Pharmacometabolomics-aided Pharmacogenomics in Autoimmune Disease

Pharmacometabolomics: An emerging platform for understanding the pathophysiological processes and therapeutic interventions

Pharmacometabolomics has emerged as a new subclass of metabolomics, aiming to predict an individual's response to a drug or optimize therapy based on prior information on an individual's metabolic profile. Pharmacometabolomics is being explored in drug discovery, biomarker identification, disease diagnosis, monitoring of disease progression, and therapeutic intervention. The time points-based sample collection is essential to measure the response of individuals to pathophysiological processes and therapeutic interventions. Analytical techniques such as NMR, LC-MS, and GC-MS have been employed to assess a huge number of metabolites present in biological systems. NMR has an advantage over other analytical techniques as it provides a snapshot of tissue and biological fluids, however, it requires higher magnetic fields to achieve better resolution. GC-MS could cover a wide range of metabolites due to high resolution but requires derivatization for certain metabolites. LC-MS is equally competitive and separates a wide range of metabolites with diverse polarities but requires extensive method development. Several platforms have been developed to analyze the analytical data and provide meaningful results via data reduction methods. PCA and PLS-DA are the most common methods for reduction dimensionality through simplified multivariate data modeling. This manuscript brings insights into the overview of pharmacometabolomics experimental design and the application of various analytical techniques and multivariate statistical analysis in the various fields of medical research.

Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades

Objective

Autoimmune diseases have long been recognized for their intricate nature and elusive mechanisms, presenting significant challenges in both diagnosis and treatment. The advent of artificial intelligence technology has opened up new possibilities for understanding, diagnosing, predicting, and managing autoimmune disorders. This study aims to explore the current state and emerging trends in the field through bibliometric analysis, providing guidance for future research directions.

Methods

The study employed the Web of Science Core Collection database for data acquisition and performed bibliometric analysis using CiteSpace, HistCite Pro, and VOSviewer.

Results

Over the past two decades, 1,695 publications emerged in this research field, including 1,409 research articles and 286 reviews. This investigation unveils the global development landscape predominantly led by the United States and China. The research identifies key institutions, such as Brigham & Women's Hospital, influential journals like the Annals of the Rheumatic Diseases, distinguished authors including Katherine P. Liao, and pivotal articles. It visually maps out the research clusters' evolutionary path over time and explores their applications in patient identification, risk factors, prognosis assessment, diagnosis, classification of disease subtypes, monitoring and decision support, and drug discovery.

Conclusion

AI is increasingly recognized for its potential in the field of autoimmune diseases, yet it continues to face numerous challenges, including insufficient model validation and difficulties in data integration and computational power. Significant advancements have been demanded to enhance diagnostic precision, improve treatment methodologies, and establish robust frameworks for data protection, thereby facilitating more effective management of these complex conditions.

Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication

Indiscriminate drug administration may lead to drug therapy results with varying effects on patients, and the proposal of personalized medication can help patients to receive effective drug therapy. Conventional ways of personalized medication, such as pharmacogenomics and therapeutic drug monitoring (TDM), can only be implemented from a single perspective. The development of pharmacometabolomics provides a research method for the realization of precise drug administration, which integrates the environmental and genetic factors, and applies metabolomics technology to study how to predict different drug therapeutic responses of organisms based on baseline metabolic levels. The published research on pharmacometabolomics has achieved satisfactory results in predicting the pharmacokinetics, pharmacodynamics, and the discovery of biomarkers of drugs. Among them, the pharmacokinetics related to pharmacometabolomics are used to explore individual variability in drug metabolism from the level of metabolism of the drugs in vivo and the level of endogenous metabolite changes. By searching for relevant literature with the keyword "pharmacometabolomics" on the two major literature retrieval websites, PubMed and Web of Science, from 2006 to 2023, we reviewed articles in the field of pharmacometabolomics that incorporated pharmacokinetics into their research. This review explains the therapeutic effects of drugs on the body from the perspective of endogenous metabolites and pharmacokinetic principles, and reports the latest advances in pharmacometabolomics related to pharmacokinetics to provide research ideas and methods for advancing the implementation of personalized medication.

