Pharmacogenomics and Global Precision Medicine in the Context of Adverse Drug Reactions: Top 10 Opportunities and Challenges for the Next Decade

The future of personalized medicine in Saudi Arabia: Opportunities and challenges

Personalized medicine is a healthcare approach that designs treatment plans of each patient, considering genetic, environmental, and lifestyle factors. This model leverages genomic information, advanced diagnostics, and data analytics to predict disease risk, optimize prevention strategies, and provide customized treatments. In Saudi Arabia, personalized medicine is gaining momentum, driven by the country's Vision 2030 initiative, which aims to transform the healthcare sector by integrating advanced medical technologies and improving healthcare delivery. The Kingdom has made significant strides in genomics and bioinformatics, with initiatives such as the Saudi Human Genome Program and advancements in institutions i.e., King Faisal Specialist Hospital and Research Centre. Continued investment in research, education, and technology, alongside international collaborations, will be crucial in overcoming these challenges and realizing the full potential of personalized medicine. This review explores the current state, challenges, and future prospects of personalized medicine in Saudi Arabia, highlighting its transformative impact on healthcare delivery and patient outcomes.

The Frequency of DPYD c.557A>G in the Dominican Population and Its Association with African Ancestry

Background/Objectives: Genetic polymorphism of the dihydropyrimidine dehydrogenase gene (DPYD) is responsible for the variability found in the metabolism of fluoropyrimidines such as 5-fluorouracil (5-FU), capecitabine, or tegafur. The DPYD genotype is linked to variability in enzyme activity, 5-FU elimination, and toxicity. Approximately 10-40% of patients treated with fluoropyrimidines develop severe toxicity. The interethnic variability of DPYD gene variants in Afro-Latin Americans is poorly studied, thereby establishing a barrier to the implementation of personalized medicine in these populations. Therefore, the present study aims to analyze the frequency of DPYD variants with clinical relevance in the Dominican population and their association with genomic ancestry components. Methods: For this study, 196 healthy volunteers from the Dominican Republic were genotyped for DPYD variants by qPCR, and individual genomic ancestry analysis was performed in 178 individuals using 90 informative ancestry markers. Data from the 1000 Genomes project were also retrieved for comparison and increased statistical power. Results and Conclusions: The c.557A>G variant (decreased dihydropyrimidine dehydrogenase function) presented a frequency of 2.6% in the Dominican population. Moreover, the frequency of this variant is positively associated with African ancestry (r2 = 0.67, p = 1 × 10-7), which implies that individuals with high levels of African ancestry are more likely to present this variant. HapB3 is completely absent in Dominican, Mexican, Peruvian, Bangladeshi, and all East Asian and African populations, which probably makes its analysis dispensable in these populations. The implementation of pharmacogenetics in oncology, specifically DPYD, in populations of Afro-Latin American ancestry should include c.557A>G, to be able to carry out the safe and effective treatment of patients treated with fluoropyrimidines.

Using machine learning to develop a clinical prediction model for SSRI-associated bleeding: a feasibility study

INTRODUCTION

Adverse drug events (ADEs) are associated with poor outcomes and increased costs but may be prevented with prediction tools. With the National Institute of Health All of Us (AoU) database, we employed machine learning (ML) to predict selective serotonin reuptake inhibitor (SSRI)-associated bleeding.

METHODS

The AoU program, beginning in 05/2018, continues to recruit ≥ 18 years old individuals across the United States. Participants completed surveys and consented to contribute electronic health record (EHR) for research. Using the EHR, we determined participants who were exposed to SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, vortioxetine). Features (n = 88) were selected with clinicians' input and comprised sociodemographic, lifestyle, comorbidities, and medication use information. We identified bleeding events with validated EHR algorithms and applied logistic regression, decision tree, random forest, and extreme gradient boost to predict bleeding during SSRI exposure. We assessed model performance with area under the receiver operating characteristic curve statistic (AUC) and defined clinically significant features as resulting in > 0.01 decline in AUC after removal from the model, in three of four ML models.

RESULTS

There were 10,362 participants exposed to SSRIs, with 9.6% experiencing a bleeding event during SSRI exposure. For each SSRI, performance across all four ML models was relatively consistent. AUCs from the best models ranged 0.632-0.698. Clinically significant features included health literacy for escitalopram, and bleeding history and socioeconomic status for all SSRIs.

