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Mitterhauser, Markus, and Wolfgang Wadsak. "Imaging Biomarkers or Biomarker Imaging?" Pharmaceuticals 7, no. 7 (June 25, 2014): 765–78. http://dx.doi.org/10.3390/ph7070765.
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Zhao, Xuemei, Vijay Modur, Leonidas N. Carayannopoulos, and Omar F. Laterza. "Biomarkers in Pharmaceutical Research." Clinical Chemistry 61, no. 11 (November 1, 2015): 1343–53. http://dx.doi.org/10.1373/clinchem.2014.231712.
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Abstract BACKGROUND Biomarkers are important tools in drug development and are used throughout pharmaceutical research. CONTENT This review focuses on molecular biomarkers in drug development. It contains sections on how biomarkers are used to assess target engagement, pharmacodynamics, safety, and proof-of-concept. It also covers the use of biomarkers as surrogate end points and patient selection/companion diagnostics and provides insights into clinical biomarker discovery and biomarker development/validation with regulatory implications. To survey biomarkers used in drug development—acknowledging that many pharmaceutical development biomarkers are not published—we performed a focused PubMed search employing “biomarker” and the names of the largest pharmaceutical companies as keywords and filtering on clinical trials and publications in the last 10 years. This yielded almost 500 entries, the majority of which included disease-related (approximately 60%) or prognostic/predictive (approximately 20%) biomarkers. A notable portion (approximately 8%) included HER2 (human epidermal growth factor receptor 2) testing, highlighting the utility of biomarkers for patient selection. The remaining publications included target engagement, safety, and drug metabolism biomarkers. Oncology, cardiovascular disease, and osteoporosis were the areas with the most citations, followed by diabetes and Alzheimer disease. SUMMARY Judicious biomarker use can improve pharmaceutical development efficiency by helping to select patients most appropriate for treatment using a given mechanism, optimize dose selection, and provide earlier confidence in accelerating or discontinuing compounds in clinical development. Optimal application of biomarker technology requires understanding of candidate drug pharmacology, detailed modeling of biomarker readouts relative to pharmacokinetics, rigorous validation and qualification of biomarker assays, and creative application of these elements to drug development problems.
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Grandjean, P. "Biomarkers in epidemiology." Clinical Chemistry 41, no. 12 (December 1, 1995): 1800–1803. http://dx.doi.org/10.1093/clinchem/41.12.1800.
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Abstract A biomarker is a measurable event occurring in a biological system, such as the human body. In environmental epidemiology, a biomarker represents a subclinical and reversible change; it is not a diagnostic test, but an indicator that an early change has occurred that could later lead to clinical disease. Although some biomarkers may belong to more than one class, they are often separated into biomarkers of exposure, biomarkers of effect, and biomarkers of susceptibility. Biomarkers can be used to classify and quantify environmental exposures and their related effects, and many methods may be applicable in toxicological experiments as well as in epidemiology. Accordingly, biomarker epidemiology is undergoing rapid development and expansion and is becoming one of the most promising areas of environmental research. Although expanded applications should be encouraged, many biomarkers are poorly characterized, and attention should be paid to defining their properties in detail.
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Wishart, David S., Brendan Bartok, Eponine Oler, Kevin Y. H. Liang, Zachary Budinski, Mark Berjanskii, AnChi Guo, Xuan Cao, and Michael Wilson. "MarkerDB: an online database of molecular biomarkers." Nucleic Acids Research 49, no. D1 (November 27, 2020): D1259—D1267. http://dx.doi.org/10.1093/nar/gkaa1067.
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Abstract MarkerDB is a freely available electronic database that attempts to consolidate information on all known clinical and a selected set of pre-clinical molecular biomarkers into a single resource. The database includes four major types of molecular biomarkers (chemical, protein, DNA [genetic] and karyotypic) and four biomarker categories (diagnostic, predictive, prognostic and exposure). MarkerDB provides information such as: biomarker names and synonyms, associated conditions or pathologies, detailed disease descriptions, detailed biomarker descriptions, biomarker specificity, sensitivity and ROC curves, standard reference values (for protein and chemical markers), variants (for SNP or genetic markers), sequence information (for genetic and protein markers), molecular structures (for protein and chemical markers), tissue or biofluid sources (for protein and chemical markers), chromosomal location and structure (for genetic and karyotype markers), clinical approval status and relevant literature references. Users can browse the data by conditions, condition categories, biomarker types, biomarker categories or search by sequence similarity through the advanced search function. Currently, the database contains 142 protein biomarkers, 1089 chemical biomarkers, 154 karyotype biomarkers and 26 374 genetic markers. These are categorized into 25 560 diagnostic biomarkers, 102 prognostic biomarkers, 265 exposure biomarkers and 6746 predictive biomarkers or biomarker panels. Collectively, these markers can be used to detect, monitor or predict 670 specific human conditions which are grouped into 27 broad condition categories. MarkerDB is available at https://markerdb.ca.
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De Jesus, J. R., and Marco Arruda. "Human disease biomarkers: challenges, advances, and trends in their validation." Journal of Integrated OMICS 11, no. 2 (December 29, 2021): 16–28. http://dx.doi.org/10.5584/jiomics.v11i2.207.
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Biomarkers are important tools in the medical field, once they allow better prediction, characterization, and treatment of diseases. In this scenario, it is essential that biomarkers are highly accurate. Thus, biomarker validation is an essential part of ensuring the effectiveness of a biomarker. Validation of biomarkers is the process by which biomarkers are evaluated for accuracy and consistency, as well as their ability to inform the condition of health or disease. Although, there is no unique measure that can be used to determine the validity for all biomarkers, there are general criteria that all biomarkers must meet to be useful. In this work, we review the definition of biomarkers and discuss the validity components. We then critically discuss the main methods used to validate biomarkers and consider some examples of biomarkers of the diseases which most killer in the world (cardiovascular diseases, cancer, and viral infections), highlighting the potential biochemical pathways of these biomarkers in the biological system. In addition, we also comment on the omic strategies used in the biomarker discovery process and conclude with information about perspectives in biomarker validation through imaging techniques.
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Dean, Brian. "Dissecting the Syndrome of Schizophrenia: Progress toward Clinically Useful Biomarkers." Schizophrenia Research and Treatment 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/614730.
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The search for clinically useful biomarkers has been one of the holy grails of schizophrenia research. This paper will outline the evolving notion of biomarkers and then outline outcomes from a variety of biomarkers discovery strategies. In particular, the impact of high-throughput screening technologies on biomarker discovery will be highlighted and how new or improved technologies may allow the discovery of either diagnostic biomarkers for schizophrenia or biomarkers that will be useful in determining appropriate treatments for people with the disorder. History tells those involved in biomarker research that the discovery and validation of useful biomarkers is a long process and current progress must always be viewed in that light. However, the approval of the first biomarker screen with some value in predicting responsiveness to antipsychotic drugs suggests that biomarkers can be identified and that these biomarkers that will be useful in diagnosing and treating people with schizophrenia.
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Weickert, Cynthia S., Thomas W. Weickert, Anil Pillai, and Peter F. Buckley. "Biomarkers in Schizophrenia: A Brief Conceptual Consideration." Disease Markers 35 (2013): 3–9. http://dx.doi.org/10.1155/2013/510402.
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Biomarkers have been sought after in the field of schizophrenia research for decades. In this paper, we discuss some of the concepts around developing biomarkers in an effort to understand why the use of biomarkers for schizophrenia has not been realized. In particular, we address the following 4 questions. Why would we need a diagnostic biomarker for schizophrenia? How is a biomarker typically defined and how does that influence the discovery of biomarkers in schizophrenia? What is the best use of biomarkers in schizophrenia? Do any biomarkers for schizophrenia currently exist? Thus, while we suggest that no biomarker currently exists for schizophrenia, the heterogeneity associated with schizophrenia will most likely need to be taken into account which will result in multiple biomarkers that identify the multiple underlying pathophysiological processes involved in schizophrenia. Therefore, much additional work will be required prior to obtaining any well-established biomarkers for schizophrenia.
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Califf, Robert M. "Biomarker definitions and their applications." Experimental Biology and Medicine 243, no. 3 (February 2018): 213–21. http://dx.doi.org/10.1177/1535370217750088.
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Biomarkers are critical to the rational development of medical therapeutics, but significant confusion persists regarding fundamental definitions and concepts involved in their use in research and clinical practice, particularly in the fields of chronic disease and nutrition. Clarification of the definitions of different biomarkers and a better understanding of their appropriate application could result in substantial benefits. This review examines biomarker definitions recently established by the U.S. Food and Drug Administration and the National Institutes of Health as part of their joint Biomarkers, EndpointS, and other Tools (BEST) resource. These definitions are placed in context of their respective uses in patient care, clinical research, or therapeutic development. We explore the distinctions between biomarkers and clinical outcome assessments and discuss the specific definitions and applications of diagnostic, monitoring, pharmacodynamic/response, predictive, prognostic, safety, and susceptibility/risk biomarkers. We also explore the implications of current biomarker development trends, including complex composite biomarkers and digital biomarkers derived from sensors and mobile technologies. Finally, we discuss the challenges and potential benefits of biomarker-driven predictive toxicology and systems pharmacology, the need to ensure quality and reproducibility of the science underlying biomarker development, and the importance of fostering collaboration across the entire ecosystem of medical product development. Impact statement Biomarkers are critical to the rational development of medical diagnostics and therapeutics, but significant confusion persists regarding fundamental definitions and concepts involved in their use in research and clinical practice. Clarification of the definitions of different biomarker classes and a better understanding of their appropriate application could yield substantial benefits. Biomarker definitions recently established in a joint FDA-NIH resource place different classes of biomarkers in the context of their respective uses in patient care, clinical research, or therapeutic development. Complex composite biomarkers and digital biomarkers derived from sensors and mobile technologies, together with biomarker-driven predictive toxicology and systems pharmacology, are reshaping development of diagnostic and therapeutic technologies. An approach to biomarker development that prioritizes the quality and reproducibility of the science underlying biomarker development and incorporates collaborative regulatory science involving multiple disciplines will lead to rational, evidence-based biomarker development that keeps pace with scientific and clinical need.
