The search for drug leads targeted to the beta-secretase: an example of the roles of computer assisted approaches in drug discovery

The inhibition of beta-secretase has become a very promising approach to control the onset and progression of Alzheimer's disease due to its involvement in the generation of amyloid plaques. The main goal of the many drug discovery projects targeting this enzyme is the identification of highly specific, non-peptidic compounds with low molecular weight, characteristics that are desirable for drug leads with low toxicity that have to transverse the blood brain barrier. We describe the main approaches used in the discovery of novel inhibitors, including substrate specificity, target structure based design, and high throughput screening (HTS), both in vitro and in silico. We place special emphasis in the receptor based design and in silico HTS, two strategies that make wide use of computer assisted tools. To exemplify the usefulness and versatility of computer tools in this endeavor we use the computer generated 'enzyme's binding site cast' to rationalize qualitatively some salient structural features of known beta-secretase second generation inhibitors, and for guiding the review of many of the ligands whose complexes with the enzyme have been studied by X-ray crystallography. We discuss the use made by other authors of molecular modelling for the understanding of the very special characteristics of ligand binding to beta-secretase and for the design of new inhibitors. Finally, we review the quest for non-peptidic inhibitors that has led to the use of HTS in vitro and in silico. The screening of extensive libraries resulted in a few low affinity compounds that do not fit into the key S1/S1' pockets, indicating that this is not an easy target to block.     

Current topics in computer-aided drug design

The addition of computer-aided drug design (CADD) technologies to the research and drug discovery approaches could lead to a reduction of up to 50% in the cost of drug design. Designing a drug is the process of finding or creating a molecule which has a specific activity on a biological organism. Development and drug discovery is a time-consuming, expensive, and interdisciplinary process whereas scientific advancements during the past two decades have altered the way pharmaceutical research produces new bioactive molecules. Advances in computational techniques and hardware solutions have enabled in silico methods to speed up lead optimization and identification. We will review current topics in computer-aided molecular design underscoring some of the most recent approaches and interdisciplinary processes. We will discuss some of the most efficient pathways and design.     

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

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 expediting the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.     

From laptop to benchtop to bedside: structure-based drug design on protein targets

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting proteinligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/ optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches.     

Shape Signatures: speeding up computer aided drug discovery

Identifying potential lead molecules is becoming a more automated process. We review Shape Signatures, a tool that is effective and easy to use compared with most computer aided drug design techniques. Laboratory researchers can apply this in silico technique cost-effectively without the need for specialized computer backgrounds. Identifying a potential lead molecule requires database screening, and this becomes rate-limiting once the database becomes too large. The use of Shape Signatures eliminates this concern and offers molecule screening rates that are in advance of any currently available method. Shape Signatures provides a conduit for researchers to conduct rapid identification of potential active molecules, and studies with this tool can be initiated with only one bioactive lead or receptor site.     

Focus on success: using a probabilistic approach to achieve an optimal balance of compound properties in drug discovery

The success of any drug will depend on how closely it achieves an ideal combination of potency, selectivity, pharmacokinetics and safety. The key to achieving this success efficiently is to consider the overall balance of molecular properties of compounds against the ideal profile for the therapeutic indication from the earliest stages of a drug discovery project. The use of in silico predictive models of absorption, distribution, metabolism and elimination (ADME) and physicochemical properties is a major aid in this exercise, as it enables virtual molecules to be assessed across a broad range of properties from initial library generation, through to candidate selection. Of course, no measurement, whether in silico, in vitro or in vivo, is perfect and the uncertainties in any data should be explicitly taken into account when basing conclusions on test results. In addition, in the early stages of drug discovery, when designing a library that is lead seeking or building compound structure-activity relationships, the quality of any set of molecules should also be balanced against the chemical diversity covered. Here, a scheme is presented for achieving these goals based on a suite of predictive ADME models, probabilistic scoring and multiobjective optimisation for library design. The use of this platform for applications in lead identification and optimisation is illustrated.     

