Pharmacophylogenomics: genes, evolution and drug targets

Prediction of chronic toxicity of pharmaceuticals in Daphnia magna by combining ortholog prediction, pharmacological effects, and quantitative structure-activity relationship

To develop a method for predicting chronic toxicity of pharmaceuticals in Daphnia, we investigated the feasibility of combining the presence of drug-target orthologs in Daphnia magna, classification based on pharmacological effects, and ecotoxicity quantitative structure-activity relationship (QSAR) prediction. We established datasets on the chronic toxicity of pharmaceuticals in Daphnia, including information on therapeutic categories, target proteins, and the presence or absence of drug-target orthologs in D. magna, using literature and databases. Chronic toxicity was predicted using ecotoxicity prediction QSAR (Ecological Structure Activity Relationship and Kashinhou Tool for Ecotoxicity), and the differences between the predicted and measured values and the presence or absence of drug-target orthologs were examined. For pharmaceuticals without drug-target orthologs in D. magna or without expected specific actions, the ecotoxicity prediction QSAR analysis yielded acceptable predictions of the chronic toxicity of pharmaceuticals. In addition, a workflow model to assess the chronic toxicity of pharmaceuticals in Daphnia was proposed based on these evaluations and verified using an additional dataset. The addition of biological aspects such as drug-target orthologs and pharmacological effects would support the use of QSARs for predicting the chronic toxicity of pharmaceuticals in Daphnia.

Unraveling TIMP1: a multifaceted biomarker in colorectal cancer

Background: The pathogenic genes of colorectal cancer (CRC) have not yet been fully elucidated, and there is currently a lack of effective therapeutic targets. This study used bioinformatics methods to explore and experimentally validate the most valuable biomarkers for colorectal cancer and further investigate their potential as targets. Methods: We analyzed differentially expressed genes (DEGs) based on the Gene Expression Omnibus (GEO) dataset and screened out hub genes. ROC curve and univariate Cox analysis of The Cancer Genome Atlas (TCGA) dataset revealed the most diagnostically and prognostically valuable genes. Immunohistochemistry (IHC) experiments were then conducted to validate the expression level of these selected genes in colorectal cancer. Gene set enrichment analysis (GSEA) was performed to evaluate the enriched signaling pathways associated with the gene. Using the CIBERSORT algorithm in R software, we analyzed the immune infiltrating cell abundance in both high and low gene expression groups and examined the gene's correlation with immune cells and immune checkpoints. Additionally, we performed drug sensitivity analysis utilizing the DepMap database, and explored the correlation between gene expression levels and ferroptosis based on the The Cancer Genome Atlas dataset. Results: The study identified a total of 159 DEGs, including 7 hub genes: SPP1, MMP1, CXCL8, CXCL1, TIMP1, MMP3, and CXCL10. Further analysis revealed TIMP1 as the most valuable diagnostic and prognostic biomarker for colorectal cancer, with IHC experiments verifying its high expression. Additionally, GSEA results showed that the high TIMP1 expression group was involved in many cancer signaling pathways. Analysis of the TCGA database revealed a positive correlation between TIMP1 expression and infiltration of macrophages (M0, M1, M2) and neutrophils, as well as the expression of immune checkpoint genes, including CTLA-4 and HAVCR2. Drug sensitivity analysis, conducted using the DepMap database, revealed that colorectal cancer cell lines exhibiting elevated levels of TIMP1 expression were more responsive to certain drugs, such as CC-90003, Pitavastatin, Atuveciclib, and CT7001, compared to those with low levels of TIMP1. Furthermore, TIMP1 expression was positively correlated with that of ferroptosis-related genes, such as GPX4 and HSPA5. Conclusion: TIMP1 can be used as a biomarker for colorectal cancer and is associated with the immunological microenvironment, drug sensitivity, and ferroptosis inhibition in this disease.

Seeing the Woods for the Trees Again: Analyzing Evolutionary Diagrams in German and US University-Level Textbooks

Phylogenetic trees are important tools for teaching and understanding evolution, yet students struggle to read and interpret them correctly. In this study, we extend a study conducted by Catley and Novick (2008) by investigating depictions of evolutionary trees in US textbooks. We investigated 1197 diagrams from 11 German and 11 United States university textbooks, conducting a cross-country comparison and comparing the results with data from the 2008 study. A coding manual was developed based on the 2008 study, with extensions focused on additional important aspects of evolutionary trees. The US and German books showed only a low number of significant differences, typically with very small impacts. In both samples, some characteristics that can render reading trees more difficult or foster misconceptions were found to be prevalent in various portions of the diagrams. Furthermore, US textbooks showed fewer problematic properties in our sample than in the 2008 sample. We conclude that evolutionary trees in US and German textbooks are represented comparably and that depictions in US textbooks have improved over the past 12 years. As students are confronted with comparable depictions of evolutionary relatedness, we argue that findings and materials from one country should easily be transferable to the other.

A new phylogenetic protocol: dealing with model misspecification and confirmation bias in molecular phylogenetics

Molecular phylogenetics plays a key role in comparative genomics and has increasingly significant impacts on science, industry, government, public health and society. In this paper, we posit that the current phylogenetic protocol is missing two critical steps, and that their absence allows model misspecification and confirmation bias to unduly influence phylogenetic estimates. Based on the potential offered by well-established but under-used procedures, such as assessment of phylogenetic assumptions and tests of goodness of fit, we introduce a new phylogenetic protocol that will reduce confirmation bias and increase the accuracy of phylogenetic estimates.

Leucine rich repeat kinase 2: a paradigm for pleiotropy

The LRRK2 gene, coding for leucine rich repeat kinase 2 (LRRK2), is a key player in the genetics of Parkinson's disease. Despite extensive efforts, LRRK2 has proved remarkably evasive with regard to attempts to understand both the role it plays in disease and its normal physiological function. At least part of why LRRK2 has been so difficult to define is that it appears to be many things to many cellular functions and diseases - a pleiotropic actor at both the genetic and the molecular level. Gaining greater insight into the mechanisms and pathways allowing LRRK2 to act in this manner will have implications for our understanding of the role of genes in the aetiology of complex disease, the molecular underpinnings of signal transduction pathways in the cell, and drug discovery in the genome era.

