Biomarkers and personalized medicine: current status and further perspectives with special focus on dermatology

Unveiling the revolutionary approach for psoriasis management: Leveraging the breakthrough capabilities of hyaluronic acid

Psoriasis is an unexceptional autoimmune-mediated, inflammatory skin disorder impacting systemic skin functions. The pathophysiology of psoriasis comprises hyperproliferation of cells on the epidermis, differentiation of keratinocytes, and the impaired barrier function of the epidermal layer of the skin, developing in the thickening of the epidermal layer. From a range of inflammatory mediators concerned during the pathogenesis of psoriasis, IL-17, -23, and TNF-α exert a significant influence on the upregulation of the symptoms. There are diverse conventional approaches dealing with psoriasis, including topical, systemic, biological, and herbal formulations. The demand for innovative formulations has emerged as several adverse effects correspond to conventionally pre-existing formulations. As hyaluronic acid (HA) has manifold structural and functional characteristics that can be worthwhile in regulating the symptoms of multiple skin inflammatory conditions, it can be used in novel formulations to amplify therapeutic effectiveness and achieve enviable responses. Moreover, HA can also serve the role of a biomarker for psoriasis according to its molecular weight. Furthermore, the mechanistic role of HA in its native form can be advantageous in ameliorating the symptoms of psoriasis. This review unequivocally covers fundamental aspects and the latest advancements in HA-based formulations for mitigating psoriasis symptoms. Furthermore, we deliberated on the role of HA as a biomarker in the physiological system of humans, in accordance with its molecular weight, the rationale behind its selection, and its mechanistic role, and how HA profoundly augments the impactfulness of various formulations in eliciting a prominent therapeutic improvement and mitigating symptoms associated with this disease.

Biomarker-guided adaptive enrichment design with threshold detection for clinical trials with time-to-event outcome

Biomarker-guided designs are increasingly used to evaluate personalized treatments based on patients' biomarker status in Phase II and III clinical trials. With adaptive enrichment, these designs can improve the efficiency of evaluating the treatment effect in biomarker-positive patients by increasing their proportion in the randomized trial. While time-to-event outcomes are often used as the primary endpoint to measure treatment effects for a new therapy in severe diseases like cancer and cardiovascular diseases, there is limited research on biomarker-guided adaptive enrichment trials in this context. Such trials almost always adopt hazard ratio methods for statistical measurement of treatment effects. In contrast, restricted mean survival time (RMST) has gained popularity for analyzing time-to-event outcomes because it offers more straightforward interpretations of treatment effects and does not require the proportional hazard assumption. This paper proposes a two-stage biomarker-guided adaptive RMST design with threshold detection and patient enrichment. We develop sophisticated methods for identifying the optimal biomarker threshold and biomarker-positive subgroup, treatment effect estimators, and approaches for type I error rate, power analysis, and sample size calculation. We present a numerical example of re-designing an oncology trial. An extensive simulation study is conducted to evaluate the performance of the proposed design.

Optimization of glycopeptide enrichment techniques for the identification of clinical biomarkers

INTRODUCTION

The identification and characterization of glycopeptides through LC-MS/MS and advanced enrichment techniques are crucial for advancing clinical glycoproteomics, significantly impacting the discovery of disease biomarkers and therapeutic targets. Despite progress in enrichment methods like Lectin Affinity Chromatography (LAC), Hydrophilic Interaction Liquid Chromatography (HILIC), and Electrostatic Repulsion Hydrophilic Interaction Chromatography (ERLIC), issues with specificity, efficiency, and scalability remain, impeding thorough analysis of complex glycosylation patterns crucial for disease understanding.

AREAS COVERED

This review explores the current challenges and innovative solutions in glycopeptide enrichment and mass spectrometry analysis, highlighting the importance of novel materials and computational advances for improving sensitivity and specificity. It outlines the potential future directions of these technologies in clinical glycoproteomics, emphasizing their transformative impact on medical diagnostics and therapeutic strategies.

EXPERT OPINION

The application of innovative materials such as Metal-Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs), functional nanomaterials, and online enrichment shows promise in addressing challenges associated with glycoproteomics analysis by providing more selective and robust enrichment platforms. Moreover, the integration of artificial intelligence and machine learning is revolutionizing glycoproteomics by enhancing the processing and interpretation of extensive data from LC-MS/MS, boosting biomarker discovery, and improving predictive accuracy, thus supporting personalized medicine.

