Proteomics-based discovery of biomarkers and therapeutic targets in neurodegenerative diseases: perspective of microglia and neuroinflammation

Proteomics is a field of study that characterises a complete profile of proteins present in a given cell, tissue or biological system. Recent advances in proteomics research made possible the identification of novel biomarkers and therapeutic targets in neurodegenerative diseases. The field of neurodegenerative diseases is particularly well suited for proteomic analysis, given the multifactorial nature of the diseases and complexity of the nervous system. As increasing evidence indicates that microglia-associated chronic neuroinflammation plays an important role in the pathophysiology of neurodegenerative diseases, microglial activation may be a potential drug target in CNS disorders. On the basis of both the neuroprotective and neurotoxic role of microglia, the proteomic analysis of microglial signalling and neuroinflammation may thus be used for the identification and validation of novel drug targets as well as for the discovery of useful biomarkers of disease diagnosis and progression in the CNS.     

Implications of salivary proteomics in drug discovery and development: a focus on cancer drug discovery

Human saliva proteomics has proven to be a novel approach in the search for protein biomarkers for non-invasive detection of human cancers. This approach may also have implications within the process of anti-cancer drug discovery. Information from saliva proteomic measurements may contribute to the target discovery and validation, assessment of efficacy and toxicity of candidate drugs, identification of disease subgroups, and prediction of responses of individual patients. In this article, we aim to give a brief overview on human saliva proteome analysis, as well as its applications to cancer biomarker discovery. Potential applications of saliva proteomics in anticancer drug discovery and development will also be discussed.     

Quantitative and targeted proteomics-based identification and validation of drug efficacy biomarkers

Proteomics refers to the large-scale study of proteins, providing comprehensive and quantitative information on proteins in tissue, blood, and cell samples. In many studies, proteomics utilizes liquid chromatography-mass spectrometry. Proteomics has developed from a qualitative methodology of protein identification to a quantitative methodology for comparing protein expression, and it is currently classified into two distinct methodologies: quantitative and targeted proteomics. Quantitative proteomics comprehensively identifies proteins in samples, providing quantitative information on large-scale comparative profiles of protein expression. Targeted proteomics simultaneously quantifies only target proteins with high sensitivity and specificity. Therefore, in biomarker research, quantitative proteomics is used for the identification of biomarker candidates, and targeted proteomics is used for the validation of biomarkers. Understanding the specific characteristics of each method is important for conducting appropriate proteomics studies. In this review, we introduced the different characteristics and applications of quantitative and targeted proteomics, and then discussed the results of our recent proteomics studies that focused on the identification and validation of biomarkers of drug efficacy. These findings may enable us to predict the outcomes of cancer therapy and drug-drug interactions with antibiotics through changes in the intestinal microbiome.     

Transgenic systems in drug discovery: from target identification to humanized mice

Pharmaceutical companies are faced with the challenge that only approximately 10% of compounds tested in costly clinical trials eventually become a new drug. Investment in early discovery research can decrease this attrition in late-stage R&D and focus resources on the best targets. Transgenic technology influences decision-making in target identification, target validation, and can also provide better models for human diseases, as well as models designed to alert researchers early about potential issues with drug metabolism and toxicity. Here we review how transgenic technology can reduce the late-stage attrition by increasing the quality of both the target and the compound.     

Scientific process, pharmacology and drug discovery

Scientific method in drug discovery has centered on generating a hypothesis (target identification) and hypothesis testing (target validation). Traditionally, both processes were performed using animal data, with the basic pharmacologist being pivotal. Many therapeutic areas that rely on human data to validate targets as animal models are seen as poorly predictive. Failures of molecules in Phase III for poor efficacy raise questions about target identification and validation. The clinical pharmacologist, working with the basic pharmacologist can play a major role in aiding target identification and by developing trial designs using small patient populations, mitigating the need for full Phase III studies to test the hypothesis. Technologies such as genomics, non-invasive imaging and proteomics are in the forefront of improving target identification and in some cases in providing paradigms for target validation in man.     

