Traditional drug development is a slow and costly process that leads to the production of new drugs. Virtual screening (VS) is a computational procedure that measures the similarity of molecules as one of its primary tasks. Many techniques for capturing the biological similarity between a test compound and a known target ligand have been established in ligand-based virtual screens (LBVSs). However, despite the good performances of the above methods compared to their predecessors, especially when dealing with molecules that have structurally homogenous active elements, they are not satisfied when dealing with molecules that are structurally heterogeneous. The main aim of this study is to improve the performance of similarity searching, especially with molecules that are structurally heterogeneous. The Siamese network will be used due to its capability to deal with complicated data samples in many fields. The Siamese multi-layer perceptron architecture will be enhanced by using two similarity distance layers with one fused layer, then multiple layers will be added after the fusion layer, and then the nodes of the model that contribute less or nothing during inference according to their signal-to-noise ratio values will be pruned. Several benchmark datasets will be used, which are: the MDL Drug Data Report (MDDR-DS1, MDDR-DS2, and MDDR-DS3), the Maximum Unbiased Validation (MUV), and the Directory of Useful Decoys (DUD). The results show the outperformance of the proposed method on standard Tanimoto coefficient (TAN) and other methods. Additionally, it is possible to reduce the number of nodes in the Siamese multilayer perceptron model while still keeping the effectiveness of recall on the same level.
Query expansion is the process of reformulating an original query to improve retrieval performance in information retrieval systems. Relevance feedback is one of the most useful query modification techniques in information retrieval systems. In this paper, we introduce query expansion into ligand-based virtual screening (LBVS) using the relevance feedback technique. In this approach, a few high-ranking molecules of unknown activity are filtered from the outputs of a Bayesian inference network based on a single ligand molecule to form a set of ligand molecules. This set of ligand molecules is used to form a new ligand molecule. Simulated virtual screening experiments with the MDL Drug Data Report and maximum unbiased validation data sets show that the use of ligand expansion provides a very simple way of improving the LBVS, especially when the active molecules being sought have a high degree of structural heterogeneity. However, the effectiveness of the ligand expansion is slightly less when structurally-homogeneous sets of actives are being sought.
Matched MeSH terms: Drug Evaluation, Preclinical/methods*
Many of the conventional similarity methods assume that molecular fragments that do not relate to biological activity carry the same weight as the important ones. One possible approach to this problem is to use the Bayesian inference network (BIN), which models molecules and reference structures as probabilistic inference networks. The relationships between molecules and reference structures in the Bayesian network are encoded using a set of conditional probability distributions, which can be estimated by the fragment weighting function, a function of the frequencies of the fragments in the molecule or the reference structure as well as throughout the collection. The weighting function combines one or more fragment weighting schemes. In this paper, we have investigated five different weighting functions and present a new fragment weighting scheme. Later on, these functions were modified to combine the new weighting scheme. Simulated virtual screening experiments with the MDL Drug Data Report (23) and maximum unbiased validation data sets show that the use of new weighting scheme can provide significantly more effective screening when compared with the use of current weighting schemes.
Matched MeSH terms: Drug Evaluation, Preclinical/methods*
Many methods have been developed to capture the biological similarity between two compounds for use in drug discovery. A variety of similarity metrics have been introduced, the Tanimoto coefficient being the most prominent. Many of the approaches assume that molecular features or descriptors that do not relate to the biological activity carry the same weight as the important aspects in terms of biological similarity. Herein, a novel similarity searching approach using a Bayesian inference network is discussed. Similarity searching is regarded as an inference or evidential reasoning process in which the probability that a given compound has biological similarity with the query is estimated and used as evidence. Our experiments demonstrate that the similarity approach based on Bayesian inference networks is likely to outperform the Tanimoto similarity search and offer a promising alternative to existing similarity search approaches.