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Birkeland, Marian L., and Joan S. McClure. "Optimizing the Clinical Utility of Biomarkers in Oncology: The NCCN Biomarkers Compendium." Archives of Pathology & Laboratory Medicine 139, no. 5 (October 21, 2014): 608–11. http://dx.doi.org/10.5858/arpa.2014-0146-ra.
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Context The rapid development of commercial biomarker tests for oncology indications has led to confusion about which tests are clinically indicated for oncology care. By consolidating biomarker testing information recommended within National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology (NCCN Guidelines), the NCCN Biomarkers Compendium aims to ensure that patients have access to appropriate biomarker testing based on the evaluations and recommendations of the expert NCCN panel members. Objectives To present the recently launched NCCN Biomarkers Compendium. Data Sources Biomarker testing information recommended within NCCN Clinical Treatment Guidelines as well as published resources for genetic and biological information. Conclusions The NCCN Biomarkers Compendium is a continuously updated resource for clinicians who need access to relevant and succinct information about biomarker testing in oncology and is linked directly to the recommendations provided within the NCCN Clinical Practice Guidelines.
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Prentice, Ross L. "Criteria for Acceptable Dietary Intake Biomarkers." Cancer Epidemiology, Biomarkers & Prevention 31, no. 6 (June 1, 2022): 1151–53. http://dx.doi.org/10.1158/1055-9965.epi-22-0180.
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Abstract Dietary intake biomarkers that can be written as actual intake, plus ‘error’ that is independent of actual intake and confounding factors can substitute for actual intake in disease association analyses. Also, such biomarkers can be used to develop calibration equations using self-reported diet and participant measures, and biomarker-calibrated intakes can be calculated in larger cohorts for use in disease association analyses. Criteria for biomarkers, and for biomarker-calibrated intakes, arise by working back from properties needed for valid disease association analyses. Accordingly, arguments for a potential biomarker are strengthened if error components are small relative to actual intakes, and important sources of reduced sensitivity or specificity are not apparent. Feeding study biomarker development can then involve regression of actual intake on putative biomarkers, with regression R2 values playing a role in biomarker evaluation. In comparison, ‘predictive’ biomarker status, as argued in this issue by Freedman and colleagues for 24-hour urinary sucrose plus fructose as biomarker for total sugars, involves regression of potential biomarker on actual intake and other variables, with parameter stability across populations and limited within-person variability as criteria. The choice of criteria for biomarkers and for biomarker-calibrated intakes, is discussed here, in the context of total sugars intake. See related article by Freedman et al., p. 1227
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Mankhong, Sakulrat, Sujin Kim, Seongju Lee, Hyo-Bum Kwak, Dong-Ho Park, Kyung-Lim Joa, and Ju-Hee Kang. "Development of Alzheimer’s Disease Biomarkers: From CSF- to Blood-Based Biomarkers." Biomedicines 10, no. 4 (April 5, 2022): 850. http://dx.doi.org/10.3390/biomedicines10040850.
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In the 115 years since the discovery of Alzheimer’s disease (AD), our knowledge, diagnosis, and therapeutics have significantly improved. Biomarkers are the primary tools for clinical research, diagnostics, and therapeutic monitoring in clinical trials. They provide much insightful information, and while they are not clinically used routinely, they help us to understand the mechanisms of this disease. This review charts the journey of AD biomarker discovery and development from cerebrospinal fluid (CSF) amyloid-beta 1-42 (Aβ42), total tau (T-tau), and phosphorylated tau (p-tau) biomarkers and imaging technologies to the next generation of biomarkers. We also discuss advanced high-sensitivity assay platforms for CSF Aβ42, T-tau, p-tau, and blood analysis. The recently proposed Aβ deposition/tau biomarker/neurodegeneration or neuronal injury (ATN) scheme might facilitate the definition of the biological status underpinning AD and offer a common language among researchers across biochemical biomarkers and imaging. Moreover, we highlight blood-based biomarkers for AD that offer a scalable alternative to CSF biomarkers through cost-saving and reduced invasiveness, and may provide an understanding of disease initiation and development. We discuss different groups of blood-based biomarker candidates, their advantages and limitations, and paths forward, from identification and analysis to clinical validation. The development of valid blood-based biomarkers may facilitate the implementation of future AD therapeutics and diagnostics.
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Irani-Shemirani, Mahnaz. "Biomarkers approach in the diagnosis and prognosis of sepsis: A review." International Journal of Public Health Research 12, no. 02 (September 1, 2022): 1617–24. http://dx.doi.org/10.17576/ijphr.1202.2022.04.
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Introduction Sepsis is a systematic host response to infection accompanied by suppression of immune system and organ failure. Rapid diagnosis of sepsis has prompted researchers to use circulating biomarkers for diagnosis sepsis. Method The PubMed, and Google Scholar databases were searched from 2010 until 2020 using the keyorganis “sepsis”, “biomarker”, “pathophysiology”, “pathogenesis”, and “diagnose” with restriction by language to English. For each individual biomarker the databases were searched again by the biomarker name. Results Although CRP and Procalcitonin are the most common biomarkers in the diagnosis of sepsis, other biomarkers such as pro-inflammatory and anti-inflammatory cytokines and chemokines, monocyte and lymphocyte biomarkers, antibody and nucleic acid biomarkers may help in diagnosis of sepsis. Conclusion The biomarkers verified capability in disease progress, prognosis of disorder, risk stratification, and treatment effect rather than diagnosis at early stage of sepsis. Defining molecular properties in septic patients opens up new means to diagnose and manage sepsis in a shorter time compared to conventional methods currently used at hospitals however further clinical evaluation of biomarkers should be performed. Keywords. Sepsis. Biomarkers. Prognosis. Diagnosis
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Gluhovschi, Cristina, Gheorghe Gluhovschi, Ligia Petrica, Romulus Timar, Silvia Velciov, Ioana Ionita, Adriana Kaycsa, and Bogdan Timar. "Urinary Biomarkers in the Assessment of Early Diabetic Nephropathy." Journal of Diabetes Research 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/4626125.
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Diabetic nephropathy (DN) is a frequent and severe complication of diabetes mellitus (DM). Its diagnosis in incipient stages may allow prompt interventions and an improved prognosis. Towards this aim, biomarkers for detecting early DN can be used. Microalbuminuria has been proven a remarkably useful biomarker, being used for diagnosis of DN, for assessing its associated condition—mainly cardiovascular ones—and for monitoring its progression. New researches are pointing that some of these biomarkers (i.e., glomerular, tubular, inflammation markers, and biomarkers of oxidative stress) precede albuminuria in some patients. However, their usefulness is widely debated in the literature and has not yet led to the validation of a new “gold standard” biomarker for the early diagnosis of DN. Currently, microalbuminuria is an important biomarker for both glomerular and tubular injury. Other glomerular biomarkers (transferrin and ceruloplasmin) are under evaluation. Tubular biomarkers in DN seem to be of a paramount importance in the early diagnosis of DN since tubular lesions occur early. Additionally, biomarkers of inflammation, oxidative stress, podocyte biomarkers, and vascular biomarkers have been employed for assessing early DN. The purpose of this review is to provide an overview of the current biomarkers used for the diagnosis of early DN.
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Cummings, Jeffrey, and Jefferson Kinney. "Biomarkers for Alzheimer’s Disease: Context of Use, Qualification, and Roadmap for Clinical Implementation." Medicina 58, no. 7 (July 19, 2022): 952. http://dx.doi.org/10.3390/medicina58070952.
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Background and Objectives: The US Food and Drug Administration (FDA) defines a biomarker as a characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention. Biomarkers may be used in clinical care or as drug development tools (DDTs) in clinical trials. The goal of this review and perspective is to provide insight into the regulatory guidance for the use of biomarkers in clinical trials and clinical care. Materials and Methods: We reviewed FDA guidances relevant to biomarker use in clinical trials and their transition to use in clinical care. We identified instructive examples of these biomarkers in Alzheimer’s disease (AD) drug development and their application in clinical practice. Results: For use in clinical trials, biomarkers must have a defined context of use (COU) as a risk/susceptibility, diagnostic, monitoring, predictive, prognostic, pharmacodynamic, or safety biomarker. A four-stage process defines the pathway to establish the regulatory acceptance of the COU for a biomarker including submission of a letter of intent, description of the qualification plan, submission of a full qualification package, and acceptance through a qualification recommendation. Biomarkers used in clinical care may be companion biomarkers, in vitro diagnostic devices (IVDs), or laboratory developed tests (LDTs). A five-phase biomarker development process has been proposed to structure the biomarker development process. Conclusions: Biomarkers are increasingly important in drug development and clinical care. Adherence to regulatory guidance for biomarkers used in clinical trials and patient care is required to advance these important drug development and clinical tools.
