discovery and development, with increasing evidence suggesting that genomically defined targets have an increased success rate during clinical development.

Leveraging clinical decision support to reduce the risk of discordant pharmacogenomics results

Pharmacogenomics (PGx) testing is commonly utilized to predict a patient's response to medications based on the presence of genetic variants. However, certain conditions have been associated with potentially inaccurate PGx results. The majority of medications are predominantly metabolized in the liver; therefore, in the case of liver transplantation, PGx results may be misinterpreted in the context of drug-metabolizing enzymes. Other instances of ambiguous PGx results have been reported in the literature in conditions such as allogeneic stem cell or bone marrow transplant, chronic lymphocytic leukemia, acute or chronic myeloid leukemia and blood transfusion. In order to prevent potential inaccuracies in PGx testing, Sanford Imagenetics developed an active, interruptive alert to inform providers of the potential for inaccurate PGx results.

The Net Benefit of Personalized Medicine: A Systematic Literature Review and Regression Analysis

OBJECTIVES

Amidst conflicting expectations about the benefits of personalized medicine (PM) and the potentially high implementation costs, we reviewed the available evidence on the cost-effectiveness of PM relative to non-PM.

METHODS

We conducted a systematic literature review of economic evaluations of PM and extracted data, including incremental quality-adjusted life-years (ΔQALYs) and incremental costs (Δcosts). ΔQALYs and Δcosts were combined with estimates of national cost-effectiveness thresholds to calculate incremental net monetary benefit (ΔNMB). Regression analyses were performed with these variables as dependent variables and PM intervention characteristics as independent variables. Random intercepts were used to cluster studies according to country.

RESULTS

Of 4774 studies reviewed, 128 were selected, providing cost-effectiveness data for 279 PM interventions. Most studies were set in the United States (48%) and the United Kingdom (16%) and adopted a healthcare perspective (82%). Cancer treatments (60%) and pharmaceutical interventions (72%) occurred frequently. Prognostic tests (19%) and tests to identify (non)responders (37%) were least and most common, respectively. Industry sponsorship occurred in 32%. Median ΔQALYs, Δcosts, and ΔNMB per individual were 0.03, Int$575, and Int$18, respectively. We found large heterogeneity in cost-effectiveness. Regression analysis showed that gene therapies were associated with higher ΔQALYs than other interventions. PM interventions for neoplasms brought higher ΔNMB than PM interventions for other conditions. Nonetheless, average ΔNMB in the 'neoplasm' group was found to be negative.

CONCLUSIONS

PM brings improvements in health but often at a high cost, resulting in 0 to negative ΔNMB on average. Pricing policies may be needed to reduce the costs of interventions with negative ΔNMB.

Cost-Effectiveness of Pharmacogenomics-Guided Prescribing to Prevent Gene-Drug-Related Deaths: A Decision-Analytic Model

Aim: Prospective studies support the clinical impact of pharmacogenomics (PGx)-guided prescribing to reduce severe and potentially fatal adverse effects. Drug-gene interactions (DGIs) preventing potential drug-related deaths have been categorized as “essential” by the Dutch Pharmacogenetics Working Group (DPWG). The collective clinical impact and cost-effectiveness of this sub-set is yet undetermined. Therefore, we aim to assess impact and cost-effectiveness of “essential” PGx tests for prevention of gene-drug-related deaths, when adopted nation-wide.

Methods: We used a decision-analytic model to quantify the number and cost per gene-drug-related death prevented, from a 1-year Dutch healthcare perspective. The modelled intervention is a single gene PGx-test for CYP2C19, DPYD, TPMT or UGT1A1 to guide prescribing based on the DPWG recommendations among patients in the Netherlands initiating interacting drugs (clopidogrel, capecitabine, systemic fluorouracil, azathioprine, mercaptopurine, tioguanine or irinotecan).

Results: For 148,128 patients initiating one of seven drugs in a given year, costs for PGx-testing, interpretation, and drugs would increase by €21.4 million. Of these drug initiators, 35,762 (24.1%) would require an alternative dose or drug. PGx-guided prescribing would relatively reduce gene-drug related mortality by 10.6% (range per DGI: 8.1–14.5%) and prevent 419 (0.3% of initiators) deaths a year. Cost-effectiveness is estimated at €51,000 per prevented gene-drug-related death (range per DGI: €-752,000–€633,000).

Conclusion: Adoption of PGx-guided prescribing for “essential” DGIs potentially saves the lives of 0.3% of drug initiators, at reasonable costs.