CONCLUSIONS

We demonstrated feasibility of predicting ADEs using ML. Incorporating genomic features and drug interactions with deep learning models may improve ADE prediction.

Pharmacogenetics and Adverse Events in the Use of Fluoropyrimidine in a Cohort of Cancer Patients on Standard of Care Treatment in Zimbabwe

Fluoropyrimidines are commonly used in the treatment of colorectal cancer. They are, however, associated with adverse events (AEs), of which gastrointestinal, myelosuppression and palmar-plantar erythrodysesthesia are the most common. Clinical guidelines are used for fluoropyrimidine dosing based on dihydropyrimidine dehydrogenase (DPYD) genetic polymorphism and have been shown to reduce these AEs in patients of European ancestry. This study aimed to evaluate, for the first time, the clinical applicability of these guidelines in a cohort of cancer patients on fluoropyrimidine standard of care treatment in Zimbabwe. DNA was extracted from whole blood and used for DPYD genotyping. Adverse events were monitored for six months using the Common Terminology Criteria for AEs (CTCAE) v.5.0. None of the 150 genotyped patients was a carrier of any of the pathogenic variants (DPYD*2A, DPYD*13, rs67376798, or rs75017182). However, severe AEs were high (36%) compared to those reported in the literature from other populations. There was a statistically significant association between BSA (p = 0.0074) and BMI (p = 0.0001) with severe global AEs. This study has shown the absence of the currently known actionable DPYD variants in the Zimbabwean cancer patient cohort. Therefore, the current pathogenic variants in the guidelines might not be feasible for all populations hence the call for modification of the current DPYD guidelines to include minority populations for the benefit of all diverse patients.

Barriers to genetic testing in clinical psychiatry and ways to overcome them: from clinicians' attitudes to sociocultural differences between patients across the globe

Genetic testing has evolved rapidly over recent years and new developments have the potential to provide insights that could improve the ability to diagnose, treat, and prevent diseases. Information obtained through genetic testing has proven useful in other specialties, such as cardiology and oncology. Nonetheless, a range of barriers impedes techniques, such as whole-exome or whole-genome sequencing, pharmacogenomics, and polygenic risk scoring, from being implemented in psychiatric practice. These barriers may be procedural (e.g., limitations in extrapolating results to the individual level), economic (e.g., perceived relatively elevated costs precluding insurance coverage), or related to clinicians' knowledge, attitudes, and practices (e.g., perceived unfavorable cost-effectiveness, insufficient understanding of probability statistics, and concerns regarding genetic counseling). Additionally, several ethical concerns may arise (e.g., increased stigma and discrimination through exclusion from health insurance). Here, we provide an overview of potential barriers for the implementation of genetic testing in psychiatry, as well as an in-depth discussion of strategies to address these challenges.

A blockchain-based framework to support pharmacogenetic data sharing

The successful implementation of pharmacogenetics (PGx) into clinical practice requires patient genomic data to be shared between stakeholders in multiple settings. This creates a number of barriers to widespread adoption of PGx, including privacy concerns related to the storage and movement of identifiable genomic data. Informatic solutions that support secure and equitable data access for genomic data are therefore important to PGx. Here we propose a methodology that uses smart contracts implemented on a blockchain-based framework, PGxChain, to address this issue. The design requirements for PGxChain were identified through a systematic literature review, identifying technical challenges and barriers impeding the clinical implementation of pharmacogenomics. These requirements included security and privacy, accessibility, interoperability, traceability and legal compliance. A proof-of-concept implementation based on Ethereum was then developed that met the design requirements. PGxChain's performance was examined using Hyperledger Caliper for latency, throughput, and transaction success rate. The findings clearly indicate that blockchain technology offers considerable potential to advance pharmacogenetic data sharing, particularly with regard to PGx data security and privacy, large-scale accessibility of PGx data, PGx data interoperability between multiple health care providers and compliance with data-sharing laws and regulations.