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Savage, William J., and Allen D. Everett. "Biomarkers in pediatrics: Children as biomarker orphans." PROTEOMICS - Clinical Applications 4, no. 12 (November 29, 2010): 915–21. http://dx.doi.org/10.1002/prca.201000062.
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Kirkwood, Sandra C., and Richard D. Hockett. "Pharmacogenomic Biomarkers." Disease Markers 18, no. 2 (2002): 63–71. http://dx.doi.org/10.1155/2002/341708.
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Pharmacogenomic biomarkers hold great promise for the future of medicine and have been touted as a means to personalize prescriptions. Genetic biomarkers for disease susceptibility including both Mendelian and complex disease promise to result in improved understanding of the pathophysiology of disease, identification of new potential therapeutic targets, and improved molecular classification of disease. However essential to fulfilling the promise of individualized therapeutic intervention is the identification of drug activity biomarkers that stratify individuals based on likely response to a particular therapeutic, both positive response, efficacy, and negative response, development of side effect or toxicity. Prior to the widespread clinical application of a genetic biomarker multiple scientific studies must be completed to identify the genetic variants and delineate their functional significance in the pathophysiology of a carefully defined phenotype. The applicability of the genetic biomarker in the human population must then be verified through both retrospective studies utilizing stored or clinical trial samples, and through clinical trials prospectively stratifying patients based on the biomarker. The risk conferred by the polymorphism and the applicability in the general population must be clearly understood. Thus, the development and widespread application of a pharmacogenomic biomarker is an involved process and for most disease states we are just at the beginning of the journey towards individualized therapy and improved clinical outcome.
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Salas, Maribel, Maxine Gossell-Williams, Priyanka Yalamanchili, Sameer Dhingra, Marina A. Malikova, Omar Aimer, and Toluwalope Junaid. "The Use of Biomarkers in Pharmacovigilance: A Systematic Review of the Literature." Biomarker Insights 18 (January 2023): 117727192311645. http://dx.doi.org/10.1177/11772719231164528.
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Background: The use of biomarkers varies from disease etiognosis and diagnosis to signal detection, risk prediction, and management. Biomarker use has expanded in recent years, however, there are limited reviews on the use of biomarkers in pharmacovigilance and specifically in the monitoring and management of adverse drug reactions (ADRs). Objective: The objective of this manuscript is to identify the multiple uses of biomarkers in pharmacovigilance irrespective of the therapeutic area. Design: This is a systematic review of the literature. Data Sources and Methods: Embase and MEDLINE database searches were conducted for literature published between 2010-March 19, 2021. Scientific articles that described the potential use of biomarkers in pharmacovigilance in sufficient detail were reviewed. Papers that did not fulfill the United States Food and Drug Administration (US FDA) definition of a biomarker were excluded, which is based on the International Conference on Harmonisation (ICH)−E16 guidance. Results: Twenty-seven articles were identified for evaluation. Most articles involved predictive biomarkers (41%), followed by safety biomarkers (38%), pharmacodynamic/response biomarkers (14%), and diagnostic biomarkers (7%). Some articles described biomarkers that applied to multiple categories. Conclusion: Various categories of biomarkers including safety, predictive, pharmacodynamic/response, and diagnostic biomarkers are being investigated for potential use in pharmacovigilance. The most frequent potential uses of biomarkers in pharmacovigilance in the literature were the prediction of the severity of an ADR, mortality, response, safety, and toxicity. The safety biomarkers identified were used to evaluate patient safety during dose escalation, identify patients who may benefit from further biomarker testing during treatment, and monitor ADRs.
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Gromova, Mariya, Annegret Vaggelas, Gabriele Dallmann, and Diane Seimetz. "Biomarkers: Opportunities and Challenges for Drug Development in the Current Regulatory Landscape." Biomarker Insights 15 (January 2020): 117727192097465. http://dx.doi.org/10.1177/1177271920974652.
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Biomarkers are widely used at every stage of drug discovery and development. Utilisation of biomarkers has a potential to make drug discovery, development and approval processes more efficient. An overview of the current global regulatory landscape is presented in this article with particular emphasis on the validation and qualification of biomarkers, as well as legal framework for companion diagnostics. Furthermore, this article shows how the number of approved drugs with at least 1 biomarker used during development (biomarker acceptance) is affected by the recent advances in the biomarker regulations. More than half of analysed approvals were supported by biomarker data and there has been a slight increase in acceptance of biomarkers in recent years, even though the growth is not continuous. For certain pharmacotherapeutic groups, approvals with biomarkers are more common than without. Examples include immunosuppressants, immunostimulants, drugs used in diabetes, antithrombotic drugs, antineoplastic agents and antivirals. As a conclusion, potential benefits, challenges and opportunities of using biomarkers in drug discovery and development in the current regulatory landscape are summarised and discussed.
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Nie, Zhaobo, Fa Lin, Runting Li, Xiaolin Chen, and Yuanli Zhao. "A Pooled Analysis of Preoperative Inflammatory Biomarkers to Predict 90-Day Outcomes in Patients with an Aneurysmal Subarachnoid Hemorrhage: A Single-Center Retrospective Study." Brain Sciences 13, no. 2 (February 2, 2023): 257. http://dx.doi.org/10.3390/brainsci13020257.
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An inflammatory response after an aneurysmal subarachnoid hemorrhage (aSAH) has always been in the spotlight. However, few studies have compared the prognostic impact of inflammatory biomarkers. Moreover, why these inflammatory biomarkers contribute to a poor prognosis is also unclear. We retrospectively reviewed aSAH patients admitted to our institution between January 2015 and December 2020. The 90-day unfavorable functional outcome was defined as a modified Rankin scale (mRS) of ≥ 3. Independent inflammatory biomarker-related risk factors associated with 90-day unfavorable outcomes were derived from a forward stepwise multivariate analysis. Receiver operating characteristic curve analysis was conducted to identify the best cut-off value of inflammatory biomarkers. Then, patients were divided into two groups according to each biomarker’s cut-off value. To eliminate the imbalances in baseline characteristics, propensity score matching (PSM) was carried out to assess the impact of each biomarker on in-hospital complications. A total of 543 patients were enrolled in this study and 96 (17.7%) patients had unfavorable 90-day outcomes. A multivariate analysis showed that the white blood cell (WBC) count, the systemic inflammation response index, the neutrophil count, the neutrophil-to-albumin ratio, the monocyte count, and the monocyte-to-lymphocyte ratio were independently associated with 90-day unfavorable outcomes. The WBC count showed the best predictive ability (area under the curve (AUC) = 0.710, 95% CI = 0.652–0.769, p < 0.001). After PSM, almost all abnormal levels of inflammatory biomarkers were associated with a higher incidence of pneumonia during hospitalization. The WBC count had the strongest association with poor outcomes. Similar to nearly all other inflammatory biomarkers, the cause of poor prognosis may be the higher incidence of in-hospital pneumonia.
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Pavlou, Maria P., Eleftherios P. Diamandis, and Ivan M. Blasutig. "The Long Journey of Cancer Biomarkers from the Bench to the Clinic." Clinical Chemistry 59, no. 1 (January 1, 2013): 147–57. http://dx.doi.org/10.1373/clinchem.2012.184614.
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BACKGROUND Protein cancer biomarkers serve multiple clinical purposes, both early and late, during disease progression. The search for new and better biomarkers has become an integral component of contemporary cancer research. However, the number of new biomarkers cleared by the US Food and Drug Administration has declined substantially over the last 10 years, raising concerns regarding the efficiency of the biomarker-development pipeline. CONTENT We describe different clinical uses of cancer biomarkers and their performance requirements. We also present examples of protein cancer biomarkers currently in clinical use and their limitations. The major barriers that candidate biomarkers need to overcome to reach the clinic are addressed. Finally, the long and arduous journey of a protein cancer biomarker from the bench to the clinic is outlined with an example. SUMMARY The journey of a protein biomarker from the bench to the clinic is long and challenging. Every step needs to be meticulously planned and executed to succeed. The history of clinically useful biomarkers suggests that at least a decade is required for the transition of a marker from the bench to the bedside. Therefore, it may be too early to expect that the new technological advances will catalyze the anticipated biomarker revolution any time soon.
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Barboriak, Daniel, Jon Steingrimsson, Constantine Gatsonis, David Schiff, and Lawrence Kleinberg. "CLRM-07. INCREASING EFFICIENCY IN EARLY PHASE MULTICENTER IMAGING BIOMARKER TRIALS: EMERGING STRATEGIES FROM THE GABLE (GLIOBLASTOMA ACCELERATED BIOMARKER LEARNING ENVIRONMENT) TRIAL." Neuro-Oncology Advances 3, Supplement_4 (September 21, 2021): iv2. http://dx.doi.org/10.1093/noajnl/vdab112.006.
