Precision medicine journey through omics approach

Pharmacometabolomics Detects Various Unreported Metoprolol Metabolites in Urine of (Potential) Living Kidney Donors and Kidney Transplant Recipients

BACKGROUND AND OBJECTIVE

Metoprolol is primarily metabolized via the polymorphic cytochrome P450-2D6 (CYP2D6) enzyme, which underlies interindividual variation in conversion rates and may benefit from pharmacogenetics-driven therapy personalization. However, the field relies heavily on knowledge of a drug's metabolism, often originating from early-phase clinical trials with single-dose administration in small samples of healthy volunteers. Pharmacogenetics could thus benefit from real-world drug metabolism studies.

METHODS

We conducted a real-world drug metabolism study for metoprolol in 18 (potential) living kidney donors and 374 kidney transplant recipients from the Transplant Lines Food and Nutrition Biobank and Cohort Study (NCT02811835) using existing liquid chromatography-high resolution mass spectrometry pharmacometabolomic data.

RESULTS

In both groups, we confirmed the presence of seven expected metabolites, including the high-abundance substances metoprolol acid and hydroxymetoprolol. We were unable to detect deisopropylmetoprolol and a metabolite known as "H 119/68". However, we did find putative further oxidized forms, namely the expected variant of deisopropylmetoprolol in which the primary amine is removed and the leftover methyl group is oxidized into a carboxylic acid ("H 104/83") and an unknown/unreported metoprolol metabolite that we refer to as "metoprolol benzoic acid". Moreover, we found nine other previously unknown/unreported metabolites, putatively reflecting N-glucuronidated metoprolol, four glucuronidated versions of hydroxymetoprolol, and a formylated, a glucuronidated, and two hydroxylated versions of metoprolol acid. Interestingly, the same metabolites were detected in potential living kidney donors and kidney transplant recipients, and metabolite profiles did not differ between both groups in principal component analysis.

CONCLUSION

We found more metoprolol metabolites than previously reported, calling for replication studies and evaluation of pharmacogenetic testing approaches to realize safer, more effective metoprolol therapy.

Are Hospital Pharmacists Ready for Precision Medicine in Nigerian Healthcare? Insights From a Multi-Center Study

Background

Precision medicine (PM) has taken center stage in healthcare since the completion of the genomic project. Developed countries have gradually integrated PM into mainstream patient management. However, Nigeria still grapples with wide acceptance, key translational research and implementation of PM. This study sought to explore the knowledge and attitude of PM among pharmacists as key stakeholders in the healthcare team.

Methods

A cross-sectional study was conducted in selected tertiary hospitals across the country. A 21-item semi-structured questionnaire was administered by hybrid online and physical methods and the results analyzed with Statistical Package for the Social Sciences Version 25. Descriptive statistics were used to summarize the data. A chi-square test was employed to determine the association of knowledge of PM and the sociodemographic characteristics of the study population.

Results

A total of 167 hospital pharmacists participated in the study. A high proportion of the participants are familiar with artificial intelligence (91.75%), Pharmacogenomics (84.5%), and precision medicine (61%). Overall, 38.9% of the pharmacists had a good knowledge while 13.2% had a poor knowledge of PM and associated terms. The level of knowledge did not correlate significantly with gender (X 2 = 3.21, p = 0.201), age (X 2 = 5, p = 0.27), marital status (X 2 = 3.21, p = 0.201), and professional level (X 2 = 6.85, p = 0.144). The most important value of precision medicine to hospital pharmacists is the ability to minimize the impact of disease through preventive medicine (49%) while a large portion are pursuing and or actively planning to pursue additional education in precision medicine.

Conclusions

There is a highly positive attitude toward the prospect of PM among hospital pharmacists in Nigeria. Education modules in this field are highly recommended as most do not have a holistic knowledge of terms used in PM. Also, more research aimed at translating PM knowledge into clinical practice is recommended.

Inflammation and resolution in obesity

Inflammation is an essential physiological defence mechanism, but prolonged or excessive inflammation can cause disease. Indeed, unresolved systemic and adipose tissue inflammation drives obesity-related cardiovascular disease and type 2 diabetes mellitus. Drugs targeting pro-inflammatory cytokine pathways or inflammasome activation have been approved for clinical use for the past two decades. However, potentially serious adverse effects, such as drug-induced weight gain and increased susceptibility to infections, prevented their wider clinical implementation. Furthermore, these drugs do not modulate the resolution phase of inflammation. This phase is an active process orchestrated by specialized pro-resolving mediators, such as lipoxins, and other endogenous resolution mechanisms. Pro-resolving mediators mitigate inflammation and development of obesity-related disease, for instance, alleviating insulin resistance and atherosclerosis in experimental disease models, so mechanisms to modulate their activity are, therefore, of great therapeutic interest. Here, we review current clinical attempts to either target pro-inflammatory mediators (IL-1β, NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) inflammasome, tumour necrosis factor (TNF) and IL-6) or utilize endogenous resolution pathways to reduce obesity-related inflammation and improve cardiometabolic outcomes. A remaining challenge in the field is to establish more precise biomarkers that can differentiate between acute and chronic inflammation and to assess the functionality of individual leukocyte populations. Such advancements would improve the monitoring of drug effects and support personalized treatment strategies that battle obesity-related inflammation and cardiometabolic disease.