Potentials and Pitfalls of Cross-Translational Models of Cognitive Impairment

A number of clinical disorders that are either neurodevelopmental or neurodegenerative exhibit significant cognitive impairments that require some form of intervention. However, the current paucity of pro-cognitive treatments that are available, due to the lack of knowledge of biological targets and symptomologies, impedes the treatment of individuals with cognitive impairments. In this review article, we explore three critical steps that need to be established in order to lead to the development of effective and appropriate treatments for cognitive impairments. The first step specifically involves the ability to efficiently reproduce and standardize current animal models of disease. The second step involves establishing well-controlled and standardized animal models across different species, such as rodents and monkeys, that link to human disease conditions. The third step involves building these animal models from both a translational and a reverse translational perspective in order to gain critical insight into the etiologies of specific cognitive impairments and the development of their early physiological and behavioral biomarkers. This bidirectional translational approach is important to improve the investigation of disease biomarkers, the underlying mechanisms of novel therapeutics on cognition, and to validate preclinical findings of drug discovery. Overall, even though animal models play an important role in investigating the pathophysiological processes and mechanisms associated with typical and atypical behavior, we discuss the ongoing challenges associated with these three critical steps of cross-translational research that has led to the current lack of success of developing effective new compounds for potential treatments and suggest approaches to stimulate advances in the field.

Gene tree species tree reconciliation with gene conversion

Gene tree/species tree reconciliation is a recent decisive progress in phylogenetic methods, accounting for the possible differences between gene histories and species histories. Reconciliation consists in explaining these differences by gene-scale events such as duplication, loss, transfer, which translates mathematically into a mapping between gene tree nodes and species tree nodes or branches. Gene conversion is a frequent and important evolutionary event, which results in the replacement of a gene by a copy of another from the same species and in the same gene tree. Including this event in reconciliation models has never been attempted because it introduces a dependency between lineages, and standard algorithms based on dynamic programming become ineffective. We propose here a novel mathematical framework including gene conversion as an evolutionary event in gene tree/species tree reconciliation. We describe a randomized algorithm that finds, in polynomial running time, a reconciliation minimizing the number of duplications, losses and conversions in the case when their weights are equal. We show that the space of optimal reconciliations includes an analog of the last common ancestor reconciliation, but is not limited to it. Our algorithm outputs any optimal reconciliation with a non-null probability. We argue that this study opens a research avenue on including gene conversion in reconciliation, and discuss its possible importance in biology.

The role of natural selection in shaping genetic variation in a promising Chagas disease drug target: Trypanosoma cruzi trans-sialidase

Rational drug design creates innovative therapeutics based on knowledge of the biological target to provide more effective and responsible therapeutics. Chagas disease, endemic throughout Latin America, is caused by Trypanosoma cruzi, a protozoan parasite. Current therapeutics are problematic with widespread calls for new approaches. Researchers are using rational drug design for Chagas disease and one target receiving considerable attention is the T. cruzi trans-sialidase protein (TcTS). In T. cruzi, trans-sialidase catalyzes the transfer of sialic acid from a mammalian host to coat the parasite surface membrane and avoid immuno-detection. However, the role of TcTS in pathology variance among and within genetic variants of the parasite is not well understood despite numerous studies. Previous studies reported the crystalline structure of TcTS and the TS protein structure in other trypanosomes where the enzyme is often inactive. However, no study has examined the role of natural selection in genetic variation in TcTS. To understand the role of natural selection in TcTS DNA sequence and protein variation, we examined a 471 bp portion of the TcTS gene from 48 T. cruzi samples isolated from insect vectors. Because there may be multiple parasite genotypes infecting one insect and there are multiple copies of TcTS per parasite genome, all 48 sequences had multiple polymorphic bases. To resolve these polymorphisms, we examined cloned sequences from two insect vectors. The data are analyzed to understand the role of natural selection in shaping genetic variation in TcTS and interpreted in light of the possible role of TcTS as a drug target. The analysis highlights negative or purifying selection on three amino acids previously shown to be important in TcTS transfer activity. One amino acid in particular, Tyr342, is a strong candidate for a drug target because it is under negative selection and amino acid substitutions inactivate TcTS transfer activity. AUTHOR SUMMARY: Chagas disease is caused by the protozoan parasite Trypanosoma cruzi and transmitted to humans and other mammals primarily by Triatomine insects. Being endemic in many South and Central American countries and affecting millions of people the need for new more effective and safe therapies is evident. Here, we examine genetic variation and natural selection on DNA (471 bp) and amino acid (157 aa) sequence data of the T. cruzi trans-sialdiase (TcTS) protein, often suggested as a candidate for rational drug design. In our surveyed region of the protein there were five amino acid residues that have been shown to be integral to the function of TcTS. We found that three were under strong negative selection making them ideal candidates for drug design; however, one was under balancing selection and should be avoided as a drug target. Our study provides new information into identifying potential targets for a new Chagas drug.

Evolutionary Perspectives of Genotype-Phenotype Factors in Leishmania Metabolism

The sandfly midgut and the human macrophage phagolysosome provide antagonistic metabolic niches for the endoparasite Leishmania to survive and populate. Although these environments fluctuate across developmental stages, the relative changes in both these environments across parasite generations might remain gradual. Such environmental restrictions might endow parasite metabolism with a choice of specific genotypic and phenotypic factors that can constrain enzyme evolution for successful adaptation to the host. With respect to the available cellular information for Leishmania species, for the first time, we measure the relative contribution of eight inter-correlated predictors related to codon usage, GC content, gene expression, gene length, multi-functionality, and flux-coupling potential of an enzyme on the evolutionary rates of singleton metabolic genes and further compare their effects across three Leishmania species. Our analysis reveals that codon adaptation, multi-functionality, and flux-coupling potential of an enzyme are independent contributors of enzyme evolutionary rates, which can together explain a large variation in enzyme evolutionary rates across species. We also hypothesize that a species-specific occurrence of duplicated genes in novel subcellular locations can create new flux routes through certain singleton flux-coupled enzymes, thereby constraining their evolution. A cross-species comparison revealed both common and species-specific genes whose evolutionary divergence was constrained by multiple independent factors. Out of these, previously known pharmacological targets and virulence factors in Leishmania were identified, suggesting their evolutionary reasons for being important survival factors to the parasite. All these results provide a fundamental understanding of the factors underlying adaptive strategies of the parasite, which can be further targeted.

iCAVE: an open source tool for visualizing biomolecular networks in 3D, stereoscopic 3D and immersive 3D

Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field.

Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications.

The rat is a widely-used model for human biology, but we must be aware of metabolic differences. Here, the authors reconstruct the genome-scale metabolic network of the rat, and after reconciling it with an improved human metabolic model, demonstrate the power of the models to integrate toxicogenomics data, providing species-specific biomarker predictions in response to a panel of drugs.

Genomics and Evolution in Traditional Medicinal Plants: Road to a Healthier Life

Medicinal plants have long been utilized in traditional medicine and ethnomedicine worldwide. This review presents a glimpse of the current status of and future trends in medicinal plant genomics, evolution, and phylogeny. These dynamic fields are at the intersection of phytochemistry and plant biology and are concerned with the evolution mechanisms and systematics of medicinal plant genomes, origin and evolution of the plant genotype and metabolic phenotype, interaction between medicinal plant genomes and their environment, the correlation between genomic diversity and metabolite diversity, and so on. Use of the emerging high-end genomic technologies can be expanded from crop plants to traditional medicinal plants, in order to expedite medicinal plant breeding and transform them into living factories of medicinal compounds. The utility of molecular phylogeny and phylogenomics in predicting chemodiversity and bioprospecting is also highlighted within the context of natural-product-based drug discovery and development. Representative case studies of medicinal plant genome, phylogeny, and evolution are summarized to exemplify the expansion of knowledge pedigree and the paradigm shift to the omics-based approaches, which update our awareness about plant genome evolution and enable the molecular breeding of medicinal plants and the sustainable utilization of plant pharmaceutical resources.

TM6SF2 and MAC30, new enzyme homologs in sterol metabolism and common metabolic disease

Carriers of the Glu167Lys coding variant in the TM6SF2 gene have recently been identified as being more susceptible to non-alcoholic fatty liver disease (NAFLD), yet exhibit lower levels of circulating lipids and hence are protected against cardiovascular disease. Despite the physiological importance of these observations, the molecular function of TM6SF2 remains unknown, and no sequence similarity with functionally characterized proteins has been identified. In order to trace its evolutionary history and to identify functional domains, we embarked on a computational protein sequence analysis of TM6SF2. We identified a new domain, the EXPERA domain, which is conserved among TM6SF, MAC30/TMEM97 and EBP (D8, D7 sterol isomerase) protein families. EBP mutations are the cause of chondrodysplasia punctata 2 X-linked dominant (CDPX2), also known as Conradi-Hünermann-Happle syndrome, a defective cholesterol biosynthesis disorder. Our analysis of evolutionary conservation among EXPERA domain-containing families and the previously suggested catalytic mechanism for the EBP enzyme, indicate that TM6SF and MAC30/TMEM97 families are both highly likely to possess, as for the EBP family, catalytic activity as sterol isomerases. This unexpected prediction of enzymatic functions for TM6SF and MAC30/TMEM97 is important because it now permits detailed experiments to investigate the function of these key proteins in various human pathologies, from cardiovascular disease to cancer.

Network biology in development of monoclonal antibody therapeutics

Monoclonal antibodies (mAbs) are large glycoproteins that recognize and remove/neutralize a specific target. Inflammation and inflammatory diseases are often treated with mAb-based therapeutics. Mathematical modeling is widely used in development of mAbs. Bioinformatics and structural modeling is used for humanization of mAbs and PK/PD modeling is extensively used in preclinical and clinical development. The objective of this commentary is to introduce systems biology-based modeling that can accelerate and improve development of mAbs.

Antiangiogenic cancer drug using the zebrafish model

The process of de novo vessel formation, called angiogenesis, is essential for tumor progression and spreading. Targeting of molecular pathways involved in such tumor angiogenetic processes by using specific drugs or inhibitors is important for developing new anticancer therapies. Drug discovery remains to be the main focus for biomedical research and represents the essence of antiangiogenesis cancer research. To pursue these molecular and pharmacological goals, researchers need to use animal models that facilitate the elucidation of tumor angiogenesis mechanisms and the testing of antiangiogenic therapies. The past few years have seen the zebrafish system emerge as a valid model organism to study developmental angiogenesis and, more recently, as an alternative vertebrate model for cancer research. In this review, we will discuss why the zebrafish model system has the advantage of being a vertebrate model equipped with easy and powerful transgenesis as well as imaging tools to investigate not only physiological angiogenesis but also tumor angiogenesis. We will also highlight the potential of zebrafish for identifying antitumor angiogenesis drugs to block tumor development and progression. We foresee the zebrafish model as an important system that can possibly complement well-established mouse models in cancer research to generate novel insights into the molecular mechanism of the tumor angiogenesis.

In silico analysis of the conservation of human toxicity and endocrine disruption targets in aquatic species

Pharmaceuticals and industrial chemicals, both in the environment and in research settings, commonly interact with aquatic vertebrates. Due to their short life-cycles and the traits that can be generalized to other organisms, fish and amphibians are attractive models for the evaluation of toxicity caused by endocrine disrupting chemicals (EDCs) and adverse drug reactions. EDCs, such as pharmaceuticals or plasticizers, alter the normal function of the endocrine system and pose a significant hazard to human health and the environment. The selection of suitable animal models for toxicity testing is often reliant on high sequence identity between the human proteins and their animal orthologs. Herein, we compare in silico the ligand-binding sites of 28 human "side-effect" targets to their corresponding orthologs in Danio rerio, Pimephales promelas, Takifugu rubripes, Xenopus laevis, and Xenopus tropicalis, as well as subpockets involved in protein interactions with specific chemicals. We found that the ligand-binding pockets had much higher conservation than the full proteins, while the peroxisome proliferator-activated receptor γ and corticotropin-releasing factor receptor 1 were notable exceptions. Furthermore, we demonstrated that the conservation of subpockets may vary dramatically. Finally, we identified the aquatic model(s) with the highest binding site similarity, compared to the corresponding human toxicity target.