Principles of early drug discovery

Developing a new drug from original idea to the launch of a finished product is a complex process which can take 12-15 years and cost in excess of $1 billion. The idea for a target can come from a variety of sources including academic and clinical research and from the commercial sector. It may take many years to build up a body of supporting evidence before selecting a target for a costly drug discovery programme. Once a target has been chosen, the pharmaceutical industry and more recently some academic centres have streamlined a number of early processes to identify molecules which possess suitable characteristics to make acceptable drugs. This review will look at key preclinical stages of the drug discovery process, from initial target identification and validation, through assay development, high throughput screening, hit identification, lead optimization and finally the selection of a candidate molecule for clinical development.     

Optimizing the use of open-source software applications in drug discovery

Drug discovery is a time consuming and costly process. Recently, a trend towards the use of in silico computational chemistry and molecular modeling for computer-aided drug design has gained significant momentum. This review investigates the application of free and/or open-source software in the drug discovery process. Among the reviewed software programs are applications programmed in JAVA, Perl and Python, as well as resources including software libraries. These programs might be useful for cheminformatics approaches to drug discovery, including QSAR studies, energy minimization and docking studies in drug design endeavors. Furthermore, this review explores options for integrating available computer modeling open-source software applications in drug discovery programs.     

In silico research in drug discovery

Target and lead discovery constitute the main components of today's early pharmaceutical research. The aim of target discovery is the identification and validation of suitable drug targets for therapeutic intervention, whereas lead discovery identifies novel chemical molecules that act on those targets. With the near completion of the human genome sequencing, bioinformatics has established itself as an essential tool in target discovery and the in silico analysis of gene expression and gene function are now an integral part of it, facilitating the selection of the most relevant targets for a disease under study. In lead discovery, advances in chemoinformatics have led to the design of compound libraries in silico that can be screened virtually. Moreover, computational methods are being developed to predict the drug-likeness of compounds. Thus, drug discovery is already on the road towards electronic R&D.     

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models 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 aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.     

药物分子设计的策略：苗头和先导物的品质决定新药的成败

苗头化合物-先导物-候选药物是创制新药的三个重要里程碑，其中候选药物的确定是新药创制的关键环节，将创制过程分成研究和开发两个阶段，并且开发阶段所有环节都取决于候选药物的化学结构，所以决定了临床前和临床研究的命运。候选药物质量的高低又受制于先导化合物的类药性和苗头化合物的品质，苗头化合物演化成先导物是将新药的研究植根于有研发前景的结构上，先导物的优化是将活性化合物转化成候选药物的过程，是在药效、药代、安全性和物化性质等多维空间中的优化操作。本文结合实例讨论了发现苗头、确定先导物、先导物优化和确定候选药物的策略原则。     

The devil is still in the details--driving early drug discovery forward with biophysical experimental methods

This review comments on some recent trends and insights in the field of lead identification and optimization with a bias toward the increased use of biophysical methods, particularly in combination with three-dimensional structural information. While high-throughput screening, combinatorial chemistry and, most recently, in silico virtual screening techniques have made well-resourced but only partially successful attempts to meet the challenge of identifying new drug candidates by playing 'the large numbers game', another group of technologies are now approaching the same challenge from what might be considered the opposite extreme. The common strategy of these technologies is to focus on a smaller set of low-molecular-weight compounds whose interactions with a target are characterized with the aid of sensitive assays, most often high-quality biophysical techniques such as biosensors, calorimetry, nuclear magnetic resonance spectroscopy and X-ray crystallography. The advantages of such an approach include more optimal and chemically attractive starting points, immediate access to reliable measurements of binding properties, the mapping of ligand interactions on the atomic level and, most importantly, a greater control of experimental errors at the initial stages of drug discovery where compounds are either discovered or lost. When correctly supported, this more careful approach appears to deliver quality leads, even for the so-called 'difficult' targets. As these techniques are complementary to traditional methods, companies should be less hesitant to invest in them. The biophysical methods that are used to drive this approach have made something of a return to drug discovery after having been discarded for being too slow, too expensive or too old-fashioned by the over-optimistic supporters of high-throughput and statistical/computational in silico methods.     