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Abstract Validated biomarkers that more accurately predict prognosis and/or measure disease burden in patients with high-grade gliomas would help triage which treatment strategies are most promising for evaluation in Phase III multicenter trials. Multicenter trials to evaluate imaging biomarkers in this group face particular challenges; these trials have historically been slow to accrue and have not recently succeeded in validating new imaging biomarkers useful in treatment development. Due to variability in image acquisition protocols, scanner hardware, image analysis, and interpretive schemes, promising results obtained in single centers are poor predictors of success in the multicenter setting. Multicenter preliminary data to support further evaluation of imaging biomarkers is rarely available. The need for more efficient trial designs that bring multicenter data earlier into the process of biomarker development has become increasingly clear. In this presentation, the planning process within ECOG-ACRIN’s Brain Tumor Working Group for a platform multicenter trial called GABLE (Glioblastoma Accelerated Biomarker Learning Environment trial) designed to evaluate biomarkers for distinguishing pseudoprogression from true progression in patients with newly diagnosed GBM is described. In our planning process, it was determined that efficiencies can be gained from evaluating multiple biomarker types in parallel rather than serially; in the context of the proposed trial, not only conventional imaging biomarkers but plasma biomarkers and radiomic biomarkers can be evaluated simultaneously. Patient tolerance limits the feasibility of evaluating multiple non-standard-of-care imaging biomarkers in parallel. For this group of biomarkers, a “fast-switching” serial evaluation strategy using multiple interim analyses was developed to triage out biomarkers unlikely to succeed in identifying patient groups with clinically significant differences in median survival. For biomarker triage, an endpoint of event-free survival (events of either death or NANO progression) was proposed. Simulations were used to evaluate alpha and beta error using this evaluation strategy.
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Rubin, Daniel, Victoria Handy, Steven Gilmore, Aimee Ginsburg, Andrea Dickens, Josh Howell, and Shannon Hough. "Impact on biomarker documentation in community oncology by optimizing clinical decision support." Journal of Clinical Oncology 42, no. 16_suppl (June 1, 2024): 11182. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.11182.
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11182 Background: Timing of biomarker results is critical to informing the selection of appropriate precision medicine-based treatment options for patients. Prior studies have demonstrated that low rates of biomarker testing may be driven by poor documentation. Our clinical decision support (CDS) tool aims to provide community oncology clinicians with a clear picture of the ever-growing available treatments. Evaluation of CDS data revealed an excess of biomarker results documented as “unknown” thought to be related to results availability. Methods: During treatment selection in the CDS tool, providers answer various prompts related to the patient’s disease, staging, treatment intent, and biomarker results. Prompts are aligned with NCCN guidelines for the respective cancer type. Changes were implemented to ease providers’ documentation of biomarker results. For adjuvant (adjNSCLC) and metastatic NSCLC (mNSCLC), prompts were relocated to align with when biomarker results were likely to be available. For metastatic castrate resistant prostate cancer (mCRPC), some biomarkers are only needed after a patient has received prior docetaxel and hormone therapy, thus prompts were moved to that scenario. Re-prompting of biomarker status for subsequent therapy was also implemented if the initial response was “unknown.” We report changes in percentage of known biomarkers specifically in adjNSCLC, mNSCLC, and mCRPC. Descriptive statistics were used to describe the rates of known biomarkers, and a chi-square test was used to analyze differences from pre and post changes. Results: In this evaluation, 85,493 biomarkers were documented. In adjNSCLC and mNSCLC, biomarker prompt changes increased documentation of known biomarkers from 69% to 78% and 59% to 79% pre- to post-implementation. In adjNSCLC, ALK and PD-L1 recording increased by ≥10% (P<0.00001). For mNSCLC, documentation increased for all known biomarkers (P<0.00001), including ≥15% increases for 8 of 10 biomarkers. In mCRPC, known biomarker documentation improved from 34% pre-implementation to 61% post-implementation. BRCA1/2, MSI, MMR, TMB, and PSMA biomarker recording increased by ≥20% (P<0.00001) after prompt changes. Selected biomarkers are shown in the table. Conclusions: Adjusting prompts in a CDS tool to align with the availability of biomarkers in clinical practice increased the documentation of known biomarker results for adjNSCLC, mNSCLC, and mCRPC. Based on these findings, similar changes are being implemented for other cancer types in the CDS tool. [Table: see text]
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Pastare, Daina, Mohamed Ridha Bennour, Elīna Polunosika, and Guntis Karelis. "Biomarkers of Multiple Sclerosis." Open Immunology Journal 9, no. 1 (December 20, 2019): 1–13. http://dx.doi.org/10.2174/1874226201909010001.
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The search for an ideal multiple sclerosis biomarker with good diagnostic value, prognostic reference and an impact on clinical outcome has yet to be realized and is still ongoing. The aim of this review is to establish an overview of the frequent biomarkers for multiple sclerosis that exist to date. The review summarizes the results obtained from electronic databases, as well as thorough manual searches. In this review the sources and methods of biomarkers extraction are described; in addition to the description of each biomarker, determination of the prognostic, diagnostic, disease monitoring and treatment response values besides clinical impact they might possess. We divided the biomarkers into three categories according to the achievement method: laboratory markers, genetic-immunogenetic markers and imaging markers. We have found two biomarkers at the time being considered the gold standard for MS diagnostics. Unfortunately, there does not exist a single solitary marker being able to present reliable diagnostic value, prognostic value, high sensitivity and specificity as well as clinical impact. We need more studies to find the best biomarker for MS.
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Shore, David. "Exclusive Drug Labeling Rights as a New Incentive for Contribution to a Communal Biomarker Resource." American Journal of Law & Medicine 44, no. 4 (November 2018): 607–26. http://dx.doi.org/10.1177/0098858818821138.
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Biomarkers are an important tool in modern drug development. The FDA has posited that increased use of biomarkers in clinical trials can accelerate pharmaceutical industry productivity, ushering new drugs to market. Accordingly, the FDA has created two pathways for evaluation of biomarker utility. Biomarkers incorporated into clinical trials, the traditional pathway, are effectively private to a therapeutic sponsor and to the scope of the trial. By contrast, in Biomarker Qualification (“BQ”), the second pathway, a biomarker is certified as a publicly available tool. The FDA has hoped that academic, non-profit, and industry stakeholders would work together in consortia to qualify biomarkers, cumulatively generating a common resource of broad utility. In practice, utilization of Biomarker Qualification has been paltry. Incentives for BQ that align with the interests of industry resource holders are necessary to fuel increased utilization of biomarkers in clinical trials and create the communal biomarker toolkit envisioned by the FDA. A blanket extension of exclusivity for any drug successfully paired with a companion biomarker would diminish public access to medicine by encouraging spurious biomarkers and correspondingly narrowed clinical trials. As a measured alternative, an exclusive right to include a qualified companion biomarker on an FDA drug label would balance public access externalities. This exclusivity would waylay label approval, and thus marketability, of later drugs relying on the qualified biomarker for clinical safety or efficacy. Accordingly, sponsors would find no incentive to portage an ineffective or unnecessary biomarker through clinical trials, as there would be no benefit to securing exclusive rights in a tool others saw no value in using.
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Rijs, Zeger, A. Naweed Shifai, Sarah E. Bosma, Peter J. K. Kuppen, Alexander L. Vahrmeijer, Stijn Keereweer, Judith V. M. G. Bovée, Michiel A. J. van de Sande, Cornelis F. M. Sier, and Pieter B. A. A. van Driel. "Candidate Biomarkers for Specific Intraoperative Near-Infrared Imaging of Soft Tissue Sarcomas: A Systematic Review." Cancers 13, no. 3 (February 1, 2021): 557. http://dx.doi.org/10.3390/cancers13030557.
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Surgery is the mainstay of treatment for localized soft tissue sarcomas (STS). The curative treatment highly depends on complete tumor resection, as positive margins are associated with local recurrence (LR) and prognosis. However, determining the tumor margin during surgery is challenging. Real-time tumor-specific imaging can facilitate complete resection by visualizing tumor tissue during surgery. Unfortunately, STS specific tracers are presently not clinically available. In this review, STS-associated cell surface-expressed biomarkers, which are currently already clinically targeted with monoclonal antibodies for therapeutic purposes, are evaluated for their use in near-infrared fluorescence (NIRF) imaging of STS. Clinically targeted biomarkers in STS were extracted from clinical trial registers and a PubMed search was performed. Data on biomarker characteristics, sample size, percentage of biomarker-positive STS samples, pattern of biomarker expression, biomarker internalization features, and previous applications of the biomarker in imaging were extracted. The biomarkers were ranked utilizing a previously described scoring system. Eleven cell surface-expressed biomarkers were identified from which 7 were selected as potential biomarkers for NIRF imaging: TEM1, VEGFR-1, EGFR, VEGFR-2, IGF-1R, PDGFRα, and CD40. Promising biomarkers in common and aggressive STS subtypes are TEM1 for myxofibrosarcoma, TEM1, and PDGFRα for undifferentiated soft tissue sarcoma and EGFR for synovial sarcoma.
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Janssen, Joëlle J. E., Bart Lagerwaard, Arie G. Nieuwenhuizen, Xavier Escoté, Núria Canela, Josep M. del Bas, Vincent C. J. de Boer, and Jaap Keijer. "Single and Joined Behaviour of Circulating Biomarkers and Metabolic Parameters in High-Fit and Low-Fit Healthy Females." International Journal of Molecular Sciences 24, no. 4 (February 20, 2023): 4202. http://dx.doi.org/10.3390/ijms24044202.