Evolutionary survey of druggable protein targets with respect to their subcellular localizations

The druggable subset of the human genome, termed the “druggable genome,” provides the pharmaceutical industry with a unique opportunity for the advancement of new therapeutic interventions for a multitude of diseases and disorders. To date, there is no systematic assessment of the evolutionary history and nature of the defined druggable proteins derived from the contemporary druggable genome (i.e., proteins that bind or are predicted to bind with high affinity to a biologic). An understanding of drug–protein target interactions in specific cellular compartments is crucial for the optimal therapeutic delivery of pharmaceutical agents, as well as for preclinical drug trials in model animals. This study applied the concept of pharmacophylogenomics, the study of genes, evolution, and drug targets, to conduct an evolutionary survey of drug targets with respect to their subcellular localizations. Using multiple models and modes of druggable genome comparison, the results concordantly indicated that orthologous drug targets with a nuclear localization in the human, macaque, mouse, and rat showed a higher trend for evolutionary conservation compared with drug targets in the cell membrane and the extracellular compartment. As such, this study provides important information regarding druggable protein targets and the druggable genome at the pharmacophylogenomics level.

Animal models of human disease: challenges in enabling translation

Animal models have historically played a critical role in the exploration and characterization of disease pathophysiology, target identification, and in the in vivo evaluation of novel therapeutic agents and treatments. In the wake of numerous clinical trial failures of new chemical entities (NCEs) with promising preclinical profiles, animal models in all therapeutic areas have been increasingly criticized for their limited ability to predict NCE efficacy, safety and toxicity in humans. The present review discusses some of the challenges associated with the evaluation and predictive validation of animal models, as well as methodological flaws in both preclinical and clinical study designs that may contribute to the current translational failure rate. The testing of disease hypotheses and NCEs in multiple disease models necessitates evaluation of pharmacokinetic/pharmacodynamic (PK/PD) relationships and the earlier development of validated disease-associated biomarkers to assess target engagement and NCE efficacy. Additionally, the transparent integration of efficacy and safety data derived from animal models into the hierarchical data sets generated preclinically is essential in order to derive a level of predictive utility consistent with the degree of validation and inherent limitations of current animal models. The predictive value of an animal model is thus only as useful as the context in which it is interpreted. Finally, rather than dismissing animal models as not very useful in the drug discovery process, additional resources, like those successfully used in the preclinical PK assessment used for the selection of lead NCEs, must be focused on improving existing and developing new animal models.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

The repertoires of ubiquitinating and deubiquitinating enzymes in eukaryotic genomes

Reversible protein ubiquitination regulates virtually all known cellular activities. Here, we present a quantitatively evaluated and broadly applicable method to predict eukaryotic ubiquitinating enzymes (UBE) and deubiquitinating enzymes (DUB) and its application to 50 distinct genomes belonging to four of the five major phylogenetic supergroups of eukaryotes: unikonts (including metazoans, fungi, choanozoa, and amoebozoa), excavates, chromalveolates, and plants. Our method relies on a collection of profile hidden Markov models, and we demonstrate its superior performance (coverage and classification accuracy >99%) by identifying approximately 25% and approximately 35% additional UBE and DUB genes in yeast and human, which had not been reported before. In yeast, we predict 85 UBE and 24 DUB genes, for 814 UBE and 107 DUB genes in the human genome. Most UBE and DUB families are present in all eukaryotic lineages, with plants and animals harboring massively enlarged repertoires of ubiquitin ligases. Unicellular organisms, on the other hand, typically harbor less than 300 UBEs and less than 40 DUBs per genome. Ninety-one UBE/DUB genes are orthologous across all four eukaryotic supergroups, and these likely represent a primordial core of enzymes of the ubiquitination system probably dating back to the first eukaryotes approximately 2 billion years ago. Our genome-wide predictions are available through the Database of Ubiquitinating and Deubiquitinating Enzymes (www.DUDE-db.org), where users can also perform advanced sequence and phylogenetic analyses and submit their own predictions.

The evolutionary rate of antibacterial drug targets

Background

One of the major issues in the fight against infectious diseases is the notable increase in multiple drug resistance in pathogenic species. For that reason, newly acquired high-throughput data on virulent microbial agents attract the attention of many researchers seeking potential new drug targets. Many approaches have been used to evaluate proteins from infectious pathogens, including, but not limited to, similarity analysis, reverse docking, statistical 3D structure analysis, machine learning, topological properties of interaction networks or a combination of the aforementioned methods. From a biological perspective, most essential proteins (knockout lethal for bacteria) or highly conserved proteins (broad spectrum activity) are potential drug targets. Ribosomal proteins comprise such an example. Many of them are well-known drug targets in bacteria. It is intuitive that we should learn from nature how to design good drugs. Firstly, known antibiotics are mainly originating from natural products of microorganisms targeting other microorganisms. Secondly, paleontological data suggests that antibiotics have been used by microorganisms for million years. Thus, we have hypothesized that good drug targets are evolutionary constrained and are subject of evolutionary selection. This means that mutations in such proteins are deleterious and removed by selection, which makes them less susceptible to random development of resistance. Analysis of the speed of evolution seems to be good approach to test this hypothesis.

Results

In this study we show that pN/pS ratio of genes coding for known drug targets is significantly lower than the genome average and also lower than that for essential genes identified by experimental methods. Similar results are observed in the case of dN/dS analysis. Both analyzes suggest that drug targets tend to evolve slowly and that the rate of evolution is a better predictor of drugability than essentiality.

Conclusions

Evolutionary rate can be used to score and find potential drug targets. The results presented here may become a useful addition to a repertoire of drug target prediction methods. As a proof of concept, we analyzed GO enrichment among the slowest evolving genes. These may become the starting point in the search for antibiotics with a novel mechanism.