Pharmacometabolomics-Based Translational Biomarkers: How to Navigate the Data Ocean

Metabolome is the end point of the genome-environment interplay, and enables an important holistic overview of individual adaptability and host responses to environmental, ecological, as well as endogenous changes such as disease. Pharmacometabolomics is the application of metabolome knowledge to decipher the mechanisms of interindividual and intraindividual variations in drug efficacy and safety. Pharmacometabolomics also contributes to prediction of drug treatment outcomes on the basis of baseline (predose) and postdose metabotypes through mathematical modeling. Thus, pharmacometabolomics is a strong asset for a diverse community of stakeholders interested in theory and practice of evidence-based and precision/personalized medicine: academic researchers, public health scholars, health professionals, pharmaceutical, diagnostics, and biotechnology industries, among others. In this expert review, we discuss pharmacometabolomics in four contexts: (1) an interdisciplinary omics tool and field to map the mechanisms and scale of interindividual variability in drug effects, (2) discovery and development of translational biomarkers, (3) advance digital biomarkers, and (4) empower drug repurposing, a field that is increasingly proving useful in the current era of Covid-19. As the applications of pharmacometabolomics are growing rapidly in the current postgenome era, next-generation proteomics and metabolomics follow the example of next-generation sequencing analyses. Pharmacometabolomics can also empower data reliability and reproducibility through multiomics integration strategies, which use each data layer to correct, connect with, and inform each other. Finally, we underscore here that contextual data remain crucial for precision medicine and drug development that stand the test of time and clinical relevance.

Pharmacometabolomics Applied to Personalized Medicine in Urological Cancers

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most common urological cancers, and their incidence has been rising over time. Surgery is the standard treatment for these cancers, but this procedure is only effective when the disease is localized. For metastatic disease, PCa is typically treated with androgen deprivation therapy, while BCa is treated with chemotherapy, and RCC is managed primarily with targeted therapies. However, response rates to these therapeutic options remain unsatisfactory due to the development of resistance and treatment-related toxicity. Thus, the discovery of biomarkers with prognostic and predictive value is needed to stratify patients into different risk groups, minimizing overtreatment and the risk of drug resistance development. Pharmacometabolomics, a branch of metabolomics, is an attractive tool to predict drug response in an individual based on its own metabolic signature, which can be collected before, during, and after drug exposure. Hence, this review focuses on the application of pharmacometabolomic approaches to identify the metabolic responses to hormone therapy, targeted therapy, immunotherapy, and chemotherapy for the most prevalent urological cancers.

Metabolomics in Autoimmune Diseases: Focus on Rheumatoid Arthritis, Systemic Lupus Erythematous, and Multiple Sclerosis

The metabolomics approach represents the last downstream phenotype and is widely used in clinical studies and drug discovery. In this paper, we outline recent advances in the metabolomics research of autoimmune diseases (ADs) such as rheumatoid arthritis (RA), multiple sclerosis (MuS), and systemic lupus erythematosus (SLE). The newly discovered biomarkers and the metabolic mechanism studies for these ADs are described here. In addition, studies elucidating the metabolic mechanisms underlying these ADs are presented. Metabolomics has the potential to contribute to pharmacotherapy personalization; thus, we summarize the biomarker studies performed to predict the personalization of medicine and drug response.

Challenges in identifying large germline structural variants for clinical use by long read sequencing

Genomic structural variations, previously considered rare events, are widely recognized as a major source of inter-individual variability and hence, a major hurdle in optimum patient stratification and disease management. Herein, we focus on large complex germline structural variations and present challenges towards target treatment via the synergy of state-of-the-art approaches and information technology tools. A complex structural variation detection remains challenging, as there is no gold standard for identifying such genomic variations with long reads, especially when the chromosomal rearrangement in question is a few Mb in length. A clinical case with a large complex chromosomal rearrangement serves as a paradigm. We feel that functional validation and data interpretation are of outmost importance for information growth to be translated into knowledge growth and hence, new working practices are highlighted.

