Showing papers in "Combinatorial Chemistry & High Throughput Screening in 2017"
TL;DR: The results indicate that TCM exerts its anticancer effects through apoptosis induction, proliferation inhibition, metastasis suppression, multidrug resistance reversal and immune function regulation, and may represent a promising therapeutic approach for patients with diverse cancers.
Abstract: Background Cancer is a systemic disease. Cancer occurrence is associated with several factors, including cell proliferation, apoptosis, metastasis, tumor microenvironment and immune system. Traditional Chinese medicine (TCM) has been widely used for thousands of years in China for its anti-cancer therapeutic effect. The advantage of using TCM is related to its action on multiple signaling pathways and molecular targets related to cancer, whilst causing few adverse effects. Objective The review focuses on the most recent studies on TCM through examples that demonstrate the anticancer effects and the mechanisms. Conclusion The review shows a large number of works which demonstrated that TCM is useful in the treatment of various types of cancers through different mechanisms of action. The results indicate that TCM exerts its anticancer effects through apoptosis induction, proliferation inhibition, metastasis suppression, multidrug resistance reversal and immune function regulation. Moreover, TCM can improve patients' quality of life. The advantage of TCM suggests that TCM may represent a promising therapeutic approach for patients with diverse cancers.
94 citations
TL;DR: The biological features obtained in this pioneering work would provide some useful insights into the formation and function of citrullination and the optimal classifier could be a useful tool to identify citruLLination sites in protein sequences.
Abstract: Background: As one of essential post-translational modifications (PTMs), the citrullination or deimination on an arginine residue would change the molecular weight and electrostatic charge of its side-chain. And it has been found that the citrullination in protein sequences was catalyzed by a type of Ca2+-dependent enzyme family called peptidylarginine deiminase (PAD), which include five isotypes: PAD1, 2, 3, 4/5, and 6. Citrullinated proteins participate in many biological processes, e.g. the citrullination of myelin basic protein (MBP) assists the early development of central nervous system. However, abnormal modifications on citrullinated proteins would also lead to some severe human diseases including multiple sclerosis and rheumatoid arthritis. Objective: Therefore, it is necessary and important to identify the citrullination sites in protein sequences. The information about the location of citrulliantion sites in protein sequences will be useful to investigate the molecular functions and disease mechanisms related to citrullinated proteins. Materials and Methods: In this study, we investigated the peptide segments that contain the citrullination sites in the centers, which were encoded into numeric digits from four aspects. Thus, we yielded a training set with 116 positive samples and 232 negative samples. Then, a reliable feature selection technique, called maximum-relevance-minimum-redundancy (mRMR), was applied to analyze these features, and four algorithms, including random forest (RF), Dagging, nearest neighbor algorithm (NNA), and support vector machine (SVM), together with the incremental feature selection (IFS) method were adopted to extract important features. Results: Finally an optimal classifier derived from RF algorithm was constructed to predict citrullination sites. 44 most prominent features were comprehensively analyzed and their biological characteristics in citrullination catalysis were also revealed. Conclusion: We believed that the biological features obtained in this pioneering work would provide some useful insights into the formation and function of citrullination and the optimal classifier could be a useful tool to identify citrullination sites in protein sequences.
27 citations
TL;DR: A computational method to predict malonylation sites and to analyzemalonylation pattern is proposed and is expected to be a promising tool to identify malonylated sites.
Abstract: Aim and objective Protein malonylation is a newly discovered post-translational modification. Malonylation is known to closely be associated with type 2 diabetes and to play its regulatory role in fatty acid oxidation and the associated genetic disease. Identifying protein malonylations might lay a solid foundation to explore malonylation function. Due to the limitations of experimental techniques, it is a great challenge to fast and accurately identify malonylation sites. Methods We proposed a computational method to predict malonylation sites and to analyze malonylation pattern. We firstly extracted protein segments so that the lysine is at the center of each segment. Then, each segment was encoded by the pseudo amino acid compositions. The support vector machine classifier trained by a training dataset was built to distinguish malonylation sites from non-malonylation ones. Results The leave-one-out test on the training dataset reached the accuracy of 0.7733, and the independent test on the testing dataset got 0.8889. Furthermore, the classifier also successfully identified 144 of 160 putative malonylation sites. Analyses on the differences between malonylation and non-malonylation segments implicated that lysine malonylation should follow a specific pattern, e.g. lysine with its neighbors being Glycine and Alanine might be more likely to be malonylated. Therefore, the proposed method is expected to be a promising tool to identify malonylation sites.
27 citations
TL;DR: The crucial ingredients involved in UPS are exhibited and the current situation of small molecules targeting various components of ubiquitination pathway in cancer treatment is discussed.
Abstract: The ubiquitin-proteasome system (UPS) is responsible for the degradation of majority of the intracellular proteins. The fundamental importance of UPS was highlighted when Rose, Hershko, and Ciechanover were awarded the 2004 Nobel Prize in Chemistry. The alterations in this process have been shown to contribute to the cancer progression. Therefore, pharmacological targeting of the UPS can potentially provide chemotherapeutics for the treatment of tumours. The application of bortezomib proved that interfering with UPS activity could be very effective against haematological malignancies. Many compounds are being screened and evaluated in recent pharmacological advances, either as single agents or in synergy with other drugs, and more to be revealed. In this review, we present the crucial components involved in the ubiquitin-proteasome pathway and discuss the current state of small molecules targeting various elements of ubiquitination in cancer treatment.
