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A Structure-Based Approach for Mapping Adverse Drug Reactions to the Perturbation of Underlying Biological Pathways  [PDF]
Izhar Wallach,Navdeep Jaitly,Ryan Lilien
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0012063
Abstract: Adverse drug reactions (ADR), also known as side-effects, are complex undesired physiologic phenomena observed secondary to the administration of pharmaceuticals. Several phenomena underlie the emergence of each ADR; however, a dominant factor is the drug's ability to modulate one or more biological pathways. Understanding the biological processes behind the occurrence of ADRs would lead to the development of safer and more effective drugs. At present, no method exists to discover these ADR-pathway associations. In this paper we introduce a computational framework for identifying a subset of these associations based on the assumption that drugs capable of modulating the same pathway may induce similar ADRs. Our model exploits multiple information resources. First, we utilize a publicly available dataset pairing drugs with their observed ADRs. Second, we identify putative protein targets for each drug using the protein structure database and in-silico virtual docking. Third, we label each protein target with its known involvement in one or more biological pathways. Finally, the relationships among these information sources are mined using multiple stages of logistic-regression while controlling for over-fitting and multiple-hypothesis testing. As proof-of-concept, we examined a dataset of 506 ADRs, 730 drugs, and 830 human protein targets. Our method yielded 185 ADR-pathway associations of which 45 were selected to undergo a manual literature review. We found 32 associations to be supported by the scientific literature.
Predicting drug side-effects by chemical systems biology
Nicholas P Tatonetti, Tianyun Liu, Russ B Altman
Genome Biology , 2009, DOI: 10.1186/gb-2009-10-9-238
Abstract: Drug-related adverse events affect approximately 2 million patients in the United States each year, resulting in about 100,000 deaths [1]. For example, highly publicized cases of severe adverse reactions recently resulted in a US Food and Drug Administration advisory panel suggesting that the popular pain relievers Percocet and Vicodin be banned [2]. Some adverse events are predictable consequences of the known mechanism of a drug, but others are not predicted and seem to result from 'off-target' pathways.When developing novel chemical entities (NCEs) for a therapeutic application, knowledge of binding partners and affected biological pathways is useful for predicting both efficacy and side-effects. Traditional drug design has relied heavily on the one drug-one target paradigm [3], but this may overlook system-wide effects that cause the drug to be unsuccessful. Adverse side-effects and lack of efficacy are the two most important reasons a drug will fail clinical trials, each accounting for around 30% of failures [3]. The development of tools that can predict adverse events and system-wide effects might thus reduce the attrition rate. Such tools will most certainly include emerging information about protein-protein interactions, signaling pathways, and pathways of drug action and metabolism. A systems view of the body's responses to a drug threatens the simplicity of the one drug-one target paradigm, but could provide a framework for considering all effects, and not just those that are targeted.The laboratory assays currently used to evaluate potential adverse drug effects can be costly and time-consuming. For example, an expensive two-year rodent bioassay is the current gold standard for determining the carcinogenicity of a NCE [4]. Some assays are also of doubtful utility - only around 15% of gene knockouts in the standard pharmaceutical model organisms show any fitness defect [3]. Therefore, drugs designed with a single target in mind may prove ineffective, not b
Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity  [PDF]
Kejian Wang,Jiazhi Sun,Shufeng Zhou,Chunling Wan,Shengying Qin,Can Li,Lin He ,Lun Yang
PLOS Computational Biology , 2013, DOI: 10.1371/journal.pcbi.1003315
Abstract: Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI). However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap). Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1) some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2) in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects.
Network Neighbors of Drug Targets Contribute to Drug Side-Effect Similarity  [PDF]
Lucas Brouwers, Murat Iskar, Georg Zeller, Vera van Noort, Peer Bork
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0022187
Abstract: In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects.