Use of comparative genomics approaches to characterize interspecies differences in response to environmental chemicals: challenges, opportunities, and research needs

A critical challenge for environmental chemical risk assessment is the characterization and reduction of uncertainties introduced when extrapolating inferences from one species to another. The purpose of this article is to explore the challenges, opportunities, and research needs surrounding the issue of how genomics data and computational and systems level approaches can be applied to inform differences in response to environmental chemical exposure across species. We propose that the data, tools, and evolutionary framework of comparative genomics be adapted to inform interspecies differences in chemical mechanisms of action. We compare and contrast existing approaches, from disciplines as varied as evolutionary biology, systems biology, mathematics, and computer science, that can be used, modified, and combined in new ways to discover and characterize interspecies differences in chemical mechanism of action which, in turn, can be explored for application to risk assessment. We consider how genetic, protein, pathway, and network information can be interrogated from an evolutionary biology perspective to effectively characterize variations in biological processes of toxicological relevance among organisms. We conclude that comparative genomics approaches show promise for characterizing interspecies differences in mechanisms of action, and further, for improving our understanding of the uncertainties inherent in extrapolating inferences across species in both ecological and human health risk assessment. To achieve long-term relevance and consistent use in environmental chemical risk assessment, improved bioinformatics tools, computational methods robust to data gaps, and quantitative approaches for conducting extrapolations across species are critically needed. Specific areas ripe for research to address these needs are recommended.

Enzyme sequence similarity improves the reaction alignment method for cross-species pathway comparison

Pathway-based information has become an important source of information for both establishing evolutionary relationships and understanding the mode of action of a chemical or pharmaceutical among species. Cross-species comparison of pathways can address two broad questions: comparison in order to inform evolutionary relationships and to extrapolate species differences used in a number of different applications including drug and toxicity testing. Cross-species comparison of metabolic pathways is complex as there are multiple features of a pathway that can be modeled and compared. Among the various methods that have been proposed, reaction alignment has emerged as the most successful at predicting phylogenetic relationships based on NCBI taxonomy. We propose an improvement of the reaction alignment method by accounting for sequence similarity in addition to reaction alignment method. Using nine species, including human and some model organisms and test species, we evaluate the standard and improved comparison methods by analyzing glycolysis and citrate cycle pathways conservation. In addition, we demonstrate how organism comparison can be conducted by accounting for the cumulative information retrieved from nine pathways in central metabolism as well as a more complete study involving 36 pathways common in all nine species. Our results indicate that reaction alignment with enzyme sequence similarity results in a more accurate representation of pathway specific cross-species similarities and differences based on NCBI taxonomy.

Probabilistic orthology analysis

Orthology analysis aims at identifying orthologous genes and gene products from different organisms and, therefore, is a powerful tool in modern computational and experimental biology. Although reconciliation-based orthology methods are generally considered more accurate than distance-based ones, the traditional parsimony-based implementation of reconciliation-based orthology analysis (most parsimonious reconciliation [MPR]) suffers from a number of shortcomings. For example, 1) it is limited to orthology predictions from the reconciliation that minimizes the number of gene duplication and loss events, 2) it cannot evaluate the support of this reconciliation in relation to the other reconciliations, and 3) it cannot make use of prior knowledge (e.g., about species divergence times) that provides auxiliary information for orthology predictions. We present a probabilistic approach to reconciliation-based orthology analysis that addresses all these issues by estimating orthology probabilities. The method is based on the gene evolution model, an explicit evolutionary model for gene duplication and gene loss inside a species tree, that generalizes the standard birth-death process. We describe the probabilistic approach to orthology analysis using 2 experimental data sets and show that the use of orthology probabilities allows a more informative analysis than MPR and, in particular, that it is less sensitive to taxon sampling problems. We generalize these anecdotal observations and show, using data generated under biologically realistic conditions, that MPR give false orthology predictions at a substantial frequency. Last, we provide a new orthology prediction method that allows an orthology and paralogy classification with any chosen sensitivity/specificity combination from the spectra of achievable combinations. We conclude that probabilistic orthology analysis is a strong and more advanced alternative to traditional orthology analysis and that it provides a framework for sophisticated comparative studies of processes in genome evolution.

Evolutionary patterning: a novel approach to the identification of potential drug target sites in Plasmodium falciparum

Malaria continues to be the most lethal protozoan disease of humans. Drug development programs exhibit a high attrition rate and parasite resistance to chemotherapeutic drugs exacerbates the problem. Strategies that limit the development of resistance and minimize host side-effects are therefore of major importance. In this study, a novel approach, termed evolutionary patterning (EP), was used to identify suitable drug target sites that would minimize the emergence of parasite resistance. EP uses the ratio of non-synonymous to synonymous substitutions (ω) to assess the patterns of evolutionary change at individual codons in a gene and to identify codons under the most intense purifying selection (ω≤0.1). The extreme evolutionary pressure to maintain these residues implies that resistance mutations are highly unlikely to develop, which makes them attractive chemotherapeutic targets. Method validation included a demonstration that none of the residues providing pyrimethamine resistance in the Plasmodium falciparum dihydrofolate reductase enzyme were under extreme purifying selection. To illustrate the EP approach, the putative P. falciparum glycerol kinase (PfGK) was used as an example. The gene was cloned and the recombinant protein was active in vitro, verifying the database annotation. Parasite and human GK gene sequences were analyzed separately as part of protozoan and metazoan clades, respectively, and key differences in the evolutionary patterns of the two molecules were identified. Potential drug target sites containing residues under extreme evolutionary constraints were selected. Structural modeling was used to evaluate the functional importance and drug accessibility of these sites, which narrowed down the number of candidates. The strategy of evolutionary patterning and refinement with structural modeling addresses the problem of targeting sites to minimize the development of drug resistance. This represents a significant advance for drug discovery programs in malaria and other infectious diseases.

The role of positive selection in determining the molecular cause of species differences in disease

BACKGROUND

Related species, such as humans and chimpanzees, often experience the same disease with varying degrees of pathology, as seen in the cases of Alzheimer's disease, or differing symptomatology as in AIDS. Furthermore, certain diseases such as schizophrenia, epithelial cancers and autoimmune disorders are far more frequent in humans than in other species for reasons not associated with lifestyle. Genes that have undergone positive selection during species evolution are indicative of functional adaptations that drive species differences. Thus we investigate whether biomedical disease differences between species can be attributed to positively selected genes.