Pharmacogenomics of Alzheimer's Disease: Novel Strategies for Drug Utilization and Development

Alzheimer's disease (AD) is a priority health problem in developed countries with a high cost to society. Approximately 20% of direct costs are associated with pharmacological treatment. Over 90% of patients require multifactorial treatments, with risk of adverse drug reactions (ADRs) and drug-drug interactions (DDIs) for the treatment of concomitant diseases such as hypertension (>25%), obesity (>70%), diabetes mellitus type 2 (>25%), hypercholesterolemia (40%), hypertriglyceridemia (20%), metabolic syndrome (20%), hepatobiliary disorder (15%), endocrine/metabolic disorders (>20%), cardiovascular disorder (40%), cerebrovascular disorder (60-90%), neuropsychiatric disorders (60-90%), and cancer (10%).For the past decades, pharmacological studies in search of potential treatments for AD focused on the following categories: neurotransmitter enhancers (11.38%), multitarget drugs (2.45%), anti-amyloid agents (13.30%), anti-tau agents (2.03%), natural products and derivatives (25.58%), novel synthetic drugs (8.13%), novel targets (5.66%), repository drugs (11.77%), anti-inflammatory drugs (1.20%), neuroprotective peptides (1.25%), stem cell therapy (1.85%), nanocarriers/nanotherapeutics (1.52%), and other compounds (<1%).Pharmacogenetic studies have shown that the therapeutic response to drugs in AD is genotype-specific in close association with the gene clusters that constitute the pharmacogenetic machinery (pathogenic, mechanistic, metabolic, transporter, pleiotropic genes) under the regulatory control of epigenetic mechanisms (DNA methylation, histone/chromatin remodeling, microRNA regulation). Most AD patients (>60%) are carriers of over ten pathogenic genes. The genes that most frequently (>50%) accumulate pathogenic variants in the same AD case are A2M (54.38%), ACE (78.94%), BIN1 (57.89%), CLU (63.15%), CPZ (63.15%), LHFPL6 (52.63%), MS4A4E (50.87%), MS4A6A (63.15%), PICALM (54.38%), PRNP (80.7059), and PSEN1 (77.19%). There is also an accumulation of 15 to 26 defective pharmagenes in approximately 85% of AD patients. About 50% of AD patients are carriers of at least 20 mutant pharmagenes, and over 80% are deficient metabolizers for the most common drugs, which are metabolized via the CYP2D6, CYP2C9, CYP2C19, and CYP3A4/5 enzymes.The implementation of pharmacogenetics can help optimize drug development and the limited therapeutic resources available to treat AD, and personalize the use of anti-dementia drugs in combination with other medications for the treatment of concomitant disorders.

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.

Host pharmacogenetic factors that may affect liver neoplasm incidence upon using direct-acting antivirals for treating hepatitis C infection

Introduction

Direct-acting antivirals (DAAs) represent a breakthrough in hepatitis C virus (HCV) treatment as they directly inhibit HCV nonstructural (NS) proteins (NS3/4A, NS5A, and NS5B). However, ongoing debates exist regarding their relationship with hepatocellular carcinoma (HCC) whose incidence is widely debated among investigators. This study was conducted to identify host pharmacogenetic factors that may influence HCC incidence upon using HCV DAAs.

Materials and methods

Details regarding 16 HCV DAAs were collected from literature and DrugBank database. Digital structures of these drugs were fed into the pharmacogenomics/pharmacovigilance in-silico pipeline (PHARMIP) to predict the genetic factors that may underpin HCC development.

Results

We identified 184 unique genes and 40 unique variants that may have key answers for the DAA/HCC paradox. These findings could be used in different methods to aid in the precise application of HCV DAAs and minimize the proposed risk for HCC. All results could be accessed at: https://doi.org/10.17632/8ws8258hn3.2.

Discussion

All the identified factors are evidence related to HCC and significantly predicted by PHARMIP as DAA targets. We discuss some examples of the methods of using these results to address the DAA/HCC controversy based on the following three primary levels: 1 - individual DAA drug, 2 - DAA subclass, and 3 - the entire DAA class. Further wet laboratory investigation is required to evaluate these results.