Clinical Importance of Potential Genetic Determinants Affecting Propofol Pharmacokinetics and Pharmacodynamics

Interindividual variability in response to drugs used in anesthesia has long been considered the rule, not the exception. It is important to mention that in anesthesiology, the variability in response to drugs is multifactorial, i.e., genetic and environmental factors interact with each other and thus affect the metabolism, efficacy, and side effects of drugs. Propofol (2,6-diisopropylphenol) is the most common intravenous anesthetic used in modern medicine. Individual differences in genetic factors [single nucleotide polymorphisms (SNPs)] in the genes encoding metabolic enzymes, molecular transporters, and molecular binding sites of propofol can be responsible for susceptibility to propofol effects. The objective of this review (through the analysis of published research) was to systematize the influence of gene polymorphisms on the pharmacokinetics and pharmacodynamics of propofol, to explain whether and to what extent the gene profile has an impact on variations observed in the clinical response to propofol, and to estimate the benefit of genotyping in anesthesiology. Despite the fact that there has been a considerable advance in this type of research in recent years, which has been largely limited to one or a group of genes, interindividual differences in propofol pharmacokinetics and pharmacodynamics may be best explained by the contribution of multiple pathways and need to be further investigated.

Role of Precision Oncology in Type II Endometrial and Prostate Cancers in the African Population: Global Cancer Genomics Disparities

Precision oncology can be defined as molecular profiling of tumors to identify targetable alterations. Emerging research reports the high mortality rates associated with type II endometrial cancer in black women and with prostate cancer in men of African ancestry. The lack of adequate genetic reference information from the African genome is one of the major obstacles in exploring the benefits of precision oncology in the African context. Whilst external factors such as the geography, environment, health-care access and socio-economic status may contribute greatly towards the disparities observed in type II endometrial and prostate cancers in black populations compared to Caucasians, the contribution of African ancestry to the contribution of genetics to the etiology of these cancers cannot be ignored. Non-coding RNAs (ncRNAs) continue to emerge as important regulators of gene expression and the key molecular pathways involved in tumorigenesis. Particular attention is focused on activated/repressed genes and associated pathways, while the redundant pathways (pathways that have the same outcome or activate the same downstream effectors) are often ignored. However, comprehensive evidence to understand the relationship between type II endometrial cancer, prostate cancer and African ancestry remains poorly understood. The sub-Saharan African (SSA) region has both the highest incidence and mortality of both type II endometrial and prostate cancers. Understanding how the entire transcriptomic landscape of these two reproductive cancers is regulated by ncRNAs in an African cohort may help elucidate the relationship between race and pathological disparities of these two diseases. This review focuses on global disparities in medicine, PCa and ECa. The role of precision oncology in PCa and ECa in the African population will also be discussed.

Genophenotypic Factors and Pharmacogenomics in Adverse Drug Reactions

Adverse drug reactions (ADRs) rank as one of the top 10 leading causes of death and illness in developed countries. ADRs show differential features depending upon genotype, age, sex, race, pathology, drug category, route of administration, and drug–drug interactions. Pharmacogenomics (PGx) provides the physician effective clues for optimizing drug efficacy and safety in major problems of health such as cardiovascular disease and associated disorders, cancer and brain disorders. Important aspects to be considered are also the impact of immunopharmacogenomics in cutaneous ADRs as well as the influence of genomic factors associated with COVID-19 and vaccination strategies. Major limitations for the routine use of PGx procedures for ADRs prevention are the lack of education and training in physicians and pharmacists, poor characterization of drug-related PGx, unspecific biomarkers of drug efficacy and toxicity, cost-effectiveness, administrative problems in health organizations, and insufficient regulation for the generalized use of PGx in the clinical setting. The implementation of PGx requires: (i) education of physicians and all other parties involved in the use and benefits of PGx; (ii) prospective studies to demonstrate the benefits of PGx genotyping; (iii) standardization of PGx procedures and development of clinical guidelines; (iv) NGS and microarrays to cover genes with high PGx potential; and (v) new regulations for PGx-related drug development and PGx drug labelling.

Critical Review of Gaps in the Diagnosis and Management of Drug-Induced Liver Injury Associated with Severe Cutaneous Adverse Reactions

Drug-induced liver injury (DILI) encompasses the unexpected damage that drugs can cause to the liver. DILI may develop in the context of an immunoallergic syndrome with cutaneous manifestations, which are sometimes severe (SCARs). Nevirapine, allopurinol, anti-epileptics, sulfonamides, and antibiotics are the most frequent culprit drugs for DILI associated with SCARs. Interestingly, alleles HLA-B*58:01 and HLA-A*31:01 are associated with both adverse reactions. However, there is no consensus about the criteria used for the characterization of liver injury in this context, and the different thresholds for DILI definition make it difficult to gain insight into this complex disorder. Moreover, current limitations when evaluating causality in patients with DILI associated with SCARs are related to the plethora of causality assessment methods and the lack of consensual complementary tools. Finally, the management of this condition encompasses the treatment of liver and skin injury. Although the use of immunomodulant agents is accepted for SCARs, their role in treating liver injury remains controversial. Further randomized clinical trials are needed to test their efficacy and safety to address this complex entity. Therefore, this review aims to identify the current gaps in the definition, diagnosis, prognosis, and management of DILI associated with SCARs, proposing different strategies to fill in these gaps.

