Drug discovery in the next millennium

Discovery of CCL18 antagonist blocking breast cancer metastasis

Our previous studies have proved that CCL18 is the most secreted chemokine in breast cancer microenvironment by tumor associated macrophages (TAMs). CCL18 promotes breast cancer invasiveness by binding to its cognate receptor PITPNM3 and activating the downstream signaling pathways. The high level of CCL18 in serum or tumor stroma is associated with tumor metastasis and poor patients overall survival. In this study, we identify an effective small molecular compound (SMC) to antagonize the effect of CCL18. We screen more than 1000 SMCs from Sun Yat-sen University SMC library and select 15 top scored SMCs by using computer-aided virtual screening based on the structure of CCL18. Then in vitro cell migration assay narrows down the selected 15 SMCs to the most effective SMC-21598. We find 10 µM SMC-21598 significantly inhibits CCL18-induced breast cancer cells adherence, invasiveness, and migration. Our further surface plasmon resonance (SPR), fluorescence spectroscopy and isothermal titration calorimetry (ITC) assays reveal that SMC-21598 binds tightly to CCL18, which blocks the binding of CCL18 with its receptor PITPNM3. The in vivo animal experiments show that SMC-21598 doesn't significantly affect xenografts growth, but inhibits lung metastasis. Our study provides a potential lead compound to antagonize CCL18 function. It would be of great significance to develop SMC drugs to ameliorate breast cancer metastasis and prolong patients' survival.

Pharmaceutical applications of affinity-ultrafiltration mass spectrometry: Recent advances and future prospects

The immunoaffinity of protein with ligand is broadly involved in many bioanalytical methods. Affinity-ultrafiltration mass spectrometry (AUF-MS), a platform based on interaction of protein-ligand affinity, has been developed to fish out interesting molecules from complex matrixes. Here we reviewed the basics of AUF-MS and its recent applications to pharmaceutical field, i.e. target-oriented discovery of lead compounds from combinatorial libraries and natural product extracts, and determination of free drug concentration in biosamples. Selected practical examples were highlighted to illustrate the advances of AUF-MS in pharmaceutical fields. The future prospects were also presented.

Multi-algorithm and multi-model based drug target prediction and web server

AIM

To develop a reliable computational approach for predicting potential drug targets based merely on protein sequence.

METHODS

With drug target and non-target datasets prepared and 3 classification algorithms (Support Vector Machine, Neural Network and Decision Tree), a multi-algorithm and multi-model based strategy was employed for constructing models to predict potential drug targets.

RESULTS

Twenty one prediction models for each of the 3 algorithms were successfully developed. Our evaluation results showed that ∼30% of human proteins were potential drug targets, and ∼40% of putative targets for the drugs undergoing phase II clinical trials were probably non-targets. A public web server named D3TPredictor (http://www.d3pharma.com/d3tpredictor) was constructed to provide easy access.

CONCLUSION

Reliable and robust drug target prediction based on protein sequences is achieved using the multi-algorithm and multi-model strategy.

The role of molecular imaging in modern drug development

Drug development represents a highly complex, inefficient and costly process. Over the past decade, the widespread use of nuclear imaging, owing to its functional and molecular nature, has proven to be a determinant in improving the efficiency in selecting the candidate drugs that should either be abandoned or moved forward into clinical trials. This helps not only with the development of safer and effective drugs but also with the shortening of time-to-market. The modern concept and future trends concerning molecular imaging will assumedly be hybrid or multimodality imaging, including combinations between high sensitivity and functional (molecular) modalities with high spatial resolution and morphological techniques.

Neoclassic drug discovery: the case for lead generation using phenotypic and functional approaches

Innovation and new molecular entity production by the pharmaceutical industry has been below expectations. Surprisingly, more first-in-class small-molecule drugs approved by the U.S. Food and Drug Administration (FDA) between 1999 and 2008 were identified by functional phenotypic lead generation strategies reminiscent of pre-genomics pharmacology than contemporary molecular targeted strategies that encompass the vast majority of lead generation efforts. This observation, in conjunction with the difficulty in validating molecular targets for drug discovery, has diminished the impact of the "genomics revolution" and has led to a growing grassroots movement and now broader trend in pharma to reconsider the use of modern physiology-based or phenotypic drug discovery (PDD) strategies. This "From the Guest Editors" column provides an introduction and overview of the two-part special issues of Journal of Biomolecular Screening on PDD. Terminology and the business case for use of PDD are defined. Key issues such as assay performance, chemical optimization, target identification, and challenges to the organization and implementation of PDD are discussed. Possible solutions for these challenges and a new neoclassic vision for PDD that combines phenotypic and functional approaches with technology innovations resulting from the genomics-driven era of target-based drug discovery (TDD) are also described. Finally, an overview of the manuscripts in this special edition is provided.