26 citations
TL;DR: Experimental results indicate that the constructed model is much superior to models using single algorithm and other models that only adopt feature selection procedure or algorithm selection procedure for building an ensemble prediction model that can yield a better performance.
Abstract: AIM AND OBJECTIVE Correct prediction of protein structural class is beneficial to investigation on protein functions, regulations and interactions. In recent years, several computational methods have been proposed in this regard. However, based on various features, it is still a great challenge to select proper classification algorithm and extract essential features to participate in classification. MATERIAL AND METHODS In this study, a feature and algorithm selection method was presented for improving the accuracy of protein structural class prediction. The amino acid compositions and physiochemical features were adopted to represent features and thirty-eight machine learning algorithms collected in Weka were employed. All features were first analyzed by a feature selection method, minimum redundancy maximum relevance (mRMR), producing a feature list. Then, several feature sets were constructed by adding features in the list one by one. For each feature set, thirtyeight algorithms were executed on a dataset, in which proteins were represented by features in the set. The predicted classes yielded by these algorithms and true class of each protein were collected to construct a dataset, which were analyzed by mRMR method, yielding an algorithm list. From the algorithm list, the algorithm was taken one by one to build an ensemble prediction model. Finally, we selected the ensemble prediction model with the best performance as the optimal ensemble prediction model. RESULTS Experimental results indicate that the constructed model is much superior to models using single algorithm and other models that only adopt feature selection procedure or algorithm selection procedure. CONCLUSION The feature selection procedure or algorithm selection procedure are really helpful for building an ensemble prediction model that can yield a better performance.
24 citations
TL;DR: EU can be suggested as a potential natural source of antioxidants appropriate for utilization in nutritional/pharmaceutical fields and its antioxidant activities were found higher than standard antioxidants.
Abstract: Aim and objective Due to the common ethnopharmacological used or scientifically examined biochemical properties, Elaeagnaceae family, Elaeagnus umbellate (Thunb.) (EU, Guz yemisi) was worth investigating. Materials and methods In this investigation, we revealed antioxidant, antiproliferative and enzyme inhibition activities of the water, methanol, ethanol, acetone, ethyl acetate and hexane extracts of EU as well as the contents of their phenolic, flavonoid, anthocyanin, ascorbic acid, lycopene and β- carotene. The antioxidant activity was screened by total antioxidant (phosphomolybdenum), inhibition of linoleic acid peroxidation, reducing power, 2-deoxyribose degradation assay, H2O2 scavenging and metal chelating activities of the samples were tested in vitro. Additionally, the scavenging activities of the extracts were determined against 1,1-diphenyl-2-picrylhydrazyl (DPPH˙), 2,2-azino-bis(3-ethylbenzothiazloine-6-sulfonicacid (ABTS˙+), superoxide anion and peroxide radicals. The samples were determined for their inhibitory activities against urease, acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE). In vitro, antiproliferative activities of six different extracts were tested using the xCELLigence system against HeLa and HT29 cell lines. Results The antioxidant activities of the extracts were found higher than standard antioxidants. The water extracts of fruit and leaf showed the best antioxidant activity. In inhibition assays of urease, AChE and BuChE, all extracts exhibited remarkable inhibition potential. Ethyl acetate extracts, especially, showed better inhibition capacity. It was found that the antioxidant activities of the extracts presented consistently with their chemical contents. The antiproliferative activities of leaf extracts were more effective than the fruit extracts. The chromatographic methods were applied to the different solvents to analyses phenolic secondery metabolites. It was found that fumaric acid, 4- hydroxybenzoic acid, rutin and quercetin-3-β-D-glucoside, neohesperidin, hesperidin determined to have higher contents all the extracts. Conclusion EU can be suggested as a potential natural source of antioxidants appropriate for utilization in nutritional/pharmaceutical fields.
24 citations
TL;DR: The synthesized KIT-6 mesoporous silica coated magnetite nanoparticles (MMNPs) are investigated as a mild and efficient catalyst for the synthesis of novel diindolylmethanes coupled with pyrazole moiety in aqua media.
Abstract: Korea advanced institute of science and technology cubic ordered mesoporous silica (KIT-6 mesoporous) silica coated magnetite nanoparticles, is an effective, eco-benign and recyclable catalyst for the electrophilic substitution reactions of indoles with various synthetized aldehydes to afford the corresponding novel diindolylmethanes in high yields and short reaction times. The catalyst can be recovered and reused without loss of activity. The work-up of the reaction consists of a simple separation, followed by concentration of the crude product and purification. The present methodology offers several advantages such as aqueous media, excellent yields, simple procedure, mild conditions and reduced environmental consequences. All of synthesized compounds are new and were characterized by IR, NMR and elemental analyses.
23 citations
TL;DR: It can be deduced that these EGB761 compounds can be regarded as a promising starting point for developing AChE inhibitors against AD.