Insulin resistance-related circulating predictive markers in the metabolic syndrome: a systematic review in the Iranian population

Background

Specific biomarkers for metabolic syndrome (MetS) may improve diagnostic specificity for clinical information. One of the main pathophysiological mechanisms of MetS is insulin resistance (IR). This systematic review aimed to summarize IR-related biomarkers that predict MetS and have been investigated in Iranian populations.

Methods

An electronic literature search was done using the PubMed and Scopus databases up to June 2022. The risk of bias was assessed for the selected articles using the instrument suggested by the Joanna Briggs Institute (JBI). This systematic review protocol was registered with PROSPERO (registration number CRD42022372415).

Results

Among the reviewed articles, 46 studies investigated the association between IR biomarkers and MetS in the Iranian population. The selected studies were published between 2009 and 2022, with the majority being conducted on adults and seven on children and adolescents. The adult treatment panel III (ATP III) was the most commonly used criteria to define MetS. At least four studies were conducted for each IR biomarker, with LDL-C being the most frequently evaluated biomarker. Some studies have assessed the diagnostic potency of markers using the area under the curve (AUC) with sensitivity, specificity, and an optimal cut-off value. Among the reported values, lipid ratios and the difference between non-HDL-C and LDL-C levels showed the highest AUCs (≥ 0.80) for predicting MetS.

Conclusions

Considering the findings of the reviewed studies, fasting insulin, HOMA-IR, leptin, HbA1c, and visfatin levels were positively associated with MetS, whereas adiponectin and ghrelin levels were negatively correlated with this syndrome. Among the investigated IR biomarkers, the association between adiponectin levels and components of MetS was well established.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01347-6.

Precision Dermatology: A Review of Molecular Biomarkers and Personalized Therapies

The evolution of personalized medicine in dermatology signifies a transformative shift towards individualized treatments, driven by the integration of biomarkers. These molecular indicators serve beyond diagnostics, offering insights into disease staging, prognosis, and therapeutic monitoring. Specific criteria guide biomarker selection, ensuring attributes like specificity, sensitivity, cost feasibility, stability, rapid detection, and reproducibility. This literature review, based on data from PubMed, SCOPUS, and Web of Science, explores biomarkers in Hidradenitis Suppurativa (HS), Psoriasis, Atopic Dermatitis (AD), Alopecia Areata (AA), Vitiligo, and Chronic Spontaneous Urticaria (CSU). In HS, TNF-α, IL-1β, and MMPs serve as biomarkers, influencing targeted therapies like adalimumab and anakinra. Psoriasis involves biomarkers such as TNF-α, IL-23, and HLA genes, shaping treatments like IL23 and IL17 inhibitors. AD biomarkers include ECP, IL-4, IL-13, guiding therapies like dupilumab and tralokinumab. For AA, lipocalin-2, cytokines, and genetic polymorphisms inform JAK inhibitors' use. Vitiligo biomarkers range from cytokines to genetic markers like TYR, TYRP1, guiding treatments like JAK inhibitors. CSU biomarkers encompass IgE, cytokines, and autologous serum tests, influencing therapies like omalizumab and cyclosporine. Comparing conditions, common proinflammatory markers reveal limited specificity. While some biomarkers aid diagnosis and standard treatments, others hold more scientific than clinical value. Precision medicine, driven by biomarkers, has shown success in skin malignancies. Future directions involve AI-powered algorithms, nanotechnology, and multi-omics integration for personalized dermatological care.

Biomarkers for Monitoring Treatment Response of Omalizumab in Patients with Chronic Urticaria

Chronic urticaria (CU) is a debilitating skin disease affecting around 1% of the population. CU can be subdivided into chronic spontaneous urticaria (CSU) and chronic inducible urticaria (CIndU). Different pathophysiological mechanisms have been proposed to play a role in the development of CU, and these are also being investigated as potential biomarkers in the diagnosis and management of the disease. As of now the only assessment tools available for treatment response are patient reported outcomes (PROs). Although these tools are both validated and widely used, they leave a desire for more objective measurements. A biomarker is a broad subcategory of observations that can be used as an accurate, reproducible, and objective indicator of clinically relevant outcomes. This could be normal biological or pathogenic processes, or a response to an intervention or exposure, e.g., treatment response. Herein we provide an overview of biomarkers for CU, with a focus on prognostic biomarkers for treatment response to omalizumab, thereby potentially aiding physicians in personalizing treatments.

