The Impact of Pharmacogenomics in Personalized Medicine

A Systematic Review Exploring Empirical Pharmacogenomics Research Within Global Indigenous Populations

BACKGROUND

This systematic review aims to highlight the scope of pharmacogenomics research within global Indigenous populations. This review also explores the barriers and facilitators of pharmacogenomics research within this population.

METHODOLOGY

A systematic review of literature was conducted to identify and present an understanding of current empirical evidence demonstrating the conduct of genomics or pharmacogenomics research within global Indigenous populations (PROSPERO registration: CRD42021257226). Using key search terms, relevant databases were searched for articles published between January 2010 and July 2022. Screening, data extraction, and analysis was conducted using well-defined inclusion criteria. Quality assessment and risk of bias appraisal was conducted using the mixed methods appraisal tool. Indigenous community engagement and participation in genomics research was assessed using the social-ecological framework.

RESULTS

From the 427 articles identified, 77 articles met inclusion criteria and underwent full-text screening. Of these, 30 articles were included in the final review, with 16 being quantitative and 14 either qualitative or mixed methods studies. Most studies were conducted with native Indigenous populations from the United States of America (36%). Content analysis revealed that studies either explored genetic variations associated with disease in Indigenous populations (23%) or markers for drug metabolism (30%) or were designed to understand perspectives of genomics research within this population (47%). Perspectives included the exploration of the role of participants in research, benefits or outcomes achieved from participation in genomics research, and levels of Indigenous engagement and participation in genomics research.

CONCLUSIONS

This review highlights a growing gap in Indigenous genomics research globally. It presents several important considerations from Indigenous participants, identifying how researchers can co-create culturally safe and inclusive design, implementation, analysis, and subsequent outcomes of genomics research involving Indigenous people. Indigenous governance, self-determination and leadership is essential, with researchers required to be responsive to such fundamental partnerships for research to progress.

Connecting Gene Variation to Treatment Outcomes in Metastatic Castration-Resistant Prostate Adenocarcinoma: Insights into Second-Generation Androgen Receptor Axis-Targeted Therapies

Prostate cancer (PC) is one of the most commonly diagnosed tumours among men. Second-generation androgen receptor axis-targeted (ARAT) agents, namely abiraterone acetate (AbA) and enzalutamide (ENZ), are currently used in the management of metastatic castration-resistant PC (mCRPC). However, the treatment is challenging due to the lack of prognostic biomarkers. Meanwhile, single-nucleotide polymorphisms (SNPs) have emerged as potential prognostic indicators of mCRPC. Thus, this study evaluated the impact of relevant SNPs on the treatment outcomes of 123 mCRPC patients enrolled in a hospital-based cohort study. The CYP17A1 rs2486758 C allele was associated with a 50% reduction in the risk of developing castration resistance (hazard ratio (HR) = 0.55; p = 0.003). Among patients without metastasis at tumour diagnosis and under AbA, a marginal association between YBX1 rs10493112 and progression-free survival was detected (log-rank test, p = 0.056). In the same subgroup, significant associations of HSD3B1 rs1047303 (CC/CA vs. AA; HR = 3.41; p = 0.025), YBX1 rs12030724 (AT vs. AA; HR = 3.54; p = 0.039) and YBX1 rs10493112 (log-rank test, p = 0.041; CC vs. AA/AC; HR = 3.22; p = 0.053) with overall survival were also observed, which were confirmed by multivariate Cox analyses. Although validation with larger cohorts is required, these findings suggest that SNPs could enhance the prognosis assessment of mCRPC patients, leading to a more personalised treatment.

From Drug Discovery to Drug Approval: A Comprehensive Review of the Pharmacogenomics Status Quo with a Special Focus on Egypt