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Biomarkers are important in the assessment of health and disease, but are poorly studied in still healthy individuals with a (potential) different risk for metabolic disease. This study investigated, first, how single biomarkers and metabolic parameters, functional biomarker and metabolic parameter categories, and total biomarker and metabolic parameter profiles behave in young healthy female adults of different aerobic fitness and, second, how these biomarkers and metabolic parameters are affected by recent exercise in these healthy individuals. A total of 102 biomarkers and metabolic parameters were analysed in serum or plasma samples from 30 young, healthy, female adults divided into a high-fit (V̇O2peak ≥ 47 mL/kg/min, N = 15) and a low-fit (V̇O2peak ≤ 37 mL/kg/min, N = 15) group, at baseline and overnight after a single bout of exercise (60 min, 70% V̇O2peak). Our results show that total biomarker and metabolic parameter profiles were similar between high-fit and low-fit females. Recent exercise significantly affected several single biomarkers and metabolic parameters, mostly related to inflammation and lipid metabolism. Furthermore, functional biomarker and metabolic parameter categories corresponded to biomarker and metabolic parameter clusters generated via hierarchical clustering models. In conclusion, this study provides insight into the single and joined behavior of circulating biomarkers and metabolic parameters in healthy females, and identified functional biomarker and metabolic parameter categories that may be used for the characterisation of human health physiology.
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Trippa, Lorenzo, and Brian Michael Alexander. "Bayesian Baskets: A Novel Design for Biomarker-Based Clinical Trials." Journal of Clinical Oncology 35, no. 6 (February 2017): JCO.2016.68.286. http://dx.doi.org/10.1200/jco.2016.68.2864.
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Purpose Biomarker-based clinical trials provide efficiencies during therapeutic development and form the foundation for precision medicine. These trials must generate information on both experimental therapeutics and putative predictive biomarkers in the context of varying pretrial information. We generated an efficient, flexible design that accommodates various pretrial levels of evidence supporting the predictive capacity of biomarkers while making pretrial design choices explicit. Methods We generated a randomization procedure that explicitly incorporates pretrial estimates of the predictive capacity of biomarkers. To compare the utility of this Bayesian basket (BB) design with that of a balanced randomized, biomarker agnostic (BA) design and a traditional basket (TB) design that includes only biomarker-positive patients, we iteratively simulated hypothetical multiarm clinical trials under various scenarios. Results BB was more efficient than BA while generating more information on the predictive capacity of putative biomarkers than both BA and TB. For simulations of hypothetical multiarm trials of experimental therapies and associated biomarkers of varying incident frequency, BB increased power over BA in cases when the biomarker was predictive and when the experimental therapeutic worked in all patients in a variety of scenarios. BB also generated more information about the predictive capacity of biomarkers than BA and categorically relative to TB, which generates no new biomarker information. Conclusion The BB design offers an efficient way to generate information on both experimental therapeutics and the predictive capacity of putative biomarkers. The design is flexible enough to accommodate varying levels of pretrial biomarker confidence within the same platform structure and makes clinical trial design decisions more explicit.
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Fox, Natalie S., Emilie Lalonde, Julie Livingstone, Julia Hopkins, Yu-Jia Shiah, Vincent Huang, Takafumi Yamaguchi, et al. "Integrated somatic subtypes of localized intermediate-risk prostate cancer." Journal of Clinical Oncology 35, no. 6_suppl (February 20, 2017): e560-e560. http://dx.doi.org/10.1200/jco.2017.35.6_suppl.e560.
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e560 Background: Approximately two thirds of intermediate risk prostate cancer patients are over- or under- treated because we cannot correctly prognose this risk group; therefore we require novel biomarkers to better direct patient therapies and avoid subjecting patients to side effects without benefit. One reason genomic biomarkers are not currently used in clinical settings is because they are notoriously difficult to validate in follow-up studies. Furthermore, the lack of clear prostate cancer subtypes prevents the development of subtype specific biomarkers as is standard practice in breast cancer. We aim to improve biomarker validation rates by defining prostate cancer subtypes that can be used to create subtype specific biomarkers. Methods: First, we assess large scale genomic patterns using whole genome sequencing and methylation data and create integrative subtypes for intermediate risk prostate cancer. Second, we assess associations between specific aberrations and subtypes, and determine whether certain types of molecular aberrations are more important background aberrations for subtype specific biomarker development. Finally, we assess biases in prognostic performance of the published Lalonde biomarker between groups associated with patient subtypes to show that subtype aware biomarkers are necessary. Results: We demonstrate that the Lalonde biomarker is biased by the cohorts’ proportion of TMPRSS2-ERG (T2E) aberrations illustrating the need to develop different biomarkers for patients with T2E and patients without T2E. Further, we suggest integrative subtypes can be used to select patients with similar genomic profiles for biomarker analysis to improve biomarker validation rates. Conclusions: This analysis provides direct guidance for future biomarker development and addresses an important barrier to clinical use of genomic biomarkers for prostate cancer.
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Wagner, John A. "Overview of Biomarkers and Surrogate Endpoints in Drug Development." Disease Markers 18, no. 2 (2002): 41–46. http://dx.doi.org/10.1155/2002/929274.
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There are numerous factors that recommend the use of biomarkers in drug development including the ability to provide a rational basis for selection of lead compounds, as an aid in determining or refining mechanism of action or pathophysiology, and the ability to work towards qualification and use of a biomarker as a surrogate endpoint. Examples of biomarkers come from many different means of clinical and laboratory measurement. Total cholesterol is an example of a clinically useful biomarker that was successfully qualified for use as a surrogate endpoint. Biomarkers require validation in most circumstances. Validation of biomarker assays is a necessary component to delivery of high-quality research data necessary for effective use of biomarkers. Qualification is necessary for use of a biomarker as a surrogate endpoint. Putative biomarkers are typically identified because of a relationship to known or hypothetical steps in a pathophysiologic cascade. Biomarker discovery can also be effected by expression profiling experiment using a variety of array technologies and related methods. For example, expression profiling experiments enabled the discovery of adipocyte related complement protein of 30 kD (Acrp30 or adiponectin) as a biomarker forin vivoactivation of peroxisome proliferator-activated receptors (PPAR)γactivity.
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Shah, Ankeet, Dominic C. Grimberg, and Brant A. Inman. "Classification of Molecular Biomarkers." Société Internationale d’Urologie Journal 1, no. 1 (October 13, 2020): 8–15. http://dx.doi.org/10.48083/akui6936.
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A “biomarker” is any measurable characteristic that indicates the presence or absence of disease or the biological response to a stimulus, typically an exposure or intervention. The FDA-NIH Biomarker Working Group has produced a document called Biomarkers, EndpointS and other Tools (BEST), which defines 7 categories of biomarkers according to their clinical usage: susceptibility and risk, diagnostic, monitoring, prognostic, predictive, pharmacodynamic and treatment response, and safety. We approach the classification of biomarkers in 2 additional ways: their bodily source and their measurement type. In the context of their use in genitourinary malignancy, we also consider factors that influence their use and reliability in clinical and research applications.
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Wilson, Jennifer L., and Russ B. Altman. "Biomarkers: Delivering on the expectation of molecularly driven, quantitative health." Experimental Biology and Medicine 243, no. 3 (December 3, 2017): 313–22. http://dx.doi.org/10.1177/1535370217744775.
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Biomarkers are the pillars of precision medicine and are delivering on expectations of molecular, quantitative health. These features have made clinical decisions more precise and personalized, but require a high bar for validation. Biomarkers have improved health outcomes in a few areas such as cancer, pharmacogenetics, and safety. Burgeoning big data research infrastructure, the internet of things, and increased patient participation will accelerate discovery in the many areas that have not yet realized the full potential of biomarkers for precision health. Here we review themes of biomarker discovery, current implementations of biomarkers for precision health, and future opportunities and challenges for biomarker discovery. Impact statement Precision medicine evolved because of the understanding that human disease is molecularly driven and is highly variable across patients. This understanding has made biomarkers, a diverse class of biological measurements, more relevant for disease diagnosis, monitoring, and selection of treatment strategy. Biomarkers’ impact on precision medicine can be seen in cancer, pharmacogenomics, and safety. The successes in these cases suggest many more applications for biomarkers and a greater impact for precision medicine across the spectrum of human disease. The authors assess the status of biomarker-guided medical practice by analyzing themes for biomarker discovery, reviewing the impact of these markers in the clinic, and highlight future and ongoing challenges for biomarker discovery. This work is timely and relevant, as the molecular, quantitative approach of precision medicine is spreading to many disease indications.
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Porter, Amy C., Jiri Aubrecht, Chandler Birch, Jonathan Braun, Carolyn Cuff, Suryasarathi Dasgupta, Jeremy D. Gale, et al. "Biomarkers of Crohn’s Disease to Support the Development of New Therapeutic Interventions." Inflammatory Bowel Diseases 26, no. 10 (August 25, 2020): 1498–508. http://dx.doi.org/10.1093/ibd/izaa215.