Drug interference with biochemical laboratory tests

Clinical laboratory practice represents an essential part of clinical decision-making, as it influences 60-70% of medical decisions at all levels of health care. Results of biochemical laboratory tests (BLTs) have a key role in establishment of adequate diagnosis as well as in evaluation of treatment progress and outcome. The prevalence of drug-laboratory test interactions (DLTIs) is up to 43% of patients who had laboratory results influenced by drugs. Unrecognized DLTIs may lead to misinterpreted BLTs results, incorrect or delayed diagnosis, extra costs for unnecessary additional tests or inadequate therapy, as all may cause false clinical decisions. The significance of timely and adequate recognition of DLTIs is to prevent common clinical consequences such as incorrectly interpreted test results, delayed or non-treated condition due to erroneous diagnosis or unnecessary extra tests or therapy. Medical professionals should be educated that it is essential to obtain patient data about medications especially for the drugs used in the last 10 days before biological material collection. Our mini-review aims to provide a comprehensive overview of the current state in this important domain of medical biochemistry with detailed analysis of the effect of drugs on BLTs and to give detailed information to medical specialists.

Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review

The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.

Personalizing therapy selection in inflammatory bowel disease

INTRODUCTION

Inflammatory bowel disease (IBD) is a complex disease, caused by aberrant immune responses to environmental stimuli where genetic, metabolomic, and environmental variables interact to cause mucosal inflammation. This review sheds light on the different drug and patient related factors that affect personalization of biologics in IBD treatment.

AREAS COVERED

We utilized the online research database PubMed to carry out literature search pertaining to therapies in IBD. We incorporated a combination of primary literature as well as review articles and meta-analyses in writing this clinical review. In this paper, we discuss the mechanisms of action for different biologics, the genotype and phenotype of patients, and pharmacokinetics/pharmacodynamics of drugs, as factors that influence response rates. We also touch upon the role of artificial intelligence in treatment personalization.

EXPERT OPINION

The future of IBD therapeutics is one of precision medicine, based on the identification of aberrant signaling pathways unique to individual patients as well as exploring the exposome, diet, viruses, and epithelial cell dysfunction as part of disease pathogenesis. We need global cooperation for pragmatic study designs as well as equitable access to machine learning/artificial intelligence technology to reach the unfulfilled potential of IBD care.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Integrated bioinformatic analysis of gene expression profiling data to identify combinatorial biomarkers in inflammatory skin disease

Selection of appropriate biomarker to identify inflammatory skin diseases is complicated by the involvement of thousands of differentially expressed genes (DEGs) across multiple cell types and organs. This study aimed to identify combinatorial biomarkers in inflammatory skin diseases. From one gene expression microarray profiling dataset, we performed bioinformatic analyses on dataset from lesional skin biopsies of patients with inflammatory skin diseases (atopic dermatitis [AD], contact eczema [KE], lichen planus [Li], psoriasis vulgaris [Pso]) and healthy controls to identify the involved pathways, predict upstream regulators, and potential measurable extracellular biomarkers. Overall, 434, 629, 581, and 738 DEGs were mapped in AD, KE, Li, and Pso, respectively; 238 identified DEGs were shared among four different inflammatory skin diseases. Bioinformatic analysis on four inflammatory skin diseases showed significant activation of pathways with known pathogenic relevance. Common upstream regulators, with upregulated predicted activity, identified were CNR1 and BMP4. We found the following common serum biomarkers: ACR, APOE, ASIP, CRISP1, DKK1, IL12B, IL9, MANF, MDK, NRTN, PCSK5, and VEGFC. Considerable differences of gene expression changes, involved pathways, upstream regulators, and biomarkers were found in different inflammatory skin diseases. Integrated bioinformatic analysis identified 12 potential common biomarkers of inflammatory skin diseases requiring further evaluation.