Pragmatic pharmacoeconomic analyses by using post-market adverse drug reaction reports: an illustration using infliximab, adalimumab, and the Canada vigilance adverse reaction database

Background

The prediction of the real-world cost of adverse drug reactions (ADRs) has historically relied on the data from randomized controlled trials (RCT). However, trial conditions do not always reflect the real-world applications of pharmaceutical products; hence, they may not accurately portray the actual risks of ADRs associated with them. The objective of this study is two-fold: (a) demonstrate whether and how post-market and RCT ADR data could lead to different conclusions for a set of drugs of interest, and (b) evaluate the potential economic impact of the post-market ADRs associated with those drugs.

Methods

We selected two TNF-α inhibitor biologics, infliximab and adalimumab, and used the Canada Vigilance Adverse Reaction (CVAR) online database as a source of post-market ADR data. Adverse reaction data from RCTs were obtained from ClinicalTrials.gov. Direct healthcare costs associated with adverse reactions were obtained from Canadian Institute for Health Information (CIHI) or Interactive Health Data Application, Alberta. We calculated post-market ADR rates and compared them with those found in the randomized controlled trials of these two drugs. Using the post-market data, we estimated the costs associated with serious ADRs from three perspectives: patient, health system, and societal.

Results

For both drugs, the post-market and RCT data exhibited significantly different adverse reaction rates for several different clinical outcomes. As a general trend, more serious adverse reactions, such as death, appeared to have a higher rate in post-market applications compared to the clinical trials. The estimated average annual economic burden of the severe adverse reaction outcomes ranged from $10 million to $20 million for infliximab and $6 million to $19 million for adalimumab.

Conclusions

The frequency and severity of post-market adverse reactions associated with pharmaceutical products may significantly differ from those detected in the clinical trials. Despite possible methodological differences, this is due to the fact that post-market data reflect the externalities of the real-world that are absent in RCTs. The economic burden of adverse reactions can be substantial, and the cost calculated using post-market data is better reflective of the cost of ADRs in the real-world.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07260-z.

Pharmacogenetic testing for adverse drug reaction prevention: systematic review of economic evaluations and the appraisal of quality matters for clinical practice and implementation

Background

Genetic testing has potential roles in identifying whether an individual would have risk of adverse drug reactions (ADRs) from a particular medicine. Robust cost-effectiveness results on genetic testing would be useful for clinical practice and policy decision-making on allocating resources effectively. This study aimed to update a systematic review on economic evaluations of pharmacogenetic testing to prevent ADRs and critically appraise the quality of reporting and sources of evidence for model input parameters.

Methods

We searched studies through Medline via PubMed, Scopus and CRD’s NHS Economic Evaluation up to October 2019. Studies investigating polymorphism-based pharmacogenetic testing, which guided drug therapies to prevent ADRs, using economic evaluation methods were included. Two reviewers independently performed data extraction and assessed the quality of reporting using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guidelines and the quality of data sources using the hierarchy of evidence developed by Cooper et al.

Results

Fifty-nine economic evaluations of pharmacogenetic testing to avoid drug-induced ADRs were found between 2002 and 2018. Cost-utility and cost-effectiveness analyses were the most common methods of economic evaluation of pharmacogenetic testing. Most studies complied with the CHEERS checklist, except for single study-based economic evaluations which did not report uncertainty analysis (78%). There was a lack of high-quality evidence not only for estimating the clinical effectiveness of pharmacogenetic testing, but also baseline clinical data. About 14% of the studies obtained clinical effectiveness data of testing from a meta-analysis of case-control studies with direct comparison, which was not listed in the hierarchy of evidence used.

Conclusions

Our review suggested that future single study-based economic evaluations of pharmacogenetic testing should report uncertainty analysis, as this could significantly affect the robustness of economic evaluation results. A specific ranking system for the quality of evidence is needed for the economic evaluation of pharmacogenetic testing of ADRs. Differences in parameters, methods and outcomes across studies, as well as population-level and system-level differences, may lead to the difficulty of comparing cost-effectiveness results across countries.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07025-8.