Target Essentiality and Centrality Characterize Drug Side Effects  [PDF]
Xiujuan Wang ,Bram Thijssen ,Haiyuan Yu
PLOS Computational Biology , 2013, DOI: 10.1371/journal.pcbi.1003119
Abstract: To investigate factors contributing to drug side effects, we systematically examine relationships between 4,199 side effects associated with 996 drugs and their 647 human protein targets. We find that it is the number of essential targets, not the number of total targets, that determines the side effects of corresponding drugs. Furthermore, within the context of a three-dimensional interaction network with atomic-resolution interaction interfaces, we find that drugs causing more side effects are also characterized by high degree and betweenness of their targets and highly shared interaction interfaces on these targets. Our findings suggest that both essentiality and centrality of a drug target are key factors contributing to side effects and should be taken into consideration in rational drug design.
Predicting drug side-effect profiles: a chemical fragment-based approach
Edouard Pauwels, Véronique Stoven, Yoshihiro Yamanishi
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-169
Abstract: In the present work, we propose a new method to predict potential side-effects of drug candidate molecules based on their chemical structures, applicable on large molecular databanks. A unique feature of the proposed method is its ability to extract correlated sets of chemical substructures (or chemical fragments) and side-effects. This is made possible using sparse canonical correlation analysis (SCCA). In the results, we show the usefulness of the proposed method by predicting 1385 side-effects in the SIDER database from the chemical structures of 888 approved drugs. These predictions are performed with simultaneous extraction of correlated ensembles formed by a set of chemical substructures shared by drugs that are likely to have a set of side-effects. We also conduct a comprehensive side-effect prediction for many uncharacterized drug molecules stored in DrugBank, and were able to confirm interesting predictions using independent source of information.The proposed method is expected to be useful in various stages of the drug development process.Drug side-effects, or adverse drug reactions, have become a major public health concern. It is one of the main causes of failure in the process of drug development, and of drug withdrawal once they have reached the market. As an illustration of the extent of this problem, serious drug side-effects are estimated to be the fourth largest cause of death in the United States, resulting in 100,000 deaths per year [1]. In order to reduce these risks, many efforts have been devoted to relate severe side-effects to some specific genetic biomarkers. This so-called pharmacogenomics strategy is a rapidly developing field, especially in oncology [2]. The aim is to prescribe a drug to patients who will benefit from it, while avoiding life threatening side-effects [3].From the viewpoint of systems biology, drugs can be regarded as molecules that induce perturbations to biological systems consisting of various molecular interactions suc
Recycling side-effects into clinical markers for drug repositioning
Miquel Duran-Frigola, Patrick Aloy
Genome Medicine , 2012, DOI: 10.1186/gm302
Abstract: For almost a century, drug discovery was driven by the quest for magic bullets, which act by targeting one critical step in a disease process and elicit a cure with few other consequences. However, this concept is far from biological reality, and even the most successful rationally designed drugs (such as Gleevec?) show a quite promiscuous binding behavior, which has opened novel therapeutic possibilities [1]. Today, the emerging picture is that drugs rarely bind specifically to a single target, and this challenges the concept of a magic bullet. Indeed, recent analyses of drug and drug-target networks show a rich pattern of interactions among drugs and their targets, where drugs acting on a single target seem to be the exception. Likewise, many proteins are targeted by several drugs with quite distinct chemical structures [2].Drug-repositioning strategies seek to exploit the notion of polypharmacology [3], together with the high connectivity among apparently unrelated cellular processes, to identify new therapeutic uses for already approved drugs. The main advantage of this approach is that, since it starts from approved compounds with well-characterized pharmacology and safety profiles, it should drastically reduce the risk of attrition in clinical phases. There are several successful examples of drug repositioning (for example, thalidomide to treat leprosy or finasteride for the prevention of baldness), although they were all found by serendipity and are not the result of well-thought strategies.More recently, and following the observation that most novel entities are found by phenotypic profiling techniques [4], systematic initiatives to find new indications for old drugs have flourished. These approaches rely mostly on genome-wide transcriptional expression data from cultured human cells treated with small molecules, and pattern-matching algorithms to discover functional connections between drugs, genes and diseases through concerted gene-expression changes [5].