Glial Fibrillary Acidic Protein in Blood as a Disease Biomarker of Neuromyelitis Optica Spectrum Disorders

Glial fibrillary acidic protein (GFAP) is a type III intermediate filament protein found in astrocytes in the brain. Damaged astrocytes release GFAP into cerebrospinal fluid and blood. Thus, GFAP levels in these body fluids may reflect the disease state of neuromyelitis optica spectrum disorder (NMOSD), which includes astrocytopathy, characterized by pathogenic antibodies against aquaporin 4 located on astrocytes. Recently, single-molecule array technology that can detect these synaptic proteins in blood, even in the subfemtomolar range, has been developed. Emerging evidence suggests that GFAP protein is a strong biomarker candidate for NMOSD. This mini-review provides basic information about GFAP protein and innovative clinical data that show the potential clinical value of blood GFAP levels as a biomarker for NMOSD.

Biomarker qualification at the European Medicines Agency: a review of biomarker qualification procedures from 2008 to 2020

Regulatory qualification of biomarkers facilitates their harmonized use across drug developers, enabling more personalized medicine. This study reviews various aspects of the European Medicines Agency’s (EMA’s) biomarker qualification procedure, including frequency and outcome, common challenges, and biomarker characteristics. Our findings provide insights into the EMA’s biomarker qualification process and will thereby support future applications. All biomarker‐related “Qualification of Novel Methodologies for Medicine Development” procedures that started from 2008 to 2020 were included. Procedural data were extracted from relevant documents and analyzed descriptively. In total, 86 biomarker qualification procedures were identified, of which 13 resulted in qualified biomarkers. Whereas initially many biomarker qualification procedures were linked to a single company and specific drug development program, a shift was observed to qualification efforts by consortia. Most biomarkers were proposed (n = 45) and qualified (n = 9) for use in patient selection, stratification, and/or enrichment, followed by efficacy biomarkers (37 proposed, 4 qualified). Overall, many issues were raised during qualification procedures, mostly related to biomarker properties and assay validation (in 79% and 77% of all procedures, respectively). Issues related to the proposed context of use and rationale were least common yet were still raised in 54% of all procedures. While few qualified biomarkers are currently available, procedures focus increasingly on biomarkers for general use instead of those linked to specific drug compounds. The issues raised during qualification procedures illustrate the thorough discussions taking place between applicants and regulators—highlighting aspects that need careful consideration and underlining the importance of an appropriate validation strategy.

Atopic Dermatitis Biomarkers and the Movement Toward Personalized Treatment

Atopic dermatitis (AD) is a heterogeneous disorder with varying phenotypes. Although AD has long been associated with barrier dysfunction, the pathogenesis of this disease is more complex, involving many molecular markers in different functional domains. Biomarkers can be helpful in different ways, including predicting prognosis, measuring treatment response, and gauging disease severity. With the advent of targeted immunomodulators, biomarkers have the potential to take on new significance in terms of selecting appropriate therapies for patients. In this review, we have summarized the key findings related to biomarkers and AD, including the specific subtype differences. Clinicians will use this information to better understand the potential of biomarkers in AD and have a guide because more specific treatments are developed that are tailored toward individual molecular profiles.

Immunotherapy in psoriasis

Over the past two decades, significant progress has been achieved in the treatment of psoriasis by targeting the human cytokine network. At present, 11 biologicals - antibodies, and a soluble receptor - are used to neutralize key inflammatory cytokines. Based on their targets, they can be grouped into the following four classes: TNF-α-, IL-12/23-, IL-17- and IL-23-inhibitors. The range of available substances, as well as their different modes of action can be challenging when selecting the right drug for an individual patient. In this article, we provide an overview of the approved biologicals for the treatment of psoriasis, including their advantages and limitations, and summarize criteria for therapy selection.

Milestones in Personalized Medicine: From the Ancient Time to Nowadays-the Provocation of COVID-19

The first evidence of individual targeting medicine appeared in ancient times thousands of years ago. Various therapeutic approaches have been established since then. However, even nowadays, conventional therapies do not take into consideration individuals' idiosyncrasy and genetic make-up, failing thus to be effective in some cases. Over time, the necessity of a more precise and effective treatment resulted in the development of a scientific field currently known as “personalized medicine.” The numerous technological breakthroughs in this field have acknowledged personalized medicine as the next generation of diagnosis and treatment. Although personalized medicine has attracted a lot of attention the last years, there are still several obstacles hindering its application in clinical practice. These limitations have come to light recently, due to the COVID-19 pandemic. This review describes the “journey” of personalized medicine over time, emphasizing on important milestones achieved through time. Starting from the treatment of malaria, as a first more personalized therapeutic approach, it highlights the need of new diagnostic tools and therapeutic regimens based on individuals' genetic background. Furthermore, it aims at raising global awareness regarding the current limitations and the necessity of a personalized strategy to overpass healthcare problems and hence, the current crisis.

