Van Eyk, Dunn - Proteomic and Genomic Analysis of Cardiovascular Disease - 2003 (522919), страница 96
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However, the random testing of a large number ofcompounds has had limited success in accelerating the rate of drug discovery andthe results have been compounded by the limitation of cell-based assays to reflectthe complexity of the molecular biology of living organisms. Thus, the focus is toward the utilization of tools of structural genomics to simplify the screening process and support the results with biology evidence.34935020 Genomics Perspective for Drug DiscoveryThe process of drug design and sampling of the compounds against the targetprotein has been significantly accelerated by advances in structural genomics.Tools of structural biology, such as X-ray crystallography and magnetic resonancespectroscopy in conjunction with bio-informatics have led to accelerated characterization of the three dimensional structure of proteins, which could provide majorclues for appropriate selection and design of drugs.
The three dimensional structure of a variety of proteins expressed in the cardiovascular system have been determined, including cardiac troponin C [64], myosin heavy chain [65], FKBP12.6,an isoform of FKBP12 that selectively binds to the cardiac ryanodine receptor(RyR2) [66], HERG potassium channel N terminus [67], calsequestrin [68], cytochrome c oxidase [69], integrin-binding fragment of human VCAM-1 [70], humanrecombinant cystathionine beta-synthase [71], hormone-binding domain of a guanylyl-cyclase-coupled receptor [72], N-terminal two domains of intercellular adhesion molecule-1 [73], VEGF in complex with domain 2 of the Flt-1 receptor [74],among many others.The initial lead compound usually does not have the desirable therapeutic index, necessitating further refinement of the lead molecule.
Typically, structuralmodifications are made to the lead molecule and the modified molecules aretested in cell cultures and animal models until a compound with a desirable therapeutic index is identified and selected for testing in humans. Delineating thecrystal structure of the target and lead molecules could accelerate the process oflead refinement. This is illustrated for HMG CoA reductase and its inhibitors(statins). Despite HMG CoA reductase inhibitors being the cornerstone of treatment of dyslipidemia, there is substantial inter-individual variability in the response of plasma lipids to treatment with statins. In an attempt to further refineand develop new HMG Co A reductase inhibitors, the structural basis for bindingof statins to HMG CoA reductase has been delineated [75].
Accordingly, statins occupy the enzymatic active site of HMG CoA reductase by forming multiple polarand van der Waals interactions with the enzyme, blocking its access to the substrate [75]. However, statins do not occupy the nicotinamide-binding site of the enzyme. This information provides the opportunity to design and develop statinswith the nicotinamide-like moiety. Such drugs, through covalent attachment tothe nicotinamide-binding site, in addition to binding to the active enzymatic site,will be expected to confer higher potency.Genomic tools also have been utilized in designing and developing more clotspecific thrombolytic agents, such as the mutant forms of tissue type plasminogen (tPA) [76, 77] and streptokinase [78]. Molecular biology studies suggest a catalytic domain in the N terminus of streptokinase can activate plasminogen throughboth fibrin-dependent and fibrin-independent mechanisms. Deletion of 59 aminoacids from the N terminus catalytic domain decreases the fibrin-independent plasminogen activity by > 600-fold [78].
As a result, in vitro assays show the mutantstreptokinase confers total clot lysis with minimal fibrinogen degradation [78].These findings raise the possibility of developing safer and more effective mutantforms of streptokinase. Similarly, several variants of tPA have been developed andtested with the goal of establishing safer, more effective and simple to use agents.20.3 Genomics and the Process of Drug DiscoveryAn example of mutant tPA is the TNK-tPA, which has a longer plasma half-life,enhanced fibrin-specificity, and increased resistance to the plasminogen activatorinhibitor-1 [76, 77].While structural genomics provides major clues for the design, selection, andrefinement of the lead compounds, protein-protein interactions also could lead toadditional target and lead identification.
Molecular biology tools, such as yeasttwo-hybrid screening, could identify proteins that interact with the original targetas the potential secondary targets for drug design and validation. Yeast, a unicellular organism, has mammalian homologue genes and could be used for screeningand identification of lead compounds [79]. Thus, lead compounds are identifiedbased on the structural and biological function of the target molecule.20.3.4Genomics and Lead ValidationTools of genomics are utilized in conjunction with pharmacological experimentsto validate leads as therapeutic agents, to establish their safety, efficacy and potency and define their side effects. In general, lead validation encompasses threephases of in vitro and cell culture studies, testing the compounds in animal models of human disease, and ultimately through performing clinical trials in humans.