UniDrug-target: a computational tool to identify unique drug targets in pathogenic bacteria

Background

Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed.

Methods

A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein's critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed.

Results

The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets.

Conclusions/Significance

UniDrug-Target is expected to accelerate pathogen-specific drug targets identification which will increase their success and durability as drugs developed against them have less chance to develop resistances and adverse impact on environment. The server is freely available at http://117.211.115.67/UDT/main.html. The standalone application (source codes) is available at http://www.bioinformatics.org/ftp/pub/bioinfojuit/UDT.rar.

COMPUTATIONAL METHOD FOR DRUG TARGET SEARCH AND APPLICATION IN DRUG DISCOVERY

Ligand-protein inverse docking has recently been introduced as a computer method for identification of potential protein targets of a drug. A protein structure database is searched to find proteins to which a drug can bind or weakly bind. Examples of potential applications of this method in facilitating drug discovery include: (1) identification of unknown and secondary therapeutic targets of a drug, (2) prediction of potential toxicity and side effect of an investigative drug, and (3) probing molecular mechanism of bioactive herbal compounds such as those extracted from plants used in traditional medicines. This method and recent results on its applications in solving various drug discovery problems are reviewed.

Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery

Knowledge and investigation of therapeutic targets (responsible for drug efficacy) and the targeted drugs facilitate target and drug discovery and validation. Therapeutic Target Database (TTD, http://bidd.nus.edu.sg/group/ttd/ttd.asp) has been developed to provide comprehensive information about efficacy targets and the corresponding approved, clinical trial and investigative drugs. Since its last update, major improvements and updates have been made to TTD. In addition to the significant increase of data content (from 1894 targets and 5028 drugs to 2025 targets and 17,816 drugs), we added target validation information (drug potency against target, effect against disease models and effect of target knockout, knockdown or genetic variations) for 932 targets, and 841 quantitative structure activity relationship models for active compounds of 228 chemical types against 121 targets. Moreover, we added the data from our previous drug studies including 3681 multi-target agents against 108 target pairs, 116 drug combinations with their synergistic, additive, antagonistic, potentiative or reductive mechanisms, 1427 natural product-derived approved, clinical trial and pre-clinical drugs and cross-links to the clinical trial information page in the ClinicalTrials.gov database for 770 clinical trial drugs. These updates are useful for facilitating target discovery and validation, drug lead discovery and optimization, and the development of multi-target drugs and drug combinations.

A Computer Experiment Model to Investigate the Effects of Drug Dosage in Animals, for Use in Pharmacological Education and Research

The ACD-IDEA database, which was originally developed by the authors in 2004, is an ongoing compilation of existing data on the in vivo doses of compounds at which various responses in certain animal species have been observed. It can provide an infrastructure for various research/educational efforts, and creates a synergy for new applications. In this paper, some of these applications are described. Specific interfaces within the database are designed for users who are not computer specialists. Users can search the database to find the answer to a query, or they can design a simple virtual animal experiment. In the second case, the interface is used to undertake a dialogue with the system, in order to test the user's knowledge regarding an experiment under consideration, and to allow the user to glean additional information on better experimental planning. The use of this virtual experimental tool should lead to savings in time, animals, materials, and monetary costs, while the effective learning outcomes of pharmacological experiments are maintained or enhanced.

Genetic epidemiology of Chagas disease

Genetic epidemiological approaches hold great promise for improving the understanding of the determinants of susceptibility to infection with Trypanosoma cruzi and the causes of differential disease outcome in T. cruzi-infected individuals. To date, a variety of approaches have been used to understand the role of genetic factors in Chagas disease. Quantitative genetic techniques have been used to estimate the heritabilities for seropositivity for T. cruzi infection and traits that are associated with disease progression in chronic T. cruzi infection. These studies have demonstrated that a significant proportion of the variation in seropositivity and a number of traits related to Chagas disease progression is attributable to genetic factors. Candidate gene studies have provided intriguing evidence for the roles of numerous individual genes in determining cardiac outcomes in chronically infected individuals. Recent results from a long-term study of Chagas disease in a rural area of Brazil have documented that over 60% of the variation in seropositivity status is attributable to genetic factors in that population. Additionally, there are significant genetic effects on a number of electrocardiographic measures and other Chagas disease-related traits. The application of genome-wide approaches will yield new evidence for the roles of specific genes in Chagas disease.