RESULTS

We identified genes that putatively underwent positive selection during the evolution of humans and four mammals which are often used to model human diseases (mouse, rat, chimpanzee and dog). We show that genes predicted to have been subject to positive selection pressure during human evolution are implicated in diseases such as epithelial cancers, schizophrenia, autoimmune diseases and Alzheimer's disease, all of which differ in prevalence and symptomatology between humans and their mammalian relatives. In agreement with previous studies, the chimpanzee lineage was found to have more genes under positive selection than any of the other lineages. In addition, we found new evidence to support the hypothesis that genes that have undergone positive selection tend to interact with each other. This is the first such evidence to be detected widely among mammalian genes and may be important in identifying molecular pathways causative of species differences.

CONCLUSION

Our dataset of genes predicted to have been subject to positive selection in five species serves as an informative resource that can be consulted prior to selecting appropriate animal models during drug target validation. We conclude that studying the evolution of functional and biomedical disease differences between species is an important way to gain insight into their molecular causes and may provide a method to predict when animal models do not mirror human biology.

Reconciliation with non-binary species trees

Reconciliation extracts information from the topological incongruence between gene and species trees to infer duplications and losses in the history of a gene family. The inferred duplication-loss histories provide valuable information for a broad range of biological applications, including ortholog identification, estimating gene duplication times, and rooting and correcting gene trees. While reconciliation for binary trees is a tractable and well studied problem, there are no algorithms for reconciliation with non-binary species trees. Yet a striking proportion of species trees are non-binary. For example, 64% of branch points in the NCBI taxonomy have three or more children. When applied to non-binary species trees, current algorithms overestimate the number of duplications because they cannot distinguish between duplication and incomplete lineage sorting. We present the first algorithms for reconciling binary gene trees with non-binary species trees under a duplication-loss parsimony model. Our algorithms utilize an efficient mapping from gene to species trees to infer the minimum number of duplications in O(|V(G) | x (k(S) + h(S))) time, where |V(G)| is the number of nodes in the gene tree, h(S) is the height of the species tree and k(S) is the size of its largest polytomy. We present a dynamic programming algorithm which also minimizes the total number of losses. Although this algorithm is exponential in the size of the largest polytomy, it performs well in practice for polytomies with outdegree of 12 or less. We also present a heuristic which estimates the minimal number of losses in polynomial time. In empirical tests, this algorithm finds an optimal loss history 99% of the time. Our algorithms have been implemented in NOTUNG, a robust, production quality, tree-fitting program, which provides a graphical user interface for exploratory analysis and also supports automated, high-throughput analysis of large data sets.

Evolutionary conservation of human drug targets in organisms used for environmental risk assessments

Pharmaceuticals are typically found in very low concentrations in the aquatic environment. Accordingly, environmental effects clearly assigned to residual drugs are consistent with high affinity interactions with conserved targets in affected wildlife species rather than with a general toxic effect. Thus, evolutionarily well-conserved targets in a given species are associated with an increased risk. In this study orthologs for 1318 human drug targets were predicted in 16 species of which several are relevant for ecotoxicity testing. The conservation of different functional categories of targets was also analyzed. Zebrafish had orthologs to 86% of the drug targets while only 61% were conserved in Daphnia and 35% in green alga. The predicted presence and absence of orthologs agrees well with published experimental data on the potential for specific drug target interaction in various species. Based on the conservation of targets we propose that aquatic environmental risk assessments for human drugs should always include comprehensive studies on aquatic vertebrates. Furthermore, individual targets, especially enzymes, are well conserved suggesting that tests on evolutionarily distant organisms would be highly relevant for certain drugs. We propose that the results can guide environmental risk assessments by improving the possibilities to identify species sensitive to certain types of pharmaceuticals or to other contaminants that act through well defined mechanisms of action. Moreover, we suggest that the results can be used to interpret the relevance of existing ecotoxicity data.

Drug-target network

The global set of relationships between protein targets of all drugs and all disease-gene products in the human protein-protein interaction or 'interactome' network remains uncharacterized. We built a bipartite graph composed of US Food and Drug Administration-approved drugs and proteins linked by drug-target binary associations. The resulting network connects most drugs into a highly interlinked giant component, with strong local clustering of drugs of similar types according to Anatomical Therapeutic Chemical classification. Topological analyses of this network quantitatively showed an overabundance of 'follow-on' drugs, that is, drugs that target already targeted proteins. By including drugs currently under investigation, we identified a trend toward more functionally diverse targets improving polypharmacology. To analyze the relationships between drug targets and disease-gene products, we measured the shortest distance between both sets of proteins in current models of the human interactome network. Significant differences in distance were found between etiological and palliative drugs. A recent trend toward more rational drug design was observed.

Stimulating the development of mechanism-based, individualized pain therapies

Biomedical science has greatly improved our understanding of pain in recent decades, but few novel molecular entities that address fundamentally new pain mechanisms have entered the clinic, despite dramatically increased pharmaceutical investment. Indeed, virtually all new analgesics approved over the past 25 years are derivatives or reformulations of opioids or aspirin-like drugs, existing drugs given for a new indication or older drugs given by a different route of administration. Here, we discuss factors contributing to this lack of innovation in therapies for pain and advocate public-private partnerships (PPPs) to translate new knowledge into more efficacious and safer treatments.

Drug discovery and computational evolutionary analysis

Drug discovery remains a difficult business with a very high level of attrition. Many steps in this long process use data generated from various species. One key challenge is to successfully translate the pre-clinical findings of target validation and safety studies in animal models to diverse human beings in the clinic. Advanced computational evolutionary analysis techniques combined with the increasing availability of sequence information enable the application of systematic evolutionary approaches to targets and pathways of interest to drug discovery. These analyses have the potential to increase our understanding of experimental differences observed between species.