Metabolomics-based treatment for chronic diseases: results from a multidisciplinary clinical study

Background

Non-communicable diseases (NCDs), known as chronic diseases, significantly impact patients' quality of life (QoL) and increase medical expenses. The majority of risk factors are modifiable, and metabolomics has been suggested as a promising strategy for their evaluation, though real-world data are scarce. This study evaluated the QoL improvement and cost-effectiveness of a metabolomics-based treatment for NCDs, aiming to restore metabolic dysfunctions and nutritional deficiencies.

Methods

We performed a pre-post intervention analysis using clinical, metabolomics, QoL and economic data obtained from the electronic health records of 765 patients visiting a private practice. The intervention consisted of personalised treatment to restore metabolic dysfunctions and nutritional deficiencies identified by metabolomics alongside the standard treatment for their condition. The mean intervention duration was 401 days.

Results

Significant improvement was identified in energy levels, sleep quality, gastrointestinal function and physical activity (p<0.001). 67.9% of participants reported significant improvement in the overall QoL, and the average quality-adjusted life-years (QALYs) increased by 0.064 (95% uncertainty interval 0.050 to 0.078) post-treatment. The incremental cost-effectiveness ratio was estimated at €49.774/QALY (95% CI €40.110 to €61.433). Metabolic profiling demonstrated that 16/35 organic acids and 11/24 total fatty acids were significantly changed post-treatment (p<0.001), participating in key pathways such as energy metabolism, microbiome and neurotransmitter turnover. Vitamin D and 5-methyltetrahydrofolate insufficiency was significantly restored (p=0.036).

Conclusion

This is the first study providing evidence that the integration of metabolomics in clinical practice can have a clinical benefit for patients' QoL and may be a cost-effective method.

Allele frequency of genetic variations related to the UGT1A1 gene-drug pair in a group of Iranian population

Objectives

The efficacy and safety of drug treatments vary widely due to genetic variations. Pharmacogenomics investigates the impact of genetic variations on patient drug response. This research investigates the frequency of UGT1A1 genetic variations in the Iranian population, comparing them with global data to provide insights into the pharmacogenomic approach in the Iranian population.

Methods

The study was conducted using the data of the Bushehr Elderly Health (BEH) program, a population-based cohort study of the elderly population aged ≥ 60 years. Genotyping of three UGT1A1 variant alleles (UGT1A1*6, UGT1A1*27, and UGT1A1*80) was performed on a group of 2730 elderly Iranian participants with the Infinium Global Screening Array.

Results

The genotyping analysis revealed significant differences compared to major global populations that were addressed in the gnomAD database. UGT1A1*80 was found at a high frequency (32.34%), and followed by UGT1A1*6 (0.76%) and UGT1A1*27 (0.018) at a low frequency in the Iranian group.

Conclusions

The UGT1A1*80 was the more prevalent allele between investigated alleles in the present study which can be considered as an important allele for pharmacogenomic testing.

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite-gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

Innovative Detection of Biomarkers Based on Chemiluminescent Nanoparticles and a Lensless Optical Sensor

The identification and quantification of biomarkers with innovative technologies is an urgent need for the precise diagnosis and follow up of human diseases. Body fluids offer a variety of informative biomarkers, which are traditionally measured with time-consuming and expensive methods. In this context, lateral flow tests (LFTs) represent a rapid and low-cost technology with a sensitivity that is potentially improvable by chemiluminescence biosensing. Here, an LFT based on gold nanoparticles functionalized with antibodies labeled with the enzyme horseradish peroxidase is combined with a lensless biosensor. This biosensor comprises four Silicon Photomultipliers (SiPM) coupled in close proximity to the LFT strip. Microfluidics for liquid handling complete the system. The development and the setup of the biosensor is carefully described and characterized. C-reactive protein was selected as a proof-of-concept biomarker to define the limit of detection, which resulted in about 0.8 pM when gold nanoparticles were used. The rapid readout (less than 5 min) and the absence of sample preparation make this biosensor promising for the direct and fast detection of human biomarkers.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Proteomics-Driven Biomarkers in Pancreatic Cancer