Applications of genetically modified tools to safety assessment in drug development

The process of new drug development consists of several stages; after identifying potential candidate compounds, preclinical studies using animal models link the laboratory and human clinical trials. Among many steps in preclinical studies, toxicology and safety assessments contribute to identify potential adverse events and provide rationale for setting the initial doses in clinical trials. Gene modulation is one of the important tools of modern biology, and is commonly employed to examine the function of genes of interest. Advances in new drug development have been achieved by exploding information on target selection and validation using genetically modified animal models as well as those of cells. In this review, a recent trend of genetically modified methods is discussed with reference to safety assessments, and the exemplary applications of gene-modulating tools to the tests in new drug development were summarized.

Update of TTD: Therapeutic Target Database

Increasing numbers of proteins, nucleic acids and other molecular entities have been explored as therapeutic targets, hundreds of which are targets of approved and clinical trial drugs. Knowledge of these targets and corresponding drugs, particularly those in clinical uses and trials, is highly useful for facilitating drug discovery. Therapeutic Target Database (TTD) has been developed to provide information about therapeutic targets and corresponding drugs. In order to accommodate increasing demand for comprehensive knowledge about the primary targets of the approved, clinical trial and experimental drugs, numerous improvements and updates have been made to TTD. These updates include information about 348 successful, 292 clinical trial and 1254 research targets, 1514 approved, 1212 clinical trial and 2302 experimental drugs linked to their primary targets (3382 small molecule and 649 antisense drugs with available structure and sequence), new ways to access data by drug mode of action, recursive search of related targets or drugs, similarity target and drug searching, customized and whole data download, standardized target ID, and significant increase of data (1894 targets, 560 diseases and 5028 drugs compared with the 433 targets, 125 diseases and 809 drugs in the original release described in previous paper). This database can be accessed at http://bidd.nus.edu.sg/group/cjttd/TTD.asp.

What are next generation innovative therapeutic targets? Clues from genetic, structural, physicochemical, and systems profiles of successful targets

Low target discovery rate has been linked to inadequate consideration of multiple factors that collectively contribute to druggability. These factors include sequence, structural, physicochemical, and systems profiles. Methods individually exploring each of these profiles for target identification have been developed, but they have not been collectively used. We evaluated the collective capability of these methods in identifying promising targets from 1019 research targets based on the multiple profiles of up to 348 successful targets. The collective method combining at least three profiles identified 50, 25, 10, and 4% of the 30, 84, 41, and 864 phase III, II, I, and nonclinical trial targets as promising, including eight to nine targets of positive phase III results. This method dropped 89% of the 19 discontinued clinical trial targets and 97% of the 65 targets failed in high-throughput screening or knockout studies. Collective consideration of multiple profiles demonstrated promising potential in identifying innovative targets.

Exploring functional genomics for drug target and therapeutics discovery in Plasmodia

Functional genomics approaches are indispensable tools in the drug discovery arena and have recently attained increased attention in antibacterial drug discovery research. However, the application of functional genomics to post-genomics research of Plasmodia is still in comparatively early stages. Nonetheless, with this genus having the most species sequenced of any eukaryotic organism so far, the Plasmodia could provide unique opportunities for the study of intracellular eukaryotic pathogens. This review presents the status quo of functional genomics of the malaria parasite including descriptions of the transcriptome, proteome and interactome. We provide examples for the in silico mining of the X-ome data sets and illustrate how X-omic data from drug challenged parasites might be used in elucidating amongst others, the mode-of-action of inhibitory compounds, validate potential targets and discover novel targets/therapeutics.

Quantitative analysis on the characteristics of targets with FDA approved drugs

Accumulated knowledge of genomic information, systems biology, and disease mechanisms provide an unprecedented opportunity to elucidate the genetic basis of diseases, and to discover new and novel therapeutic targets from the wealth of genomic data. With hundreds to a few thousand potential targets available in the human genome alone, target selection and validation has become a critical component of drug discovery process. The explorations on quantitative characteristics of the currently explored targets (those without any marketed drug) and successful targets (targeted by at least one marketed drug) could help discern simple rules for selecting a putative successful target. Here we use integrative in silico (computational) approaches to quantitatively analyze the characteristics of 133 targets with FDA approved drugs and 3120 human disease genes (therapeutic targets) not targeted by FDA approved drugs. This is the first attempt to comparatively analyze targets with FDA approved drugs and targets with no FDA approved drug or no drugs available for them. Our results show that proteins with 5 or fewer number of homologs outside their own family, proteins with single-exon gene architecture and proteins interacting with more than 3 partners are more likely to be targetable. These quantitative characteristics could serve as criteria to search for promising targetable disease genes.