Ensemble docking can be a successful virtual screening technique that addresses the innate conformational heterogeneity of macromolecular drug targets. Yet, lacking a method to identify a subset of conformational states that effectively segregates active and inactive small molecules, ensemble docking may result in the recommendation of a large number of false positives. Here, three knowledge-based methods that construct structural ensembles for virtual screening are presented. Each method selects ensembles by optimizing an objective function calculated using the receiver operating characteristic (ROC) curve: either the area under the ROC curve (AUC) or a ROC enrichment factor (EF). As the number of receptor conformations, N, becomes large, the methods differ in their asymptotic scaling. Given a set of small molecules with known activities and a collection of target conformations, the most resource intense method is guaranteed to find the optimal ensemble but scales as O(2(N)). A recursive approximation to the optimal solution scales as O(N(2)), and a more severe approximation leads to a faster method that scales linearly, O(N). The techniques are generally applicable to any system, and we demonstrate their effectiveness on the androgen nuclear hormone receptor (AR), cyclin-dependent kinase 2 (CDK2), and the peroxisome proliferator-activated receptor δ (PPAR-δ) drug targets. Conformations that consisted of a crystal structure and molecular dynamics simulation cluster centroids were used to form AR and CDK2 ensembles. Multiple available crystal structures were used to form PPAR-δ ensembles. For each target, we show that the three methods perform similarly to one another on both the training and test sets.
Matched MeSH terms: Drug Evaluation, Preclinical/methods*
Glycyrrhiza glabra L. (Family: Fabaceae) is one of the important traditional medicinal plant used extensively in folk medicine. It is known for its ethnopharmacological value in curing a wide variety of ailments. Glycyrrhizin, an active compound of G. glabra, possesses anti-inflammatory activity due to which it is mostly used in traditional herbal medicine for the treatment and management of chronic diseases. The present review is focused extensively on the pharmacology, pharmacokinetics, toxicology, and potential effects of Glycyrrhizic Acid (GA). A thorough literature survey was conducted to identify various studies that reported on the GA on PubMed, Science Direct and Google Scholar.
A Bayesian inference network (BIN) provides an interesting alternative to existing tools for similarity-based virtual screening. The BIN is particularly effective when the active molecules being sought have a high degree of structural homogeneity but has been found to perform less well with structurally heterogeneous sets of actives. In this paper, we introduce an alternative network model, called a Bayesian belief network (BBN), that seeks to overcome this limitation of the BIN approach. Simulated virtual screening experiments with the MDDR, WOMBAT and MUV data sets show that the BIN and BBN methods allow effective screening searches to be carried out. However, the results obtained are not obviously superior to those obtained using a much simpler approach that is based on the use of the Tanimoto coefficient and of the square roots of fragment occurrence frequencies.
Matched MeSH terms: Drug Evaluation, Preclinical/methods*
Quantifying the similarity of molecules is considered one of the major tasks in virtual screening. There are many similarity measures that have been proposed for this purpose, some of which have been derived from document and text retrieving areas as most often these similarity methods give good results in document retrieval and can achieve good results in virtual screening. In this work, we propose a similarity measure for ligand-based virtual screening, which has been derived from a text processing similarity measure. It has been adopted to be suitable for virtual screening; we called this proposed measure the Adapted Similarity Measure of Text Processing (ASMTP). For evaluating and testing the proposed ASMTP we conducted several experiments on two different benchmark datasets: the Maximum Unbiased Validation (MUV) and the MDL Drug Data Report (MDDR). The experiments have been conducted by choosing 10 reference structures from each class randomly as queries and evaluate them in the recall of cut-offs at 1% and 5%. The overall obtained results are compared with some similarity methods including the Tanimoto coefficient, which are considered to be the conventional and standard similarity coefficients for fingerprint-based similarity calculations. The achieved results show that the performance of ligand-based virtual screening is better and outperforms the Tanimoto coefficients and other methods.