Animal models of human disease: Challenges in enabling translation

Animal models have historically played a critical role in the exploration and characterization of disease pathophysiology, target identification, and in the in vivo evaluation of novel therapeutic agents and treatments. In the wake of numerous clinical trial failures of new chemical entities (NCEs) with promising preclinical profiles, animal models in all therapeutic areas have been increasingly criticized for their limited ability to predict NCE efficacy, safety and toxicity in humans. The present review discusses some of the challenges associated with the evaluation and predictive validation of animal models, as well as methodological flaws in both preclinical and clinical study designs that may contribute to the current translational failure rate. The testing of disease hypotheses and NCEs in multiple disease models necessitates evaluation of pharmacokinetic/pharmacodynamic (PK/PD) relationships and the earlier development of validated disease-associated biomarkers to assess target engagement and NCE efficacy. Additionally, the transparent integration of efficacy and safety data derived from animal models into the hierarchical data sets generated preclinically is essential in order to derive a level of predictive utility consistent with the degree of validation and inherent limitations of current animal models. The predictive value of an animal model is thus only as useful as the context in which it is interpreted. Finally, rather than dismissing animal models as not very useful in the drug discovery process, additional resources, like those successfully used in the preclinical PK assessment used for the selection of lead NCEs, must be focused on improving existing and developing new animal models.     

Search for breast cancer-related biomarker proteins for drug discovery

The identification of biomarkers is a promising approach for the diagnosis and effective therapy of cancer. In particular, disease proteomics is a potentially useful method for identifying such biomarkers. However, very few biomarker proteins for drug development have been discovered using this approach. The main difficulty is to efficiently select potential biomarkers from the many candidate proteins identified by the proteomics approach. To circumvent this problem, we have developed "antibody proteomics technology" that can screen for biomarker proteins by isolating antibodies against each candidate in a rapid and comprehensive manner. Here, we applied "antibody proteomics technology" to breast cancer-related biomarker discovery and evaluated the utility of this novel technology. Cell extracts derived from breast tumor cells (SKBR3) and normal cells (184A1) were analyzed by two-dimensional differential gel electrophoresis (2D-DIGE) to identify proteins over-expressed in the tumor cells. Candidate proteins were extracted from the gel pieces, immobilized onto a nitrocellulose membrane using a dot blot apparatus and then used as target antigens in scFv-phage enrichment and selection. Following this in vitro phage selection procedure, scFvs binding to 21 different over-expressed proteins in tumor cells were successfully isolated within several weeks. The expression profiles of the identified proteins were then determined by tissue microarray analysis using the scFv-phages. Consequently, we identified three breast tumor-specific proteins. Our data demonstrates the utility of an antibody proteomics system for discovering and validating tumor-related proteins in pharmaceutical proteomics. Currently, we are analyzing the functions of these proteins to use them as diagnostic markers or therapeutic targets.     

Proteomics as a tool in the pharmaceutical drug design process

Proteomics is a technology platform that is gaining widespread use in drug discovery and drug development programs. Defined as the protein complement of the genome, the proteome is a varied and dynamic repertoire of molecules that in many ways dictates the functional form that is taken by the genome. The importance of proteomics is a direct consequence of the central role that proteins play in establishing the biological phenotype of organisms in healthy and diseased states. Moreover, proteins constitute the vast majority of drug targets against which pharmaceutical drug design processes are initiated. By studying interrelationships between proteins that occur in health and disease and following drug treatment, proteomics contributes important insight that can be used to determine the pathophysiological basis for disease and to study the mechanistic basis for drug action and toxicity. Proteomics is also an effective means to identify biomarkers that have the potential to improve decision making surrounding drug efficacy and safety issues based on data derived from the study of key tissues and the discovery and appropriate utilization of biomarkers.     