Bioinformatic discovery of novel bioactive peptides

Short synthetic oligopeptides based on regions of human proteins that encompass functional motifs are versatile reagents for understanding protein signaling and interactions. They can either mimic or inhibit the parent protein's activity and have been used in drug development. Peptide studies typically either derive peptides from a single identified protein or (at the other extreme) screen random combinatorial peptides, often without knowledge of the signaling pathways targeted. Our objective was to determine whether rational bioinformatic design of oligopeptides specifically targeted to potentially signaling-rich juxtamembrane regions could identify modulators of human platelet function. High-throughput in vitro platelet function assays of palmitylated cell-permeable oligopeptides corresponding to these regions identified many agonists and antagonists of platelet function. Many bioactive peptides were from adhesion molecules, including a specific CD226-derived inhibitor of inside-out platelet signaling. Systematic screens of this nature are highly efficient tools for discovering short signaling motifs in molecular signaling pathways.

An integrated view of protein evolution

Why do proteins evolve at different rates? Advances in systems biology and genomics have facilitated a move from studying individual proteins to characterizing global cellular factors. Systematic surveys indicate that protein evolution is not determined exclusively by selection on protein structure and function, but is also affected by the genomic position of the encoding genes, their expression patterns, their position in biological networks and possibly their robustness to mistranslation. Recent work has allowed insights into the relative importance of these factors. We discuss the status of a much-needed coherent view that integrates studies on protein evolution with biochemistry and functional and structural genomics.

Multi-target strategies for the improved treatment of depressive states: Conceptual foundations and neuronal substrates, drug discovery and therapeutic application

Major depression is a debilitating and recurrent disorder with a substantial lifetime risk and a high social cost. Depressed patients generally display co-morbid symptoms, and depression frequently accompanies other serious disorders. Currently available drugs display limited efficacy and a pronounced delay to onset of action, and all provoke distressing side effects. Cloning of the human genome has fuelled expectations that symptomatic treatment may soon become more rapid and effective, and that depressive states may ultimately be "prevented" or "cured". In pursuing these objectives, in particular for genome-derived, non-monoaminergic targets, "specificity" of drug actions is often emphasized. That is, priority is afforded to agents that interact exclusively with a single site hypothesized as critically involved in the pathogenesis and/or control of depression. Certain highly selective drugs may prove effective, and they remain indispensable in the experimental (and clinical) evaluation of the significance of novel mechanisms. However, by analogy to other multifactorial disorders, "multi-target" agents may be better adapted to the improved treatment of depressive states. Support for this contention is garnered from a broad palette of observations, ranging from mechanisms of action of adjunctive drug combinations and electroconvulsive therapy to "network theory" analysis of the etiology and management of depressive states. The review also outlines opportunities to be exploited, and challenges to be addressed, in the discovery and characterization of drugs recognizing multiple targets. Finally, a diversity of multi-target strategies is proposed for the more efficacious and rapid control of core and co-morbid symptoms of depression, together with improved tolerance relative to currently available agents.

Application of DETECTER, an evolutionary genomic tool to analyze genetic variation, to the cystic fibrosis gene family

BACKGROUND

The medical community requires computational tools that distinguish missense genetic differences having phenotypic impact within the vast number of sense mutations that do not. Tools that do this will become increasingly important for those seeking to use human genome sequence data to predict disease, make prognoses, and customize therapy to individual patients.

RESULTS

An approach, termed DETECTER, is proposed to identify sites in a protein sequence where amino acid replacements are likely to have a significant effect on phenotype, including causing genetic disease. This approach uses a model-dependent tool to estimate the normalized replacement rate at individual sites in a protein sequence, based on a history of those sites extracted from an evolutionary analysis of the corresponding protein family. This tool identifies sites that have higher-than-average, average, or lower-than-average rates of change in the lineage leading to the sequence in the population of interest. The rates are then combined with sequence data to determine the likelihoods that particular amino acids were present at individual sites in the evolutionary history of the gene family. These likelihoods are used to predict whether any specific amino acid replacements, if introduced at the site in a modern human population, would have a significant impact on fitness. The DETECTER tool is used to analyze the cystic fibrosis transmembrane conductance regulator (CFTR) gene family.

CONCLUSION

In this system, DETECTER retrodicts amino acid replacements associated with the cystic fibrosis disease with greater accuracy than alternative approaches. While this result validates this approach for this particular family of proteins only, the approach may be applicable to the analysis of polymorphisms generally, including SNPs in a human population.

Pharmacogenetics in drug development

Over the last two decades, identification of polymorphisms that influence human diseases has begun to have an impact on the provision of medical care. The promise of genetics lies in its ability to provide insights into an individual's susceptibility to disease, the likely nature of the disease and the most appropriate therapy. For much of its history, pharmacogenetics (PGx-the use of genetic information to impact drug choice) has been limited to comparatively simple phenotypes such as plasma drug levels. Progress in genetics technologies has broadened the scope of PGx efficacy and safety studies that can be implemented, impacting on a broad spectrum of drug discovery and development activities. Recent PGx data show the ability of this approach to generate information that can be applied to dose selection, efficacy determination and safety issues. This in turn will lead to significant opportunities to affect both the approach to clinical development and the probability of success--the latter being an important aspect for pharmaceutical companies and for the patients who will benefit from these new medicines.

Therapeutics in renal disease: the road ahead for antiproliferative targets

Discovery into the molecular basis of renal disease is occurring at an unprecedented rate. With the advent of the NIH Roadmap, there is a greater expectation of translating this knowledge into new treatments. Here, we review the therapeutic strategy to preserve renal function in proliferative renal diseases by directly inhibiting the mitogenic pathways within renal parenchymal cells that promote G0 to G1/S cell-cycle phase progression. Reductionist methodologies have identified several antiproliferative molecular targets, and promising preclinical testing of leading small-molecule drugs to modulate these targets has now led to landmark clinical trials. Yet, this advancement into targeted therapy highlights important differences between the therapeutic goals of molecular nephrology versus molecular oncology and, by extension, the poorly understood role of alternative target activity in drug efficacy. Systems research to clarify these issues should accelerate the development of this promising therapeutic strategy.