HLA Allele-Restricted Immune-Mediated Adverse Drug Reactions: Framework for Genetic Prediction

Human leukocyte antigen (HLA) is a hallmark genetic marker for the prediction of certain immune-mediated adverse drug reactions (ADRs). Numerous basic and clinical research studies have provided the evidence base to push forward the clinical implementation of HLA testing for the prevention of such ADRs in susceptible patients. This review explores current translational progress in using HLA as a key susceptibility factor for immune ADRs and highlights gaps in our knowledge. Furthermore, relevant findings of HLA-mediated drug-specific T cell activation are covered, focusing on cellular approaches to link genetic associations to drug-HLA binding as a complementary approach to understand disease pathogenesis. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Economic Impact of the Application of a Precision Medicine Model (5SPM) on Psychotic Patients

BACKGROUND

Schizophrenia is a severe mental disorder that often manifests within the first three decades of life. Its prognosis is uncertain and may result in a prolonged treatment that could extend throughout the entire lifespan of the patient. Antipsychotic drugs are characterized by a high interindividual variability when considering therapeutic effect and emergence of adverse effects. Such interindividual variability is thought to be associated primarily with pharmacokinetic matters.

OBJECTIVE

The objective of this study was to evaluate the economic impact of the application of the 5-Step Precision Medicine model (5SPM), an approach based on the pharmacogenetic analysis of the primary genes involved in the metabolism of the therapy for each patient, restructuring treatment as necessary.

PATIENTS AND METHODS

One hundred eighty-eight psychiatry patients were analysed for single nucleotide polymorphisms on genes CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A5 and ABCB1. Information on patients' diagnosis, pharmacotherapy, and hospitalizations was collected.

RESULTS

We achieved a cost-benefit ratio of 3.31-3.59 with a reduction of direct cost (hospitalizations plus pharmacotherapy) with a reduction of total cost in 67% of the patients who underwent the clinical intervention.

CONCLUSION

A rational Precision Medicine-based approach to psychiatric patients could result in a reduction on number of drugs required to control exacerbations, and the underlying pathologies, reducing the risk of adverse effects and improving adherence to treatment, leading to a potential decrease in direct costs. This methodology has been shown to be cost-dominant and, being based on a pharmacogenetic analysis, it has a lifelong nature, as the data obtained can be applied to other medical disciplines.

Pharmacogenetic testing in psychiatry and neurology: an overview of reviews

Pharmacogenetic testing is available to healthcare professionals to guide drug selection and prevent adverse events. However, its implementation in the clinical practice of psychiatry/neurology still has barriers, mainly due to a lack of evidence. We conducted a literature search on Cochrane Library, Embase and Pubmed, from their inception to 18 June 2020. We included 16 published systematic reviews. The most studied drug categories were anticonvulsants and selective serotonin reuptake inhibitors associated with human leukocyte antigen and cytochrome P450 genes (HLA-A, HLA-B, CYP2C9, CYP2D6, CYP2C19), classified as critically low quality/low quality. There is a need for more robust studies with adequate design to assess the potential benefits of adopting pharmacogenetics in health systems and services.

Strategies and settings of clinical pharmacogenetic implementation: a scoping review of pharmacogenetics programs

Pharmacogenetic (PGx) literature has shown beneficial outcomes in safety, efficacy and cost when evidence-based gene-drug decision making is incorporated into clinical practice. PGx programs with successfully implemented clinical services have been published in a variety of settings including academic health centers and community practice. The primary objective was to systematically scope the literature to characterize the current trends, extent, range and nature of clinical PGx programs. Forty articles representing 19 clinical PGx programs were included in analysis. Most programs are in urban, academic institutions. Education, governance and workflow were commonly described while billing/reimbursement and consent were not. This review provides an overview of current PGx models that can be used as a reference for institutions beginning the implementation process.

Integrating disease and drug-related phenotypes for improved identification of pharmacogenomic variants

Aim: To improve the identification and interpretation of pharmacogenetic variants through the integration of disease and drug-related traits. Materials & methods: We hypothesized that integrating genome-wide disease and pharmacogenomic data may drive new insights into drug toxicity and response by identifying shared genetic architecture. Pleiotropic variants were identified using a methodological framework incorporating colocalization analysis. Results: Using genome-wide association studies summary statistics from the UK Biobank, European Bioinformatics Institute genome-wide association studies catalog and the Pharmacogenomics Research Network, we validated pleiotropy at the ABCG2 locus between allopurinol response and gout and identified novel pleiotropy between antihypertensive-induced new-onset diabetes, Crohn's disease and inflammatory bowel disease at the IL18RAP/SLC9A4 locus. Conclusion: New mechanistic insights and genetic loci can be uncovered by identifying pleiotropy between disease and drug-related traits.

Predictive association of ABCB1 C3435T genetic polymorphism with the efficacy or safety of lopinavir and ritonavir in COVID-19 patients

Lopinavir and ritonavir are substrates of permeability glycoprotein encoded by ABCB1. The efficacy and safety of these drugs is unknown in COVID-19 patients affected by ABCB1 genetic variability. Patients carrying one or two copies of the ABCB1 C3435T were predictively considered as risk phenotypes. It was predicted that risk phenotypes due to carrying either one or two copies of ABCB1 C3435T were highly prevalent in Europe (76.8%; 95% CI: 75-78), followed by America (67%; 95% CI: 65-69), Asia (63.5%; 95% CI: 62-65) and Africa (41.4%; 95% CI: 37-46), respectively. It is hypothesized that a considerable proportion of COVID-19 patients treated with lopinavir/ritonavir inheriting ABCB1 C3435T genetic polymorphism may be predisposed to either therapeutic failure or toxicity.