How to look for new drugs? What guidelines to use? Have we to continue investigations on plant and marine organisms? These questions arise frequently today. A pharmacological effect results from the addition of many effects at a molecular level, i.e. the interaction between a ligand and a receptor. As long as the chemical structure of this receptor remains unknown, studies of Nature's resources will yield the largest reservoir of new drugs. Nature provides our imagination with the pattern of novel biologically active molecules. Criteria classically used in the past to select plants for study were chemotaxonomy, ethnopharmacology or pharmacotaxonomy. Examples will be taken from personal experience, to illustrate work done according to the chemotaxonomical approach (Ochrosia and ellipticines), and the ethnopharmacological approach (antiinflammatory properties of Euphorbiaceae from New Caledonia). Taking into account that one of the major problems we have to face is the unsatisfactory classical pharmacological testing procedure, we have tried to set up a network grouping biologists and chemists. Among many results obtained, one concerns the use of the mammalian hypothalamo-pituitary system to screen effects of alkaloids extracted from Psychotria oleoides, a Rubiaceae collected in New Caledonia. Psycholeine exhibits an intriguing activity on GH release. Another result concerns the influence of a Labiatae extract on the adenylate cyclase system: 9 HODE extracted from Glechoma hederacea stimulates the basal level of enzyme activity in platelets, this activity being possibly involved in the folk uses claimed. Using the tubulin test to screen antimitotic activities of plant extracts, the biological activity of rhazinilam has been demonstrated as responsible for the antitubulin activity of a Malaysian plant, Kopsia singapurensis.(ABSTRACT TRUNCATED AT 250 WORDS)
The Sarawak Biodiversity Centre (SBC) is a state government agency which regulates research and promotes the sustainable use of biodiversity. It has a program on documentation of traditional knowledge (TK) and is well-equipped with facilities for natural product research. SBC maintains a Natural Product Library (NPL) consisting of local plant and microbial extracts for bioprospecting. The NPL is a core discovery platform for screening of bioactive compounds by researchers through a formal agreement with clear benefit sharing obligations. SBC aims to develop partnerships with leading institutions and the industries to explore the benefits of biodiversity.
Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.
Diabetes now is the most common chronic disease in the world inducing heavy burden for the people's health. Based on this, diabetes research such as islet function has become a hot topic in medical institutes of the world. Today, in medical institutes, the conventional experiment platform in vitro is monolayer cell culture. However, with the development of micro- and nano-technologies, several microengineering methods have been developed to fabricate three-dimensional (3D) islet models in vitro which can better mimic the islet of pancreases in vivo. These in vitro islet models have shown better cell function than monolayer cells, indicating their great potential as better experimental platforms to elucidate islet behaviors under both physiological and pathological conditions, such as the molecular mechanisms of diabetes and clinical islet transplantation. In this review, we present the state-of-the-art advances in the microengineering methods for fabricating microscale islet models in vitro. We hope this will help researchers to better understand the progress in the engineering 3D islet models and their biomedical applications such as drug screening and islet transplantation.
Induced pluripotent stem cell-based therapy for treating genetic disorders has become an interesting field of research in recent years. However, there is a paucity of information regarding the applicability of induced pluripotent stem cells in dental research. Recent advances in the use of induced pluripotent stem cells have the potential for developing disease-specific iPSC lines in vitro from patients. Indeed, this has provided a perfect cell source for disease modeling and a better understanding of genetic aberrations, pathogenicity, and drug screening. In this paper, we will summarize the recent progress of the disease-specific iPSC development for various human diseases and try to evaluate the possibility of application of iPS technology in dentistry, including its capacity for reprogramming some genetic orodental diseases. In addition to the easy availability and suitability of dental stem cells, the approach of generating patient-specific pluripotent stem cells will undoubtedly benefit patients suffering from orodental disorders.
Four binuclear phosphanesilver(I) dithiocarbamates, {cyclohexyl3PAg(S2CNRR')}2 for R = R' = Et (1), CH2CH2 (2), CH2CH2OH (3) and R = Me, R' = CH2CH2OH (4) have been synthesised and characterised by spectroscopy and crystallography, and feature tri-connective, μ2-bridging dithiocarbamate ligands and distorted tetrahedral geometries based on PS3 donor sets. The compounds were evaluated for anti-bacterial activity against a total of 12 clinically important pathogens. Based on minimum inhibitory concentration (MIC) and cell viability tests (human embryonic kidney cells, HEK 293), 1-4 are specifically active against Gram-positive bacteria while demonstrating low toxicity; 3 and 4 are active against methicillin resistant S. aureus (MRSA). Across the series, 4 was most effective and was more active than the standard anti-biotic chloramphenicol. Time kill assays reveal 1-4 to exhibit both time- and concentration-dependent pharmacokinetics against susceptible bacteria. Compound 4 demonstrates rapid (within 2 h) bactericidal activity at 1 and 2 × MIC to reach a maximum decrease of 5.2 log10 CFU/mL against S. aureus (MRSA).