Biomarkers,validation and pharmacokinetic-pharmacodynamic modelling

Four elements are crucial to successful pharmacokinetic-pharmacodynamic (PK/PD) modelling and simulation for efficient and effective rational drug development: (i) mechanism-based biomarker selection and correlation to clinical endpoints; (ii) quantification of drug and/or metabolites in biological fluids under good laboratory practices (GLP); (iii) GLP-like biomarker method validation and measurements and; (iv) mechanism-based PK/PD modelling and validation. Biomarkers can provide great predictive value in early drug development if they reflect the mechanism of action for the intervention even if they do not become surrogate endpoints. PK/PD modelling and simulation can play a critical role in this process. Data from genomic and proteomics differentiating healthy versus disease states lead to biomarker discovery and identification. Multiple genes control complex diseases via hosts of gene products in biometabolic pathways and cell/organ signal transduction. Pilot exploratory studies should be conducted to identify pivotal biomarkers to be used for predictive clinical assessment of disease progression and the effect of drug intervention. Most biomarkers are endogenous macromolecules, which could be measured in biological fluids. Many exist in heterogeneous forms with varying activity and immunoreactivity, posting challenges for bioanalysis. Reliable and selective assays could be validated under a GLP-like environment for quantitative methods. While the need for consistent reference standards and quality control monitoring during sample analysis for biomarker assays are similar to that of drug molecules, many biomarkers have special requirements for sample collection that demand a well coordinated team management. Bioanalytical methods should be validated to meet study objectives at various drug development stages, and possess adequate performance to quantify biochemical responses specific to the target disease progression and drug intervention. Protocol design to produce sufficient data for PK/PD modelling would be more complex than that of PK. Knowledge of mechanism from discovery and preclinical studies are helpful for planning clinical study designs in cascade, sequential, crossover or replicate mode. The appropriate combination of biomarker identification and selection, bioanalytical methods development and validation for drugs and biomarkers, and mechanism-based PK/PD models for fitting data and predicting future clinical endpoints/outcomes provide powerful insights and guidance for effective and efficient rational drug development, toward safe and efficacious medicine for individual patients.     

Biomarkers in oncology drug development: rescuers or troublemakers?

Oncology is considered as the pioneer indication for the clinical application of molecular biomarkers. Newly developed targeted anticancer therapies call for the implementation of molecular biomarker strategies but even novel cytotoxic treatments use biomarkers for the assessment of efficacy and toxicity. Biomarkers may play several roles in the progression of a drug from research to personalised medicine. In particular biomarkers are used to understand the mechanism of action of a drug, monitor the modulation of the intended target, assess efficacy and safety, adapt dosing and schedule, select patients and prognosticate the clinical outcome. Nowadays, the use of biomarkers in oncology is still challenged as only a limited number of oncology drugs on the market have a companion biomarker test to be mandatorily performed before treatment. This is in contradiction with the current major investment the pharmaceutical sector is devoting to biomarker identification and development. What are the measurable milestones and outcomes of these investments? How does biomarker development contribute to reaching the ultimate goal of finding the right molecules for the right targets at the right doses and schedules for the right patients? This review provides a critical overview of recent salient achievements in the identification and development of biomarkers.     

Gene-Expression - Based Responses to Drug Treatment

Perfect drugs are potent, specific and nontoxic. Many compounds fail because of unexpected toxicity and lack of efficacy in later stages of clinical development. Therefore, more complete knowledge and understanding of the properties of a drug is needed at an earlier stage of drug development. DNA microarrays can yield gene expression profiles from cells or tissues treated with a compound. Such "expression fingerprints" are used in drug discovery for drug target identification and validation and for elucidating the mode of action of novel compounds during lead identification and optimization. Moreover, during drug development, DNA microarrays help in the discovery of new diagnostic and prognostic biomarkers, as well as in the prediction of resistance and toxic side effects. This review aims to assess to what extent the promise of gene expression profiling has already materialized for the different stages of drug discovery and development. (c) 2002 Prous Science. All rights reserved.     

Accelerating Drug Development Using Biomarkers: A Case Study with Sitagliptin, A Novel DPP4 Inhibitor for Type 2 Diabetes

The leveraged use of biomarkers presents an opportunity in understanding target engagement and disease impact while accelerating drug development. For effective integration in drug development, it is essential for biomarkers to aid in the elucidation of mechanisms of action and disease progression. The recent years have witnessed significant progress in biomarker selection, validation, and qualification, while enabling surrogate and clinical endpoint qualification and application. Biomarkers play a central role in target validation for novel mechanisms. They also play a central role in the learning/confirming paradigm, particularly when utilized in concert with pharmacokinetic/pharmacodynamic modeling. Clearly, these attributes make biomarker integration attractive for scientific and regulatory applications to new drug development. In this review, applications of proximal, or target engagement, and distal, or disease-related, biomarkers are highlighted using the example of the recent development of sitagliptin for type 2 diabetes, wherein elucidation of target engagement and disease-related biomarkers significantly accelerated sitagliptin drug development. Importantly, use of biomarkers as tools facilitated design of clinical efficacy trials while streamlining dose focus and optimization, the net impact of which reduced overall cycle time to filing as compared to the industry average.     