MUFFIN: Multi-Scale Feature Fusion for Drug-Drug Interaction Prediction

MOTIVATION

Adverse drug-drug interactions (DDIs) are crucial for drug research and mainly cause morbidity and mortality. Thus, the identification of potential DDIs is essential for doctors, patients, and the society. Existing traditional machine learning models rely heavily on handcraft features and lack generalization. Recently, the deep learning approaches that can automatically learn drug features from the molecular graph or drug-related network have improved the ability of computational models to predict unknown DDIs. However, previous works utilized large labeled data and merely considered the structure or sequence information of drugs without considering the relations or topological information between drug and other biomedical objects (e.g., gene, disease, and pathway), or considered knowledge graph (KG) without considering the information from the drug molecular structure.

RESULTS

Accordingly, to effectively explore the joint effect of drug molecular structure and semantic information of drugs in knowledge graph for DDI prediction, we propose a multi-scale feature fusion deep learning model named MUFFIN. MUFFIN can jointly learn the drug representation based on both the drug-self structure information and the KG with rich bio-medical information. In MUFFIN, we designed a bi-level cross strategy that includes cross- and scalar-level components to fuse multi-modal features well. MUFFIN can alleviate the restriction of limited labeled data on deep learning models by crossing the features learned from large-scale KG and drug molecular graph. We evaluated our approach on three datasets and three different tasks including binary-class, multi-class, and multi-label DDI prediction tasks. The results showed that MUFFIN outperformed other state-of-the-art baselines.

AVAILABILITY

The source code and data are available at https://github.com/xzenglab/MUFFIN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Synthesis of major pharmacogenomics pretest counseling themes: a multisite comparison

The accessibility of pharmacogenomic (PGx) testing has grown substantially over the last decade and with it has arisen a demand for patients to be counseled on the use of these tests. While guidelines exist for the use of PGx results; objective determinants for who should receive PGx testing remain incomplete. PGx clinical services have been created to meet these screening and education needs and significant variability exists between these programs. This article describes the practices of four PGx clinics during pretest counseling sessions. A description of the major tenets of the benefits, limitations and risks of testing are compiled. Additional tools are provided to serve as a foundation for those wishing to begin or expand their own counseling service.

Pharmacogenetic Testing: A Tool for Personalized Drug Therapy Optimization

Pharmacogenomics is a study of how the genome background is associated with drug resistance and how therapy strategy can be modified for a certain person to achieve benefit. The pharmacogenomics (PGx) testing becomes of great opportunity for physicians to make the proper decision regarding each non-trivial patient that does not respond to therapy. Although pharmacogenomics has become of growing interest to the healthcare market during the past five to ten years the exact mechanisms linking the genetic polymorphisms and observable responses to drug therapy are not always clear. Therefore, the success of PGx testing depends on the physician's ability to understand the obtained results in a standardized way for each particular patient. The review aims to lead the reader through the general conception of PGx and related issues of PGx testing efficiency, personal data security, and health safety at a current clinical level.

A Tutorial for Pharmacogenomics Implementation Through End-to-End Clinical Decision Support Based on Ten Years of Experience from PREDICT

Vanderbilt University Medical Center implemented pharmacogenomics (PGx) testing with the Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment (PREDICT) initiative in 2010. This tutorial reviews the laboratory considerations, technical infrastructure, and programmatic support required to deliver panel-based PGx testing across a large health system with examples and experiences from the first decade of the PREDICT initiative. From the time of inception, automated clinical decision support (CDS) has been a critical capability for delivering PGx results to the point-of-care. Key features of the CDS include human-readable interpretations and clinical guidance that is anticipatory, actionable, and adaptable to changes in the scientific literature. Implementing CDS requires that structured results from the laboratory be encoded in standards-based messages that are securely ingested by electronic health records. Translating results to guidance also requires an informatics infrastructure with multiple components: (1) to manage the interpretation of raw genomic data to "star allele" results to expected phenotype, (2) to define the rules that associate a phenotype with recommended changes to clinical care, and (3) to manage and update the knowledge base. Knowledge base management is key to processing new results with the latest guidelines, and to ensure that historical genomic results can be reinterpreted with revised CDS. We recommend that these components be deployed with institutional authorization, programmatic support, and clinician education to govern the CDS content and policies around delivery.