A Quantitative and Narrative Evaluation of Goodman and Gilman's Pharmacological Basis of Therapeutics

Goodman and Gilman's The Pharmacological Basis of Therapeutics (_GGPBT) has been a cornerstone in the education of pharmacists, physicians, and pharmacologists for decades. The objectives of this study were to describe and evaluate the 13^th edition of _GGPBT on bases including: (1) author characteristics; (2) recency of citations; (3) conflict of interest (CoI) disclosure; (4) expert evaluation of chapters. Contributors' (N = 115) sex, professional degrees, and presence of undisclosed potential CoI-as reported by the Center for Medicare and Medicaid's Open Payments (2013-2017)-were examined. The year of publication of citations was extracted relative to Katzung's Basic and Clinical Pharmacology (_KatBCP), and DiPiro's Pharmacotherapy: A Pathophysiologic Approach (_DiPPAPA). Content experts provided thorough chapter reviews. The percent of _GGPBT contributors that were female (20.9%) was equivalent to those in _KatBCP (17.0%). Citations in _GGPBT (11.5 ± 0.2 years) were significantly older than those in _KatBCP (10.4 ± 0.2) and _DiPPAPA (9.1 ± 0.1, p < 0.0001). Contributors to _GGPBT received USD 3 million in undisclosed remuneration (Maximum author = USD 743,718). In contrast, _DiPPAPA made CoI information available. Reviewers noted several strengths but also some areas for improvement. _GGPBT will continue to be an important component of the biomedical curriculum. Areas of improvement include a more diverse authorship, improved conflict of interest transparency, and a greater inclusion of more recent citations.

Roles of the matricellular protein Tenascin-C in T-lymphocyte trafficking and etiopathogenesis of Oral Lichen Planus

OBJECTIVE

This study was aimed at examining the role of Tenascin-C in T cell trafficking in Oral Lichen Planus (OLP).

DESIGN

For the in vivo immunohistochemical analyses, 115 OLP samples were collected from patients and immunostaining was performed. The intensity and distribution of TN-C expression were quantified and correlated with histological analyses of basement membrane integrity and presence of inflammatory infiltrate. For the in vitro study, TN-C and collagen were coated on culture plates and migration of T lymphocytes was assessed.

RESULTS

TN-C immunoexpression was increased in terms of both distribution and intensity along the basement membrane zone. These changes were significantly associated with basement membrane duplication (distribution p < 0.002 and intensity p < 0.001) and bands of inflammation (distribution p < 0.002 and intensity p < 0.001) assessed by Chi-square test. T lymphocytes demonstrated significant migration towards TN-C as compared to collagen (n = 3, p < 0.05).

CONCLUSIONS

These findings indicate TN-C may have a key role in promoting T cell migration at the epithelial-mesenchymal junction in OLP. These observations suggest TN-C could be a good target for therapeutic intervention, either in itself or synergistically with anti-inflammatory directed strategies in this chronic disease management.

Pharmacometabolomics Informs Quantitative Radiomics for Glioblastoma Diagnostic Innovation

Applications of omics systems biology technologies have enormous promise for radiology and diagnostics in surgical fields. In this context, the emerging fields of radiomics (a systems scale approach to radiology using a host of technologies, including omics) and pharmacometabolomics (use of metabolomics for patient and disease stratification and guiding precision medicine) offer much synergy for diagnostic innovation in surgery, particularly in neurosurgery. This synthesis of omics fields and applications is timely because diagnostic accuracy in central nervous system tumors still challenges decision-making. Considering the vast heterogeneity in brain tumors, disease phenotypes, and interindividual variability in surgical and chemotherapy outcomes, we believe that diagnostic accuracy can be markedly improved by quantitative radiomics coupled to pharmacometabolomics and related health information technologies while optimizing economic costs of traditional diagnostics. In this expert review, we present an innovation analysis on a systems-level multi-omics approach toward diagnostic accuracy in central nervous system tumors. For this, we suggest that glioblastomas serve as a useful application paradigm. We performed a literature search on PubMed for articles published in English between 2006 and 2016. We used the search terms "radiomics," "glioblastoma," "biomarkers," "pharmacogenomics," "pharmacometabolomics," "pharmacometabonomics/pharmacometabolomics," "collaborative informatics," and "precision medicine." A list of the top 4 insights we derived from this literature analysis is presented in this study. For example, we found that (i) tumor grading needs to be better refined, (ii) diagnostic precision should be improved, (iii) standardization in radiomics is lacking, and (iv) quantitative radiomics needs to prove clinical implementation. We conclude with an interdisciplinary call to the metabolomics, pharmacy/pharmacology, radiology, and surgery communities that pharmacometabolomics coupled to information technologies (chemoinformatics tools, databases, collaborative systems) can inform quantitative radiomics, thus translating Big Data and information growth to knowledge growth, rational drug development and diagnostics innovation for glioblastomas, and possibly in other brain tumors.

