Drug Repositioning by Integrating Known Disease-Gene and Drug-Target Associations in a Semi-supervised Learning Model

Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs and diseases. These methods approach the problem using either machine learning- or network-based models with an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarities between drugs and between diseases are usually used as inputs. In addition, known drug-disease associations are also needed for the methods as prior information. It should be noted that those associations are still not well established due to the fact that many of marketed drugs have been withdrawn and this could affect the outcome of the methods. In this study, we propose a novel method named RLSDR (Regularized Least Square for Drug Repositioning) to find new uses of drugs. More specifically, it relies on a semi-supervised learning model, Regularized Least Square, thus it does not require definition of non-drug-disease associations as previously proposed machine learning-based methods. In addition, the similarity between drugs measured by chemical structures of drug compounds and the similarity between diseases which share phenotypes can be represented in a form of either similarity network or similarity matrix as inputs of the method. Moreover, instead of using a gold-standard set of known drug-disease associations, we construct an artificial set of the associations based on known disease-gene and drug-target associations. Experiment results demonstrate that RLSDR achieves better prediction performance on the artificial set of drug-disease associations than that on the gold-standard ones in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC). In addition, it outperforms two representative network-based methods irrespective of the prior information of drug-disease associations. Novel indications for a number of drugs are also identified and validated by evidences from a different data resource.     

Studies on DNA binding of caffeine and derivatives: evidence of intercalation by DNA-unwinding experiments

Caffeine and derivatives are compounds with pleiotropic effects on the genetic material which are supposed to originate from drugs binding to DNA. Here we show, by using two different topological methods, that methylated oxypurines, at biologically relevant concentrations, unwind DNA in a fashion similar to known intercalators. Methylated oxypurines could be ranked by decreasing unwinding potency: 8-methoxycaffeine greater than 8-ethoxycaffeine greater than 8-chlorocaffeine greater than caffeine greater than theophylline. These findings confirm, with a different assay, interaction of caffeine with DNA and add additional support to an intercalative mode of binding of these drugs to DNA.     

The importance of stereoselective determination of drugs in the clinical laboratory

About 56% of the drugs currently in use are chiral compounds, and 88% of these chiral synthetic drugs are used therapeutically as racemates. Only a few of these drugs qualify for a stereospecific determination in a clinical laboratory for therapeutic drug monitoring of patients. If the qualitative and quantitative pharmacokinetic and pharmacodynamic effects are similar, the enantiomers do not need to be separated. However, if the metabolism of the different stereoisomers is handled by different enzymes which are either polymorphic or can be induced or inhibited, and if their pharmacodynamic effects have differences either in strength or in quality, enantiospecific analysis is urgently needed. Unfortunately, there are many racemic drugs where the stereospecificity of the metabolism and/or the pharmacodynamic effects of the enantiomers is not known today. For these drugs, there is a great need for studies concentrating on these differences to improve treatment of the patients.     

Finding Candidate Drugs for Hepatitis C Based on Chemical-Chemical and Chemical-Protein Interactions

Hepatitis C virus (HCV) is an infectious virus that can cause serious illnesses. Only a few drugs have been reported to effectively treat hepatitis C. To have greater diversity in drug choice and better treatment options, it is necessary to develop more drugs to treat the infection. However, it is time-consuming and expensive to discover candidate drugs using experimental methods, and computational methods may complement experimental approaches as a preliminary filtering process. This type of approach was proposed by using known chemical-chemical interactions to extract interactive compounds with three known drug compounds of HCV, and the probabilities of these drug compounds being able to treat hepatitis C were calculated using chemical-protein interactions between the interactive compounds and HCV target genes. Moreover, the randomization test and expectation-maximization (EM) algorithm were both employed to exclude false discoveries. Analysis of the selected compounds, including acyclovir and ganciclovir, indicated that some of these compounds had potential to treat the HCV. Hopefully, this proposed method could provide new insights into the discovery of candidate drugs for the treatment of HCV and other diseases.     

Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds

Several pathogen parasite species show different susceptibilities to different antiparasite drugs. Unfortunately, almost all structure-based methods are one-task or one-target Quantitative Structure-Activity Relationships (ot-QSAR) that predict the biological activity of drugs against only one parasite species. Consequently, multi-tasking learning to predict drugs activity against different species by a single model (mt-QSAR) is vitally important. In the two previous works of the present series we reported two single mt-QSAR models in order to predict the antimicrobial activity against different fungal (Bioorg. Med. Chem.2006, 14, 5973-5980) or bacterial species (Bioorg. Med. Chem.2007, 15, 897-902). These mt-QSARs offer a good opportunity (unpractical with ot-QSAR) to construct drug-drug similarity Complex Networks and to map the contribution of sub-structures to function for multiple species. These possibilities were unattended in our previous works. In the present work, we continue this series toward other important direction of chemotherapy (antiparasite drugs) with the development of an mt-QSAR for more than 500 drugs tested in the literature against different parasites. The data were processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 212 out of 244 (87.0%) cases in training series and 207 out of 243 compounds (85.4%) in external validation series. In order to illustrate the performance of the QSAR for the selection of active drugs we carried out an additional virtual screening of antiparasite compounds not used in training or predicting series; the model recognized 97 out of 114 (85.1%) of them. We also give the procedures to construct back-projection maps and to calculate sub-structures contribution to the biological activity. Finally, we used the outputs of the QSAR to construct, by the first time, a multi-species Complex Networks of antiparasite drugs. The network predicted has 380 nodes (compounds), 634 edges (pairs of compounds with similar activity). This network allows us to cluster different compounds and identify on average three known compounds similar to a new query compound according to their profile of biological activity. This is the first attempt to calculate probabilities of antiparasitic action of drugs against different parasites.     

Stage-specific activity of potential antimalarial compounds measured in vitro by flow cytometry in comparison to optical microscopy and hypoxanthine uptake

The evaluation of new antimalarial agents using older methods of monitoring sensitivity to antimalarial drugs are laborious and poorly suited to discriminate stage-specific activity. We used flow cytometry to study the effect of established antimalarial compounds, cysteine protease inhibitors, and a quinolone against asexual stages of Plasmodium falciparum. Cultured P. falciparum parasites were treated for 48 h with different drug concentrations and the parasitemia was determined by flow cytometry methods after DNA staining with propidium iodide. P. falciparum erythrocytic life cycle stages were readily distinguished by flow cytometry. Activities of established and new antimalarial compounds measured by flow cytometry were equivalent to results obtained with microscopy and metabolite uptake assays. The antimalarial activity of all compounds was higher against P. falciparum trophozoite stages. Advantages of flow cytometry analysis over traditional assays included higher throughput for data collection, insight into the stage-specificity of antimalarial activity avoiding use of radioactive isotopes.     

Stereoselective chromatography of cardiovascular drugs: an update

This review reports the latest achievements in chromatographic enantioseparations of various classes of cardiovascular drugs and selected applications of these methods in pharmaceutical and clinical analysis. The use of these drugs as test compounds for new chiral stationary phases and different parameters of chromatographic processes is also presented.     

What else can green monkeys tell us about AIDS?

Although the development of an HIV vaccine may eventually provide a means of controlling AIDS in developed countries, more immediate and less sophisticated methods are going to have to be developed for use in the rural regions of Africa where AIDS may already have reached epidemic proportions. Evidence from several studies of wild primates suggests that plant secondary compounds may commonly act as control agents for a variety of different pathogens. Although studies of laboratory populations of green monkeys indicate that their resistance to AIDS is likely to be genetic, we argue that it may be worth screening some of the plants eaten by African primates in the hope of coming up with compounds that exhibit suitable anti-viral activity. Any compounds isolated in this way are likely to be much cheaper to manufacture than laboratory-produced drugs and may also have been already screened for unpleasant side-effects.     

Drug Transfer Across the Blood-Brain Barrier: Correlation Between In Vitro and In Vivo Models

To assess the drug transport across the blood-brain barrier (BBB), we compared the maximal brain extraction values at time 0 [E(0) values] obtained using either in vitro or in vivo methods. The in vitro BBB model consisted of a coculture of brain capillary endothelial cells growing on one side of a filter and astrocytes on the other. The in vivo model used intracarotid injection in anesthetized rats. Eleven compounds were tested. They were selected because they exhibit quantitatively different brain extraction rates: very low for inulin and sucrose, low for oxicam-related nonsteroidal antiinflammatory drugs and diclofenac, and high for propranolol and diazepam. As these compounds are apparently transferred by a passive diffusion mechanism, two others, glucose and leucine, were added that cross the BBB by a known carrier-mediated process. The in vivo and in vitro E(0) values showed a strong correlation as indicated by the Spearman's correlation coefficient (r = 0.88, p less than 0.01). The relative ease with which such cocultures can be produced in large quantities could facilitate the screening of new centrally acting drugs.     