A Review of the Challenges of Using Biomedical Big Data for Economic Evaluations of Precision Medicine

There is potential value in incorporating biomedical big data (BBD)-observational real-world patient-level genomic and clinical data in multiple sub-populations-into economic evaluations of precision medicine. However, health economists face practical and methodological challenges when using BBD in this context. We conducted a literature review to identify and summarise these challenges. Relevant articles were identified in MEDLINE, EMBASE, EconLit, University of York Centre for Reviews and Dissemination and Cochrane Library from 2000 to 2018. Articles were included if they studied issues relevant to the interconnectedness of biomedical big data, precision medicine, and health economic evaluation. Nineteen articles were included in the review. Challenges identified related to data management, data quality and data analysis. The availability of large volumes of data from multiple sources, the need to conduct data linkages within an environment of opaque data access and sharing procedures, and other data management challenges are primarily practical and may not be long-term obstacles if procedures for data sharing and access are improved. However, the existence of missing data across linked datasets, the need to accommodate dynamic data, and other data quality and analysis challenges may require an evolution in economic evaluation methods. Health economists face challenges when using BBD in economic evaluations of technologies that facilitate precision medicine. Potential solutions to some of these challenges do, however, exist. Going forward, health economists who present work that uses BBD should document challenges and the solutions they have applied to the challenges to support future researcher endeavours.

Integrative Biology and Big-Data-Centrism: Mapping out a Bioscience Ethics Perspective with a S.W.O.T. Matrix

In current biomedicine, omics technologies drive systems-oriented modes of research to achieve a more holistic and personalized view of health and disease. This shift in scientific approach co-occurs with an era of biocapitalism characterized by markets for biomaterial (e.g., DNA, cells, and tissues) as exploitable resources, high-throughput technologies as tools, and "Big Data" as currency. Prediagnostics and genomics-based analyses successfully entered the public domain more or less unfiltered, offering numerous business opportunities envisioning individuals to contribute to the health sector by providing biomaterial and data as well as by using technology, thus becoming participants and informed coproducers of health. Exploring strengths and weaknesses, as well as opportunities and threats by S.W.O.T. analysis, we highlight some chances, pitfalls, and biases of this sector from a bioscience ethics stance. We conclude that the shift from diagnostic to predictive interpretation of data that comes along with integrative biology seems to escape the general and sometimes the experts' awareness. Moreover, rapid translation into products for the global health market is based on marketable views on health and disease that in turn affect basic research through, for example, funding policies and the research questions being asked. Along with this, biological reductionism is revived fuelling simplified understandings of the genotype phenotype relationship in terms of biology and the human dimension in a broader sense, as well as visions of achieving human perfection through novel biotechnologies.

The future of myeloma precision medicine: integrating the compendium of known drug resistance mechanisms with emerging tumor profiling technologies

Multiple myeloma (MM) is a hematologic malignancy that is considered mostly incurable in large part due to the inability of standard of care therapies to overcome refractory disease and inevitable drug-resistant relapse. The post-genomic era has been a productive period of discovery where modern sequencing methods have been applied to large MM patient cohorts to modernize our current perception of myeloma pathobiology and establish an appreciation for the vast heterogeneity that exists between and within MM patients. Numerous pre-clinical studies conducted in the last two decades have unveiled a compendium of mechanisms by which malignant plasma cells can escape standard therapies, many of which have potentially quantifiable biomarkers. Exhaustive pre-clinical efforts have evaluated countless putative anti-MM therapeutic agents and many of these have begun to enter clinical trial evaluation. While the palette of available anti-MM therapies is continuing to expand it is also clear that malignant plasma cells still have mechanistic avenues by which they can evade even the most promising new therapies. It is therefore becoming increasingly clear that there is an outstanding need to develop and employ precision medicine strategies in MM management that harness emerging tumor profiling technologies to identify biomarkers that predict efficacy or resistance within an individual's sub-clonally heterogeneous tumor. In this review we present an updated overview of broad classes of therapeutic resistance mechanisms and describe selected examples of putative biomarkers. We also outline several emerging tumor profiling technologies that have the potential to accurately quantify biomarkers for therapeutic sensitivity and resistance at genomic, transcriptomic and proteomic levels. Finally, we comment on the future of implementation for precision medicine strategies in MM and the clear need for a paradigm shift in clinical trial design and disease management.