In silico search for drug targets of natural compounds

Natural compounds represent a significant source for the development of novel medicines. Finding the target proteins for a natural compound is the most important step towards understanding its molecular mechanism for therapeutic usage. In fact, the search for target proteins could be considered the first step of the drug discovery and development pipeline. While experimental determination of compound-protein interactions remains very challenging, effective in silico approaches have been developed and have demonstrated appealing advantages, including their low-cost and capability to scale up easily. The goal of this article is to provide an introduction to in silico search for drug targets of natural compounds. I first review currently available natural compounds databases and human gene/protein databases, and the rapidly emerging databases for known drug-target interactions. These resources provide the 'materials' for in silico approaches and define the gold standard of 'positives' for evaluating them. I then introduce three classes of computational methods for target identification of natural compounds, namely molecular docking, quantitative structure-activity relationship (QSAR) modeling, and data mining and integrative analysis. Use of these methods is explained using real examples, and the advantages and disadvantages of each method are compared. As these state-of-the-art methods continue to mature amid significant challenges, this field appears poised for a period of significant growth, with untold benefits to drug discovery and natural product development.     

Chemical genomics strategy for the discovery of new anticancer agents

Chemical genomics represents a cooperation of biology and chemistry to identify and intervene the biological targets. Small molecules with diverse structural characteristics should be used to validate the target through interfering with the biological processes. Because of the limitation of existing chemical libraries, the diversity can be exploited using both the molecular design techniques; structure-based design and ligand-based design. These methods can guide the selection of small molecules with optimal binding properties to desired biological targets. Studies of potential molecular targets for novel anticancer drug discovery including in silico screening, QSAR, and de novo design demonstrated the importance of chemical genomics strategy to find the chemical probes and drug lead compounds.     

Development of drugs for Epstein-Barr virus using high-throughput in silico virtual screening

Importance of the field: Epstein-Barr virus (EBV) is a ubiquitous human herpesvirus that is causally associated with endemic forms of Burkitt's lymphoma, nasopharyngeal carcinoma and lymphoproliferative disease in immunosuppressed individuals. On a global scale, EBV infects > 90% of the adult population and is responsible for ～ 1% of all human cancers. To date, there is no efficacious drug or therapy for the treatment of EBV infection and EBV-related diseases. Areas covered in this review: In this review, we discuss the existing anti-EBV inhibitors and those under development. We discuss the value of different molecular targets, including EBV lytic DNA replication enzymes as well as proteins that are expressed exclusively during latent infection, such as EBV nuclear antigen 1 (EBNA-1) and latent membrane protein 1. As the atomic structure of the EBNA-1 DNA binding domain has been described, it is an attractive target for in silico methods of drug design and small molecule screening. We discuss the use of computational methods that can greatly facilitate the development of novel inhibitors and how in silico screening methods can be applied to target proteins with known structures, such as EBNA-1, to treat EBV infection and disease. What the reader will gain: The reader is familiarized with the problems in targeting of EBV for inhibition by small molecules and how computational methods can greatly facilitate this process. Take home message: Despite the impressive efficacy of nucleoside analogs for the treatment of herpesvirus lytic infection, there remain few effective treatments for latent infections. As EBV latent infection persists within and contributes to the formation of EBV-associated cancers, targeting EBV latent proteins is an unmet medical need. High-throughput in silico screening can accelerate the process of drug discovery for novel and selective agents that inhibit EBV latent infection and associated disease.     