Pharmacogenomics (PGx) is the hope for the full optimization of drug therapy while minimizing the accompanying adverse drug events that cost billions of dollars annually. Since years before the century, it has been known that inter-individual variations contribute to differences in specific drug responses. It is the bridge to what is well-known today as "personalized medicine". Addressing the drug's pharmacokinetics and pharmacodynamics is one of the features of this science, owing to patient characteristics that vary on so many occasions. Mainly in the liver parenchymal cells, intricate interactions between the drug molecules and enzymes family of so-called "Cytochrome P450" occur which hugely affects how the body will react to the drug in terms of metabolism, efficacy, and safety. Single nucleotide polymorphisms, once validated for a transparent and credible clinical utility, can be used to guide and ensure the succession of the pharmacotherapy plan. Novel tools of pharmacoeconomics science are utilized extensively to assess cost-effective pharmacogenes preceding the translation to the bedside. Drug development and discovery incorporate a drug-gene perspective and save more resources. Regulations and laws shaping the clinical PGx practice can be misconceived; however, these pre-/post approval processes ensure the product's safety and efficacy. National and international regulatory agencies seek guidance on maintaining conduct in PGx practice. In this patient-centric era, social and legal considerations manifest in a way that makes them unavoidable, involving patients and other stakeholders in a deliberate journey toward utmost patient well-being. In this comprehensive review, we contemporarily addressed the scientific leaps in PGx, along with various challenges that face the proper implementation of personalized medicine in Egypt. These informative insights were drawn to serve what the Egyptian population, in particular, would benefit from in terms of knowledge and know-how while maintaining the latest global trends. Moreover, this review is the first to discuss various modalities and challenges faced in Egypt regarding PGx, which we believe could be used as a pilot piece of literature for future studies locally, regionally, and internationally.

Efficacy of AI-Guided (GenAISTM) Dietary Supplement Prescriptions versus Traditional Methods for Lowering LDL Cholesterol: A Randomized Parallel-Group Pilot Study

Emerging evidence suggests that personalized dietary supplement regimens can significantly influence lipid metabolism and cardiovascular risk. The efficacy of AI-guided dietary supplement prescriptions, compared with standard physician-guided prescriptions, remains underexplored. In a randomized, parallel-group pilot study, 70 patients aged 40-75 years with LDL-C levels between 70 and 190 mg/dL were enrolled. Participants were randomized to receive either AI-guided dietary supplement prescriptions or standard physician-guided prescriptions for 90 days. The primary endpoint was the percent change in LDL-C levels. Secondary endpoints included changes in total cholesterol, HDL-C, triglycerides, and hsCRP. Supplement adherence and side effects were monitored. Sixty-seven participants completed the study. The AI-guided group experienced a 25.3% reduction in LDL-C levels (95% CI: -28.7% to -21.9%), significantly greater than the 15.2% reduction in the physician-guided group (95% CI: -18.5% to -11.9%; p < 0.01). Total cholesterol decreased by 15.4% (95% CI: -19.1% to -11.7%) in the AI-guided group compared with 8.1% (95% CI: -11.5% to -4.7%) in the physician-guided group (p < 0.05). Triglycerides were reduced by 22.1% (95% CI: -27.2% to -17.0%) in the AI-guided group versus 12.3% (95% CI: -16.7% to -7.9%) in the physician-guided group (p < 0.01). HDL-C and hsCRP changes were not significantly different between groups. The AI-guided group received a broader variety of supplements, including plant sterols, omega-3 fatty acids, red yeast rice, coenzyme Q10, niacin, and fiber supplements. Side effects were minimal and comparable between groups. AI-guided dietary supplement prescriptions significantly reduce LDL-C and triglycerides more effectively than standard physician-guided prescriptions, highlighting the potential for AI-driven personalization in managing hypercholesterolemia.

Electroanalysis in Pharmacogenomic Studies: Mechanisms of Drug Interaction with DNA

The review discusses electrochemical methods for analysis of drug interactions with DNA. The electroanalysis method is based on the registration of interaction-induced changes in the electrochemical oxidation potential of heterocyclic nitrogenous bases in the DNA molecule and in the maximum oxidation current amplitude. The mechanisms of DNA-drug interactions can be identified based on the shift in the electrooxidation potential of heterocyclic nitrogenous bases toward more negative (cathodic) or positive (anodic) values. Drug intercalation into DNA shifts the electrochemical oxidation potential to positive values, indicating thermodynamically unfavorable process that hinders oxidation of nitrogenous bases in DNA. The potential shift toward the negative values indicates electrostatic interactions, e.g., drug binding in the DNA minor groove, since this process does not interfere with the electrochemical oxidation of bases. The concentration-dependent decrease in the intensity of electrochemical oxidation of DNA bases allows to quantify the type of interaction and calculate the binding constants.

DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer

Some of the recent studies on drug sensitivity prediction have applied graph neural networks to leverage prior knowledge on the drug structure or gene network, and other studies have focused on the interpretability of the model to delineate the mechanism governing the drug response. However, it is crucial to make a prediction model that is both knowledge-guided and interpretable, so that the prediction accuracy is improved and practical use of the model can be enhanced. We propose an interpretable model called DRPreter (drug response predictor and interpreter) that predicts the anticancer drug response. DRPreter learns cell line and drug information with graph neural networks; the cell-line graph is further divided into multiple subgraphs with domain knowledge on biological pathways. A type-aware transformer in DRPreter helps detect relationships between pathways and a drug, highlighting important pathways that are involved in the drug response. Extensive experiments on the GDSC (Genomics of Drug Sensitivity and Cancer) dataset demonstrate that the proposed method outperforms state-of-the-art graph-based models for drug response prediction. In addition, DRPreter detected putative key genes and pathways for specific drug-cell-line pairs with supporting evidence in the literature, implying that our model can help interpret the mechanism of action of the drug.

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.

Integration of advanced methods and models to study drug absorption and related processes: An UNGAP perspective

This collection of contributions from the European Network on Understanding Gastrointestinal Absorption-related Processes (UNGAP) community assembly aims to provide information on some of the current and newer methods employed to study the behaviour of medicines. It is the product of interactions in the immediate pre-Covid period when UNGAP members were able to meet and set up workshops and to discuss progress across the disciplines. UNGAP activities are divided into work packages that cover special treatment populations, absorption processes in different regions of the gut, the development of advanced formulations and the integration of food and pharmaceutical scientists in the food-drug interface. This involves both new and established technical approaches in which we have attempted to define best practice and highlight areas where further research is needed. Over the last months we have been able to reflect on some of the key innovative approaches which we were tasked with mapping, including theoretical, in silico, in vitro, in vivo and ex vivo, preclinical and clinical approaches. This is the product of some of us in a snapshot of where UNGAP has travelled and what aspects of innovative technologies are important. It is not a comprehensive review of all methods used in research to study drug dissolution and absorption, but provides an ample panorama of current and advanced methods generally and potentially useful in this area. This collection starts from a consideration of advances in a priori approaches: an understanding of the molecular properties of the compound to predict biological characteristics relevant to absorption. The next four sections discuss a major activity in the UNGAP initiative, the pursuit of more representative conditions to study lumenal dissolution of drug formulations developed independently by academic teams. They are important because they illustrate examples of in vitro simulation systems that have begun to provide a useful understanding of formulation behaviour in the upper GI tract for industry. The Leuven team highlights the importance of the physiology of the digestive tract, as they describe the relevance of gastric and intestinal fluids on the behaviour of drugs along the tract. This provides the introduction to microdosing as an early tool to study drug disposition. Microdosing in oncology is starting to use gamma-emitting tracers, which provides a link through SPECT to the next section on nuclear medicine. The last two papers link the modelling approaches used by the pharmaceutical industry, in silico to Pop-PK linking to Darwich and Aarons, who provide discussion on pharmacometric modelling, completing the loop of molecule to man.

Knowledge, Attitudes, and Perceived Barriers toward Genetic Testing and Pharmacogenomics among Healthcare Workers in the United Arab Emirates: A Cross-Sectional Study

In order to successfully translate the scientific models of genetic testing and pharmacogenomics into clinical practice, empowering healthcare workers with the right knowledge and functional understanding on the subject is essential. Limited research in the United Arab Emirates (UAE) have assessed healthcare worker stances towards genomics. This study aimed to assess healthcare workers’ knowledge and attitudes on genetic testing. A cross-sectional study was conducted among healthcare workers practicing in either public or private hospitals or clinics as pharmacists, nurses, physicians, managers, and allied health. Participants were recruited randomly and via snowball techniques. Surveys were collected between April and September 2019; out of 552 respondents, 63.4% were female, the mean age was 38 (±9.6) years old. The mean knowledge score was 5.2 (±2.3) out of nine, which shows a fair level of knowledge. The scores of respondents of pharmacy were 5.1 (±2.5), medicine 6.0 (±2.0), and nursing 4.8 (±2.1). All participants exhibited a fair knowledge level about genetic testing and pharmacogenomics. Of the respondents, 91.9% showed a positive attitude regarding availability of genetic testing. The top identified barrier to implementation was the cost of testing (62%), followed by lack of training or education and insurance coverage (57.8% and 57.2%, respectively). Building upon the positive attitudes and tackling the barriers and challenges will pave the road for full implementation of genetic testing and pharmacogenomics in the UAE. We recommend empowering healthcare workers by improving needed and tailored competencies related to their area of practice. We strongly urge the stakeholders to streamline and benchmark the workflow, algorithm, and guidelines to standardize the health and electronic system. Lastly, we advocate utilizing technology and electronic decision support as well as the translational report to back up healthcare workers in the UAE.