Unsaturated didehydrodeoxycytidine drugs. 1. Impact of C=C positions in the sugar ring

The chemical names of a pair of recently synthesized antitumor drugs are given in the present study as 1',2'-didehydro-3',4'-deoxycytidine and 3',4'-didehydro-2',4'-deoxycytidine. The order of stabilities, geometries, and ionization potentials of the unsaturated sugar-modified cytidine derivatives is investigated quantum mechanically. Our density functional theory calculations based on the B3LYP/6-311++G** model reveal that 3',4'-didehydro-2',4'-deoxycytidine (SD-C2) is slightly more stable than its isomer, 1',2'-didehydro-3',4'-deoxycytidine, by an energy of 5.28 kJ x mol(-1) in isolation. The isomers structurally differ by only the C=C location in the sugar ring. However, the compounds exhibit an unusual orientation with a less puckered sugar ring; that is, 3',4'-didehydro-2',4'-deoxycytidine is determined to be a beta-nucleoside, which is a C1'-endo, north conformer with an anticlinal sugar ring, whereas 1',2'-didehydro-3',4'-deoxycytidine is neither an alpha-nucleoside nor a beta-nucleoside but is a C4'-endo, south conformer with an antiperiplanar sugar ring. The present study further indicates that the C=C double bond location imposes significant effects on their ionization potentials (IPs) and other important molecular properties such as molecular electrostatic potential (MEP). In addition, inner shell binding energy spectral variations with respect to the C=C bond exhibit more site dependence. The valence shell binding energy spectral changes are, on the other hand, significant and delocalized. The latter indicates that such changes in valence space are not isolated effects but are within the entire nucleoside. Finally, the present study suggests that the nearly 0.6 eV difference in the first ionization potentials (highest occupied molecular orbital) of the isomers is sufficiently large to identify them by further spectroscopic measures.

Engineered Chimeric Enzymes as Tools for Drug Discovery: Generating Reliable Bacterial Screens for the Detection, Discovery, and Assessment of Estrogen Receptor Modulators

Engineered protein-based sensors of ligand binding have emerged as attractive tools for the discovery of therapeutic compounds through simple screening systems. We have previously shown that engineered chimeric enzymes, which combine the ligand-binding domains of nuclear hormone receptors with a highly sensitive thymidylate synthase reporter, yield simple sensors that report the presence of hormone-like compounds through changes in bacterial growth. This work describes an optimized estrogen sensor in Escherichia coli with extraordinary reliability in identifying diverse estrogenic compounds and in differentiating between their agonistic/antagonistic pharmacological effects. The ability of this system to assist the discovery of new estrogen-mimicking compounds was validated by screening a small compound library, which led to the identification of two structurally novel estrogen receptor modulators and the accurate prediction of their agonistic/antagonistic biocharacter in human cells. Strong evidence is presented here that the ability of our sensor to detect ligand binding and recognize pharmacologically critical properties arises from allosteric communication between the artificially combined protein domains, where different ligand-induced conformational changes in the receptor are transmitted to the catalytic domain and translated to distinct levels of enzymic efficiency. To the best of our knowledge, this is one of the first examples of an engineered enzyme with the ability to sense multiple receptor conformations and to be either activated or inactivated depending on the nature of the bound effector molecule. Because the proposed mechanism of ligand dependence is not specific to nuclear hormone receptors, we anticipate that our protein engineering strategy will be applicable to the construction of simple sensors for different classes of (therapeutic) binding proteins.

Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness

Identification and validation of viable targets is an important first step in drug discovery and new methods, and integrated approaches are continuously explored to improve the discovery rate and exploration of new drug targets. An in silico machine learning method, support vector machines, has been explored as a new method for predicting druggable proteins from amino acid sequence independent of sequence similarity, thereby facilitating the prediction of druggable proteins that exhibit no or low homology to known targets.

Serendipitous discovery of novel bacterial methionine aminopeptidase inhibitors

In this article we describe the application of structural biology methods to the discovery of novel potent inhibitors of methionine aminopeptidases. These enzymes are employed by the cells to cleave the N-terminal methionine from nascent peptides and proteins. As this is one of the critical steps in protein maturation, it is very likely that inhibitors of these enzymes may prove useful as novel antibacterial agents. Involvement of crystallography at the very early stages of the inhibitor design process resulted in serendipitous discovery of a new inhibitor class, the pyrazole-diamines. Atomic-resolution structures of several inhibitors bound to the enzyme illuminate a new mode of inhibitor binding.