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Abstract Background Currently, 2 coprimary end points are used by health authorities to determine the effectiveness of therapeutic interventions in patients with Crohn’s disease (CD): symptomatic remission (patient-reported outcome assessment) and endoscopic remission (ileocolonoscopy). However, there is lack of accepted biomarkers to facilitate regulatory decision-making in the development of novel therapeutics for the treatment of CD. Methods With support from the Helmsley Charitable Trust, Critical Path Institute formed the Crohn’s Disease Biomarkers preconsortium (CDBpC) with members from the pharmaceutical industry, academia, and nonprofit organizations to evaluate the CD biomarker landscape. Biomarkers were evaluated based on biological relevance, availability of biomarker assays, and clinical validation data. Results The CDBpC identified the most critical need as pharmacodynamic/response biomarkers to monitor disease activity in response to therapeutic intervention. Fecal calprotectin (FC) and serum C-reactive protein (CRP) were identified as biomarkers ready for the regulatory qualification process. A number of exploratory biomarkers and potential panels of these biomarkers was also identified for additional development. Given the different factors involved in CD and disease progression, a combination of biomarkers, including inflammatory, tissue injury, genetic, and microbiome-associated biomarkers, will likely have the most utility. Conclusions The primary focus of the Inflammatory Bowel Disease Regulatory Science Consortium will be development of exploratory biomarkers and the qualification of FC and CRP for IBD. The Inflammatory Bowel Disease Regulatory Science Consortium, focused on tools to support IBD drug development, will operate in the precompetitive space to share data, biological samples for biomarker testing, and assay information for novel biomarkers.
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Tjerkstra, Maud, Homeyra Labib, Bert A. Coert, René Spijker, Jonathan M. Coutinho, W. Peter Vandertop, and Dagmar Verbaan. "Laboratory biomarkers of delayed cerebral ischemia following subarachnoid hemorrhage: A systematic review." Journal of Circulating Biomarkers 12 (April 5, 2023): 17–25. http://dx.doi.org/10.33393/jcb.2023.2502.
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Delayed cerebral ischemia (DCI) substantially contributes to disability and death in subarachnoid hemorrhage (SAH) patients; however, its pathophysiology is incompletely understood and diagnostic and therapeutic strategies are lacking. Biomarkers may help to elucidate the pathophysiology, optimize early diagnosis, or provide treatment targets. We systematically searched PubMed and Embase on October 13, 2021, for studies that evaluated at least one laboratory biomarker in patients with DCI, using the most up-to-date definition of DCI as proposed by a panel of experts in 2010. Quality of studies was assessed using the Newcastle-Ottawa Scale or Cochrane Collaboration’s risk of bias assessment tool. Biomarkers of clinical and radiological DCI were analyzed separately. Results were meta-analyzed if possible, otherwise narratively reviewed. Biomarkers were classified as significant, inconclusive, or nonsignificant. We defined validated biomarkers as those with significant results in meta-analyses, or in at least two studies using similar methodologies within the same time interval after SAH. The search yielded 209 articles with 724 different biomarkers; 166 studies evaluated 646 biomarkers of clinical DCI, of which 141 were significant and 7 were validated biomarkers (haptoglobulin 2-1 and 2-2, ADAMTS13, vWF, NLR, P-selectin, F2-isoprostane); 78 studies evaluated 165 biomarkers of radiological DCI, of which 63 were significant and 1 was a validated biomarker (LPR). Hence, this review provides a selection of seven biomarkers of clinical DCI and one biomarker of radiological DCI as most promising biomarkers of DCI. Future research should focus on determining the exact predictive, diagnostic, and therapeutic potentials of these biomarkers.
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Johnson, Danielle, Dyfrig Hughes, Sir Munir Pirmohamed, and Andrea Jorgensen. "Evidence to Support Inclusion of Pharmacogenetic Biomarkers in Randomised Controlled Trials." Journal of Personalized Medicine 9, no. 3 (September 1, 2019): 42. http://dx.doi.org/10.3390/jpm9030042.
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Pharmacogenetics and biomarkers are becoming normalised as important technologies to improve drug efficacy rates, reduce the incidence of adverse drug reactions, and make informed choices for targeted therapies. However, their wider clinical implementation has been limited by a lack of robust evidence. Suitable evidence is required before a biomarker’s clinical use, and also before its use in a clinical trial. We have undertaken a review of five pharmacogenetic biomarker-guided randomised controlled trials (RCTs) and evaluated the evidence used by these trials to justify biomarker inclusion. We assessed and quantified the evidence cited in published rationale papers, or where these were not available, obtained protocols from trial authors. Very different levels of evidence were provided by the trials. We used these observations to write recommendations for future justifications of biomarker use in RCTs and encourage regulatory authorities to write clear guidelines.
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Bera, Aishi, and Preeti Roy. "Biomarkers associated with early detection of aging in human: a review." YMER Digital 21, no. 08 (August 19, 2022): 732–48. http://dx.doi.org/10.37896/ymer21.08/61.
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Most chronic illnesses as well as functional deficits happen to be exacerbated by aging. There happens to be an existing significant heterogeneity inside of the level that belongs to illness as well as functional impairment risk within an existing homogenous age group, highlighting the need that is going to belong to appropriate biomarkers to help inside of describing the complicated aging processes. The diversity that belongs to biological living environments, lifestyle activities, as well as medical treatments complicates the discovery that belongs to biomarkers even further. During the same time as an existing result, no one biomarker or gold standard instrument that is going to belong to monitoring effective or healthy aging has been identified. The current state that belongs to knowledge on top of possible biomarkers happens to be described inside of this brief overview, with an existing emphasis on top of their application to the primary physiological systems influenced by the aging process, such during the same time that physical capacity, nutritional status, body composition, as well as endocrine as well as immunological function. Keywords - Biomarkers, Aging, MARK-AGE consortium, biomarkers of different human organ systems, frailty syndrome biomarker, DNA methylation biomarker, Phenotypic biomarker, Genetic biomarker, Immunological biomarker, Cellular biomarker, Deep - Aging clocks
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Bocanegra, Victoria, Cintia Celina Vaquer, Clara García Samartino, Sebastián Arbona, Rodrigo Luis Ongay, Erika Gudiño, Paula Valdemoros, et al. "A novel liquid biopsy methylation-based technology and early detection of colorectal cancer in patients." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): e15527-e15527. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e15527.
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e15527 Background: Since colorectal cancer (CRC) remains as the third most common and second most deadly malignant neoplasm, accurate and non-invasive detection technologies became a promising strategy to enhance early diagnosis. Our goal is to develop a liquid biopsy test able to detect methylation-based biomarkers in circulating tumor DNA (ct-DNA) collected from CRC patients' plasma. We are currently working on a digital PCR (dPCR)-based technology focused on detecting with high sensitivity and specificity our proprietary CRC specific epigenetic biomarker panel. Methods: We developed a bioinformatic platform that identifies methylation-based biomarkers from genome-wide Illumina 450K methylation data available in GDC-TCGA public databases. The platform uses multidimensional analysis algorithms and, through a predictive model, establishes a ranking with the most appropriate diagnostic epigenetic biomarkers. First, we tested the selected biomarkers in tumor tissue and in distant colonic mucosa from average risk CRC patients using qPCR. Then, ct-DNA from plasma samples of CRC patients and circulating DNA (cf-DNA) from risk matched negative colonoscopy-confirmed healthy individuals was analyzed by a novel dPCR-based technology in order to detect our panel of epigenetic biomarkers. Lastly, we applied a ROC analysis to the Area Under the Curve (AUC) values and obtained the corresponding sensitivity and specificity results. Results: Illumina 450K methylation data from primary tumor and normal tissue was incorporated into our bioinformatic platform that subsequently ranked 275 candidate genes. Based on several classification criteria we selected a panel composed of the three best ranked candidates. We validated the bioinformatic platform performance in identifying sensitive and specific CRC epigenetic biomarkers by detecting the presence of these biomarkers in tumor mucosa from paired CRC patients and its absence in distant colonic mucosa (n = 20) using qPCR. Furthermore, we applied our dPCR-based technology to simultaneously detect the three-gene panel of epigenetic biomarkers in ctDNA from 34 CRC patients and cfDNA from 33 healthy individuals. We obtained an overall AUC = 0.92, with 76% sensitivity (95% CI: 60-87%) and 90.9% specificity (95% CI: 76-97%). Conclusions: We validated a technology capable of detecting a novel and proprietary methylation-based biomarkers panel. This accurate liquid biopsy technology based on epigenetic biomarker detection applies dPCR and might be implemented in screening or early colorectal cancer detection. Although our technology needs further clinical validations and optimizations, we consider that the obtained results are strong enough to evolve in a complete epigenetic biomarker's blood-based test.
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Paczesny, Sophie. "Biomarkers for posttransplantation outcomes." Blood 131, no. 20 (May 17, 2018): 2193–204. http://dx.doi.org/10.1182/blood-2018-02-791509.
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Abstract During the last decade, the development of biomarkers for the complications seen after allogeneic hematopoietic stem cell transplantation has expanded tremendously, with the most progress having been made for acute graft-versus-host disease (aGVHD), a common and often fatal complication. Although many factors are known to determine transplant outcome (including the age of the recipient, comorbidity, conditioning intensity, donor source, donor-recipient HLA compatibility, conditioning regimen, posttransplant GVHD prophylaxis), they are incomplete guides for predicting outcomes. Thanks to the advances in genomics, transcriptomics, proteomics, and cytomics technologies, blood biomarkers have been identified and validated for us in promising diagnostic tests, prognostic tests stratifying for future occurrence of aGVHD, and predictive tests for responsiveness to GVHD therapy and nonrelapse mortality. These biomarkers may facilitate timely and selective therapeutic intervention. However, such blood tests are not yet available for routine clinical care. This article provides an overview of the candidate biomarkers for clinical evaluation and outlines a path from biomarker discovery to first clinical correlation, to validation in independent cohorts, to a biomarker-based clinical trial, and finally to general clinical application. This article focuses on biomarkers discovered with a large-scale proteomics platform and validated with the same reproducible assay in at least 2 independent cohorts with sufficient sample size according to the 2014 National Institutes of Health consensus on biomarker criteria, as well as on biomarkers as tests for risk stratification of outcomes, but not on their pathophysiologic contributions, which have been reviewed recently.