Pharmacogenomics for Primary Care: An Overview

Most of the prescribing and dispensing of medicines happens in primary care. Pharmacogenomics (PGx) is the study and clinical application of the role of genetic variation on drug response. Mounting evidence suggests PGx can improve the safety and/or efficacy of several medications commonly prescribed in primary care. However, implementation of PGx has generally been limited to a relatively few academic hospital centres, with little adoption in primary care. Despite this, many primary healthcare providers are optimistic about the role of PGx in their future practice. The increasing prevalence of direct-to-consumer genetic testing and primary care PGx studies herald the plausible gradual introduction of PGx into primary care and highlight the changes needed for optimal translation. In this article, the potential utility of PGx in primary care will be explored and on-going barriers to implementation discussed. The evidence base of several drug-gene pairs relevant to primary care will be outlined with a focus on antidepressants, codeine and tramadol, statins, clopidogrel, warfarin, metoprolol and allopurinol. This review is intended to provide both a general introduction to PGx with a more in-depth overview of elements relevant to primary care.

Randomised multicentre clinical trial to evaluate voriconazole pre-emptive genotyping strategy in patients with risk of aspergillosis: vorigenipharm study protocol

INTRODUCTION

Invasive aspergillosis is the most important cause of morbidity and mortality in patients with haematological diseases. At present, voriconazole is the first-line treatment for invasive fungal disease. The pharmacokinetic interindividual variability of voriconazole depends on genetic factors. CYP450 is involved in 70%-75% of total metabolism of voriconazole, mainly CYP3A4 and CYP2C19, with the remaining 25%-30% of metabolism conducted by monooxygenase flavins. CYP2C19 single nucleotide polymorphisms could explain 50%-55% of variability in voriconazole metabolism.

MATERIALS AND METHODS

The main objective is to compare efficiency of pre-emptive voriconazole genotyping with routine practice. The primary outcome is serum voriconazole on the fifth day within the therapeutic range. The secondary outcome is the combined variables of therapeutic failure and adverse events within 90 days of first administration, associated with voriconazole. A total of 146 patients at risk of invasive aspergillosis who will potentially receive voriconazole will be recruited, and CYP2C19 will be genotyped. If the patient ultimately receives voriconazole, they will be randomised (1:1 experimental/control). In the experimental arm, patients will receive a dose according to a pharmacogenetic algorithm, including CYP2C19 genotype and clinical and demographic information. In the control arm, patients will receive a dose according to clinical practice guidelines. In addition, a Spanish National Healthcare System (NHS) point-of-view cost-effectiveness evaluation will be performed. Direct cost calculations for each arm will be performed.

CONCLUSION

This trial will provide information about the viability and cost-effectiveness of the implementation of a pre-emptive voriconazole genotyping strategy in the Spanish NHS.

ETHICS AND DISSEMINATION

A Spanish version of this protocol has been evaluated and approved by the La Paz University Hospital Ethics Committee and the Spanish Agency of Medicines and Medical Devices. Trial results will be submitted for publication in an open peer-reviewed medical speciality-specific publication.

TRIAL REGISTRATION NUMBER

Eudra-CT: 2019-000376-41 and NCT04238884; Pre-results.

Systematic evidence of health economic evaluation of drugs for postmenopausal osteoporosis: A quality appraisal

This paper systematically and critically reviewed all published economic evaluations of drugs for the treatment of postmenopausal osteoporosis. A systematic search was conducted using relevant databases for economic evaluations to include all relevant English articles published between January 2008 to January 2020. After extracting the key study characteristics, methods and outcomes, we evaluated each article using the Quality of Health Economic Studies (QHES) and the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) instruments. A total of 49 studies met the inclusion criteria. Majority of studies were funded by the industry and reported favorable cost-effectiveness. Based on the QHES total scores, studies (n = 35) were found to be industry-funded with higher QHES mean 82.44 ± 8.69 as compared with nonindustry funding studies (n = 11) with mean 72.22 ± 17.67. The overall mean QHES scores were found to be higher 79.06 ± 11.84, representing high quality (75–100) compared to CHEERS scores (%) 75.03 ± 11.21. The statistical pairwise comparison between CHEERS mean (75.03 ± 11.21) and QHES mean (79.06 ± 11.84) were not statistically significant (P = 0.10) whereas, QHES score showed higher means as compared to CHEERS. This study suggests the overall quality of the published literatures was relatively few high-quality health economic evaluation demonstrating the cost-effectiveness of drugs for postmenopausal osteoporosis, and the majority of the literature highlights that methodological shortcoming.

