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      In silico pharmacology for drug discovery: applications to targets and beyond

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          Abstract

          Computational ( in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

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          Most cited references154

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          Global mapping of pharmacological space.

          We present the global mapping of pharmacological space by the integration of several vast sources of medicinal chemistry structure-activity relationships (SAR) data. Our comprehensive mapping of pharmacological space enables us to identify confidently the human targets for which chemical tools and drugs have been discovered to date. The integration of SAR data from diverse sources by unique canonical chemical structure, protein sequence and disease indication enables the construction of a ligand-target matrix to explore the global relationships between chemical structure and biological targets. Using the data matrix, we are able to catalog the links between proteins in chemical space as a polypharmacology interaction network. We demonstrate that probabilistic models can be used to predict pharmacology from a large knowledge base. The relationships between proteins, chemical structures and drug-like properties provide a framework for developing a probabilistic approach to drug discovery that can be exploited to increase research productivity.
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            A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening.

            High-throughput and virtual screening are widely used to discover novel leads for drug design. On examination, many screening hits appear non-drug-like: they act noncompetitively, show little relationship between structure and activity, and have poor selectivity. Attempts to develop these peculiar molecules into viable leads are often futile, and much time can be wasted on the characterization of these "phony" hits. Despite their common occurrence, the mechanism of action of these promiscuous molecules remains unknown. To investigate this problem, 45 diverse screening hits were studied. Fifteen of these were previously reported as inhibitors of various receptors, including beta-lactamase, malarial protease, dihydrofolate reductase, HIV Tar RNA, thymidylate synthase, kinesin, insulin receptor, tyrosine kinases, farnesyltransferase, gyrase, prions, triosephosphate isomerase, nitric oxide synthase, phosphoinositide 3-kinase, and integrase; 30 were from an in-house screening library of a major pharmaceutical company. In addition to their original targets, 35 of these 45 compounds were shown to inhibit several unrelated model enzymes. These 35 screening hits included compounds, such as fullerenes, dyes, and quercetin, that have repeatedly shown activity against diverse targets. When tested against the model enzymes, the compounds showed time-dependent but reversible inhibition that was dramatically attenuated by albumin, guanidinium, or urea. Surprisingly, increasing the concentration of the model enzymes 10-fold largely eliminated inhibition, despite a 1000-fold excess of inhibitor; a well-behaved competitive inhibitor did not show this behavior. One model to explain these observations was that the active form of the promiscuous inhibitors was an aggregate of many individual molecules. To test this hypothesis, light scattering and electron microscopy experiments were performed. The nonspecific inhibitors were observed to form particles of 30-400 nm diameter by both techniques. In control experiments, a well-behaved competitive inhibitor and an inactive dye-like molecule were not observed to form aggregates. Consistent with the hypothesis that the aggregates are the inhibitory species, the particle size and IC(50) values of the promiscuous inhibitors varied monotonically with ionic strength; a competitive inhibitor was unaffected by changes in ionic strength. Unexpectedly, aggregate formation appears to explain the activity of many nonspecific inhibitors and may account for the activity of many promiscuous screening hits. Molecules acting via this mechanism may be widespread in drug discovery screening databases. Recognition of these compounds may improve screening results in many areas of pharmaceutical interest.
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              From magic bullets to designed multiple ligands.

              Increasingly, it is being recognised that a balanced modulation of several targets can provide a superior therapeutic effect and side effect profile compared to the action of a selective ligand. Rational approaches in which structural features from selective ligands are combined have produced designed multiple ligands that span a wide variety of targets and target classes. A key challenge in the design of multiple ligands is attaining a balanced activity at each target of interest while simultaneously achieving a wider selectivity and a suitable pharmacokinetic profile. An analysis of literature examples reveals trends and insights that might help medicinal chemists discover the next generation of these types of compounds.
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                Author and article information

                Journal
                Br J Pharmacol
                British Journal of Pharmacology
                Nature Publishing Group
                0007-1188
                1476-5381
                04 June 2007
                27 August 2007
                September 2007
                : 152
                : 1
                : 21-37
                Affiliations
                [1 ]ACT LLC New York, NY, USA
                [2 ]Department of Pharmaceutical Sciences, University of Maryland Baltimore, MD, USA
                [3 ]Chemogenomics Laboratory, Research Unit on Biomedical Informatics, Institut Municipal d'Investigació Mèdica and Universitat Pompeu Fabra, Parc de Recerca Biomèdica Barcelona CAT, Spain
                [4 ]University Hospital Centre Lausanne, Switzerland
                Author notes
                Article
                0707306
                10.1038/sj.bjp.0707306
                1978280
                17549046
                07e18ba0-77c1-4721-9097-f449ad91bab9
                Copyright 2007, Nature Publishing Group
                History
                : 21 December 2006
                : 27 February 2007
                : 25 April 2007
                Categories
                Reviews
                Review Article

                Pharmacology & Pharmaceutical medicine
                quantitative structure–activity relationships,homology models,docking,pharmacology,adme/tox,in silico,in vitro,drug discovery,computational

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