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Pieters, Alanah, Eva Gijbels, Bruno Cogliati, Pieter Annaert, Lindsey Devisscher, and Mathieu Vinken. "Biomarkers of cholestasis." Biomarkers in Medicine 15, no. 6 (April 2021): 437–54. http://dx.doi.org/10.2217/bmm-2020-0691.
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Cholestasis is a major pathological manifestation, often resulting in detrimental liver conditions, which occurs in a variety of indications collectively termed cholestatic liver diseases. The frequent asymptomatic character and complexity of cholestasis, together with the lack of a straightforward biomarker, hampers early detection and treatment of the condition. The ‘omics’ era, however, has resulted in a plethora of cholestatic indicators, yet a single clinically applicable biomarker for a given cholestatic disease remains missing. The criteria to fulfil as an ideal biomarker as well as the challenging molecular pathways in cholestatic liver diseases advocate for a scenario in which multiple biomarkers, originating from different domains, will be assessed concomitantly. This review gives an overview of classical clinical and novel molecular biomarkers in cholestasis, focusing on their benefits and drawbacks.
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Duffy, Michael J., Catharine M. Sturgeon, György Sölétormos, Vivian Barak, Rafael Molina, Daniel F. Hayes, Eleftherios P. Diamandis, and Patrick M. M. Bossuyt. "Validation of New Cancer Biomarkers: A Position Statement from the European Group on Tumor Markers." Clinical Chemistry 61, no. 6 (June 1, 2015): 809–20. http://dx.doi.org/10.1373/clinchem.2015.239863.
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Abstract BACKGROUND Biomarkers are playing increasingly important roles in the detection and management of patients with cancer. Despite an enormous number of publications on cancer biomarkers, few of these biomarkers are in widespread clinical use. CONTENT In this review, we discuss the key steps in advancing a newly discovered cancer candidate biomarker from pilot studies to clinical application. Four main steps are necessary for a biomarker to reach the clinic: analytical validation of the biomarker assay, clinical validation of the biomarker test, demonstration of clinical value from performance of the biomarker test, and regulatory approval. In addition to these 4 steps, all biomarker studies should be reported in a detailed and transparent manner, using previously published checklists and guidelines. Finally, all biomarker studies relating to demonstration of clinical value should be registered before initiation of the study. SUMMARY Application of the methodology outlined above should result in a more efficient and effective approach to the development of cancer biomarkers as well as the reporting of cancer biomarker studies. With rigorous application, all stakeholders, and especially patients, would be expected to benefit.
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Menke, James Michael, Md Shahidul Ahsan, and Suan Phaik Khoo. "More Accurate Oral Cancer Screening with Fewer Salivary Biomarkers." Biomarkers in Cancer 9 (January 1, 2017): 1179299X1773200. http://dx.doi.org/10.1177/1179299x17732007.
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Signal detection and Bayesian inferential tools were applied to salivary biomarkers to improve screening accuracy and efficiency in detecting oral squamous cell carcinoma (OSCC). Potential cancer biomarkers are identified by significant differences in assay concentrations, receiver operating characteristic areas under the curve (AUCs), sensitivity, and specificity. However, the end goal is to report to individual patients their risk of having disease given positive or negative test results. Likelihood ratios (LRs) and Bayes factors (BFs) estimate evidential support and compile biomarker information to optimize screening accuracy. In total, 26 of 77 biomarkers were mentioned as having been tested at least twice in 137 studies and published in 16 summary papers through 2014. Studies represented 10 212 OSCC and 25 645 healthy patients. The measure of biomarker and panel information value was number of biomarkers needed to approximate 100% positive predictive value (PPV). As few as 5 biomarkers could achieve nearly 100% PPV for a disease prevalence of 0.2% when biomarkers were ordered from highest to lowest LR. When sequentially interpreting biomarker tests, high specificity was more important than test sensitivity in achieving rapid convergence toward a high PPV. Biomarkers ranked from highest to lowest LR were more informative and easier to interpret than AUC or Youden index. The proposed method should be applied to more recently published biomarker data to test its screening value.
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Majkić-Singh, Nada. "What is a Biomarker? From its Discovery to Clinical Application." Journal of Medical Biochemistry 30, no. 3 (July 1, 2011): 186–92. http://dx.doi.org/10.2478/v10011-011-0029-z.
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What is a Biomarker? From its Discovery to Clinical ApplicationThe term biomarker in medicine most often stands for a protein measured in the circulation (blood) whose concentration indicates a normal or a pathological response of the organism, as well as a pharmacological response to the applied therapy. From a wider perspective, a biomarker is any indicator that is used as an index of the intensity of a disease or other physiological state in the organism. This means that biomarkers have a very important role in medical research and practice providing insight into the mechanism and course of a disease. Since a large number of biomarkers exist today that are used for different purposes, they have been classified into: 1) antecedent biomarkers, indicating risk of disease occurrence, 2) screening biomarkers, used to determine a subclinical form of disease, 3) diagnostic biomarkers, revealing an existing disease, 4) staging biomarkers, that define the stage and severity of a disease, and 5) prognostic biomarkers, that confirm the course of disease, including treatment response. Regardless of their role, their clinical significance depends on their sensitivity, specificity, predictive value, and also precision, reliability, reproducibility, and the possibility of easy and wide application. For a biomarker to become successful, it must undergo the process of validation, depending on the level of use. It is very important for every suggested biomarker, according to its purpose or its nature, to possess certain characteristics and to meet the strict requirements related to sensitivity, accuracy and precision, in order for the proper outcome to be produced in the estimation of the state for which it is intended. Finally, the development of guidelines for biomarker application is very important, based on well defined and properly conducted assessments of biomarker determination, providing the means by which research is translated into practice and allowing evidence based on facts to promote the clinical application of new biomarkers.
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Rayamajhi, Sagar, Jared Sipes, Ashley L. Tetlow, Souvik Saha, Ajay Bansal, and Andrew K. Godwin. "Extracellular Vesicles as Liquid Biopsy Biomarkers across the Cancer Journey: From Early Detection to Recurrence." Clinical Chemistry 70, no. 1 (January 2024): 206–19. http://dx.doi.org/10.1093/clinchem/hvad176.
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Abstract Background Cancer is a dynamic process and thus requires highly informative and reliable biomarkers to help guide patient care. Liquid-based biopsies have emerged as a clinical tool for tracking cancer dynamics. Extracellular vesicles (EVs), lipid bilayer delimited particles secreted by cells, are a new class of liquid-based biomarkers. EVs are rich in selectively sorted biomolecule cargos, which provide a spatiotemporal fingerprint of the cell of origin, including cancer cells. Content This review summarizes the performance characteristics of EV-based biomarkers at different stages of cancer progression, from early malignancy to recurrence, while emphasizing their potential as diagnostic, prognostic, and screening biomarkers. We discuss the characteristics of effective biomarkers, consider challenges associated with the EV biomarker field, and report guidelines based on the biomarker discovery pipeline. Summary Basic science and clinical trial studies have shown the potential of EVs as precision-based biomarkers for tracking cancer status, with promising applications for diagnosing disease, predicting response to therapy, and tracking disease burden. The multi-analyte cargos of EVs enhance the performance characteristics of biomarkers. Recent technological advances in ultrasensitive detection of EVs have shown promise with high specificity and sensitivity to differentiate early-cancer cases vs healthy individuals, potentially outperforming current gold-standard imaging-based cancer diagnosis. Ultimately, clinical translation will be dictated by how these new EV biomarker-based platforms perform in larger sample cohorts. Applying ultrasensitive, scalable, and reproducible EV detection platforms with better design considerations based upon the biomarker discovery pipeline should guide the field towards clinically useful liquid biopsy biomarkers.
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Sebro, Ronnie. "Advancing Diagnostics and Patient Care: The Role of Biomarkers in Radiology." Seminars in Musculoskeletal Radiology 28, no. 01 (February 2024): 003–13. http://dx.doi.org/10.1055/s-0043-1776426.
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AbstractThe integration of biomarkers into medical practice has revolutionized the field of radiology, allowing for enhanced diagnostic accuracy, personalized treatment strategies, and improved patient care outcomes. This review offers radiologists a comprehensive understanding of the diverse applications of biomarkers in medicine. By elucidating the fundamental concepts, challenges, and recent advancements in biomarker utilization, it will serve as a bridge between the disciplines of radiology and epidemiology. Through an exploration of various biomarker types, such as imaging biomarkers, molecular biomarkers, and genetic markers, I outline their roles in disease detection, prognosis prediction, and therapeutic monitoring. I also discuss the significance of robust study designs, blinding, power and sample size calculations, performance metrics, and statistical methodologies in biomarker research. By fostering collaboration between radiologists, statisticians, and epidemiologists, I hope to accelerate the translation of biomarker discoveries into clinical practice, ultimately leading to improved patient care.
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Fengming, Yi, and Wu Jianbing. "Biomarkers of Inflammatory Bowel Disease." Disease Markers 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/710915.
