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Integrated system biology approaches to fetal medicine problems
Published in Moshe Hod, Vincenzo Berghella, Mary E. D'Alton, Gian Carlo Di Renzo, Eduard Gratacós, Vassilios Fanos, New Technologies and Perinatal Medicine, 2019
Jezid Miranda, Fátima Crispi, Eduard Gratacós
Biomarker discovery has played an important role in medicine and is defined as an indicator that “signals” events in biological samples or systems. It is clear now that pregnancy complications such as preeclampsia or spontaneous preterm birth are multifactorial diseases, and no single biomarker can predict at the population level the risk of these diseases due to their complex etiology. Perhaps the biggest difference between classical approaches and computational biology is the move from hypothesis-directed toward hypothesis-generating research combining clinical information with big data generated by omics techniques (Figure 12.1). Yet, several challenges to this approach need to be taken into consideration.
Application of Genomic, Proteomic, and Metabolomic Technologies to the Development of Countermeasures against Chemical Warfare Agents
Published in Brian J. Lukey, James A. Romano, Salem Harry, Chemical Warfare Agents, 2019
Jennifer W. Sekowski, James F. Dillman III
Biomarker discovery has been an accelerating field of research since the advent of genomics, proteomics, and metabolomics. Despite the seemingly recent emergence of biomarker discovery as an important component of medical research, biomarkers have been used clinically for decades. Cholinesterase activity is a prototypical biomarker used in both civilian and military settings to determine exposure to organophosphate pesticides or nerve agents. In the military, cholinesterase testing has been used routinely for force health protection by testing soldiers before deployment to an area where CWAs may be used (TB Med 590, 2001). In addition, cholinesterase testing is used for occupational screening of agent workers. Given the ease of the assays used, the specificity of cholinesterase inhibition by nerve agents, and the link between cholinesterase activity and exposure, cholinesterase has proved to be a useful biomarker of exposure to nerve agents. However, the tests lack the specificity necessary to unequivocally identify the agent responsible for cholinesterase inhibition. To identify the specific agent responsible for cholinesterase inhibition, an analytical chemistry technique is required (e.g., GC-MS). This requires additional sample, time, and expertise that may not be readily available in a battlefield scenario or terrorist attack. In addition, identifying exposure to and effects of low levels of CWA has been problematic. Genomic, proteomic, and metabolic technologies provide a new way to uncover novel biomarkers for rapid and accurate force health protection and monitoring.
The Need of External Validation for Metabolomics Predictive Models
Published in Raquel Cumeras, Xavier Correig, Volatile organic compound analysis in biomedical diagnosis applications, 2018
Raquel Rodríguez-Pérez, Marta Padilla, Santiago Marco
Numerous studies and research findings have been published in the domain of biomarker discovery in omics sciences (Bussche et al., 2015; Dettmer et al., 2007; Machado and Laskowski, 2005; Schmekel et al., 2014; Westhoff et al., 2011). However, the generalized lack of reproducibility of the results indicates that methodological problems plague these studies. As a result, the vast majority of proposed biomarkers in literature have never found clinical application (Ducker and Krapfenbauer, 2013).
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
Conventional drug development pipelines consist of target identification and validation, assay development and screening, hit identification, lead optimization, and the selection of the final molecule for clinical development [14], each step marking a milestone in a rigid streamlined process. Main objectives are to identify potent drugs with suitable bioavailability, toxicity profiles, chemical synthesis, selectivity against putative target and ADME (absorption, distribution, metabolism, excretion), whilst mostly neglecting the heterogeneity of patients. In order to address this, precision medicine was introduced to customize treatments based on patient profiles [15]. This concept strongly impacted the linearity of drug discovery pipelines and suggested a more integrated and looped process [16]. Strong benefits of response biomarker discovery include acceleration of drug approval due to increased success rates of clinical trials [2] and thus reduced costs. AI offers the potential to leverage the entire molecular landscape of patients, thus becoming an invaluable tool for precision medicine.
Characteristics and outcomes of participants in colorectal cancer biomarker trials versus a real-world cohort
Published in Acta Oncologica, 2021
Siavash Foroughi, Hui-li Wong, Jeanne Tie, Rachel Wong, Margaret Lee, Belinda Lee, Ian Jones, Iain Skinner, Antony W. Burgess, Peter Gibbs
Biomarkers are of ever-increasing interest in cancer. They define specific subsets of patients with a differing prognosis and/or with a differing likelihood of deriving benefit or harm from a particular intervention [20,21]. Until recently, biomarker research was largely conducted by incorporating biomarker studies into therapeutic clinical trial design or through retrospective analyses of cancer clinical trial data and specimens [22], but increasingly, existing historical non-trial patient cohorts with matching clinical data are being used for biomarker discovery or validation. For the more promising predictive or prognostic markers, prospective studies are being pursued where the primary aim is to measure the clinical impact of biomarker-guided therapy. A search of ClinicalTrials.gov for the keywords “biomarker” and “cancer” returned 4169 currently active studies.
Cancer biomarker discovery and translation: proteomics and beyond
Published in Expert Review of Proteomics, 2019
Ventzislava A. Hristova, Daniel W. Chan
Transitioning ‘from bench to bedside’ is the foundation of translational research and it requires large-scale validation studies and clinical trials that are difficult for a single academic lab or even institution [66]. Translating biomarker discovery into clinical assays requires a large team, including academic researchers, clinicians, and industry, to define the clinical utility of the test, validation process and study design. Assembling such a team can be a daunting task that requires tremendous resources and poses a major challenge in translational research. The NCI EDRN initiative is a prime example of a successful multi-organization translational research program aimed at the discovery of cancer biomarkers for early detection and risk assessment, where collaboration among academic institutions, industry, and government has been able to address challenges including analytical and clinical validation that require large specimen cohorts and standardized sample preparation [58].