Adverse Drug Reactions in the Emergency Department: Is There a Role for Pharmacogenomic Profiles at Risk?-Results from the ADRED Study

Individual differences in required drug dosages exist based on the pharmacogenomic (PGx) profiles. This study aimed to assess associations between PGx profiles and adverse drug reactions (ADR) that lead to admissions to the emergency department (ED). ADR cases of the prospective multi-center observational trial in EDs (ADRED study) were analyzed (n = 776) together with the relevant PGx phenotypes of the enzymes CYP2D6, CYP2C19, CYP2C9, and VKORC1. Overall, the allele frequency distribution in this cohort did not differ from the population frequencies. We compared the frequencies of phenotypes in the subgroups with the drugs suspected of certain ADR, in the remaining cases. The frequency distribution of CYP2C19 differed for the ADR bleeding cases suspected of clopidogrel (p = 0.020). In a logistic regression analysis, higher CYP2C19 activity (OR (95% CI): 4.97 (1.73−14.27)), together with age (1.05 (1.02−1.08)), showed an impact on the clopidogrel-suspecting ADRs, when adjusting for the clinical parameters. There was a trend for an association of phenprocoumon-risk profiles (low VKORC1 or CYP2C9 activity) with phenprocoumon-suspecting ADRs (p = 0.052). The PGx impact on serious ADRs might be highest in drugs that cannot be easily monitored or those that do not provoke mild ADR symptoms very quickly. Therefore, patients that require the intake of those drugs with PGx variability such as clopidogrel, might benefit from PGx testing.

Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders

A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category. In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.

Influence of metabolic profiles on the safety of drug therapy in routine care in Germany: protocol of the cohort study EMPAR

INTRODUCTION

Pre-emptive testing of pharmacogenetically relevant single-nucleotide polymorphisms can be an effective tool in the prevention of adverse drug reactions and therapy resistance. However, most of the tests are not used as standard in routine care in Germany because of lacking evidence for the clinical and economical benefit and their impact on the usage of healthcare services. We address this issue by investigating the influence of pharmacogenetic profiles on the use of healthcare services over an extended period of several years using routine care data from a statutory health insurance company. The goal is to provide clinical evidence whether pre-emptive pharmacogenetic testing of metabolic profiles in routine care in Germany is beneficial and cost-effective.

METHODS AND ANALYSIS

The EMPAR (Einfluss metabolischer Profile auf die Arzneimitteltherapiesicherheit in der Routineversorgung) study is a non-interventional cohort study conducted to analyse pharmacogenetic risk factors that are important for drug therapy by means of endpoints relevant for healthcare. The analysis is based on pharmacogenetic profiles and statutory health insurance data. We perform pharmacogenetic, pharmacoepidemiological and pharmacoeconomic analyses using health care utilisation scores and machine learning techniques. Therefore, we aim to include about 10 000 patients (≥18 years) insured by the health insurance provider Techniker Krankenkasse. The study focuses on patients with prescriptions of anticoagulants and prescriptions of cholesterol-lowering drugs. Also, a screening for special pharmacogenetic characteristics will be performed in patients with at least one Y57.9! diagnosis (Complication of medical and surgical care: drug or medicament, unspecified). Outcomes include the utilisation of health insurance services, the incidence of incapacity for work and costs for drugs and treatment.

ETHICS AND DISSEMINATION

The protocol was approved by the Ethics Committee of the Medical Faculty, University of Bonn (Lfd. Nr. 339/17). The results of this research project will be published in scientific open access journals and at conferences.

TRIAL REGISTRATION NUMBER

German Clinical Trials Register, DRKS00013909.

Economic Evaluations of CYP2C19 Genotype-Guided Antiplatelet Therapy Compared to the Universal Use of Antiplatelets in Patients With Acute Coronary Syndrome: A Systematic Review

Background and Objectives:

Clopidogrel is widely used after the percutaneous coronary intervention (PCI) in patients with acute coronary syndrome (ACS) and requires activation by cytochrome P450 (CYP), primarily CYP2C19. Patients with CYP2C19 loss-of-function alleles are at increased risk of major adverse cardiovascular events, while more expensive novel antiplatelet agents (ticagrelor and prasugrel) are unaffected by the CYP2C19 mutations. This systematic review aims to answer the question about whether overall evidence supports the genotype-guided selection of antiplatelet therapy as a cost-effective strategy in post-PCI ACS.

Methods:

A systematic literature search of PubMed, EMBASE, EconLit, and PharmGKB was done to identify all the economic evaluations related to genotype-guided therapy compared to the universal use of antiplatelets in ACS patients. Quality of Health Economic Studies tool was used for quality assessment.

Results:

The search identified 13 articles, where genotype-guided treatment was compared to universal clopidogrel, ticagrelor, and/or prasugrel. Six studies showed that genotype-guided therapy was cost-effective compared to universal clopidogrel, while 5 studies showed that it was dominant. One study specified that genotype-guided with ticagrelor is cost-effective only in both CYP2C19 intermediate and poor metabolizers. Genotype-guided therapy was dominant when compared to universal prasugrel, ticagrelor, or both in 5, 1, and 3 studies, respectively. Only 2 studies reported that universal ticagrelor was cost-effective compared to genotype-guided treatment. All the included articles had good quality.