Synthesis and selection of de novo proteins that bind and impede cellular functions of an essential mycobacterial protein

Recent advances in nonrational and part-rational approaches to de novo peptide/protein design have shown increasing potential for development of novel peptides and proteins of therapeutic use. We demonstrated earlier the usefulness of one such approach recently developed by us, called "codon shuffling," in creating stand-alone de novo protein libraries from which bioactive proteins could be isolated. Here, we report the synthesis and selection of codon-shuffled de novo proteins that bind to a selected Mycobacterium tuberculosis protein target, the histone-like protein HupB, believed to be essential for mycobacterial growth. Using a versatile bacterial two-hybrid system that entailed utilization of HupB and various codon-shuffled protein libraries as bait and prey, respectively, we were able to identify proteins that bound strongly to HupB. The observed interaction was also confirmed using an in vitro assay. One of the protein binders was expressed in Mycobacterium smegmatis and was shown to appreciably affect growth in the exponential phase, a period wherein HupB is selectively expressed. Furthermore, the transcription profile of hupB gene showed a significant reduction in the transcript quantity in mycobacterial strains expressing the protein binder. Electron microscopy of the affected mycobacteria elaborated on the extent of cell damage and hinted towards a cell division malfunction. It is our belief that a closer inspection of the obtained de novo proteins may bring about the generation of small-molecule analogs, peptidomimetics, or indeed the proteins themselves as realistic leads for drug candidates. Furthermore, our strategy is adaptable for large-scale targeting of the essential protein pool of Mycobacterium tuberculosis and other pathogens.

Therapeutic targets: progress of their exploration and investigation of their characteristics

Modern drug discovery is primarily based on the search and subsequent testing of drug candidates acting on a preselected therapeutic target. Progress in genomics, protein structure, proteomics, and disease mechanisms has led to a growing interest in and effort for finding new targets and more effective exploration of existing targets. The number of reported targets of marketed and investigational drugs has significantly increased in the past 8 years. There are 1535 targets collected in the therapeutic target database compared with approximately 500 targets reported in a 1996 review. Knowledge of these targets is helpful for molecular dissection of the mechanism of action of drugs and for predicting features that guide new drug design and the search for new targets. This article summarizes the progress of target exploration and investigates the characteristics of the currently explored targets to analyze their sequence, structure, family representation, pathway association, tissue distribution, and genome location features for finding clues useful for searching for new targets. Possible "rules" to guide the search for druggable proteins and the feasibility of using a statistical learning method for predicting druggable proteins directly from their sequences are discussed.

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is important to significantly increase the knowledge of the biology of its causative agents, and is motivated, on a longer term, by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should, therefore, be as reliable and versatile as possible. In this context, five aspects of the organization and mining of malaria genomic and post-genomic data were examined: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes, particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Recent progress towards a grid-enabled chemogenomic knowledge space is discussed.

Progress and problems in the exploration of therapeutic targets

Drugs exert their therapeutic effect by binding and regulating the activity of a particular protein or nucleic acid target. A large number of targets have been explored for drug discovery. Continuous effort has been directed at the search for new targets and more-extensive exploration of existing targets. Knowledge of these targets facilitates the understanding of molecular mechanisms of drugs and the effort required for drug discovery and target searches. Areas of progress, current focuses of research and development and the difficulties in target exploration are reviewed. The characteristics of the currently explored targets and their correlation to the level of difficulty for target exploration are analyzed. From these characteristics, simple rules can be derived for estimating the difficulty level of target exploration. The feasibility of predicting druggable proteins by using simple rules and sequence-derived physicochemical properties is also discussed.

Toxin detection based on action potential shape analysis using a realistic mathematical model of differentiated NG108-15 cells

The NG108-15 neuroblastoma/glioma hybrid cell line has been frequently used for toxin detection, pharmaceutical screening and as a whole-cell biosensor. However, detailed analysis of its action potentials during toxin or drug administration has not been accomplished previously using patch clamp electrophysiology. In order to explore the possibility of identifying toxins based on their effect on the shape of intracellularly or extracellularly detected action potentials, we created a computer model of the action potential generation of this cell type. To generate the experimental data to validate the model, voltage dependent sodium, potassium and high-threshold calcium currents, as well as action potentials, were recorded from NG108-15 cells with conventional whole-cell patch-clamp methods. Based on the classic Hodgkin-Huxley formalism and the linear thermodynamic description of the rate constants, ion-channel parameters were estimated using an automatic fitting method. Utilizing the established parameters, action potentials were generated in the model and were optimized to represent the actual recorded action potentials to establish baseline conditions. To demonstrate the applicability of the method for toxin detection and discrimination, the effect of tetrodotoxin (a sodium channel blocker) and tefluthrin (a pyrethroid that is a sodium channel opener) were studied. The two toxins affected the shape of the action potentials differently and their respective effects were identified based on the changes in the fitted parameters. Our results represent one of the first steps to establish a complex model of NG108-15 cells for quantitative toxin detection based on action potential shape analysis of the experimental results.