Pancreatic cancer is a devastating disease that has a grim prognosis, highlighting the need for improved screening, diagnosis, and treatment strategies. Currently, the sole biomarker for pancreatic ductal adenocarcinoma (PDAC) authorized by the U.S. Food and Drug Administration is CA 19-9, which proves to be the most beneficial in tracking treatment response rather than in early detection. In recent years, proteomics has emerged as a powerful tool for advancing our understanding of pancreatic cancer biology and identifying potential biomarkers and therapeutic targets. This review aims to offer a comprehensive survey of proteomics' current status in pancreatic cancer research, specifically accentuating its applications and its potential to drastically enhance screening, diagnosis, and treatment response. With respect to screening and diagnostic precision, proteomics carries the capacity to augment the sensitivity and specificity of extant screening and diagnostic methodologies. Nonetheless, more research is imperative for validating potential biomarkers and establishing standard procedures for sample preparation and data analysis. Furthermore, proteomics presents opportunities for unveiling new biomarkers and therapeutic targets, as well as fostering the development of personalized treatment strategies based on protein expression patterns associated with treatment response. In conclusion, proteomics holds great promise for advancing our understanding of pancreatic cancer biology and improving patient outcomes. It is essential to maintain momentum in investment and innovation in this arena to unearth more groundbreaking discoveries and transmute them into practical diagnostic and therapeutic strategies in the clinical context.

Computational Biology Helps Understand How Polyploid Giant Cancer Cells Drive Tumor Success

Precision and organization govern the cell cycle, ensuring normal proliferation. However, some cells may undergo abnormal cell divisions (neosis) or variations of mitotic cycles (endopolyploidy). Consequently, the formation of polyploid giant cancer cells (PGCCs), critical for tumor survival, resistance, and immortalization, can occur. Newly formed cells end up accessing numerous multicellular and unicellular programs that enable metastasis, drug resistance, tumor recurrence, and self-renewal or diverse clone formation. An integrative literature review was carried out, searching articles in several sites, including: PUBMED, NCBI-PMC, and Google Academic, published in English, indexed in referenced databases and without a publication time filter, but prioritizing articles from the last 3 years, to answer the following questions: (i) "What is the current knowledge about polyploidy in tumors?"; (ii) "What are the applications of computational studies for the understanding of cancer polyploidy?"; and (iii) "How do PGCCs contribute to tumorigenesis?"

AIMedGraph: a comprehensive multi-relational knowledge graph for precision medicine

The development of high-throughput molecular testing techniques has enabled the large-scale exploration of the underlying molecular causes of diseases and the development of targeted treatment for specific genetic alterations. However, knowledge to interpret the impact of genetic variants on disease or treatment is distributed in different databases, scientific literature studies and clinical guidelines. AIMedGraph was designed to comprehensively collect and interrogate standardized information about genes, genetic alterations and their therapeutic and diagnostic relevance and build a multi-relational, evidence-based knowledge graph. Graph database Neo4j was used to represent precision medicine knowledge as nodes and edges in AIMedGraph. Entities in the current release include 30 340 diseases/phenotypes, 26 140 genes, 187 541 genetic variants, 2821 drugs, 15 125 clinical trials and 797 911 supporting literature studies. Edges in this release cover 621 731 drug interactions, 9279 drug susceptibility impacts, 6330 pharmacogenomics effects, 30 339 variant pathogenicity and 1485 drug adverse reactions. The knowledge graph technique enables hidden knowledge inference and provides insight into potential disease or drug molecular mechanisms. Database URL: http://aimedgraph.tongshugene.net:8201.

From alchemy to personalised medicine: the journey of laboratory medicine

This review summarises the long period in which man has approached nature to understand its powers, and has tried to control it through physical and chemical, and also magical, practices. From the attempt to manage nature to the development of primordial drugs and medical practices and later to achieve modern biomedical science, laboratory practices always played a pivotal role. Over the years and centuries, the laboratory has acquired more and more importance in the improvement of health.In addition to the well-known importance of laboratory medicine in the early diagnosis and appropriateness, the discoveries of the last 50 years have also given the Laboratory a decisive role in regenerative and personalised medicine.This paper examines the evolution of the laboratory and is not meant to be a treatise on the history of medicine. The goal is to highlight the moments of the transition from magic and alchemy to laboratory science.-------------------------------Roberto Verna is President of the World Association of Societies of Pathology and Laboratory Medicine and President of the Academy for Health and Clinical Research.

Metabolomics as a Critical Tool for Studying Clinical Surgery

Metabolomics enables the analysis of metabolites within an organism, which offers the closest direct measurement of the physiological activity of the organism, and has advanced efforts to characterize metabolic states, identify biomarkers, and investigate metabolic pathways. A high degree of innovation in analytical techniques has promoted the application of metabolomics, especially in the study of clinical surgery. Metabolomics can be employed as a clinical testing method to maximize therapeutic outcomes, and has been applied in rapid diagnosis of diseases, timely postoperative monitoring, prognostic assessment, and personalized medicine. This review focuses on the use of mass spectrometry and nuclear magnetic resonance-based metabolomics in clinical surgery, including identifying metabolic changes before and after surgery, finding disease-associated biomarkers, and exploring the potential of personalized therapy. Challenges and opportunities of metabolomics in organ transplantation are also discussed, with a particular emphasis on metabolomics in donor organ evaluation and protection, prognostic outcome prediction, as well as postoperative adverse reaction monitoring. In the end, current limitations of metabolomics in clinical surgery and future research directions are presented.