Biomarker-guided trials: Challenges in practice

Biomarker-guided trials have drawn considerable attention as they promise to lead to improvements in the benefit-risk ratio of treatments and enhanced opportunities for drug development. A variety of such designs have been proposed in the literature, many of which have been adopted in practice. Implementing such trial designs in practice can be challenging, and identifying those challenges was the main objective of a workshop organised by the MRC Hubs for Trials Methodology Research Network's Stratified Medicine Working Group in March 2017. Participants reflected on completed and ongoing biomarker-guided trials to identify the practical challenges encountered. Here, the key challenges identified during the workshop including those related to funding, ethical and regulatory issues, recruitment, monitoring of samples and laboratories, biomarker assessment, and data sharing and resources, are discussed. Despite the complexities often associated with biomarker-guided trials, the workshop concluded that they can play an important role in advancing the field of personalised medicine. Therefore, it is important that the practical challenges surrounding their implementation are acknowledged and addressed.

A Scoping Review of Non-invasive Imaging Modalities in Dermatological Disease: Potential Novel Biomarkers in Hidradenitis Suppurativa

Background: The development of imaging-based biomarkers has the potential to overcome major challenges in the accurate and reproducible assessment of disease severity and response to novel therapies in Hidradenitis Suppurativa (HS). Understanding the advantages and limitations of existing non-invasive imaging modalities in dermatological disease will aid in the development of hypotheses and inform the design of future studies. Methods: A scoping review was performed using Medline, Embase, Web of Science Databases and evaluation of "gray literature" until June 30, 2019. Citations were examined according to pre-defined inclusion and exclusion criteria. Citations were reviewed by two independent reviewers. Narrative Synthesis was used to summarize data, structured by imaging modality. Results: Non-invasive imaging modalities, such as ultrasound, MRI, RCM, EIS, OCT, and MIT, were identified. Only ultrasound, MRI and MIT have been used in HS. Image modalities vary in image depth, resolution, cost, accessibility and correlation with known aspects of disease activity in HS. Discussion and Conclusion: The benefits and limitations of each imaging modality are products of cost, accessibility, validity and reliability. An additional hurdle to the development of image-based biomarkers in HS is a lack of established analytical benchmarks that can be correlated with existing biological, inflammatory and clinical parameters. This review has identified potential imaging biomarkers, as well as relevant analytical benchmarks that reflect the presence or absence of disease. Further investigation work is needed to analytically and clinically validate these imaging variables in order to identify potential imaging biomarkers in HS.

What evidence do we need for biomarker qualification?

Biomarkers can facilitate all aspects of the drug development process. However, biomarker qualification-the use of a biomarker that is accepted by the U.S. Food and Drug Administration-needs a clear, predictable process. We describe a multistakeholder effort including government, industry, and academia that proposes a framework for defining the amount of evidence needed for biomarker qualification. This framework is intended for broad applications across multiple biomarker categories and uses.

Computational Systems Biology of Metabolism in Infection

A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.

Can innate and autoimmune reactivity forecast early and advance stages of age-related macular degeneration?

Age-related macular degeneration (AMD) is a major cause of central vision loss in persons over 55years of age in developed countries. AMD is a complex disease in which genetic, environmental and inflammatory factors influence its onset and progression. Elevation in serum anti-retinal autoantibodies, plasma and local activation of complement proteins of the alternative pathway, and increase in secretion of proinflammatory cytokines have been seen over the course of disease. Genetic studies of AMD patients confirmed that genetic variants affecting the alternative complement pathway have a major influence on AMD risk. Because the heterogeneity of this disease, there is no sufficient strategy to identify the disease onset and progression sole based eye examination, thus identification of reliable serological biomarkers for diagnosis, prognosis and response to treatment by sampling patient's blood is necessary. This review provides an outline of the current knowledge on possible serological (autoantibodies, complement factors, cytokines, chemokines) and related genetic biomarkers relevant to the pathology of AMD, and discusses their application for prediction of disease activity and prognosis in AMD.