The New Age of -omics in Urothelial Cancer - Re-wording Its Diagnosis and Treatment

Unmet needs in urothelial cancer management represent an important challenge in our effort to improve long-term overall and disease-free survival rates with no significant compromise in quality of life. Radical cystectomy with pelvic lymph node dissection is the standard for the management of muscle-invasive, non-metastatic cancers. In spite of a 90% local disease control, up to 50% of patients ultimately die of distant metastasis. Bladder preservation using chemo-radiation is an acceptable alternative, but optimal patient selection remains elusive. Recent research is focused on the employment of tailored-made strategies in urothelial cancer exploiting the potential of theranostics in patient selection for specific therapies. Herein, we review the current knowledge on molecular theranostics in urothelial cancer and we suggest that this is the time to move toward imaging theranostics, if tailored-made disease management and patient stratification is envisaged.

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Urothelial cancer management represents an important challenge.

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Optimum patient stratification and tailored-made theranostics remain elusive.

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Imaging theranostics is envisaged as a cancer roadmap.

Recent trends on the role of epigenomics, metabolomics and noncoding RNAs in rationalizing mood stabilizing treatment

Mood stabilizers are the cornerstone in treatment of mood disorders, but their use is characterized by high interindividual variability. This feature has stimulated intensive research to identify predictive biomarkers of response and disentangle the molecular bases of their clinical efficacy. Nevertheless, findings from studies conducted so far have only explained a small proportion of the observed variability, suggesting that factors other than DNA variants could be involved. A growing body of research has been focusing on the role of epigenetics and metabolomics in response to mood stabilizers, especially lithium salts. Studies from these approaches have provided new insights into the molecular networks and processes involved in the mechanism of action of mood stabilizers, promoting a systems-level multiomics synergy. In this article, we reviewed the literature investigating the involvement of epigenetic mechanisms, noncoding RNAs and metabolomic modifications in bipolar disorder and the mechanism of action and clinical efficacy of mood stabilizers.

Key Pharmacogenomic Considerations for Sickle Cell Disease Patients

Sickle cell disease (SCD), although a monogenic disease, exhibits a complex clinical phenotype that hampers optimum patient stratification and disease management, especially on hydroxyurea treatment. Moreover, theranostics, the combination of diagnostics to individualize and optimize therapeutic interventions, has not been firmly on the forefront of SCD research and clinical management to date. We suggest that if tailor-made theranostics in SCD is envisaged, pharmacogenomics is anticipated to be the way forward. Herein, we present the current key pharmacogenomic opportunities and challenges in SCD, considering population variation, ethics, and socioeconomic aspects. We focus on pharmacogenomics and pain management, genethics, and cost-effectiveness in SCD. We searched for and synthesized data from PubMed and Google Scholar, and the references from relevant articles, using the keywords "pharmacogenomics," "sickle cell disease," "hydroxyurea," "ethics," "pain management," "morphine metabolism," "opioids," "pharmacogenomics and chronic pain," "cost-effectiveness," and "economic evaluation." Only articles published in English were included. So far, when pharmacogenomics in SCD has been considered, interindividual variability in hydroxyurea response/toxicity has been of primary interest. We underscore the need to extend pharmacogenomic considerations on other therapeutic interventions currently present using a holistic patient-centric approach, and taking disease complications into account as well. Furthermore, we raise awareness toward socioeconomic, ethical, and population differences in the way sickle cell pharmacogenomics might unfold in the future. If pharmacogenomics in SCD is to be used in the clinic in an evidence-based manner, cost-effectiveness and population-specific empirical ethics data are urgently needed.

