Osteoarthritis
Jason Liebowitz, Philip Seo, David Hellmann, Michael Zeide in Clinical Innovation in Rheumatology, 2023
Biochemical markers (also called molecular markers, signature molecules, or biomarkers) are biological molecules found in body fluids or tissues that may be used as indicators of physiological and pathophysiological processes. They can be defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” (77). Biomarkers may be used to see how well patients respond to new treatments and interventions for a disease or condition. In OA, biomarkers may be used to understand disease pathogenesis, study progression, and define the molecular endotypes (78, 79). Biomarkers have been used very effectively to identify molecular endotypes and clinical phenotypes in other disease areas. For example, in asthma, biomarkers have been used to identify phenotypes and endotypes that characterize severe asthma (80, 81). However, in the field of OA, we are lagging behind and need to catch up in order to enhance clinical trials and facilitate drug development. Biomarkers of early OA represent a major unmet need, and more research needs to be done to identify biomarkers that characterize early events in the pathogenesis of OA.
Pilot Grants
Lisa Chasan-Taber in Writing Grant Proposals in Epidemiology, Preventive Medicine, and Biostatistics, 2022
Biomarkers are measurable chemical, physical, or biological characteristics that aim to represent the severity or presence of some disease state. They are often obtained via blood or urine samples or biopsy. However, biomarkers face several limitations. First, biomarkers may not reflect the etiologically relevant time period for the impact of your exposure on your outcome of interest. For example, a cholesterol measure obtained after diagnosis of heart disease may not be representative of the cholesterol levels that preceded the heart disease. Second, degradation over time in frozen samples could reduce their validity. In addition, the biomarker may be influenced by other factors. For example, blood levels of vitamin D are influenced not only by diet but also by sunlight exposure.
Role of Biomarkers in Clinical Development of Cancer Therapies
Sherry X. Yang, Janet E. Dancey in Handbook of Therapeutic Biomarkers in Cancer, 2021
Based on the purposes in clinical studies, biomarkers can be categorized in a few types: Pharmacodynamics markers (PD Markers): Changes in molecular measurements due to the drug effect (e.g., downregulation of PAPR activity after administration of PARP inhibitors)Predictive markers: characteristics associated with response to a therapy (e.g., HER2 amplification as measured by FISH is predictive of activity with trastuzumab)Prognostic markers: characteristics associated with the inherent nature of the cancers and outcome, independent of therapy (e.g., the multi-gene OncoType Dx scores predicts survival outcome in patients with ER/PR positive breast cancer)Surrogate markers: measurements that can be used as an substitute of a clinically relevant endpoint
Laboratory testing for mitochondrial diseases: biomarkers for diagnosis and follow-up
Published in Critical Reviews in Clinical Laboratory Sciences, 2023
Abraham J. Paredes-Fuentes, Clara Oliva, Roser Urreizti, Delia Yubero, Rafael Artuch
Blood, urine, and cerebrospinal fluid (CSF) molecules are well established surrogate markers for MDs used in clinical practice. One of the main challenges is to validate specific and sensitive biomarkers both to diagnose disease and to predict disease progression. Profiling of lactate, amino acids, organic acids, and acylcarnitine species is routinely used in assessments of MD patients. As mentioned above, most of these biomarkers lack sensitivity and specificity for the diagnosis or follow-up of MDs [6,7]. New biomarkers, including some proteins [17–19] and circulating cell-free mitochondrial DNA (ccfmtDNA), with increased diagnostic specificity, have been identified in the last decade and have been proposed as potentially useful biomarkers for the assessment of clinical outcomes [20,21]. Despite these advances, even these new biomarkers are not sufficiently specific and sensitive to assess MD progression, and new biomarkers are urgently needed to monitor the success of novel therapeutic strategies [6].
Breathing new life into clinical testing and diagnostics: perspectives on volatile biomarkers from breath
Published in Critical Reviews in Clinical Laboratory Sciences, 2022
Jordan J. Haworth, Charlotte K. Pitcher, Giuseppe Ferrandino, Anthony R. Hobson, Kirk L. Pappan, Jonathan L. D. Lawson
Disease processes can modify underlying metabolic pathways, generating biomarkers that can report on aspects of disease development, progression, or treatment. Many VOCs on breath are products of endogenous biochemistry, making them relevant reporters of metabolic activity that may be altered by disease processes (Figure 2) [24]. Metabolic biomarkers offer many advantages, including responsiveness to endogenous (e.g. genetic) and exogenous (e.g. Western diet) stimuli. As such, metabolic biomarkers provide a more direct readout of current disease state, while genetics, for example, is often only suitable for predicting the risk of disease. This distinction is particularly beneficial in treatment monitoring, where biomarkers may detect signs of response or changing severity before there are notable changes in symptoms.
Artificial intelligence in early drug discovery enabling precision medicine
Published in Expert Opinion on Drug Discovery, 2021
Fabio Boniolo, Emilio Dorigatti, Alexander J. Ohnmacht, Dieter Saur, Benjamin Schubert, Michael P. Menden
Personalized medicine is the extreme case of precision medicine, in which the treatment is not only administered according to a biomarker but truly tailored to the needs of an individual. AI has been successfully used to develop individualized drug compounds themselves, with personalized cancer vaccines being one of the prime examples [43–47]. Cancer vaccines require the identification of antigen peptides that are highly specific to the patient’s tumor and MHC genotype and use those to boost the patient’s immune system [48]. Machine learning [49–51] and optimization methods have been developed to aid peptide identification and assembly of the vaccine [52–54] and have been integrated in almost all personalized vaccine design pipelines. The ability to choose a target antigen and set of MHC alleles makes such vaccine design frameworks not only applicable to personalized cancer immunotherapy [43–47], but generally useful for population-level prophylactic vaccine development against infectious diseases. Similarly, large and small molecule design has also seen recent successes [55–57] in AI-driven drug development and even some examples of personalized applications [58].
Related Knowledge Centers
- Antibody
- Biological Process
- Gtpase
- Oncogene
- Infection
- Pathogen
- Pharmacology
- Intervention
- Prostate-Specific Antigen
- Selected Reaction Monitoring
- Gtpase