Fast and slow versus strong and weak metal-DNA binding: consequences for anti-cancer activity

The binding of transition metal compounds to nucleic acids is discussed in the perspectives of kinetics and their anticancer activity. Kinetics of ligand exchange is primarily determined by the intrinsic properties of the metal ions, and to a lesser degree by the ligands coordinated already to the metal ion. Metal compounds having ligand-exchange rates of the same order of magnitude as cell-division processes, e.g. many Pt(iIIi), Ru(II) and Ru(III) compounds, are in use as chemotherapeutic drugs. Detailed knowledge of ligand exchange in such compounds is important for design of derivative and entirely new compounds. Metal coordination compounds of metal ions with much faster ligand-exchange reactions interact with DNA in a quite different way, namely primarily by compensation of negative charge of the polyanionic chain and are usually not active as anticancer agents. Examples of our recent work are presented in relation with experiments performed by others on new generations of platinum anti-cancer drugs.     

Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data

MOTIVATION: Prediction of interactions between proteins and chemical compounds is of great benefit in drug discovery processes. In this field, 3D structure-based methods such as docking analysis have been developed. However, the genomewide application of these methods is not really feasible as 3D structural information is limited in availability. RESULTS: We describe a novel method for predicting protein-chemical interaction using SVM. We utilize very general protein data, i.e. amino acid sequences, and combine these with chemical structures and mass spectrometry (MS) data. MS data can be of great use in finding new chemical compounds in the future. We assessed the validity of our method in the dataset of the binding of existing drugs and found that more than 80% accuracy could be obtained. Furthermore, we conducted comprehensive target protein predictions for MDMA, and validated the biological significance of our method by successfully finding proteins relevant to its known functions. AVAILABILITY: Available on request from the authors.     

Rediscovery of known natural compounds: nuisance or goldmine?

Do all natural compounds have a distinct biological activity, or are most of them merely biosynthetic debris? Many natural compounds have important biological functions, and certainly many more of the ample 200,000 currently known will ultimately prove to be more than just 'secondary metabolites'. The question is how to select the most promising candidates for potential new drugs. 'Rediscovery' of known natural compounds is regarded as a nuisance or disappointment by scientists involved with the identification of novel compounds. The other side of the coin, however, is that the discovery that a particular compound occurs in unrelated species can be a valuable clue toward the identification of a novel receptor or enzyme. Here, we put forward the hypothesis that when a natural compound occurs in unrelated species, it must have an important biological function by interacting with a specific molecular target. This is because it is extremely improbable that in nature one particular compound is synthesized in totally unrelated species for no reason at all. For many compounds occurring in unrelated species, it is already known that they act on specific molecular targets. For others, it is just known that they occur in different species. In some cases, biological activities are known but not the underlying mechanisms of action. It is from this category of compounds that important discoveries are likely to be made. Some (around 70) of them were identified. They represent important clues from nature offering an alternative approach to the classical screening of large numbers of compounds.     

Lack of correlation between intercalation and plasmid curing ability of some tricyclic compounds

Some tricyclic psychotropic drugs are known to have plasmid curing activity. The interaction with DNA of three plasmid curing (chlorpromazine, amitriptyline, imipramine) and four ineffective (methylene blue, 7,8-dioxo-chlorpromazine, thiazinamium, chlorpromazine sulphoxide) compounds was studied by fluorescence polarization and circular dichroism methods. Among the seven compounds three, namely chlorpromazine, 7,8-O2-chlorpromazine, and methylene blue showed an intercalation effect. Other phenothiazines such as chlorpromazine sulphoxide and thiazinamium were not able to intercalate into Escherichia coli DNA, neither did the plasmid curing drugs amitryptyline and imipramine. It is concluded that the plasmid curing ability is not necessarily related to the intercalation ability.     

手性化合物制备的方法

手性是自然界最重要的属性之一,分子手性识别在生命活动中起着极为重要的作用。同一化合物的两个对映体之间不仅具有不同的光学性质和物理化学性质,而且它们具有不同的生物活性,比如在药理上,药物作用包括酶的抑制、膜的传递、受体结合等均和药物的立体化学有关;手性药物的对映体的生物学活性、毒性、代谢和药物素质完全不同。手性化合物的制备已成为当前国内外较热门的研究课题之一。本文从非生物法和生物法两个方面较全面地综述了手性化合物的制备方法,希望为相关研究者提供参考 。     

Representation of target-bound drugs by computed conformers: implications for conformational libraries

Background  

The increasing number of known protein structures provides valuable information about pharmaceutical targets. Drug binding sites are identifiable and suitable lead compounds can be proposed. The flexibility of ligands is a critical point for the selection of potential drugs. Since computed 3D structures of millions of compounds are available, the knowledge of their binding conformations would be a great benefit for the development of efficient screening methods.     

Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species

There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by linear discriminant analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial neural networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysis.     