Conclusion:

Based on current economic evaluations in the literature, implementing CYP2C19 genotype-guided therapy is a cost-effective approach in guiding the selection of medication in patients with ACS undergoing PCI.

Pharmacogenomics at the center of precision medicine: challenges and perspective in an era of Big Data

Pharmacogenomics (PGx) is one of the core elements of personalized medicine. PGx information reduces the likelihood of adverse drug reactions and optimizes therapeutic efficacy. St Catherine Specialty Hospital in Zagreb/Zabok, Croatia has implemented a personalized patient approach using the RightMed^® Comprehensive PGx panel of 25 pharmacogenes plus Facor V Leiden, Factor II and MTHFR genes, which is interpreted by a special counseling team to offer the best quality of care. With the advent of significant technological advances comes another challenge: how can we harness the data to inform clinically actionable measures and how can we use it to develop better predictive risk models? We propose to apply the principles artificial intelligence to develop a medication optimization platform to prevent, manage and treat different diseases.

Cost-effectiveness of precision medicine: a scoping review

OBJECTIVES

Precision medicine (PM) aims to improve patient outcomes by stratifying or individualizing diagnosis and treatment decisions. Previous reviews found inconclusive evidence as to the cost-effectiveness of PM. The purpose of this scoping review was to describe current research findings on the cost-effectiveness of PM and to identify characteristics of cost-effective interventions.

METHODS

We searched PubMed with a combination of terms related to PM and economic evaluations and included studies published between 2014 and 2017.

RESULTS

A total of 83 articles were included, of which two-thirds were published in Europe and the USA. The majority of studies concluded that the PM intervention was at least cost-effective compared to usual care. However, the willingness-to-pay thresholds varied widely. Key factors influencing cost-effectiveness included the prevalence of the genetic condition in the target population, costs of genetic testing and companion treatment and the probability of complications or mortality.

CONCLUSIONS

This review may help inform decisions about reimbursement, research and development of PM interventions.

Pharmacogenomics In Pharmacy Practice: Current Perspectives

Pharmacogenomics (i.e., the application of genetic information in predicting an individual's response to drug therapy) plays an increasingly important role in drug development and decision-making regarding precision medicine. This has been shown to reduce the risk of adverse events and improve patient health-care outcomes through targeted therapies and dosing. As the field of pharmacogenomics rapidly evolves, the role of pharmacists in the education, implementation, and research applications of pharmacogenomics is becoming increasingly recognized. This paper aims to provide an overview and current perspectives of pharmacogenomics in contemporary clinical pharmacy practice and to discuss the future directions on advancing pharmacogenomics education, application, and research in pharmacy practice.

Critical review and quality-assessment of cost analyses in radiotherapy: How reliable are the data?

PURPOSE/OBJECTIVE

Health economic evaluations (HEE) are increasingly having an impact on policymakers, although the results greatly depend on the quality of the methodology used and on transparent reporting. The two main objectives of this study were to evaluate the quality of cost analyses of external beam radiotherapy (EBRT) and to assess the comprehensiveness and relevance of cost criteria defined in three validated quality-assessment instruments.

MATERIALS AND METHODS

The selection of articles was based on a previous systematic literature review of EBRT-costing studies retrieved from January 2004 to January 2015 (Period 1) in MEDLINE, Embase, and NHS-EED databases and completed in a second time period from January 2015 to November 2018 (Period 2). Three validated instruments to assess the methodology quality with the CHEC and the QHES, and the methodology with the CHEERS checklists were used. The quality was evaluated by both quantitative and qualitative analyses. The scoring robustness was examined with the Kendall coefficient of concordance and inter-class correlation coefficients.

RESULTS

In total, twenty-three articles were selected. The main geographic areas of cost analyses were Canada (n = 5), France (n = 4), and the USA (n = 4). The most commonly studied pathologies and technologies were prostate (n = 7) and head and neck cancer (n = 5) and IMRT (n = 8) and IGRT (n = 2), respectively. The mean instrument scores demonstrated a fair degree of methodological quality, with 69.7% for the CHEC, 73.6% for the QHES, as well as for the reporting quality, with 59.4% for CHEERS for Period 1 (74.4%, 71.5%, and 66.1%, respectively, for Period 2). An additional qualitative analysis per criterion revealed that certain items, essential for understanding the costing methodology and the results (e.g., the time horizon, discount rate, sensitivity analysis) were often only partially completed. Statistical analysis confirmed that the reviewers' scoring was consistent. The instruments identified the same top three articles, albeit with a degree of variation in the ranking.

CONCLUSION

Qualitative and quantitative assessment of cost analyses in EBRT exhibits a fair level of study quality in terms of the methodology and reporting transparency. The impact of cost calculations on the final HEE result appears to be underestimated, and increased transparency of the data sources and the methodologies is needed.