Construction of Drug Network Based on Side Effects and Its Application for Drug Repositioning  [PDF]
Hao Ye, Qi Liu, Jia Wei
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0087864
Abstract: Drugs with similar side-effect profiles may share similar therapeutic properties through related mechanisms of action. In this study, a drug-drug network was constructed based on the similarities between their clinical side effects. The indications of a drug may be inferred by the enriched FDA-approved functions of its neighbouring drugs in the network. We systematically screened new indications for 1234 drugs with more than 2 network neighbours, 36.87% of the drugs achieved a performance score of Normalized Discounted Cumulative Gain in the top 5 positions (NDCG@5)≥0.7, which means most of the known FDA-approved indications were well predicted at the top 5 positions. In particular, drugs for diabetes, obesity, laxatives and antimycobacterials had extremely high performance with more than 80% of them achieving NDCG@5≥0.7. Additionally, by manually checking the predicted 1858 drug-indication pairs with Expression Analysis Systematic Explorer (EASE) score≤10?5 (EASE score is a rigorously modified Fisher exact test p value), we found that 80.73% of such pairs could be verified by preclinical/clinical studies or scientific literature. Furthermore, our method could be extended to predict drugs not covered in the network. We took 98 external drugs not covered in the network as the test sample set. Based on our similarity criteria using side effects, we identified 41 drugs with significant similarities to other drugs in the network. Among them, 36.59% of the drugs achieved NDCG@5≥0.7. In all of the 106 drug-indication pairs with an EASE score≤0.05, 50.94% of them are supported by FDA approval or preclinical/clinical studies. In summary, our method which is based on the indications enriched by network neighbors may provide new clues for drug repositioning using side effects.
Synthesis of Chiral Building Blocks for Use in Drug Discovery  [PDF]
Sharon T. Marino,Danuta Stachurska-Buczek,Daniel A. Huggins,Beata M. Krywult,Craig S. Sheehan,Thao Nguyen,Neil Choi,Jack G. Parsons,Peter G. Griffiths,Ian W. James,Andrew M. Bray,Jonathan M. White,Rustum S. Boyce
Molecules , 2004, DOI: 10.3390/90600405
Abstract: In the past decade there has been a significant growth in the sales of pharmaceutical drugs worldwide, but more importantly there has been a dramatic growth in the sales of single enantiomer drugs. The pharmaceutical industry has a rising demand for chiral intermediates and research reagents because of the continuing imperative to improve drug efficacy. This in turn impacts on researchers involved in preclinical discovery work. Besides traditional chiral pool and resolution of racemates as sources of chiral building blocks, many new synthetic methods including a great variety of catalytic reactions have been developed which facilitate the production of complex chiral drug candidates for clinical trials. The most ambitious technique is to synthesise homochiral compounds from non-chiral starting materials using chiral metal catalysts and related chemistry. Examples of the synthesis of chiral building blocks from achiral materials utilizing asymmetric hydrogenation and asymmetric epoxidation are presented.
Influence of the operational environment on biological firmness of building composites
V.T. Erofeev,A.D. Bogatov,S.N. Bogatova,S.V. Kaznacheev
Magazine of Civil Engineering , 2012,
Abstract: In modern conditions durability and reliability of buildings are demanded more and more. According to it the special attention starts to be given to the danger of biological degradation of materials and constructions.Microorganisms are capable to occupy the surfaces of all tested kinds of widely used binding agents. However, the specific and quantitative compositions of microorganisms are various under identical service conditions.Having compared the quantity of kinds and types of the fungi developing on the samples of binders, it is possible to evaluate the efficiency of application of those or other building composites and to choose the most suitable materials for corresponding service conditions. It was experimentally shown that composites on a basis of alkaline glass binding agents possess the raised stability in the biologically corrosive environment.
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