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International Journal of Molecular... Jul 2022C-reactive protein (CRP) is considered a biomarker of infection/inflammation. It is a commonly used tool for early detection of infection in the emergency room or as a... (Review)
Review
C-reactive protein (CRP) is considered a biomarker of infection/inflammation. It is a commonly used tool for early detection of infection in the emergency room or as a point-of-care test and especially for differentiating between bacterial and viral infections, affecting decisions of admission and initiation of antibiotic treatments. As C-reactive protein is part of a dynamic and continuous inflammatory process, a single CRP measurement, especially at low concentrations, may erroneously lead to a wrong classification of an infection as viral over bacterial and delay appropriate antibiotic treatment. In the present review, we introduce the concept of C-reactive protein dynamics, measuring the velocity of C-reactive protein elevation, as a tool to increase this biomarker's diagnostic ability. We review the studies that helped define new metrics such as estimated C-reactive protein velocity (velocity of C-reactive protein elevation from symptoms' onset to first C-reactive protein measurement) and the measured C-reactive protein velocity (velocity between sequential C-reactive protein measurements) and the use of these metrics in different clinical scenarios. We also discuss future research directions for this novel metric.
Topics: Anti-Bacterial Agents; Bacterial Infections; Biomarkers; C-Reactive Protein; Humans; Inflammation; Virus Diseases
PubMed: 35897672
DOI: 10.3390/ijms23158100 -
Pharmaceutical Research Feb 2006Despite major advances in modern drug discovery and development, the number of new drug approvals has not kept pace with the increased cost of their development....
Despite major advances in modern drug discovery and development, the number of new drug approvals has not kept pace with the increased cost of their development. Increasingly, innovative uses of biomarkers are employed in an attempt to speed new drugs to market. Still, widespread adoption of biomarkers is impeded by limited experience interpreting biomarker data and an unclear regulatory climate. Key differences preclude the direct application of existing validation paradigms for drug analysis to biomarker research. Following the AAPS 2003 Biomarker Workshop (J. W. Lee, R. S. Weiner, J. M. Sailstad, et al. Method validation and measurement of biomarkers in nonclinical and clinical samples in drug development. A conference report. Pharm Res 22:499-511, 2005), these and other critical issues were addressed. A practical, iterative, "fit-for-purpose" approach to biomarker method development and validation is proposed, keeping in mind the intended use of the data and the attendant regulatory requirements associated with that use. Sample analysis within this context of fit-for-purpose method development and validation are well suited for successful biomarker implementation, allowing increased use of biomarkers in drug development.
Topics: Biomarkers; Calibration; Data Interpretation, Statistical; Drug Design; Models, Statistical; Quality Control; Reproducibility of Results; Terminology as Topic
PubMed: 16397743
DOI: 10.1007/s11095-005-9045-3 -
Bioanalysis Jun 2018
Topics: Biomarkers; Chemistry Techniques, Analytical; Humans; Reproducibility of Results
PubMed: 29939799
DOI: 10.4155/bio-2018-0127 -
Chinese Clinical Oncology Sep 2015Treatment of brain tumors is increasingly informed by biomarkers. One use is to appropriately group tumors with similar genetic/genomic characteristics and to design... (Review)
Review
Treatment of brain tumors is increasingly informed by biomarkers. One use is to appropriately group tumors with similar genetic/genomic characteristics and to design trials separately for the individual groups. The biomarker's use is to predict patient response so that the most effective treatment can be selected for patients based on their tumor characteristics. Trial designs that recruit unselected patients are poorly suited for identifying treatments effective only in subsets of patients given the relatively small numbers of patients available for trials. Investigators are beginning to use different designs that better account for tumor heterogeneity. In this article, an overview of the role of biomarkers in brain tumor trials is presented in the context of existing clinical trials as well as trials that may be launched within the next several years.
Topics: Biomarkers, Tumor; Brain Neoplasms; Clinical Trials as Topic; History, 20th Century; History, 21st Century; Humans; Research Design
PubMed: 26408305
DOI: 10.3978/j.issn.2304-3865.2015.09.04 -
Briefings in Bioinformatics May 2019Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated... (Review)
Review
Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.
Topics: Biomarkers; Computer Simulation; Humans; Models, Biological; Precision Medicine; Systems Biology
PubMed: 29194464
DOI: 10.1093/bib/bbx158 -
The AAPS Journal May 2022Decades of discussion and publication have gone into the guidance from the scientific community and the regulatory agencies on the use and validation of pharmacokinetic...
Decades of discussion and publication have gone into the guidance from the scientific community and the regulatory agencies on the use and validation of pharmacokinetic and toxicokinetic assays by chromatographic and ligand binding assays for the measurement of drugs and metabolites. These assay validations are well described in the FDA Guidance on Bioanalytical Methods Validation (BMV, 2018). While the BMV included biomarker assay validation, the focus was on understanding the challenges posed in validating biomarker assays and the importance of having reliable biomarker assays when used for regulatory submissions, rather than definition of the appropriate experiments to be performed. Different from PK bioanalysis, analysis of biomarkers can be challenging due to the presence of target analyte(s) in the control matrices used for calibrator and quality control sample preparation, and greater difficulty in procuring appropriate reference standards representative of the endogenous molecule. Several papers have been published offering recommendations for biomarker assay validation. The situational nature of biomarker applications necessitates fit-for-purpose (FFP) assay validation. A unifying theme for FFP analysis is that method validation requirements be consistent with the proposed context of use (COU) for any given biomarker. This communication provides specific recommendations for biomarker assay validation (BAV) by LC-MS, for both small and large molecule biomarkers. The consensus recommendations include creation of a validation plan that contains definition of the COU of the assay, use of the PK assay validation elements that support the COU, and definition of assay validation elements adapted to fit biomarker assays and the acceptance criteria for both.