Introduction to chemical proteomics for drug discovery and development

A fundamental goal of chemical proteomics is to identify target proteins for bioactive small molecules and then apply them to drug discovery and development as valid and drugable targets. Here, we introduce integrated technologies for the rapid identification of target proteins, methodologies for validating them as drugable targets, and applications of chemical proteomics in drug discovery and development.     

The applications of biomarkers in early clinical drug development to improve decision-making processes

Selecting, evaluating and applying biomarkers in drug discovery and exploratory drug development do substantially shorten the time to reach a critical decision point. Biomarkers are most useful in the early phase of clinical development when measurement of clinical endpoints or true surrogates may be too time-consuming or cumbersome to provide timely proof of principle or dose-ranging information. The use of biomarkers in early drug development helps to streamline clinical development by determining whether the drug is reaching and affecting the molecular target in humans, delivering findings that are comparable to preclinical data, and by providing a measurable endpoint that predicts desired or undesired clinical effects. Critical decisions such as candidate selection, early proof of mechanism or proof of concept, dose ranging and patient stratification as well as the assessment of development risks regarding safety, toxicity and drug interactions can be based on measurement of appropriate biomarkers that are biologically and/or clinically validated. Preclinical and phase I development plans can be focused to support an early s.d. or m.d. biomarker study in healthy volunteers or mildly diseased patients, thus saving both resources and time. Dose estimates and patient stratification may reduce the size and duration of clinical studies in later phases of development, and safety and toxicity biomarkers may help to stop or continue a programme early on. Even if a biomarker fails in the validation process there may still be a benefit of having used it as more knowledge about pathophysiology of the disease and the drug may be obtained. Thus, appropriateness of biomarkers depends on the stage of development, development strategy and the medical indication. Examples of biomarkers in exploratory clinical development are given for the development of new drugs in various indications.     

Recent advances in clinical proteomics using mass spectrometry

The ultimate objective of clinical proteomics is the successful discovery, validation and translation of biomarkers, together with new therapeutic targets into medical practices. New highly developed technologies in proteomics and their use in understanding tumor biology have significant clinical potential in the diagnosis, prognosis and treatment of disease. Areas such as MS, new labeling technologies and advancements in bioinformatics systems are now used to successfully detect disease-associated biomarkers together with therapeutic targets in complex biological specimens, including biofluids, cell lysates and tissue biopsies. Recent improvements in sample preparation (specifically focused on fractionation and enrichment) are enabling the analysis of low-abundance proteins together with many types of post-translational modifications. Targeted proteomic diagnostics will play a significant role in the development of personalized molecular medicine, a process that will be vital in modernizing healthcare structures.     

Aptamers as tools for target prioritization and lead identification

The increasing number of potential drug target candidates has driven the development of novel technologies designed to identify functionally important targets and enhance the subsequent lead discovery process. Highly specific synthetic nucleic acid ligands – also known as aptamers – offer a new exciting route in the drug discovery process by linking target validation directly with HTS. Recently, aptamers have proven to be valuable tools for modulating the function of endogenous cellular proteins in their natural environment. A set of technologies has been developed to use these sophisticated ligands for the validation of potential drug targets in disease models. Moreover, aptamers that are specific antagonists of protein function can act as substitute interaction partners in HTS assays to facilitate the identification of small-molecule lead compounds.     

Target discovery

Target discovery, which involves the identification and early validation of disease-modifying targets, is an essential first step in the drug discovery pipeline. Indeed, the drive to determine protein function has been stimulated, both in industry and academia, by the completion of the human genome project. In this article, we critically examine the strategies and methodologies used for both the identification and validation of disease-relevant proteins. In particular, we will examine the likely impact of recent technological advances, including genomics, proteomics, small interfering RNA and mouse knockout models, and conclude by speculating on future trends.