Abstract: Aim and objective EGb761, a standardized and well-defined product extract of Ginkgo biloba leaves, has beneficial role in the treatment of multiple diseases, particularly Alzheimer's disease (AD). Identification of natural acetylcholinesterase (AChE) inhibitors from EGb761 would provide a novel therapeutic approach against the Alzheimer's disease. Material and method A series of 21 kinds of promising EGb761 compounds were selected, and subsequently evaluated for their potential ability to bind AChE enzyme by molecular docking and a deep analysis of protein surface pocket features. Results Docking results indicated that these compounds can bind tightly with the active site of human AChE, with favorable distinct interactions around several important residues Asp74, Leu289, Phe295, Ser293, Tyr341, Trp286 and Val294 in the active pocket. Most EGB761 compounds could form the hydrogen bond interactions with the negatively charged Asp74 and Phe295 residues. Among these compounds, diosmetin is the one with the best-predicted docking score while three key hydrogen bonds can be formed between small molecule and corresponding residues of the binding site. Besides, other three compounds luteolin, apigenin, and isorhamnetin have better predicted docking scores towards AChE than other serine proteases, i.e. Elastase, Tryptase, Factor XA, exhibiting specificity for AChE inhibition. The RMSD and MM-GBSA results from molecular dymamic simulations indicated that the docking pose of diosmetin-AChE complex displayed highly stable, which can be used for validating the accuracy of molecular docking study. Subsequently, the AChE inhibitory activities of these compounds were evaluated by the Ellman's colorimetric method. Conclusion The obtained results revealed that all the four compounds exhibited modest AChE inhibitory activity, among which Diosmetin manifested remarkable anti-AChE activity, comparable with the reference compound, Physostigmine. It can be deduced that these EGB761 compounds can be regarded as a promising starting point for developing AChE inhibitors against AD.
23 citations
TL;DR: An integrated molecular docking approach to investigate protein-ligand interactions with a focus on the HIV-1 protease has the potential to improve docking accuracy in drug design projects focused on HIV- 1 protease.
Abstract: Background One key step in the development of inhibitors for an enzyme is the application of computational methodologies to predict protein-ligand interactions. The abundance of structural and ligand-binding information for HIV-1 protease opens up the possibility to apply computational methods to develop scoring functions targeted to this enzyme. Objective Our goal here is to develop an integrated molecular docking approach to investigate protein-ligand interactions with a focus on the HIV-1 protease. In addition, with this methodology, we intend to build target-based scoring functions to predict inhibition constant (Ki) for ligands against the HIV-1 protease system. Methods Here, we described a computational methodology to build datasets with decoys and actives directly taken from crystallographic structures to be applied in evaluation of docking performance using the program SAnDReS. Furthermore, we built a novel function using as terms MolDock and PLANTS scoring functions to predict binding affinity. To build a scoring function targeted to the HIV-1 protease, we have used machine-learning techniques. Results The integrated approach reported here has been tested against a dataset comprised of 71 crystallographic structures of HIV protease, to our knowledge the largest HIV-1 protease dataset tested so far. Comparison of our docking simulations with benchmarks indicated that the present approach is able to generate results with improved accuracy. Conclusion We developed a scoring function with performance higher than previously published benchmarks for HIV-1 protease. Taken together, we believe that the approach here described has the potential to improve docking accuracy in drug design projects focused on HIV-1 protease.
22 citations
TL;DR: This study warrants the dual inhibition activity of AS6 against IN and RNase H confirms its anti-HIV activity.
Abstract: Background HIV integrase (IN) and reverse transcriptase (RT) are key enzymes for the replication of HIV-1. DNA polymerase and ribonuclease H (RNase H) are the two catalytic domains of HIV-1 RT which are validated as drug targets because of their essence for replication. IN and RNase H domain of RT shares striking structural similarity; it contains conserved DDE triad (two aspartates and one glutamate) and a pair of divalent Mg2+/Mn2+ ions at their catalytic core domain. Objective To search for novel compounds with dual inhibition of IN and RNase H for the drug development against both wild and drug-resistant strains of HIV. Methods In the present work, attempts have been made to search compounds against both IN and the RNase H domain of RT. Using structure-based virtual screening approach; Asinex database of small molecules was screened against the viral IN. Top thirty ranked hits obtained, were further evaluated against RNase H domain of RT using Extra Precision (XP) mode of Glide docking. Furthermore, eleven common potential hits were observed which were subjected to the in-silico prediction of drug-likeness properties. Later on, molecular dynamics simulation was performed for the best common active hit (AS6), in the complex with selected enzymes. Result In silico screening of Asinex database compounds against IN and RNase H resulted in total seven compounds namely AS3, AS5, AS6, AS15, AS17, AS18, and AS20 having dual inhibition activity. Conclusion This study warrants the dual inhibition activity of AS6 against IN and RNase H confirms its anti-HIV activity.
20 citations
TL;DR: The current review focuses on the MAO-B inhibitory properties of various synthetically derived chromones with specific emphasis on the structure-activity relationships and molecular recognition of MAo-B inhibition by this class.