2-(4-Chlorobenzyl)-3-hydroxy-7,8,9,10-tetrahydrobenzo[H]quinoline-4-carboxylic acid (PSI-697): identification of a clinical candidate from the quinoline salicylic acid series of P-selectin antagonists

P-selectin-PSGL-1 interaction causes rolling of leukocytes on the endothelial cell surface, which subsequently leads to firm adherence and leukocyte transmigration through the vessel wall into the surrounding tissues. P-selectin is upregulated on the surface of both platelets and endothelium in a variety of atherosclerosis-associated conditions. Consequently, inhibition of this interaction by means of a small molecule P-selectin antagonist is an attractive strategy for the treatment of atherosclerosis. High-throughput screening and subsequent analoging had led to the identification of compound 1 as the lead candidate. Herein, we report the continuation of this work and the discovery of a second-generation series, the tetrahydrobenzoquinoline salicylic acids. These compounds have improved pharmacokinetic properties, and a number of them have shown oral efficacy in mouse and rat models of atherogenesis and vascular injury. The lead 31 (PSI-697), is currently in clinical development for the treatment of atherothrombotic vascular events.     

靶向K-ras的半合成穿心莲内酯衍生物:发现潜在抗癌药物先导物的有效方法（英文）

We embarked on the discovery of anticancer agents from andrographolide nearly 15 years ago.Thus far,a few lead semisynthetic compounds have been identified,but only recently we managed to pinpoint their potential molecular target.Through in silico and cell-based studies,these lead molecules have been found to bind K-ras oncoprotein and disrupt its function.Further molecular docking analysis suggested the compounds targeted both wild-type and oncogenic mutant K-ras.However,the binding affinity was greater for the oncogenic protein.Low binding energies to wild-type K-ras protein suggested transient binding and inhibition.The compounds showed stronger binding interactions to all three mutant K-ras proteins(G12V,G12 Cand G12D)with average free energies(ΔG bind)of-82kcal·mol-1 as compared with-61kcal·mol-1 for the wild-type protein.It is noteworthy that the binding pocket in wild-type K-ras protein,however,is different from that of the mutant proteins.SRJ23,one of the lead compounds,showed the strongest binding interactions to all three mutant K-ras proteins.Stronger binding to the mutant proteins could lead to more targeted and prolonged inhibition.Investigation into the effect of the compounds on RAS-MAPK pathway showed this pathway was disrupted in colon,breast and prostate cancer cells.In vivo studies revealed the compounds retarded the growth of human colon(HCT-116)and prostate(PC-3)cancer xenografts in mice.All of the above prompted us to synthesise derivatives of the lead compounds for improvement of binding affinity for the oncogenic K-ras.A preliminary in silico exploration found some compounds with such property and these compounds are presently undergoing extensive pharmacological investigations.     

Lipid A as a Drug Target and Therapeutic Molecule

In this review, lipid A, from its discovery to recent findings, is presented as a drug target and therapeutic molecule. First, the biosynthetic pathway for lipid A, the Raetz pathway, serves as a good drug target for antibiotic development. Several assay methods used to screen for inhibitors of lipid A synthesis will be presented, and some of the promising lead compounds will be described. Second, utilization of lipid A biosynthetic pathways by various bacterial species can generate modified lipid A molecules with therapeutic value.     

Pathway analysis tools and toxicogenomics reference databases for risk assessment

The pharmaceutical industry has begun to leverage a range of new technologies (proteomics, pharmacogenomics, metabolomics and molecular toxicology [e.g., toxicogenomics]) and analysis tools that are becoming increasingly integrated in the area of drug discovery and development. The approach of analyzing the vast amount of toxicogenomics data generated using molecular pathway and networks analysis tools in combination with analysis of reference data will be the main focus of this review. We will demonstrate how this combined approach can increase the understanding of the molecular mechanisms that lead to chemical-induced toxicity and application of this knowledge to compound risk assessment. We will provide an example of the insights achieved through a molecular toxicology analysis based on the well-known hepatotoxicant lipopolysaccharide to illustrate the utility of these new tools in the analysis of complex data sets, both in vivo and in vitro. The ultimate objective is a better lead selection process that improves the chances for success across the different stages of drug discovery and development.