What Is Trust? Ethics and Risk Governance in Precision Medicine and Predictive Analytics

Trust is a ubiquitous term used in emerging technology (e.g., Big Data, precision medicine), innovation policy, and governance literatures in particular. But what exactly is trust? Even though trust is considered a critical requirement for the successful deployment of precision medicine initiatives, nonetheless, there is a need for further conceptualization with regard to what qualifies as trust, and what factors might establish and sustain trust in precision medicine, predictive analytics, and large-scale biology. These new fields of 21st century medicine and health often deal with the "futures" and hence, trust gains a temporal and ever-present quality for both the present and the futures anticipated by new technologies and predictive analytics. We address these conceptual gaps that have important practical implications in the way we govern risk and unknowns associated with emerging technologies in biology, medicine, and health broadly. We provide an in-depth conceptual analysis and an operative definition of trust dynamics in precision medicine. In addition, we identify three main types of "trust facilitators": (1) technical, (2) ethical, and (3) institutional. This three-dimensional framework on trust is necessary to building and maintaining trust in 21st century knowledge-based innovations that governments and publics invest for progressive societal change, development, and sustainable prosperity. Importantly, we analyze, identify, and deliberate on the dimensions of precision medicine and large-scale biology that have carved out trust as a pertinent tool to its success. Moving forward, we propose a "points to consider" on how best to enhance trust in precision medicine and predictive analytics.

Clinical pharmacogenomics: patient perspectives of pharmacogenomic testing and the incidence of actionable test results in a chronic disease cohort

Aim

This study aimed to examine pharmacogenomic test results and patient perspectives at an academic cardiovascular medicine clinic.

Patients & methods

Test results for three common cardiovascular drug-gene tests (warfarin-CYP2C9-VKORC1, clopidogrel-CYP2C19 and simvastatin-SLCO1B1) of 208 patients in the Ohio State University-Coriell Personalized Medicine Collaborative were examined to determine the incidence of potentially actionable test results. A post-hoc, anonymous, patient survey was also conducted.

Results

Potentially actionable test results for at least one of the three drug-gene tests were determined in 170 (82%) patients. Survey responses (n = 134) suggested that patients generally considered their test results to be important (median of 7.5 on a 10-point scale of importance) and were interested (median of 7.3 on a 10-point scale of interest) in a Clinical Pharmacogenomic Service.

Conclusion

Attitudes toward pharmacogenomic testing were generally favorable, and potentially actionable test results were not uncommon in this cardiovascular medicine cohort.

Influence of ABCC2, CYP2C8, and CYP2J2 Polymorphisms on Tacrolimus and Mycophenolate Sodium-Based Treatment in Brazilian Kidney Transplant Recipients

STUDY OBJECTIVE

To investigate the influence of single nucleotide polymorphisms (SNPs) in genes encoding metabolizing enzymes (CYP2C8, CYP2J2, and UGT2B7) and transporters (ABCC2 and ABCG2) on dose and dose-adjusted trough blood concentrations (C:D ratio), clinical outcomes, and occurrence of adverse events of tacrolimus and mycophenolate sodium in Brazilian kidney transplant recipients.

DESIGN

Pharmacogenetic analysis of patients enrolled in a previously published study.

PATIENTS

One hundred forty-eight adult kidney transplant recipients treated with tacrolimus, enteric-coated mycophenolate sodium, and prednisone for 90 days posttransplantation.

MEASUREMENTS AND MAIN RESULTS

ABCC2 c.-24C>T and c.3972C>T, ABCG2 c.421C>A, CYP2C8*3, CYP2J2 c.-76G>T, and UGT2B7 c.372A>G SNPs were determined by real-time polymerase chain reaction. The CYP3A5*3C SNP data were used to eliminate the confounding effect of this variant on the results. ABCC2 c.3972T allele carriers showed higher tacrolimus C:D values than did carriers of the c.3972CC genotype. The CYP2C8*3 variant was also associated with slightly higher tacrolimus C:D values and higher estimated glomerular filtration rate but only in CYP3A5-nonexpressing patients (CYP3A5*3C/*3C carriers). None of the SNPs were associated with mycophenolate sodium dose or episodes of biopsy-confirmed acute rejection or delayed graft function. The CYP2J2 c.-76T allele was associated with increased risk for treatment-induced nausea and/or vomiting (OR: 5.30, 95% confidence interval 1.49-18.79, p<0.05).

CONCLUSION

The ABCC2 c.3972C >T polymorphism affected tacrolimus C:D in Brazilian kidney transplant recipients. Further, CYP2C8*3 and CYP2J2 c.-76G>T SNPs influenced the renal function of these patients and the occurrence of adverse events during treatment with tacrolimus and mycophenolate sodium.