Abstract: Aim and objective Specific inhibitors of monoamine oxidase (MAO)-B are considered useful therapeutic agents in targeting neurological disorders like Alzheimer's and Parkinson's diseases. Due to the academic challenge of designing new hMAO-B inhibitors and the possibility of discovering compounds with improved properties compared to existing MAO-B inhibitors, a number of research groups are searching for new classes of chemical compounds that may act as selective hMAO-B inhibitors. Materials and methods Among these, chromone (4H-1-benzopyran-4-one) derivatives have recently emerged as a chemotype with specific and high potency MAO-B inhibition. Chromones are structurally related to a series of coumarins and chalcones, which are well-known inhibitors of MAO-B. Results The experimental evidence has demonstrated that most of the chromone skeleton derived compounds have shown potent, reversible and selective type of hMAO-B inhibitors. Conclusion The current review focuses on the MAO-B inhibitory properties of various synthetically derived chromones with specific emphasis on the structure-activity relationships and molecular recognition of MAO-B inhibition by this class. This review covers the recent updates present in the literature and will certainly provide a greater insight for the design and development of new class of potent chromone based selective MAO-B inhibitors.
TL;DR: A systematic approach using Support vector machine with Radial basis function kernel to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development.
Abstract: AIM AND OBJECTIVE Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. MATERIALS AND METHODS In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. RESULTS The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. CONCLUSION This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development.
TL;DR: Virtual screening of natural and synthetic PTs for Nrf2 stimulatory activity reveals that the natural PTs are more promising than the most potent synthetic derivatives of oleanolic acid like CDDO, CDDO-methyl and CDDOimidazol.
Abstract: Aim and Objective: Kelch like ECH-associated protein 1 (Keap1) and Nuclear factor-E2 related factor 2 (Nrf2) binding is a key step in the ubiquitination and degradation of Nrf2. The compounds inhibiting this binding exert antioxidant actions. Naturally occurring pentacyclic triterpenoids (PTs) and their synthetic derivatives are projected as activators of Nrf2 signalling. The 16-mer Nrf2 peptide binding site on Keap-1 (PDB: 2 FLU) is proposed to be the prospective target where pentacyclic triterpenoid may exert protein-protein interaction. Material and Method: In the present study, seventy seven PTs of natural and synthetic origin are screened for Nrf2 stimulatory activity using online PASS (Prediction of Activity Spectrum of Substances) software followed by in silico molecular docking against 16-mer Nrf2 peptide binding site on Keap-1. This virtual screening reveals that Nrf2 stimulatory PTs dock on the 16-mer peptide binding site on Keap-1 and may exert their biological activities by interfering with the Keap-1 and Nrf2 binding. Results: In the present study shows that the small molecules like PT’s bind to keap 1 pocket where the 16 mer peptide of Neh2 domain of Nrf2. High docking score of -10.53, -9.08, -8.36, -7.94, -7.49 and -7.18 is shown by glycyrrhizin, asiatic acid, medecassic acid, barrigenic acid, rotundic acid, ursolic acid, respectively. Conclusion: The identified hits such as asiatic acid and medecassic acid represent a very promising starting point for the development of potent Nrf2 stimulator. The natural PTs are more promising than the most potent synthetic derivatives of oleanolic acid like CDDO, CDDO-methyl and CDDOimidazol.
TL;DR: It is revealed that tyrosine metabolism is disturbed in ESCC patients and the metabolites involved in tyrorosine pathway can be used as diagnostic biomarkers of the disease.
Abstract: Background: Esophageal Squamous Cell Carcinoma (ESCC) is a common malignant tumor in China, which causes about 200,000 deaths each year. Sensitive biomarkers are helpful to diagnose the disease in early stage. Methods: To identify biomarkers of ESCC and elucidate underlying mechanism of the disease, a targeted metabolomics strategy based on liquid chromatography-tandem mass spectrometry (LCMS/ MS) has been implemented to explore tyrosine metabolism from 40 ESCC patients and 27 healthy controls. Results: Four metabolites, i.e. phenylalanine, 4-hydroxyphenyllactic acid, 3,4-dihydroxyphenylalanine, and 3,4-dihydroxyphenylacetic acid were identified as diagnostic biomarkers for ESCC patients. Based on these biomarkers, a prediction model was constructed for ESCC diagnosis. The analysis of receiver operating characteristic (ROC) curve confirmed its effectiveness of the model. Conclusion: Our results reveal that tyrosine metabolism is disturbed in ESCC patients and the metabolites involved in tyrosine pathway can be used as diagnostic biomarkers of the disease. Findings of this study can help investigate pathogenesis of ESCC and facilitate understanding mechanism of the disease.
TL;DR: The obtained ADME and cytotoxicity data demonstrated that both MSAE and mitragynine have poor bioavailability and have the potential to be significantly cytotoxic.
Abstract: AIM AND OBJECTIVE Mitragynine, a major active alkaloid of Mitragyna speciosa, acts as an agonist on µ-opioid receptors, producing effects similar to morphine and other opioids. It has been traditionally utilized to alleviate opiate withdrawal symptoms. Besides consideration about potency and selectivity, a good drug must possess a suitable pharmacokinetic profile, with suitable absorption, distribution, metabolism, excretion and toxicity (ADME-Tox) profile, in order to have a high chance of success in clinical trials. MATERIAL AND METHOD The purity of mitragynine in a Mitragyna speciosa alkaloid extract (MSAE) was determined using Ultra-Fast Liquid Chromatography (UFLC). In vitro high throughput ADMETox studies such as aqueous solubility, plasma protein binding, metabolic stability, permeability and cytotoxicity tests were carried out to analyze the physicochemical properties of MSAE and mitragynine. The UFLC quantification revealed that the purity of mitragynine in the MSAE was 40.9%. RESULTS MSAE and mitragynine are highly soluble in aqueous solution at pH 4.0 but less soluble at pH 7.4. A parallel artificial membrane permeability assay demonstrated that it is extensively absorbed through the semi-permeable membrane at pH 7.4 but very poorly at pH 4.0. Both are relatively highly bound to plasma proteins (> 85 % bound) and are metabolically stable to liver microsomes (> 84 % remained unchanged). In comparison to MSAE, mitragynine showed higher cytotoxicity against WRL 68, HepG2 and Clone 9 hepatocytes after 72 h treatment. CONCLUSION The obtained ADME and cytotoxicity data demonstrated that both MSAE and mitragynine have poor bioavailability and have the potential to be significantly cytotoxic.