Topics: Biological Assay; Biomarkers; Chromatography, Liquid; Mass Spectrometry; Reference Standards
PubMed: 35534647
DOI: 10.1208/s12248-022-00707-z -
Seminars in Nephrology Mar 2018Technological advances in mass spectrometry-based lipidomic platforms have provided the opportunity for comprehensive profiling of lipids in biological samples and shown... (Review)
Review
Technological advances in mass spectrometry-based lipidomic platforms have provided the opportunity for comprehensive profiling of lipids in biological samples and shown alterations in the lipidome that occur in metabolic disorders. A lipidomic approach serves as a powerful tool for biomarker discovery and gaining insight to molecular mechanisms of disease, especially when integrated with other -omics platforms (ie, transcriptomics, proteomics, and metabolomics) in the context of systems biology. In this review, we describe the workflow commonly applied to the conduct of lipidomic studies including important aspects of study design, sample preparation, biomarker identification and quantification, and data processing and analysis, as well as crucial considerations in clinical applications. We also review some recent studies of the application of lipidomic platforms that highlight the potential of lipid biomarkers and add to our understanding of the molecular basis of kidney disease.
Topics: Big Data; Biomarkers; Electronic Data Processing; Humans; Kidney Diseases; Lipid Metabolism; Lipids; Quality Control; Statistics as Topic; Workflow
PubMed: 29602396
DOI: 10.1016/j.semnephrol.2018.01.004 -
Investigative and Clinical Urology Feb 2020A disease-specific biomarker (or biomarkers) is a characteristic reflecting a pathological condition in human body, which can be used as a diagnostic or prognostic tool... (Review)
Review
A disease-specific biomarker (or biomarkers) is a characteristic reflecting a pathological condition in human body, which can be used as a diagnostic or prognostic tool for the clinical management. A urine-based biomarker(s) may provide a clinical value as attractive tools for clinicians to utilize in the clinical setting in particular to bladder diseases including bladder cancer and other bladder benign dysfunctions. Urine can be easily obtained by patients with no preparation or painful procedures required from patients' side. Currently advanced omics technologies and computational power identified potential omics-based novel biomarkers. An unbiased profiling based on transcriptomics, proteomics, epigenetics, metabolomics approaches et al. found that expression at RNA, protein, and metabolite levels are linked with specific bladder diseases and outcomes. In this review, we will discuss about the urine-based biomarkers reported by many investigators including us and how these biomarkers can be applied as a diagnostic and prognostic tool in clinical trials and patient care to promote bladder health. Furthermore, we will discuss how these promising biomarkers can be developed into a smart medical device and what we should be cautious about toward being used in real clinical setting.
Topics: Biomarkers; Biomedical Research; Humans; Urinary Bladder Diseases; Urology
PubMed: 32055750
DOI: 10.4111/icu.2020.61.S1.S8 -
Current Protocols in Pharmacology Mar 2017A biomarker is a biological observation that substitutes for and ideally predicts a clinically relevant endpoint or intermediate outcome that is more difficult to... (Review)
Review
A biomarker is a biological observation that substitutes for and ideally predicts a clinically relevant endpoint or intermediate outcome that is more difficult to observe. The use of clinical biomarkers is easier and less expensive than direct measurement of the final clinical endpoint, and biomarkers are usually measured over a shorter time span. They can be used in disease screening, diagnosis, characterization, and monitoring; as prognostic indicators; for developing individualized therapeutic interventions; for predicting and treating adverse drug reactions; for identifying cell types; and for pharmacodynamic and dose-response studies. To understand the value of a biomarker, it is necessary to know the pathophysiological relationship between the biomarker and the relevant clinical endpoint. Good biomarkers should be measurable with little or no variability, should have a sizeable signal to noise ratio, and should change promptly and reliably in response to changes in the condition or its therapy. © 2017 by John Wiley & Sons, Inc.
Topics: Biomarkers; Biomarkers, Pharmacological; Diagnosis; Dose-Response Relationship, Drug; Drug Discovery; Drug-Related Side Effects and Adverse Reactions; Humans; Pharmacokinetics; Sensitivity and Specificity; Signal-To-Noise Ratio; Terminology as Topic
PubMed: 28306150
DOI: 10.1002/cpph.19 -
Journal of Pharmacy & Pharmaceutical... 2022Periostin is a matricellular, nonstructural protein belonging to the fasciclin family and is encoded by the POSTN gene in humans. Periostin plays an important role in... (Review)
Review
Periostin is a matricellular, nonstructural protein belonging to the fasciclin family and is encoded by the POSTN gene in humans. Periostin plays an important role in maintaining a normal tissue matrix in the lungs. Despite the vital role as a structural mediator in tissue growth and repair, periostin is involved in the pathogenic mechanism during tissue remodeling and fibrosis. Periostin is a chemoattractant mediator, promotes eosinophil recruitment and adhesion on the airways sub-epithelial membrane of asthmatic patients. POSTN gene was identified as one of the highly expressed genes induced by interleukins IL-13, IL-5 and IL-4 - the key cytokines of Th2 immune responses in the bronchial tissues of asthmatic patients. This review highlights the potential role of periostin as a validated biomarker in respiratory disease progression and its candidacy to predict the response to treatments targeting Th-2 cytokines in bronchial asthma. In addition, its potential role in COPD, IPF, lung cancer and lung infection, is also speculated. Keywords Periostin, Asthma, Pneumonia, COPD, Idiopathic pulmonary fibrosis, Biomarker.
Topics: Biomarkers; Fibrosis; Humans; Lung Diseases
PubMed: 35379385
DOI: 10.18433/jpps32306