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Inflammatory bowel disease (IBD) is a chronic disease mostly involved with intestine with unknown etiology. Diagnosis, evaluation of severity, and prognosis are still present as challenges for physicians. An ideal biomarker with the characters such as simple, easy to perform, noninvasive or microinvasive, cheap, rapid, and reproducible is helpful for patients and clinicians. Currently biomarkers applied in clinic include CRP, ESR, pANCA, ASCA, and fecal calprotectin. However, they are far from ideal. Lots of studies are focused on seeking for ideal biomarker for IBD. Herein, the paper reviewed recent researches on biomarkers of IBD to get advances of biomarkers in inflammatory bowel disease.
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Zou, Jinfeng, and Edwin Wang. "Cancer Biomarker Discovery for Precision Medicine: New Progress." Current Medicinal Chemistry 26, no. 42 (January 8, 2020): 7655–71. http://dx.doi.org/10.2174/0929867325666180718164712.
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Background: Precision medicine puts forward customized healthcare for cancer patients. An important way to accomplish this task is to stratify patients into those who may respond to a treatment and those who may not. For this purpose, diagnostic and prognostic biomarkers have been pursued. Objective: This review focuses on novel approaches and concepts of exploring biomarker discovery under the circumstances that technologies are developed, and data are accumulated for precision medicine. Results: The traditional mechanism-driven functional biomarkers have the advantage of actionable insights, while data-driven computational biomarkers can fulfill more needs, especially with tremendous data on the molecules of different layers (e.g. genetic mutation, mRNA, protein etc.) which are accumulated based on a plenty of technologies. Besides, the technology-driven liquid biopsy biomarker is very promising to improve patients’ survival. The developments of biomarker discovery on these aspects are promoting the understanding of cancer, helping the stratification of patients and improving patients’ survival. Conclusion: Current developments on mechanisms-, data- and technology-driven biomarker discovery are achieving the aim of precision medicine and promoting the clinical application of biomarkers. Meanwhile, the complexity of cancer requires more effective biomarkers, which could be accomplished by a comprehensive integration of multiple types of biomarkers together with a deep understanding of cancer.
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Modur, Vijay, Eric Hailman, and JC Barrett. "Evidence-Based Laboratory Medicine in Oncology Drug Development: From Biomarkers to Diagnostics." Clinical Chemistry 59, no. 1 (January 1, 2013): 102–9. http://dx.doi.org/10.1373/clinchem.2012.191072.
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BACKGROUND The promise of targeted therapies in molecularly defined subsets of cancer has led to a transformation of the process of drug development in oncology. To target cancer successfully and precisely requires high-quality translational data. Such data can be generated by the use of biomarkers that answer key questions in drug development. CONTENT Translational data for aiding in decision-making and driving cancer drug development can be generated by systematic assessments with biomarkers. Types of biomarkers that support decisions include: pharmacodynamic assessments for selecting the best compound or dosage; assessment of early tumor response with tissue biomarkers and imaging, mutation, and other assessment strategies for patient selection; and the use of markers of organ injury to detect toxicity and improve safety. Tactics used to generate biomarker data include fit-for-purpose assay validation and real-time biomarker assessments. Successfully translated and clinically informative biomarkers can mature into novel companion diagnostic tests that expand the practice of laboratory medicine. SUMMARY Systematic biomarker assessments are a key component of the clinical development of targeted therapies for cancer. The success of these biomarker assessments requires applying basic principles of laboratory medicine to generate the data required to make informed decisions. Successful biomarkers can transition into diagnostic tests that expand the laboratory medicine armamentarium.
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Hornaday, Kylie K., Eilidh M. Wood, and Donna M. Slater. "Is there a maternal blood biomarker that can predict spontaneous preterm birth prior to labour onset? A systematic review." PLOS ONE 17, no. 4 (April 4, 2022): e0265853. http://dx.doi.org/10.1371/journal.pone.0265853.
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Introduction The ability to predict spontaneous preterm birth (sPTB) prior to labour onset is a challenge, and it is currently unclear which biomarker(s), may be potentially predictive of sPTB, and whether their predictive power has any utility. A systematic review was conducted to identify maternal blood biomarkers of sPTB. Methods This study was conducted according to PRISMA protocol for systematic reviews. Four databases (MEDLINE, EMBASE, CINAHL, Scopus) were searched up to September 2021 using search terms: “preterm labor”, “biomarker” and “blood OR serum OR plasma”. Studies assessing blood biomarkers prior to labour onset against the outcome sPTB were eligible for inclusion. Risk of bias was assessed based on the Newcastle Ottawa scale. Increased odds of sPTB associated with maternal blood biomarkers, as reported by odds ratios (OR), or predictive scores were synthesized. This review was not prospectively registered. Results Seventy-seven primary research articles met the inclusion criteria, reporting 278 unique markers significantly associated with and/or predictive of sPTB in at least one study. The most frequently investigated biomarkers were those measured during maternal serum screen tests for aneuploidy, or inflammatory cytokines, though no single biomarker was clearly predictive of sPTB based on the synthesized evidence. Immune and signaling pathways were enriched within the set of biomarkers and both at the level of protein and gene expression. Conclusion There is currently no known predictive biomarker for sPTB. Inflammatory and immune biomarkers show promise, but positive reporting bias limits the utility of results. The biomarkers identified may be more predictive in multi-marker models instead of as single predictors. Omics-style studies provide promising avenues for the identification of novel (and multiple) biomarkers. This will require larger studies with adequate power, with consideration of gestational age and the heterogeneity of sPTB to identify a set of biomarkers predictive of sPTB.
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Mehta, Arpita I., Sally Ross, Mark S. Lowenthal, Vincent Fusaro, David A. Fishman, Emanuel F. Petricoin, and Lance A. Liotta. "Biomarker Amplification by Serum Carrier Protein Binding." Disease Markers 19, no. 1 (2003): 1–10. http://dx.doi.org/10.1155/2003/104879.
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Mass spectroscopic analysis of the low molecular mass (LMM) range of the serum/plasma proteome is a rapidly emerging frontier for biomarker discovery. This study examined the proportion of LMM biomarkers, which are bound to circulating carrier proteins. Mass spectroscopic analysis of human serum following molecular mass fractionation, demonstrated that the majority of LMM biomarkers exist bound to carrier proteins. Moreover, the pattern of LMM biomarkers bound specifically to albumin is distinct from those bound to non-albumin carriers. Prominent SELDI-TOF ionic species (m/z 6631.7043) identified to correlate with the presence of ovarian cancer were amplified by albumin capture. Several insights emerged: a) Accumulation of LMM biomarkers on circulating carrier proteins greatly amplifies the total serum/plasma concentration of the measurable biomarker, b) The total serum/plasma biomarker concentration is largely determined by the carrier protein clearance rate, not the unbound biomarker clearance rate itself, and c) Examination of the LMM species bound to a specific carrier protein may contain important diagnostic information. These findings shift the focus of biomarker detection to the carrier protein and its biomarker content.
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Abdullah, Muhammad Ilyas, and Waheed Ahmad. "Role of Biomarkers in Molecular and Disease Diagnostics." Global Drug Design & Development Review VI, no. I (June 30, 2021): 1–13. http://dx.doi.org/10.31703/gdddr.2021(vi-i).01.
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A biomarker is a variable which is biologically monitored and sometimes studied more as an indication of natural biological mechanisms, pathogenic activities, or therapeutic reactions. Biomarkers are beneficial in the detection and grading of disease seriousness in laboratory as well as clinical settings. The first step in the creation of the biomarker system is to explore a viable target. There are two categories of. biomarkers: biomarkers of exposure for detection of risk, and biomarkers of disease employed in disease progression, diagnosis, and surveillance. The principles of biomarkers in disease have been applied to the detection, screening, diagnosis, treatment and checking of cancer. However, more targeted therapies have now been developed that can be directed to kill cancer cells only, while sparing healthy cells. By understanding the relation amongst the biological processes and clinical outcomes, we can increase, expand, and further elaborate our ways of treatment of diseases by using biomarkers.
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Gilstrap, Lauren G., and Thomas J. Wang. "Biomarkers and Cardiovascular Risk Assessment for Primary Prevention: An Update." Clinical Chemistry 58, no. 1 (January 1, 2012): 72–82. http://dx.doi.org/10.1373/clinchem.2011.165712.
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Abstract BACKGROUND Interest in cardiovascular biomarkers in primary prevention has increased dramatically in the past decade. This increase has been fueled by an improved understanding of cardiovascular pathophysiology, as well as novel technologies for biomarker identification. CONTENT In this review we provide a brief overview of recent concepts in the evaluation of screening biomarkers, because biomarkers may behave differently when used for screening as opposed to diagnosis or disease staging. The following specific biomarker examples are then discussed, with a focus on data from primary prevention studies: high-sensitivity C-reactive protein, B-type natriuretic peptide, lipoprotein-associated phospholipase A2, and high-sensitivity troponin T. The article concludes by addressing novel platforms for biomarker discovery, reviewing recent examples from the field of metabolomics. SUMMARY An ongoing challenge is to develop screening strategies that can identify individuals at risk for cardiovascular events well before symptoms appear. For this purpose, the measurement of soluble biomarkers could be an important adjunct to traditional cardiovascular risk assessment. Recent studies highlight both the strengths and limitations of “novel” circulating biomarkers, and suggest that substantial work is still needed to identify biomarkers that are sufficiently accurate and cost-effective for routine use in primary prevention.