TL;DR: Few phytochemicals actively used in CRC research and are in clinical trials against CRC are discussed and some can act as a source for new drug or can acts as a lead compound for further modifications during the drug development against cancer.
Abstract: Background & aim Colorectal cancer (CRC) is a malignant disease whose incidence and mortality rates are greatly influenced by environmental factors. Under-treatment of CRC such as a poor diagnostic evaluation, less aggressive surgery, less intensive chemotherapy results in metastasizing of the primary tumor cells and recurrence of cancer. Prolonged chemotherapy treatment against cancer is hazardous to the patients, which also limits its use in cancer therapy. Current research in developing a novel anti-cancer agent, direct towards finding a better antimetastatic and an anti-invasive drug with reduced side effects. Method & results In this direction, plant derived chemical compounds or phytochemical act as a prominent source of new compounds for drug development. Phytochemicals have a multi-action and a multi-target capacity, and has gained attention among the research communities from last two decades. Epidemiological study shows a direct relationship between a diet and CRC development. A diet rich in plant based products such as vegetables, fruits and cereals is known to prevent CRC development. This review is an effort to explore more about the potential phytochemicals in CRC prevention and also in CRC treatment. Conclusion Here, we have discussed few phytochemicals actively used in CRC research and are in clinical trials against CRC. We have explored more on some of these phytochemicals which can act as a source for new drug or can act as a lead compound for further modifications during the drug development against cancer.
TL;DR: The extensive analysis of the identified GO terms and KEGG pathways indicate that they all play roles during the tumorigenesis, inducing that they can be key indicator for identification of carcinogenic chemicals.
Abstract: Cancer is one of the serious disease that causes several human deaths every year. Up to now, we have spent lots of time and money to investigate this disease, thereby designing effective treatments. Previous studies mainly focus on studying genetic background of different subtypes of cancer and neglect another important factor, environmental factor. Carcinogenic chemical is one of the type of environmental factor, exposure of such chemical may definitely initiate and promote the tumorigenesis. In this study, we tried to partly describe the differences between carcinogenic and non-carcinogenic chemicals using gene ontology (GO) terms and KEGG pathways. The carcinogenic and non-carcinogenic chemicals that were retrieved from Carcinogenic Potency Database (CPDB) were encoded into numeric vectors using the enrichment theories of GO terms and KEGG pathways. Then, the minimal redundancy maximal relevance (mRMR) method was adopted to analyze all features, resulting in some important GO terms and KEGG pathways. The extensive analysis of the identified GO terms and KEGG pathways indicate that they all play roles during the tumorigenesis, inducing that they can be key indicator for identification of carcinogenic chemicals.
TL;DR: Evidence is provided which implicate the imidazole-based compounds as potential prototypes for the development of anti-parasitic agents for trypanosomiasis.
Abstract: Background: Current drugs available for the treatment of Chagas disease are fraught with several challenges including severe toxicity and limited efficacy. These factors coupled with the absence of effective drugs for treating the chronic stage of the disease have rendered the development of new drugs against Chagas disease a priority. Objective: This study screened several imidazole-based compounds for anti-Trypanosoma potential. Method: Using an in vitro experimental infection model, several imidazole-based compounds were screened for anti-proliferative effect on Trypanosoma cruzi epimastigotes. Additionally, all test compounds were evaluated for unspecific cytotoxicity on L929 murine fibroblasts. Benznidazole (BZN) served as reference drug. Results: All test compounds demonstrated interesting trypanocidal potential with IC 50 values in the μM range (1 50 50 value ca. 30 μM. Conversely, most of the test compounds were highly cytotoxic, resulting in selectivity lower than that of BZN (SI > 9.42). Conclusion: We provide evidence which implicate the imidazole-based compounds as potential prototypes for the development of anti-parasitic agents. Findings have far-reaching relevance to drug discovery efforts for trypanosomiasis.
TL;DR: The comparison results indicate that the model is quite effective and suitable for the identification of whether a given chemical can participate in a given metabolic pathway.
Abstract: Background The study of metabolic pathway is one of the most important fields in biochemistry. Good comprehension of the metabolic pathway system is helpful to uncover the mechanism of some fundamental biological processes. Because chemicals are part of the main components of the metabolic pathway, correct identification of which metabolic pathways a given chemical can participate in is an important step for understanding the metabolic pathway system. Most previous methods only considered the chemical information, which tried to deal with a multilabel classification problem of assigning chemicals to proper metabolic pathways. Methods In this study, the pathway information was also employed, thereby transforming the problem into a binary classification problem of identifying the pair of chemicals and metabolic pathways, i.e., a chemical and a metabolic pathway was paired as a sample to be considered in this study. To construct the prediction model, the association between chemical pathway type pairs was evaluated by integrating the association between chemicals and association between pathway types. The support vector machine was adopted as the prediction engine. Results The extensive tests show that the constructed model yields good performance with total prediction accuracy around 0.878. Conclusion The comparison results indicate that our model is quite effective and suitable for the identification of whether a given chemical can participate in a given metabolic pathway.
TL;DR: The formulations tested in this paper can release caffeic acid with a Higuchi kinetic profile, in which release of active ingredient occurs by a diffusion process, and exhibited a lower permeation rate and higher retention in the skin, which is essential for a cosmetic product.
Abstract: Caffeic acid (CA) is a cinnamic acid derivative, found in many vegetable products, with powerful antioxidant activity, the ability to increase collagen production and capacity to prevent premature aging of the skin The classic emulsions of CA are widely used by the consumer to provide a pleasant, refreshing sensorial experience; however, preparations developed in the form of dry film are presented as a technological alternative due to its facile and safe transportation The aim of this study was to evaluate the release, permeation, and retention of CA in a film and emulsion through in vitro experiments The release evaluation of CA from the emulsion and the film was performed using modified Franz diffusion cells, with an area of 177 cm², using Microette equipment (Hanson Research) with a cellulose membrane The evaluation of the permeation of CA from the formulations was conducted using a similar technique of release, except that a biological membrane was used High release of active compound and reduced permeation was observed, indicating that CA was able to be retained in the epidermis/dermis, where it should have the desired action The concentration of caffeic acid in the skin was higher for the film formulation than for the emulsion This demonstrates a greater efficiency of this type of innovative release system, besides its facile and safe transportation Keyword: The caffeic acid (CA) is a cinnamic acid derivative, found in a lot of vegetable products with powerful antioxidant activity, increasing collagen production and preventing premature aging of the skin The classic emulsions are widely used by the consumer by providing pleasant refreshing sensorial, however, preparations developed in the form of dry film are presented as a technological alternative for its ease and safety in transportation The aim of this study was to evaluate the release, permeation and retention of CA in a film and an emulsion through in vitro experiments The release evaluation of caffeic acid from the emulsion and the film was performed using modified Franz diffusion cells with an area of 177 cm² using Microette equipment (Hanson Research) with cellulose membrane The evaluation of the permeation of CA from the formulations was conducted using a similar technique of the release, however, a biological membrane was used It was observed high release of active compound and reduced permeation, indicating that it was able to remain retained in the epidermis/dermis, where it should have action
TL;DR: The pharmacological action, the pharmacology network including mutation of signaling receptor and modulation of intracellular signaling pathway, and the combination treatment strategy of EGCG are clarified and sorted out.
Abstract: EGCG is the most important pharmacological component in tea. Researches have confirmed its effects, including anti-tumor, anti-inflammation, anti-aging, anti-obesity, anti-diabetic, cardiovascular disease prevention and protection, immunoregulation and neuroprotection. Paradoxically, the clinical application of EGCG is very rare. One of the most important reasons is its poor stability and low bioavailability. Excepting for altering the dosage form or synthesizing the analogues to overcome the loss during absorption, an increasing number of studies indicate that EGCG can exert certain auxiliary effect and enhance chemosensitivity in combined medication. The pharmacological action, the pharmacology network including mutation of signaling receptor and modulation of intracellular signaling pathway, and the combination treatment strategy of EGCG are clarified and sorted out, both the possible targets and combinatorial applications based on the characteristics of EGCG are systematically summarized.
TL;DR: All derivatives were more cytotoxic to hematopoietic neoplastic cells when compared to solid tumor derived cells and all three compounds are promising for in vivo and combination therapy studies against cancer.
Abstract: Aim and objective Cancer has become one of the leading causes of morbidity and mortality worldwide. Limitations associated with existing agents increase the need to develop more effective anticancer drugs to improve the therapeutic arsenal available. The aim of this study was to synthesize and evaluate the antiproliferative effects of three new thiazacridine derivatives. Material and methods Using a three steps synthesis reaction, three novel thiazacridine derivatives were obtained and characterized: (Z)-5-acridin-9-ylmethylene-3-(4-methyl-benzyl)-4-thioxo-thiazolidin- 2-one (LPSF/AC-99), (Z)-5-acridin-9-ylmethylene-3-(4-chloro-benzyl)-4-thioxo-thiazolidin-2- one (LPSF/AC-119) and (Z)-5-acridin-9-ylmethylene-3-(3-chloro-benzyl)-4-thioxo-thiazolidin-2- one (LPSF/AC-129). Toxicity and selectivity assays were performed by colorimetric assay. Then, changes in cell cycle and cell death induction mechanisms were assessed by flow cytometry. Results All compounds exhibited cytotoxicity to Raji (Burkitt's lymphoma) and Jurkat (acute T cell leukemia) cells, where LPSF/AC-119 showed best IC50 values (0.6 and 1.53 µ M, respectively). LPSF/AC-129 was the only cytotoxic compound in glioblastoma cell line NG97 (IC50 = 55.77 µ M). None of the compounds were toxic to normal human cells and induced neoplastic cell death primarily by apoptosis. Conclusion All derivatives were more cytotoxic to hematopoietic neoplastic cells when compared to solid tumor derived cells. All three compounds are promising for in vivo and combination therapy studies against cancer.
TL;DR: This study provided a new computational method that can be a useful tool to predict myristoylation sites from protein sequences and validated by its performance on a test dataset.
Abstract: Background: Myristoylation is an important hydrophobic post-translational modification that is covalently bound to the amino group of Gly residues on the N-terminus of proteins. The many diverse functions of myristoylation on proteins, such as membrane targeting, signal pathway regulation and apoptosis, are largely due to the lipid modification, whereas abnormal or irregular myristoylation on proteins can lead to several pathological changes in the cell. Objective: To better understand the function of myristoylated sites and to correctly identify them in protein sequences, this study conducted a novel computational investigation on identifying myristoylation sites in protein sequences. Materials and Methods: A training dataset with 196 positive and 84 negative peptide segments were obtained. Four types of features derived from the peptide segments following the myristoylation sites were used to specify myristoylatedand non-myristoylated sites. Then, feature selection methods including maximum relevance and minimum redundancy (mRMR), incremental feature selection (IFS), and a machine learning algorithm (extreme learning machine method) were adopted to extract optimal features for the algorithm to identify myristoylation sites in protein sequences, thereby building an optimal prediction model. Results: As a result, 41 key features were extracted and used to build an optimal prediction model. The effectiveness of the optimal prediction model was further validated by its performance on a test dataset. Furthermore, detailed analyses were also performed on the extracted 41 features to gain insight into the mechanism of myristoylation modification. Conclusion: This study provided a new computational method for identifying myristoylation sites in protein sequences. We believe that it can be a useful tool to predict myristoylation sites from protein sequences.
TL;DR: This review reviewed the clinical treatment of several diseases using photosensitizers formulated with nanoparticles, with the overall goal of aiding the design and development of novel photosenitizers.
Abstract: Background & aim Photosensitizers are key molecules used in photodynamic cancer therapy (PDT), which is an effective therapeutic modality option for several diseases and nononcological disorders. Due to its lower systemic toxicity and its ability to destroy tumors selectively, PDT has been considered as clinical avenue for the treatment of several cancers. Methods & results Three essential elements are involved in a PDT procedure: a photosensitizer, light of a specific wavelength, and singlet oxygen. However, the properties of conventional photosensitizers exhibit some drawbacks that may limit their use. Nanoparticles can provide significant benefits that counter these drawbacks and enable higher efficiency and biosafety. With the development of nanotechnology, nanoparticles have been used to encapsulate photosensitizers to enhance the phototoxic and pharmacokinetic properties of these agents. Conclusion Here, the main motivation for this review is to summarize recent progress in the development of photosensitizers, in particular, photosensitizers with nanoparticle modifications. In addition, we reviewed the clinical treatment of several diseases using photosensitizers formulated with nanoparticles, with the overall goal of aiding the design and development of novel photosensitizers.
TL;DR: This work tried to obtain EBOV infection-related human genes in a protein-protein interaction network using Dijkstra algorithm and hopes it could contribute to the discovery of novel effective treatments.
Abstract: BACKGROUND Ebola hemorrhagic fever (EHF) is caused by Ebola virus (EBOV). It is reported that human could be infected by EBOV with a high fatality rate. However, association factors between EBOV and host still tend to be ambiguous. OBJECTIVE According to the "guilt by association" (GBA) principle, proteins interacting with each other are very likely to function similarly or the same. Based on this assumption, we tried to obtain EBOV infection-related human genes in a protein-protein interaction network using Dijkstra algorithm. CONCLUSION We hope it could contribute to the discovery of novel effective treatments. Finally, 15 genes were selected as potential EBOV infection-related human genes.
TL;DR: The present review describes the importance of structural features of potential chemical scaffolds as well as the role of computational approaches like ligand docking, molecular dynamics, QSAR and pharmacophore modeling in the development of novel MAO inhibitors.
Abstract: Background Due to the limited number of MAO inhibitors in the clinics, several research efforts are aimed at the discovery of novel MAO inhibitors. At present, a high specificity and a reversible mode of inhibition of MAO-A/B are cited as desirable traits in drug discovery process. This will help to reduce the probability of causing target disruption and may increase the duration of action of drug. Aim Most of the existing MAO inhibitors lead to side effects due to the lack of affinity and selectivity. Therefore, there is an urgent need to design novel, potent, reversible and selective inhibitors for MAO-A/B. Selective inhibition of MAO-A results in the elevated level of serotonin and noradrenaline. Hence, MAO-A inhibitors can be used for improving the symptoms of depression. The selective MAO-B inhibitors are used with L-DOPA and/or dopamine agonists in the symptomatic treatment of Parkinson's disease. The present study was aimed to describe the recently developed hits of MAO inhibitors. Method At present, CADD techniques are gaining an attention in rationale drug discovery of MAO inhibitors, and several research groups employed CADD approaches on various chemical scaffolds to identify novel MAO inhibitors. These computational techniques assisted in the development of lead molecules with improved pharmacodynamics / pharmacokinetic properties toward MAOs. Further, CADD techniques provided a better understanding of structural aspects of molecular targets and lead molecules. Conclusions The present review describes the importance of structural features of potential chemical scaffolds as well as the role of computational approaches like ligand docking, molecular dynamics, QSAR and pharmacophore modeling in the development of novel MAO inhibitors.