Shaking the tree: mapping complex disease genes with linkage disequilibrium

Much effort and expense are being spent internationally to detect genetic polymorphisms contributing to susceptibility to complex human disease. Concomitantly, the technology for detecting and genotyping single nucleotide polymorphisms (SNPs) has undergone rapid development, yielding extensive catalogues of these polymorphisms across the genome. Population-based maps of the correlations amongst SNPs (linkage disequilibrium) are now being developed to accelerate the discovery of genes for complex human diseases. These genomic advances coincide with an increasing recognition of the importance of very large sample sizes for studying genetic effects. Together, these new genetic and epidemiological data hold renewed promise for the identification of susceptibility genes for complex traits. We review the state of knowledge about the structure of the human genome as related to SNPs and linkage disequilibrium, discuss the potential applications of this knowledge to mapping complex disease genes, and consider the issues facing whole genome association scanning using SNPs.

A Fast Exchange Algorithm for Designing Focused Libraries in Lead Optimization

Combinatorial chemistry is widely used in drug discovery. Once a lead compound has been identified, a series of R-groups and reagents can be selected and combined to generate new potential drugs. The combinatorial nature of this problem leads to chemical libraries containing usually a very large number of virtual compounds, far too large to permit their chemical synthesis. Therefore, one often wants to select a subset of "good" reagents for each R-group of reagents and synthesize all their possible combinations. In this research, one encounters some difficulties. First, the selection of reagents has to be done such that the compounds of the resulting sublibrary simultaneously optimize a series of chemical properties. For each compound, a desirability index, a concept proposed by Harrington,(20) is used to summarize those properties in one fitness value. Then a loss function is used as objective criteria to globally quantify the quality of a sublibrary. Second, there are a huge number of possible sublibraries, and the solutions space has to be explored as fast as possible. The WEALD algorithm proposed in this paper starts with a random solution and iterates by applying exchanges, a simple method proposed by Fedorov(13) and often used in the generation of optimal designs. Those exchanges are guided by a weighting of the reagents adapted recursively as the solutions space is explored. The algorithm is applied on a real database and reveals to converge rapidly. It is compared to results given by two other algorithms presented in the combinatorial chemistry literature: the Ultrafast algorithm of D. Agrafiotis and V. Lobanov and the Piccolo algorithm of W. Zheng et al.

Impact of functional genomics and proteomics on radionuclide imaging

The assessment of gene function following the completion of human genome sequencing may be performed using radionuclide imaging procedures. These procedures are needed for the evaluation of genetically manipulated animals or newly designed biomolecules, which requires a thorough understanding of physiology, biochemistry, and pharmacology. The experimental approaches will involve many new technologies, including in vivo imaging with single photon emission computed tomography and positron emission tomography. Nuclear medicine procedures may be applied for the determination of gene function and regulation using established and new tracers, or using in vivo reporter genes, such as genes encoding enzymes, receptors, antigens, or transporters. Visualization of in vivo reporter gene expression can be performed using radiolabeled substrates, antibodies, or ligands. Combinations of specific promoters and in vivo reporter genes may deliver information about the regulation of the corresponding genes. Furthermore, protein-protein interactions and activation of signal transduction pathways may be visualized noninvasively. The role of radiolabeled antisense molecules for the analysis of messenger ribonucleic acid (RNA) content has to be investigated. However, possible applications are therapeutic intervention using triplex oligonucleotides with therapeutic isotopes, which can be brought near to specific deoxyribonucleic acid sequences to induce deoxyribonucleic acid strand breaks at selected loci. Imaging of labeled siRNA makes sense if these are used for therapeutic purposes to assess the delivery of these new drugs to their target tissue. Pharmacogenomics will identify new surrogate markers for therapy monitoring, which may represent potential new tracers for imaging. Drug distribution studies for new therapeutic biomolecules are needed at least during preclinical stages of drug development. New treatment modalities, such as gene therapy with suicide genes, will need procedures for therapy planning and monitoring. Finally, new biomolecules will be developed by bioengineering methods, which may be used for the isotope-based diagnosis and treatment of disease.

Techniques: Cardiovascular pharmacology and drug discovery in the 21st century

The latter half of the 20th century has been characterized by pharmacologists as the 'age of the receptor', an era in which the bioassay, that stalwart of classical pharmacology, has played a seminal role in identifying novel cardiovascular medicines. In this article, we ask what, if anything, has changed in the pharmacologist's approach to discovering novel cardiovascular drugs on this, the 25th anniversary of the inaugural publication of Trends in Pharmacological Sciences.