Mass spectrometry and the search for moonlighting proteins

Mass spectrometry has become one of the most important techniques in proteomics because of its use to identify the proteins found in different cell types, organelles, and multiprotein complexes. This information about protein location and binding partners can provide valuable clues to infer a protein's function. However, more and more proteins are found that "moonlight," or have more than one function, and the presence of moonlighting proteins can make more difficult the identification of protein function in those studies. This review discusses examples of moonlighting proteins and how their presence can affect the results of mass spectrometry studies that identify the locations, levels, and changes in protein expression. Although the presence of moonlighting proteins can complicate the results of those studies, mass spectrometry-derived protein-expression profiles potentially provides a very powerful method to find additional moonlighting proteins because they do not require a prior hypothesis of the protein's function.

A 4'-phosphopantetheinyl transferase mediates non-ribosomal peptide synthetase activation in Aspergillus fumigatus

Aspergillus fumigatus is a significant human pathogen. Non-ribosomal peptide (NRP) synthesis is thought to be responsible for a significant proportion of toxin and siderophore production in the organism. Furthermore, it has been shown that 4'-phosphopantetheinylation is required for the activation of key enzymes involved in non-ribosomal peptide synthesis in other species. Here we report the cloning, recombinant expression and functional characterisation of a 4'-phosphopantetheinyl transferase from A. fumigatus and the identification of an atypical NRP synthetase (Afpes1), spanning 14.3 kb. Phylogenetic analysis has shown that the NRP synthetase exhibits greatest identity to NRP synthetases from Metarhizium anisolpiae (PesA) and Alternaria brassicae (AbrePsy1). Northern hybridisation and RT-PCR analysis have confirmed that both genes are expressed in A. fumigatus. A 120 kDa fragment of the A. fumigatus NRP synthetase, containing a putative thiolation domain, was cloned and expressed in the baculovirus expression system. Detection of a 4'-phosphopantetheinylated peptide (SFSAMK) from this protein, by MALDI-TOF mass spectrometric analysis after coincubation of the 4'-phosphopantetheinyl transferase with the recombinant NRP synthetase fragment and acetyl CoA, confirms that it is competent to play a role in NRP synthetase activation in A. fumigatus. The 4'-phosphopantetheinyl transferase also activates, by 4'-phosphopantetheinylation, recombinant alpha-aminoadipate reductase (Lys2p) from Candida albicans, a key enzyme involved in lysine biosynthesis.

FIGENIX: intelligent automation of genomic annotation: expertise integration in a new software platform

Background

Two of the main objectives of the genomic and post-genomic era are to structurally and functionally annotate genomes which consists of detecting genes' position and structure, and inferring their function (as well as of other features of genomes). Structural and functional annotation both require the complex chaining of numerous different software, algorithms and methods under the supervision of a biologist. The automation of these pipelines is necessary to manage huge amounts of data released by sequencing projects. Several pipelines already automate some of these complex chaining but still necessitate an important contribution of biologists for supervising and controlling the results at various steps.

Results

Here we propose an innovative automated platform, FIGENIX, which includes an expert system capable to substitute to human expertise at several key steps. FIGENIX currently automates complex pipelines of structural and functional annotation under the supervision of the expert system (which allows for example to make key decisions, check intermediate results or refine the dataset). The quality of the results produced by FIGENIX is comparable to those obtained by expert biologists with a drastic gain in terms of time costs and avoidance of errors due to the human manipulation of data.

Conclusion

The core engine and expert system of the FIGENIX platform currently handle complex annotation processes of broad interest for the genomic community. They could be easily adapted to new, or more specialized pipelines, such as for example the annotation of miRNAs, the classification of complex multigenic families, annotation of regulatory elements and other genomic features of interest.

Oracle Database 10g: a platform for BLAST search and Regular Expression pattern matching in life sciences

As database management systems expand their array of analytical functionality, they become powerful research engines for biomedical data analysis and drug discovery. Databases can hold most of the data types commonly required in life sciences and consequently can be used as flexible platforms for the implementation of knowledgebases. Performing data analysis in the database simplifies data management by minimizing the movement of data from disks to memory, allowing pre-filtering and post-processing of datasets, and enabling data to remain in a secure, highly available environment. This article describes the Oracle Database 10g implementation of BLAST and Regular Expression Searches and provides case studies of their usage in bioinformatics. http://www.oracle.com/technology/software/index.html

Data integration: challenges for drug discovery

The effective integration of data and knowledge from many disparate sources will be crucial to future drug discovery. Data integration is a key element of conducting scientific investigations with modern platform technologies, managing increasingly complex discovery portfolios and processes, and fully realizing economies of scale in large enterprises. However, viewing data integration as simply an 'IT problem' underestimates the novel and serious scientific and management challenges it embodies - challenges that could require significant methodological and even cultural changes in our approach to data.

Managing genomic and proteomic knowledge

Genomic and proteomic platform data constitute a hugely important resource to current efforts in disease understanding, systems biology and drug discovery. We review prerequisites for the adequate management of 'omic' data, the means by which such data are analyzed and converted to knowledge relevant to drug discovery and issues crucial to the integration of such data, particularly with chemical, genetic and clinical data.:

Pharmacogenetics and drug development: the path to safer and more effective drugs

Pharmacogenetics provides opportunities for informed decision-making along the pharmaceutical pipeline. There is a growing literature of retrospective studies of marketed medicines that describe efficacy or safety on the basis of patient genotypes. These studies emphasize the potential prospective use of genome information to enhance success in finding new medicines. An example of a prospective efficacy pharmacogenetic Phase-IIA proof-of-concept study is described. Inserting a rapidly performed efficacy pharmacogenetic step after initial clinical data are obtained can provide confidence for a commitment to full drug development. The rapid identification of adverse events during and after drug development using genomic mapping tools is also reviewed.

The role of bioinformatics in target validation

Bioinformatics is being increasingly used to support target validation by providing functionally predictive information mined from databases and experimental datasets using a variety of computational tools. The predictive power of these complementary approaches is strongest when information from several techniques is combined, including experimental confirmation of predictions. The aim of this review is to highlight and discuss the key approaches available in this rapidly developing area to facilitate selection of the appropriate tools and databases.: