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PubMed1999

Proteomics in drug discovery.

The promise of genomics has dramatically altered the way drug discovery is now viewed. Overshadowed by the exuberance for genomics are the observations that most disease processes and treatments are manifest at the protein level and that there may not be a good correlation between gene expression and protein expression. An alternative and complementary approach to genomics is protein expression profiling - proteomics. The authors describe the technology, its advantages and some applications.

Journal Article↗
PubMed2004

Clinical proteomics and biomarker discovery.

Early detection of disease generally provides much-improved outcomes by a definitive medical procedure or through lifestyle modification along with specific medical management strategies. For serum biomarkers, which are central to the diagnosis of many diseases, to become truly useful sentinels of pathogenesis, their sensitivity and specificity in both early detection and recurrence monitoring must be improved. Currently, the detection and monitoring of disease markers is based on solitary proteins, and this approach is not always reliable. New classes of biomarkers derived from mass spectroscopy analysis of the low molecular weight proteome have shown improved abilities in the early detection of disease and hence in patient risk stratification and outcome. The development of a modular platform technology with sufficient flexibility and design abstractions allowing for concurrent experimentation, test, and refinement will help speed the progress of mass spectroscopy-derived proteomic pattern-based diagnostics from the scientific laboratory to the medical clinic. For acceptance by scientists, physicians, and regulatory personnel, new bioinformatic tools are essential system components for data management, analysis, and intuitive display of these new and complex data. Clinically engineered mass spectroscopy systems are essential for the further development and validation of multiplexed biomarkers that have shown tremendous promise for the early detection of disease.

Biomarkers↗
PubMed2002

Proteomics: drug target discovery on an industrial scale.

The discovery of targets that are sufficiently robust to yield marketable therapeutics is an enormous challenge. Through the years, several approaches have been used with varying degrees of success. These include target-independent screening of tumor-derived cell lines (disease-dependent), reductionist approaches to identifying crucial elements of disease-affected pathways, disease-independent screening of homologs of previously drugged targets, disease-dependent 'global' examination of gene transcript levels, and disease-dependent global examination of protein expression levels. These endeavors have been enabled by several major advancements in technology, most recently, the sequencing of the human genome. This review identifies the technical issues to be addressed for industrial-scale protein-based discovery in the identification of targets for therapeutic (or diagnostic) intervention. Such approaches aim to direct discovery in a way that increases the probability of robust target identification, and decreases the probability of failure owing to variable expression in this emerging field.

Algorithms↗
PubMed2026

Bifunctional covalent organic framework for rapid isolation of extracellular vesicles and proteomics-based biomarker discovery.

Extracellular vesicles (EVs), serving as crucial carriers of biomarkers for tumor diagnosis and prognostic evaluation, as well as drug delivery vehicles and therapeutic targets, making it a research hotspot. The isolation methods represent a key aspect of EV-associated research. In this work, an alkynyl-functionalized covalent organic framework (COF) was synthesized under acidic conditions at room temperature and further modified by photo-initiated thiol-yne click reaction, yielding a bifunctionalized COF material decorated with distearoyl phosphatidylethanolamine (DSPE) and Ti4+. This bifunctional COF material (COF-DSPE-Ti) can leverage the bifunctional synergistic effect between DSPE and Ti4+ sites, thereby facilitating the efficient isolation of EVs. This synergistic effect enables the efficient isolation of EVs within 3 min. Proteomic analysis reveals that this isolation method significantly outperforms ultracentrifugation, and an effective EV isolation and analysis can be completed using only 10 μL of plasma sample. For clinical liquid biopsy, the integration of the COF-DSPE-Ti method with proteomics lead to the identification of 64 upregulated proteins in plasma samples from colorectal cancer (CRC) patients, among which S100A9 emerged as a potential EV biomarker. In addition, KLK2, KLK3, and FOLH1, which have been established as diagnostic markers for prostate cancer (PCa), are successfully identified in EVs isolated from the urine of PCa patients. These findings demonstrate the reliability of this approach for screening EV-associated biomarkers and provide a novel strategy for the early diagnosis and prognostic assessment of CRC and PCa.

Proteomics↗
PubMed2025

Proteomic-based biomarker discovery reveals panels of diagnostic biomarkers for early identification of heart failure subtypes.

BACKGROUND: Limited access to echocardiography can delay the diagnosis of suspected heart failure (HF), which in turn postpones the initiation of optimal guideline-directed medical therapy. Although natriuretic peptides like B-type natriuretic peptide (BNP) are valuable biomarkers for diagnosing and managing HF, the utility of combining BNP with other blood-based biomarkers to predict subtypes of new-onset HF remains underexplored. OBJECTIVES: This study sought to investigate and evaluate the diagnostic significance of adding blood-based biomarkers to BNP for identifying heart failure with preserved ejection fraction (HFpEF) or reduced ejection fraction (HFrEF), with the goal of enhancing diagnostic assays beyond BNP measurements. METHODS: We identified candidate blood protein biomarkers using untargeted proteomics workflows from a cohort of individuals recruited to the STOP-HF trial who were at risk of HF and subsequently developed either HFpEF or HFrEF over time ("HF progressors"; n = 40). Candidate biomarkers were verified in an independent cohort (n = 52) from a community-based rapid access HF diagnostic clinic. The biological processes associated with these proteins were assessed, and the diagnostic values of biomarker panels were evaluated using a machine learning approach. RESULTS: Within HF progressors, we identified 3 proteins associated with HFpEF development: vascular cell adhesion protein 1 (VCAM1), insulin-like growth factor 2 (IGF2), and inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3). Additionally, 4 proteins were linked to HFrEF development: C-reactive protein (CRP), interleukin-6 receptor subunit beta (IL6RB), phosphatidylinositol-glycan-specific phospholipase D (PHLD), and noelin (NOE1). These findings were verified in an independent cohort to distinguish HF subtypes from controls. Moreover, a random forest algorithm demonstrated that combining these candidate biomarkers with BNP measurement significantly improved the prediction of HF subtypes. CONCLUSIONS: We identified candidate proteins linked to HFpEF and HFrEF in a longitudinal HF progressor cohort and validated them in a community-based cohort. Adding these proteins to BNP led to a significant improvement in HF subtype prediction. Study results have clinical implications for blood-based screening of HF subtypes using panels of biomarkers, particularly in resource-limited settings.

Humans↗
PubMed2005

Structural proteomics in drug discovery.

High-throughput, automated or semiautomated methodologies implemented by companies and structural genomics initiatives have accelerated the process of acquiring structural information for proteins via x-ray crystallography. This has enabled the application of structure-based drug design technologies to a variety of new structures that have potential pharmacologic relevance. Although there remain major challenges to applying these approaches more broadly to all classes of drug discovery targets, clearly the continued development and implementation of these structure-based drug design methodologies by the scientific community at large will help to address and provide solutions to these hurdles. The result will be a growing number of protein structures of important pharmacologic targets that will help to streamline the process of identification and optimization of lead compounds for drug development. These lead agonist and antagonist pharmacophores should, in turn, help to alleviate one of the current critical bottlenecks in the drug discovery process; that is, defining the functional relevance of potential novel targets to disease modification. The prospect of generating an increasing number of potential drug candidates will serve to highlight perhaps the most significant future bottleneck for drug development, the cost and complexity of the drug approval process.

Animals↗
PubMed2003

Proteomics in the discovery of new therapeutic targets for psychiatric disease.

In terms of impact on and cost to society psychiatric disorders are among the most important health problems of today. Current estimates from the US suggest that the collective cost of psychiatric diseases could amount to one-third of the total health care budget with a cumulative lifetime prevalence of 30%. While undoubtedly improvements have been made in the diagnosis and treatment of at least the symptoms of mental illness, there has been frustratingly little progress in elucidating the molecular mechanisms. However, a fundamentally different approach to study molecular mechanisms of psychiatric diseases is emerging as a result of technological advances in expression profiling methods. This comprises the investigation of the expressed disease 'phenotypes', developing from the differential gene and protein expression in the central nervous system as a result of the complex interaction between genetic predisposition and environmental modulation. This paper will focus on proteomics, expression profiling at the protein level, reviewing some of the available tools and their application in the molecular analysis of psychiatric disease.

Antipsychotic Agents↗
PubMed2005

From proteomics to biomarker discovery in Alzheimer's disease.

Alzheimer's disease (AD) is the most common form of dementia in the elderly. AD is an invariably fatal neurodegenerative disorder with no effective treatment or definitive antemortem diagnostic test. Little is known about the changes in the brain preceding or accompanying initiation of the disease. Understanding the biological processes, which occur during AD onset and/or progression, will improve the diagnosis and treatment of the disease. As we will discuss in this review article, using high-throughput cDNA microarray we identified candidate genes whose expression is altered in the brain of cases at risk for AD dementia. However, it is possible that the use of the cDNA microarray technology alone may underestimate post-transcriptional modifications and therefore provides only a partial view of the biological problem of interest. As such, the combination of cDNA and protein arrays may provide a more global picture of the biological processes being studied. Based on this hypothesis, we initiated a series of high-throughput proteomic studies and found that the expressions of proteins involved in synaptic plasticity are selectively altered in the brain of cases at high risk to develop AD dementia (mild cognitive impairment; MCI). This is consistent with our cDNA microarray evidence showing that the expression of a-type synapsins is selectively altered in the brain of MCI cases. Collectively, these studies support the feasibility and usefulness of high-throughput cDNA microarray and proteomics techniques to study the sequential changes of distinctive gene expression patterns in the brain as a function of the progression of AD dementia.

Alzheimer Disease↗
PubMed2002

[Pharmaco-proteomic analysis: application of proteomic analysis to the discovery and development of new drugs].

Pharmacoproteomics may be defined as proteomics applied to the discovery of new therapeutic targets and to the study of drug effects. Proteomics is a powerful technique for analyzing the protein expression profiles in a biological system and its modifications in response to a stimulus or according to the physiological or pathophysiological states. Thus it is a technique of choice for the discovery of new drug targets. It is also an interesting approach for the study of the mode of action of treatments and preclinical drug development. This pharmacoproteomic approach may be particularly useful for the research of new molecular alterations implicated in type 2 diabetes and/or obesity and for the further characterization of existing or new drugs.

Animals↗
PubMed2003

Advances in encoding of colloids for combinatorial libraries: applications in genomics, proteomics and drug discovery.

The creation of enormous libraries of chemicals and their subsequent screening for bioactivity has been accelerated through recent developments in encoding solid supports. The ability to accurately identify the structure of a biomolecule that has exhibited activity is invaluable and is closer to realisation in the advent of smart nanoscience. In this review the evolution of encoding solid supports as platforms for combinatorial synthesis is traced. Current approaches to encoding solid supports are reviewed and their potential for use as supports for the high-throughput screening of split and mix libraries explored. Finally, a brief consideration of the status of the application of encoded libraries is provided including creative chemical and colloidal encoding.

Biopolymers↗
PubMed2003

A strategy for the comparative analysis of serum proteomes for the discovery of biomarkers for hepatocellular carcinoma.

Many of the emerging technologies for the global evaluation of gene expression, at both the RNA and protein level, are being applied to the problem of finding biomarkers for human disease progression. These analyses can be made difficult, however, by variation between samples that arises from both technical and nondisease related physiological or genetic causes. In an effort to identify serum polypeptides whose presence or absence correlates with the clinical status of patients at high risk for hepatocellular carcinoma (HCC), we have developed a strategy that helps to focus the analysis on meaningful changes in protein levels above the background of variation. For the current study we divided the patient population into four clinically defined diagnostic groups that represent a generally increasing risk for HCC. Chronic infection with hepatitis B virus (HBV) is a major risk factor for HCC and our groups included patients with no indication of liver disease (healthy), those with inactive chronic HBV, those with active chronic HBV, and patients with a diagnosis of HCC and history of chronic HBV infection. Serum polypeptides from these patients were first analyzed in two-dimensional gels by combining the serum from patients in each of the four groups to generate composite gel profiles. Analysis of these composite gels allowed us to identify two relatively abundant features that were reduced in the HCC group as compared to the healthy group. Tryptic fragment mass fingerprinting identified the features as a carboxy terminal fragment of complement C3 and an isoform of apolipoprotein A1. These two features were examined by two-dimensional gel electrophoresis of serum from each individual in the four groups in order to verify that the inter-group differences seen in composite gels reported changes in abundance for most members of the group, rather than extreme changes for a small fraction of the group. These preliminary studies suggest that a proteomic methodology can be used for the identification of serum biomarkers for HCC and other liver disease.

Amino Acid Sequence↗
PubMed2003

Prostate carcinoma tissue proteomics for biomarker discovery.

BACKGROUND: The advent of the prostate-specific antigen (PSA) test has had a profound impact on the diagnosis and treatment of prostate carcinoma. However, the use of PSA levels alone for screening for prostate carcinoma was compromised by the variations in the amount of PSA produced by the benign prostatic tissue specimens. Proteins were involved in various pathways that determine the behavior of a cell. Therefore, information regarding proteins may reveal drug targets and/or markers for early detection. METHODS: The authors used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to determine the protein profiles from fresh tissues of the prostate. Laser capture microdissection was performed to isolate pure populations of cells. RESULTS: The authors identified a protein with an average m/Z of 24,782.56 +/- 107.27 that was correlated with the presence of prostate carcinoma. Furthermore, using laser capture microdissection, they demonstrated that the origin of this protein, which the authors designated PCa-24, was derived from the epithelial cells of the prostate. PCa-24 expression was detected in 16 of 17 (94%) prostate carcinoma specimens but not in paired normal cells. In addition, this protein was not expressed in any of the 12 benign prostatic hyperplasia specimens that were assayed. CONCLUSIONS: PCa-24 may be useful a marker for prostate carcinoma.

Adult↗
PubMed2006

Plasma and cerebrospinal fluid-based protein biomarkers for motor neuron disease.

Motor neuron diseases (MNDs) and, in particular, amyotrophic lateral sclerosis (ALS), are a heterogeneous group of neurologic disorders characterized by the progressive loss of motor function. In ALS, a selective and relentless degeneration of both upper and lower motor neurons occurs, culminating in mortality typically within 5 years of symptom onset. However, survival rates vary among individual patients and can be from a few months to >10 years from diagnosis. Inadequacies in disease detection and treatment, along with a lack of diagnostic and prognostic tools, have prompted many to turn to proteomics-based biomarker discovery efforts. Proteomics refers to the study of the proteins expressed by a genome at a particular time, and the proteome can respond to and reflect the status of an organism, including health and disease states. Although an emerging field, proteomic applications promise to uncover biomarkers critical for differentiating patients with ALS and other MNDs from healthy individuals and from patients affected by other diseases. Ideally, these studies will also provide mechanistic information to facilitate identification of new drug targets for subsequent therapeutic development. In addition to proper experimental design, standard operating procedures for sample acquisition, preprocessing, and storage must be developed. Biological samples typically analyzed in proteomic studies of neurologic diseases include both plasma and cerebrospinal fluid (CSF). Recent studies have identified individual proteins and/or protein panels from blood plasma and CSF that represent putative biomarkers for ALS, although many of these proteins are not unique to this disease. Continued investigations are required to validate these initial findings and to further pursue the role of these proteins as diagnostic biomarkers or surrogate markers of disease progression. Protein biomarkers specific to ALS will additionally function to evaluate drug efficacy in clinical trials and to identify novel targets for drug design. It is hoped that proteomic technologies will soon integrate the basic biology of ALS with mechanistic disease information to achieve success in the clinical setting.

Amyotrophic Lateral Sclerosis↗
PubMed2002

Proteomics-based anticancer drug discovery and development.

Proteins are important targets for drug discovery and this applied to cancer as well because there is a defect in the protein machinery of the cell in malignancy. Proteomic technologies are now being integrated with genomic approaches for cancer drug discovery and target validation. Among the large number of proteomic technologies available for this purpose, the most important ones are 3-D protein structure determination, protein microarrays, laser capture microdissection and study of protein-protein and protein-drug interactions. Cancer biomarkers and several cell pathways are important drug targets. Several companies are involved in using proteomic technologies for drug discovery. Finally, proteomic approaches will play an important role in the discovery and development of personalized medicines.

Antineoplastic Agents↗
PubMed2002

Clinical proteomics for cancer biomarker discovery and therapeutic targeting.

As we emerge into the post-genome era, proteomics finds itself as the driving force field as we translate the nucleic acid information archive into understanding how the cell actually works and how disease processes operate. Even so, the traditionally held view of proteomics as simply cataloging and developing lists of the cellular protein repertoire of a cell are now changing, especially in the sub-discipline of clinical proteomics. The most relevant information archive to clinical applications and drug development involves the elucidation of the information flow of the cell; the "software" of protein pathway networks and circuitry. The deranged circuitry of the cell as the drug target itself as well as the effect of the drug on not just the target, but also the entire network, is what we now are striving towards. Clinical proteomics, as a new and most exciting sub-discipline of proteomics, involves the bench-to-bedside clinical application of proteomic tools. Unlike the genome, there are potentially thousands of proteomes: each cell type has its own unique proteome. Moreover, each cell type can alter its proteome depending on the unique tissue microenvironment in which it resides, giving rise to multiple permutations of a single proteome. Since there is no polymerase chain reaction equivalent to proteomics- identifying and discovering the "wiring diagram" of a human diseased cell in a biopsy specimen remains a daunting challenge. New micro-proteomic technologies are being and still need to be developed to drill down into the proteomes of clinically relevant material. Cancer, as a model disease, provides a fertile environment to study the application of proteomics at the bedside. The promise of clinical proteomics and the new technologies that are developed is that we will detect cancer earlier through discovery of biomarkers, we will discover the next generation of targets and imaging biomarkers, and we can then apply this knowledge to patient-tailored therapy.

Algorithms↗
PubMed2002

Proteomics for cancer biomarker discovery.

The emergence of novel technologies allows researchers to facilitate the comprehensive analyses of genomes, transcriptomes, and proteomes in health and disease. The information that is expected from such technologies may soon exert a dramatic change in the pace of cancer research and impact dramatically on the care of cancer patients. These approaches have already demonstrated the power of molecular medicine in discriminating among disease subtypes that are not recognizable by traditional pathologic criteria and in identifying specific genetic events involved in cancer progression. This review covers a selection of advances in the realm of proteomics and its promise for cancer biomarker discovery. It also addresses issues regarding sample preparation and specificity and discusses current challenges that need to be overcome. Finally, the review touches on the efforts of the Early Detection Research Network at the National Cancer Institute in promoting biomarker discovery for translation at the clinical level.

Affinity Labels↗
PubMed2006

Biomarker discovery from the plasma proteome using multidimensional fractionation proteomics.

Because biomarkers are typically low in abundance, the crucial step of biomarker discovery is to efficiently separate clinically relevant sets of proteins that might define disease stages and/or predict disease development. It is anticipated that a multi-dimensional fractionation system (MDFS) will provide an efficient means of separating low abundance proteins from plasma proteins, resulting in the extension of the detection limit. However, when using an MDFS to analyze the plasma proteome it is important to consider how sample processing, yield, resolution and throughput potential may influence the detection limit. This review evaluates the recent advances in MDFS research with respect to '4RS criterion' (4R: resolution, reproducibility, recovery, and robustness; 4S: simplicity, speed, selectivity and sensitivity) and discusses perspectives for future plasma-derived biomarker discovery.

Biomarkers↗