Immune response of fish to parasitic protozoa

Epizootic outbreaks of fish diseases are increasingly common as a result of intensive aquaculture, fish farming and sea ranching. Very few drugs are available for treatment or prophylaxis against fish diseases, and development of such compounds is inhibited by different national regulations governing the use of chemicals in fish for human or animal consumption. Alternative approaches are urgently needed. But although the taxonomy and biology of fish parasites have been extensively studied, relatively little is known about protective immunity in fish and the effects of parasites on the piscine immune system. In this article, Patrick Woo discusses the immune responses of fish to parasitic protozoa, showing that vaccination is a viable control strategy, and stressing the need for a coordinated global research programme on fish diseases.     

Fatal peptic ulcer complications and the use of non-steroidal anti-inflammatory drugs,aspirin, and corticosteroids.

Although non-steroidal anti-inflammatory drugs are known to cause peptic ulcer and its complications, controversy exists about the number of deaths from ulcer which are attributable to their use. A case-control study was therefore performed to determine whether prior use of non-steroidal and other anti-inflammatory compounds was associated with an increased case fatality rate from complications of peptic ulcer. Non-steroidal anti-inflammatory drugs were used by 39% of a series of 80 patients who had died from peptic ulcer complications and by 37% of 160 controls who were survivors matched for sex, age, ulcer site, and nature of complication (odds ratio 1.1; 95% confidence interval 0.6 to 2.1). Similarly, the rates of prior use of aspirin by cases and controls were almost identical (odds ratio 1.2; 95% confidence interval 0.5 to 1.9). Thus neither nonsteroidal anti-inflammatory drugs nor aspirin were associated with increased case fatality rates from peptic ulcer complications. In contrast, corticosteroids were associated with an increased mortality (odds ratio 4.2; 95% confidence interval 0.9 to 25.6). Although this increase in the estimated relative risk was not statistically significant, a review of the case records indicated that most deaths in steroid users were due to serious sepsis, indicating that there might be a causal association between use of the drugs and the mode of death.     

Design,Synthesis and Biological Evaluation of New N-Acyl Hydrazones with a Methyl Sulfonyl Moiety as Selective COX-2 Inhibitors

The mechanism of action of nonsteroidal anti-inflammatory drugs (NSAIDs) is inhibition of specific prostaglandin (PG) synthesis by inhibition of cyclooxygenase (COX) enzymes. The two COX isoenzymes show 60 % similarity. It is known that the nonspecific side effects of conventional NSAIDs are physiologically caused by inhibition of the COX-1 enzyme. Therefore, the use of COX-2 selective inhibitors is seen to be a more beneficial approach in reducing these negative effects. However, some of the existing COX-2 selective inhibitors show cardiovascular side effects. Therefore, studies on the development of new selective COX-2 inhibitors remain necessary. It is important to develop new COX-2 inhibitors in the field of medicinal chemistry. Accordingly, novel N-acyl hydrazone derivatives were synthesized as new COX-2 inhibitors in this study. The hydrazone structure, also known for its COX activity, is important in terms of many biological activities and was preferred as the main structure in the design of these compounds. A methyl sulfonyl pharmacophore was added to the structure in order to increase the affinity for the polar side pocket present in the COX-2 enzyme. It is known that methyl sulfonyl groups are suitable for polar side pockets. The synthesis of the compounds ( 3a – 3j ) was characterized by spectroscopic methods. Evaluation of in vitro COX-1/COX-2 enzyme inhibition was performed by fluorometric method. According to the enzyme inhibition results, the obtained compounds displayed the predicted selectivity for COX-2 enzyme inhibition. Compound 3j showed important COX-2 inhibition with a value of IC₅₀=0.143 uM. Interaction modes between the COX-2 enzyme and compound 3j were investigated by docking studies.     

Failla Memorial Lecture. Redox, radiation, and reductive bioactivation.

A brief review is presented of the background to, and the principles involved in, the development of redox-sensitive drugs for use in cancer therapy. The role of redox processes in the action of various types of radiosensitizers and in the activation of bioreductive drugs is described. The mechanisms by which many simple hypoxic cell radiosensitizers act are believed to involve fast electron transfer processes involving DNA. Some of these agents can also function as hypoxic cell cytotoxins, although the mechanisms involved are different. These "bioreductive drugs" are activated by intracellular metabolic reduction mediated through various cellular reductases. Usually, though not always, bioreduction is favored under hypoxic conditions, and this is why many of these compounds display differential cytotoxicity to hypoxic cells. This is one of the rationales for selectivity in solid tumors. The potencies of both hypoxic cell radiosensitizers and bioreductive drugs are strongly correlated with their electron affinities. Classes of bioreductive agents of current interest are described briefly. These include simple and dual-function nitroheterocycles including the highly potent compound RB-6145, quinone-based drugs including analogues of mitomycin C, and heterocyclic compounds containing N-oxide functions. The study of bioreductive agents for potential use as adjuncts for various approaches to cancer treatment is described.