Van Eyk, Dunn - Proteomic and Genomic Analysis of Cardiovascular Disease - 2003 (522919), страница 100
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The key to formulating the next generation of therapeutics isthe context of gene and protein expression, both in relationship to disease pathophysiology. Simplistically, any alteration in gene and protein expression can beviewed as a potential molecular target ripe for drug discovery. Traditional biochemical, pharmacology, and molecular biology will still need to be applied to theidentification of targets. However, the drug discovery process can now be augmented significantly with genomic information at the level of chromosomal DNA (human genetics), disease gene associations (via analysis of single nucleotide polymorphisms or SNPs), mRNA profiling (via gene expression or “gene chip” arrayanalysis), genetic animal models of disease (with transgenic knockouts, overex-36136221 Proteomics in Pharmaceutical R & DFig.
21.1 The modern drug discovery and development process and the role of proteomics. Depicted, from top to bottom, are thebroad discovery and development stages thatare comprised of critical phases. The specificsteps from genes to marketing of a new drugare listed in the third tier, and followed bytheir disciplinary focus. Finally, the varioustechnologies are listed relative to their application in the overall process.
The role of proteomics is noted in the shaded boxes. Whilethe technologies are illustrated in discretetimeframes, in many cases much overlap occurs that renders the overall drug discovery anddevelopment process as quite dynamic, andcertainly adaptive to the needs of any one particular project.
CIR = confidence in rationale; I,II, III = Clinical Phases 1, II, III; HTS = highthroughput screening; ADME = absorption,distribution, metabolism and elimination/excretion; SAR = structure-activity relationship;MOA = mechanism of action; KO = knockout;IVSG = in vitro; PK = pharmacokinetic;PDM = pharmacodynamics and metabolism.pression, phenotypic screening), and bioinformatic/computational searching (forparalogues of known drug targets – see the International Human Genome Consortium description of identifying eighteen new targets including dopamine andinsulin-like growth factor receptors [2]).
Additionally, proteomics and functionalgenomics can provide a critical insight into the functional role of specific proteins. Advances in analytical proteomic techniques will, one hopes, allow for agreater level of sensitivity in detection and a quantitative assessment of proteinchanges in disease states. This, coupled with bioinformatic analysis, should allowthe development of protein network maps that can provide meaningful and noveltargets for drug discovery.
Application of high-throughput automation with all theaforementioned approaches should make target identification a far more rapidprocess. Clearly, the knowledge of the drug target and its function will permit abetter understanding of the mechanism of drug action that will guide clinicaltrials and facilitate drug development.Target validation has become an increasingly important component in the drugdiscovery process as the number of potential new drug targets grows. Already21.1 Perspectives on the Drug Discovery and Development Processthere are the emerging signs that large numbers and/or poorly characterizedgenomic targets can create a bottleneck in drug discovery pipelines.
In contrast toprevious R&D paradigms where target validation was in many instances a definable stage with limited means for confirmation (i.e., low-throughput animal studies and cellular assays), the process of validating a molecular target can now beviewed as being continuous, though high-throughput approaches have not yetbeen applied. Indeed, a target can only be defined as truly validated when thedrug candidate is approved in the clinic – thus meeting its characterization asbeing an effector of a therapeutic agent that has a desired clinical use and benefitwhen modulated in humans. In addition, target validation can now be achievedthrough multiple approaches, performed independently or collectively at molecular, cellular, animal or patient levels. New technologies are improving the pace oftarget validation by assisting in the establishment of a genotype-phenotype relationship – these include SNP analysis, gene and protein expression assessmentusing microarrays and proteomics, gene- and protein-specific “knockouts” eitherin cells or animals (as performed on a high-throughput level by Deltagen, LexiconGenetics and Paradigm Therapeutics), antisense and RNAi technologies, structural biology for elucidating drug-target interactions, and chemical genomics (wheremembers of combinatorial libraries are linked with special functionalities andused in in vitro screens [e.g., as developed by Novalon, Scriptgen, Chiron, NeoGenesis] to identify specific ligand-target interactions).
Target validation now cantake 9–12 months generally, sometimes less, as compared to 18 months in thepast. However, the sheer numbers of targets and compounds being generatednow have neither provided the high-quality leads anticipated nor even increasedthe rate at which such leads are validated. The pharmaceutical industry has onlybegun to grapple with this issue, and it is likely that target validation will continue to receive considerable attention in an attempt to improve its utility in thedrug discovery and development process.The lead optimization step in drug discovery is where early chemical leads(generated from high-throughput screening (HTS) of large chemical libraries) canbe optimized for biological activity, pharmacodynamic and pharmokinetic profiles,safety, and clinical predictiveness.
Most companies have now incorporated manyof these components as early as possible, and placed them on parallel, rather thansequential tracks, to more quickly ascertain the quality of lead compounds. Typically, this comprises an iterative process between chemical modifications and biological activities, with each cycle improving the compound until results against allclinical parameters (i.e., efficacy, selectivity/specificity, toxicology, potency, metabolic stability, and delivery) are optimized.
Pharmaceutical companies havelaunched major initiatives to integrate predictive screening for absorption, metabolism, distribution, and elimination/excretion (ADME), and physiochemical optimization (i.e., permeability, solubility, logD, pKa, and stability) early in the testingschemes, sometimes even prior to in vivo efficacy studies. Broadly speaking, anacute and unmet need is the application of automated high-throughput technologies to lead optimization. The intent is to replace traditional low-throughput testing, much of it animal-based, and still impeded by considerable regulatory guide-36336421 Proteomics in Pharmaceutical R & Dlines that have not yet embraced the validity of all new technologies.
Novel technologies for lead optimization now include genetically modified animals or cellsthat can provide predictive in vivo results using “humanized” genes/proteins.Technologies such as toxicoproteomics and toxicogenomics, which deal primarilywith effects of compounds on protein and gene expression patterns in target cellsor tissues, are emerging as key approaches in the pharmaceutical industry’s current initiative to introduce high-quality, chemical entities more rapidly, and withless attrition and cost. For instance, the development of toxicology-relevant genomic and proteomic databases, as noted in sections below, is an emerging activitywithin several biotech and big pharma companies.
It will now be possible to helppredict toxicity of new compounds by comparing their profiles to those of compounds with known mechanisms of action.Another major challenge in accelerating drug development is the clinical evaluation stage. Here the pharmaceutical industry has begun to place more emphasison overlapping phases of clinical development.
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