Radionuclide imaging in the post-genomic era

The assessment of gene function, which follows the completion of human genome sequencing, may be performed using the tools of the genome program. These tools represent high-throughput methods evaluating changes in the expression of many or all genes of an organism at the same time in order to investigate genetic pathways for normal development and disease. They describe proteins on a proteome-wide scale, thereby, creating a new way of doing cell research which results in the determination of three dimensional protein structures and the description of protein networks. These descriptions may then be used for the design of new hypotheses and experiments in the traditional physiological, biochemical, and pharmacological sense. The evaluation of genetically manipulated animals or new designed biomolecules will require a thorough understanding of physiology, biochemistry, and pharmacology and the experimental approaches will involve many new technologies including in vivo imaging with SPECT and positron emission tomography (PET). Nuclear medicine procedures may be applied for the determination of gene function and regulation using established and new tracers or using in vivo reporter genes such as genes encoding enzymes, receptors, antigens, or transporters. Pharmacogenomics will identify new surrogate markers for therapy monitoring which may represent potential new tracers for imaging. Also drug distribution studies for new therapeutic biomolecules are needed at least during preclinical stages of drug development. Finally, new biomolecules will be developed by bioengineering methods, which may be used for isotope-based diagnosis and treatment of disease.

Targeting the CREB pathway for memory enhancers

Today, the clinical notion of 'memory disorder' is largely synonymous with 'Alzheimer's disease.' Only 50% of all dementias are of the Alzheimer's type though, and dementias represent only the more severe of all learning/memory disorders that derive from heredity, disease, injury or age. Perhaps as many as 30 million Americans suffer some type of clinically recognized memory disorder. To date, therapeutic drugs of only one class have been approved for the treatment of Alzheimer's disease. Fortunately, basic research during the past 25 years has begun to define a 'chemistry of brain plasticity,' which is suggesting new gene targets for the discovery of memory enhancers.

Barriers to Alzheimer disease drug discovery and development in academia

The drug discovery and the drug development processes represent a continuum of recursive activities that range from initial drug target identification to final Food and Drug Administration approval and marketing of a new therapeutic. Drug discovery, as its name implies, is more exploratory and less focused in many cases, whereas drug development has a clinically defined endpoint and a specific disease goal. Academia has historically made major contributions to this process at the early discovery phases. However, current trends in the organization of the pharmaceutical industry suggest an expanded role for academia in the near future. Megamergers among major pharmaceutical corporations indicate their movement toward a focus on end-stage clinical trials, manufacturing, and marketing. There has been a parallel increase in outsourcing of intermediate steps to specialty small pharmaceutical, biotechnology, and contract service companies. The new paradigm suggests that academia will play an increasingly important role at the proof-of-principle stage of basic and clinical drug discovery research, in training the future skilled work force, and in close partnerships with small pharmaceutical and biotechnology companies. However, academic drug discovery research faces a set of barriers to progress, the relative importance of which varies with the home institution and the details of the research area. These barriers fall into four general categories: (1) the historical administrative structure and environment of academia; (2) the structure and emphasis of peer review panels that control research funding by government and private agencies; (3) the organization and operation of the academic infrastructure; and (4) the structure and availability of specialized resources and information management. Selected examples of barriers to drug discovery and drug development research and training in academia are presented, as are some specific recommendations designed to minimize or circumvent these barriers. In some cases, precedents established by other disease-focused areas may be relevant to Alzheimer disease and related disorders, but the overall impact of any changes requires adaptation at the top of the administrative structures in academia and funding agencies to support and encourage cooperative efforts among faculty investigators.

Barriers to Alzheimer disease drug discovery and development in the biotechnology industry

The major barrier to Alzheimer disease (AD) drug discovery and development in the biotechnology industry is scale. Most biotechnology companies do not have the personnel or expertise to carry a drug from the bench to the market. Much effort in the industry has been directed toward the elucidation of molecular mechanisms of AD and the identification of new targets. Advances in biotechnology have generated new insights into disease mechanisms, increased the number of lead compounds, and accelerated biologic screening. The majority of costs associated with drug development are in clinical testing and development activities, many of which are driven by regulatory issues. For most biotechnology companies, the costs of such trials and the infrastructure necessary to support them are prohibitive. Another significant barrier is the definition of therapeutic benefit for AD drugs; Food and Drug Administration (FDA) precedent has established that a drug must show superiority to placebo on a performance-based test of cognition and a measure of global clinical function. This restrictive definition is biased toward drugs that enhance performance on memory-based tests. Newer AD drugs are targeted toward slowing disease progression; however, there is currently no accepted definition of what constitutes efficacy in disease progression. Despite these obstacles, the biotechnology industry has much to offer AD drug discovery and development. Biotechnology firms have already developed essential technology for AD drug development and will continue to do so. Biotechnology companies can move more quickly; of course, the trick is to move quickly in the right direction. Speed may offset some of the problems associated with lack of scale. Additionally, biotechnology companies can afford to address markets that may be too restricted for larger pharmaceutical companies. This advantage will have increasing importance, as therapies are developed to address subtypes of AD.

Pharmacokinetic/pharmacodynamic studies in drug product development

In the quest of ways for rationalizing and accelerating drug product development, integrated pharmacokinetic/pharmacodynamic (PK/PD) concepts provide a highly promising tool. PK/PD modeling concepts can be applied in all stages of preclinical and clinical drug development, and their benefits are multifold. At the preclinical stage, potential applications might comprise the evaluation of in vivo potency and intrinsic activity, the identification of bio-/surrogate markers, as well as dosage form and regimen selection and optimization. At the clinical stage, analytical PK/PD applications include characterization of the dose-concentration-effect/toxicity relationship, evaluation of food, age and gender effects, drug/drug and drug/disease interactions, tolerance development, and inter- and intraindividual variability in response. Predictive PK/PD applications can also involve extrapolation from preclinical data, simulation of drug responses, as well as clinical trial forecasting. Rigorous implementation of the PK/PD concepts in drug product development provides a rationale, scientifically based framework for efficient decision making regarding the selection of potential drug candidates, for maximum information gain from the performed experiments and studies, and for conducting fewer, more focused clinical trials with improved efficiency and cost effectiveness. Thus, PK/PD concepts are believed to play a pivotal role in streamlining the drug development process of the future.

Harnessing the mouse to unravel the genetics of human disease

Complex traits, i.e. those with multiple genetic and environmental determinants, represent the greatest challenge for genetic analysis, largely due to the difficulty of isolating the effects of any one gene amid the noise of other genetic and environmental influences. Methods exist for detecting and mapping the Quantitative Trait Loci (QTLs) that influence complex traits. However, once mapped, gene identification commonly involves reduction of focus to single candidate genes or isolated chromosomal regions. To reach the next level in unraveling the genetics of human disease will require moving beyond the focus on one gene at a time, to explorations of pleiotropism, epistasis and environment-dependency of genetic effects. Genetic interactions and unique environmental features must be as carefully scrutinized as are single gene effects. No one genetic approach is likely to possess all the necessary features for comprehensive analysis of a complex disease. Rather, the entire arsenal of behavioral genomic and other approaches will be needed, such as random mutagenesis, QTL analyses, transgenic and knockout models, viral mediated gene transfer, pharmacological analyses, gene expression assays, antisense approaches and importantly, revitalization of classical genetic methods. In our view, classical breeding designs are currently underutilized, and will shorten the distance to the target of understanding the complex genetic and environmental interactions associated with disease. We assert that unique combinations of classical approaches with current behavioral and molecular genomic approaches will more rapidly advance the field.

TTD: Therapeutic Target Database

A number of proteins and nucleic acids have been explored as therapeutic targets. These targets are subjects of interest in different areas of biomedical and pharmaceutical research and in the development and evaluation of bioinformatics, molecular modeling, computer-aided drug design and analytical tools. A publicly accessible database that provides comprehensive information about these targets is therefore helpful to the relevant communities. The Therapeutic Target Database (TTD) is designed to provide information about the known therapeutic protein and nucleic acid targets described in the literature, the targeted disease conditions, the pathway information and the corresponding drugs/ligands directed at each of these targets. Cross-links to other databases are also introduced to facilitate the access of information about the sequence, 3D structure, function, nomenclature, drug/ligand binding properties, drug usage and effects, and related literature for each target. This database can be accessed at http://xin.cz3.nus.edu.sg/group/ttd/ttd.asp and it currently contains entries for 433 targets covering 125 disease conditions along with 809 drugs/ligands directed at each of these targets. Each entry can be retrieved through multiple methods including target name, disease name, drug/ligand name, drug/ligand function and drug therapeutic classification.

Molecular approaches to contraceptive development

The next generation of contraceptives will be based on the identification of novel molecules essential for reproductive processes and will rely on the refinement of older as well as newer technologies. Functional analysis of naturally occurring reproductive genetic disorders and creation of mice null for specific genes would greatly assist in the choice of genetic targets for contraceptive development. Structure-based design of drugs as exemplified by the preparation of an orally active non-peptide gonadotropin releasing hormone (GnRH) would revolutionize drug formulation and delivery for a peptide analogue. This review examines some of the molecular targets that may change contraceptive choices in the future.

From symptomatic treatments to causative therapy?

The search for genes that predispose individuals to develop common chronic diseases such as asthma, diabetes and Alzheimer's promises to give insights into their molecular pathogenesis. This will lead to the development of therapies that modulate the pathology, rather than the physiology of these diseases. As academia and the pharmaceutical industry increasingly focus on this challenge, the genetic dissection of Alzheimer's is spearheading attempts to shift the therapeutic paradigm away from symptomatic to curative treatments.