TL;DR: Ovarian preservation is safe for young women with stage I EEAC, which is not significantly associated with overall and cancer-specific mortality.
Abstract: BACKGROUND Most patients with early stage endometrial endometrioid adenocarcinoma (EEAC) are treated with hysterectomy and bilateral oophorectomy. But this surgical menopause leads to long-term sequelae for premenopausal women, especially for young women of childbearing age. This population-based study was to evaluate the safety of ovarian preservation in young women with stage I EEAC. METHODS Patients of age 50 or younger with stage I EEAC were explored from the Surveillance, Epidemiology and End Results program database during 2004 to 2013. Propensity score matching was used to randomize the data set and reduce the selection biases of doctors. Univariate analysis and multivariate cox proportional hazards model were utilized to estimate the safety of ovarian preservation. RESULTS A total of 7183 patients were identified, and ovarian preservation was performed in 863 (12 %) patients. Compared with women treated with oophorectomy, patients with ovarian preservation significantly tend to be younger at diagnosis (P-value < 0.001) and more likely diagnosed as stage IA EEAC, to have better differentiated tumor tissues and smaller tumors, as well as less likely to undergo radiation and lymphadenectomy. 863 patients treated with oophorectomy were selected by propensity score matching. After propensity score matching, the differences of all characteristics between ovarian preservation and oophorectomy were not significant and potential confounders in the two groups decreased. In univariate analysis of matched population, ovarian preservation had no effect on overall (P-value=0.928) and cancer-specific (P-value=0.390) mortality. In propensityadjusted multivariate analysis, ovarian preservation was not significantly associated with overall (HR=0.69, 95%CI=0.41-1.68, P-value=0.611) and cancer-specific (HR=1.65, 95%CI=0.54-5.06, Pvalue= 0.379) survival. CONCLUSION Ovarian preservation is safe for young women with stage I EEAC, which is not significantly associated with overall and cancer-specific mortality.
TL;DR: A computational framework based on the convolutional neural network, a deep learning algorithm representing raw form of data pattern without assumption of their distribution, showed better and more balanced performance of sensitivity and specificity comparing to other machine learning algorithms.
Abstract: BACKGROUND The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big problems in cell cycle-regulated gene detection. METHODS Here, a computational framework called DLGene was proposed for cell cycle-regulated gene detection. It is based on the convolutional neural network, a deep learning algorithm representing raw form of data pattern without assumption of their distribution. First, the expression data was transformed to categorical state data to denote the changing state of gene expression, and four different expression patterns were revealed for the reported cell cycle-regulated genes. Then, DLGene was applied to discriminate the non-cell cycle gene and the four subtypes of cell cycle genes. Its performances were compared with six traditional machine learning methods. At last, the biological functions of representative cell cycle genes for each subtype are analyzed. RESULTS Our method showed better and more balanced performance of sensitivity and specificity comparing to other machine learning algorithms. The cell cycle genes had very different expression pattern with non-cell cycle genes and among the cell-cycle genes, there were four subtypes. Our method not only detects the cell cycle genes, but also describes its expression pattern, such as when its highest expression level is reached and how it changes with time. For each type, we analyzed the biological functions of the representative genes and such results provided novel insight to the cell cycle mechanisms.
TL;DR: It is apparent that, by this computational docking approach, more selective, reversible and potent molecules could be proposed as MAO inhibitors by precise modifications on the basic scaffold.
Abstract: Background This is an exciting period for research on monoamine oxidase and its effects on central nervous system. As the current hitting-one-target, therapeutic strategy has become quite inefficient for the treatment of various neurological disorders Objective: The objective of this review is to identify and critically discuss the computational development of multi-target natural and related ligand-MAO protein docking approaches in the study of monoamine oxidase (MAO) enzymes. Discussion Computational development of the new compounds from natural and related synthetic origin, active as MAO inhibitors (MAOIs) was discussed in some detail. The docking studies related to the alkaloids and their various categories secondary metabolites from plants like alkaloids, flavonoids and xanthones class of compounds specially caffeine, β-carboline, naphthoquinone, morpholine, piperine, amphetamine and furthermore curcumin, eugenol, trans-Farnesol and many other extracted plant constituents with their docking studies were discussed in detail. Conclusion It is apparent that, by this computational docking approach, more selective, reversible and potent molecules could be proposed as MAO inhibitors by precise modifications on the basic scaffold.
TL;DR: A large number of computational drug repositioning approaches have been developed over the past decades and they are summarized briefly and classified them into target-based, gene-expression- based, phenome-based and multi-omics-based categories according to strategies of drug reppositioning.
Abstract: Computational drug repositioning emerges as a new idea of drug discovery and development. Contrary to conventional routines, computational drug repositioning encompasses low risk and high safety. Some successful cases demonstrated its advantage. Therefore, a large number of computational drug repositioning approaches have been developed over the past decades. We summarized briefly these methods and classified them into target-based, gene-expression-based, phenome-based and multi-omics-based categories according to strategies of drug repositioning. We reviewed some representatives of computational drug repositioning methods in each category, with emphasis on detail of techniques and finally discussed developing trends of computational drug repositioning.