Pharmacogenomics education and research at the Department of Pharmacy, University of Patras, Greece

The Pharmacogenomics and Personalized Medicine group belongs to the Laboratory of Molecular Biology and Immunology, Department of Pharmacy and is active since 2009 mainly in the field of pharmacogenomics and personalized medicine. Herein, we describe the research interests, collaborations and accomplishments of the Pharmacogenomics and Personalized Medicine group together with the teaching activities of the group that greatly enhance the pharmacogenomics knowledge of graduate/postgraduate students and healthcare professionals.

Pharmacometabolomics Informs Viromics toward Precision Medicine

Nowadays, we are experiencing the big data era with the emerging challenge of single data interpretation. Although the advent of high-throughput technologies as well as chemo- and bio-informatics tools presents pan-omics data as the way forward to precision medicine, personalized health care and tailored-made therapeutics can be only envisaged when interindividual variability in response to/toxicity of xenobiotics can be interpreted and thus, predicted. We know that such variability is the net outcome of genetics (host and microbiota) and environmental factors (diet, lifestyle, polypharmacy, and microbiota) and for this, tremendous efforts have been made to clarify key-molecules from correlation to causality to clinical significance. Herein, we focus on the host–microbiome interplay and its direct and indirect impact on efficacy and toxicity of xenobiotics and we inevitably wonder about the role of viruses, as the least acknowledged ones. We present the emerging discipline of pharmacometabolomics-informed viromics, in which pre-dose metabotypes can assist modeling and prediction of interindividual response to/toxicity of xenobiotics. Such features, either alone or in combination with host genetics, can power biomarker discovery so long as the features are variable among patients, stable enough to be of predictive value, and better than pre-existing tools for predicting therapeutic efficacy/toxicity.

Novel genetic risk variants for pediatric celiac disease

BACKGROUND

Celiac disease is a complex chronic immune-mediated disorder of the small intestine. Today, the pathobiology of the disease is unclear, perplexing differential diagnosis, patient stratification, and decision-making in the clinic.

METHODS

Herein, we adopted a next-generation sequencing approach in a celiac disease trio of Greek descent to identify all genomic variants with the potential of celiac disease predisposition.

RESULTS

Analysis revealed six genomic variants of prime interest: SLC9A4 c.1919G>A, KIAA1109 c.2933T>C and c.4268_4269delCCinsTA, HoxB6 c.668C>A, HoxD12 c.418G>A, and NCK2 c.745_746delAAinsG, from which NCK2 c.745_746delAAinsG is novel. Data validation in pediatric celiac disease patients of Greek (n = 109) and Serbian (n = 73) descent and their healthy counterparts (n = 111 and n = 32, respectively) indicated that HoxD12 c.418G>A is more prevalent in celiac disease patients in the Serbian population (P < 0.01), while NCK2 c.745_746delAAinsG is less prevalent in celiac disease patients rather than healthy individuals of Greek descent (P = 0.03). SLC9A4 c.1919G>A and KIAA1109 c.2933T>C and c.4268_4269delCCinsTA were more abundant in patients; nevertheless, they failed to show statistical significance.

CONCLUSIONS

The next-generation sequencing-based family genomics approach described herein may serve as a paradigm towards the identification of novel functional variants with the aim of understanding complex disease pathobiology.

Metabolomics: Bridging the Gap between Pharmaceutical Development and Population Health

Metabolomics has emerged as an essential tool for studying metabolic processes, stratification of patients, as well as illuminating the fundamental metabolic alterations in disease onset, progression, or response to therapeutic intervention. Metabolomics materialized within the pharmaceutical industry as a standalone assay in toxicology and disease pathology and eventually evolved towards aiding in drug discovery and pre-clinical studies via supporting pharmacokinetic and pharmacodynamic characterization of a drug or a candidate. Recent progress in the field is illustrated by coining of the new term—Pharmacometabolomics. Integration of data from metabolomics with large-scale omics along with clinical, molecular, environmental and behavioral analysis has demonstrated the enhanced utility of deconstructing the complexity of health, disease, and pharmaceutical intervention(s), which further highlight it as an essential component of systems medicine. This review presents the current state and trend of metabolomics applications in pharmaceutical development, and highlights the importance and potential of clinical metabolomics as an essential part of multi-omics protocols that are directed towards shaping precision medicine and population health.