Cell culture and in vitro experiments determine whether the candidate leadcompound confers the expected phenotype, such as blocking the target receptor orenzyme, at desirable specificity and concentration. The lead compounds are testedin animal models to determine their efficacy, safety, and side effects. The generalgoal is to determine whether administration of the candidate compound producesthe desirable effect on the phenotype of interest without causing major side effects. Ultimately, clinical trials are performed to determine the efficacy, potencyand toxicity of the candidate compound in humans (different phases of clinicaltrials).In the first and second stage of lead validation a variety of molecular biology experiments and genetic manipulations are used.
Gene transfer studies and functional genomics in animal models could provide crucial efficacy and safety data.Examples are abound and includes a variety of studies in animal models including transgenic animal models of human diseases, such as validation of a CETP inhibitor in a rabbit model of dyslipidemia, as discussed earlier [49]. Similarly, validation studies of TNK-tPA were initially performed in a rabbit model of thrombosis, which showed significantly higher potency and clot selectivity of TNK-tPAthan the wild type tPA. Initial validation studies of mutant streptokinase D59were performed in in vitro assays [78]. Furthermore, validation studies of inhibition of bARK1 have been performed through transgenesis and crosses of transgenic mice [63].
Finally, knock out models, such as apoE-/- [80, 81] and LDLR-/[82] mice provide excellent opportunities to test the effects of lead compounds onthe phenotype of interest, such as dyslipidemia and atherosclerosis.35135220 Genomics Perspective for Drug Discovery20.3.5Genomics and Individualization of Drug TherapyInter-individual variation in response to drugs, both beneficial and harmful, hasbeen recognized since the dawning of medicine.
One of the first examples is thevariability in response to isoniazid and individuals were considered either as “fast”or “slow” metabolizers. It was subsequently recognized that differences in the rateof acetylation of isoniazid, the major route of its elimination, was responsible forthe inter-individual variability. Another well-recognized example is response towarfarin therapy, which is encountered commonly in the daily practice of cardiology. While the familial basis of variability in response to drug therapy has beenrecognized for many years, its molecular genetic basis remain largely unknown.The contribution of genomics is to identify the genetic basis of variability in drugresponses and thus, to establish individualized drug therapy.Completion of The Human Genome Project and development of SNP and haplotype maps have provided the opportunity to map and identify genes and theirvariants involved in response to drug therapy.
As a result, the field of identification of genetic determinants of response to therapy, often referred to as “pharmacogenetics”, has become the focus of extensive research by the pharmaceuticaland biotechnology industry. The ultimate goal is to tailor drug therapy accordingto the genetic profile in order to maximize the beneficial effects while minimizingthe side effects. Extensive review of this topic is beyond the scope of this Chapterand the reader is referred to an excellent recent review [83].The existing knowledge is largely based on SNP-association studies, which are subject to a high rate of spurious results [84]. Large-scale studies are ongoing to identifythe genetic determinants of response to drugs.
Nevertheless, despite the provisionalresults of association studies, it has become evident that SNPs are major determinants of variability to drug response in each stage of drug effects. SNPs and haplotypes could contribute to variability in response to therapy by affecting the pharmacokinetics, namely absorption, distribution, metabolism and elimination of the drug.This is exemplified in cloning and identification of SNPs in cytochrome p450 genes,which have led to identification of several variants that affect drug metabolism.
SNPsin CYP2C9 have been found to affect the anticoagulant effect of warfarin [85]. Similarly, SNPs in CYP2D6, encoding debrisoquine hydroxylase, which catalyze the oxidation of drugs containing a basic nitrogen, affect blood levels of b blockers [86]. Subjects with SNPs in CYP2D6 that results in loss of function are the “poor metabolizers”and subjects with gain of function SNPs are considered “rapid metabolizers”.
Indeed,inter-ethnic variation in response to drugs is partly due to differences in the prevalence of loss of function and gain of function SNPs in CYP2D6 among different ethnic populations. In addition, SNPs in the drug metabolizing genes could affect drugdrug interactions as exemplified by the inter-individual variability in interactions between erythromycin and antiarrhythmic drugs.
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