β-Tricalcium phosphate (β-TCP) has attracted much attention as an artificial bone substitute owing to its biocompatibility and osteoconductivity. In this study, osteoconductivity of β-TCP bone substitute was enhanced without using growth factors or cells. Dicalcium phosphate dihydrate (DCPD), which is known to possess the highest solubility among calcium phosphates, was coated on β-TCP granules by exposing their surface with acidic calcium phosphate solution. The amount of coated DCPD was regulated by changing the reaction time between β-TCP granules and acidic calcium phosphate solution. Histomorphometry analysis obtained from histological results revealed that the approximately 10mol% DCPD-coated β-TCP granules showed the largest new bone formation compared to DCPD-free β-TCP granules, approximately 2.5mol% DCPD-coated β-TCP granules, or approximately 27mol% DCPD-coated β-TCP granules after 2 and 4weeks of implantation. Based on this finding, we demonstrate that the osteoconductivity of β-TCP granules could be improved by coating their surface with an appropriate amount of DCPD.
Detection and quantification of DNA is critical to many areas of life sciences and health care, from
disease diagnosis to drug screening. The transduction of DNA through electrochemical methods have a fast response rate and with a conductometric device like the silicon nanowire which can be fabricated to have a similar diameter of the DNA molecule being targeted, detection is real-time. Critical to this is the interfacing of a current-source and an amplifier capable of achieving a maximum of 10 pico ampere input bias. In this project, we fabricated a silicon nanowire using the top down approach and built a circuit that can mimic the output signal as low as 12 nA and achieved a gain of 1 million to be interfaced with the nanowire for real-time DNA detection.
This study investigated the effect of manufacturing process variables of mini-tablets, in particular, the effect of process variables concerning fluidized bed granulation on tablet weight variation. Test granules were produced with different granulation conditions according to a definitive screening design (DSD). The five evaluated factors assigned to DSD were: the grinding speed of the sample mill at the grinding process of the active pharmaceutical ingredient (X1), microcrystalline cellulose content in granules (X2), inlet air temperature (X3), binder concentration (X4) and the spray speed of the binder solution (X5) at the granulation process. First, the relationships between the evaluated factors and the granule properties were investigated. As a result of the DSD analysis, the mode of action of granulation parameters on the granule properties was fully characterized. Subsequently, the variation in tablet weight was examined. In addition to mini-tablets (3 mm diameter), this experiment assessed regular tablets (8 mm diameter). From the results for regular tablets, the variation in tablet weight was affected by the flowability of granules. By contrast, regarding the mini-tablets, no significant effect on the variation of tablet weight was found from the evaluated factors. From this result, this study further focused on other important factors besides the granulation process, and then the effect of the die-hole position of the multiple-tip tooling on tablet weight variation was proven to be significant. Our findings provide a better understanding of manufacturing mini-tablets.
Dillenia (Dilleniaceae) is a genus of about 100 species of flowering plants in tropical and subtropical trees of Southern Asia, Australasia, and the Indian Ocean Islands. Until now, only eight Dillenia species have been reported to be used traditionally in different countries for various medical purposes. Out of eight species, D. pentagyna (Roxb), D. indica (Linn.) and D. suffruticosa (Griffith Ex. Hook. F. & Thomsom Martelli) have been reported to be used to treat cancerous growth.
Many of the similarity-based virtual screening approaches assume that molecular fragments that are not related to the biological activity carry the same weight as the important ones. This was the reason that led to the use of Bayesian networks as an alternative to existing tools for similarity-based virtual screening. In our recent work, the retrieval performance of the Bayesian inference network (BIN) was observed to improve significantly when molecular fragments were reweighted using the relevance feedback information. In this paper, a set of active reference structures were used to reweight the fragments in the reference structure. In this approach, higher weights were assigned to those fragments that occur more frequently in the set of active reference structures while others were penalized. Simulated virtual screening experiments with MDL Drug Data Report datasets showed that the proposed approach significantly improved the retrieval effectiveness of ligand-based virtual screening, especially when the active molecules being sought had a high degree of structural heterogeneity.
Matched MeSH terms: Drug Evaluation, Preclinical/methods*
Over the years, in vitro Franz diffusion experiments have evolved into one of the most important methods for researching transdermal drug administration. Unfortunately, this type of testing often yields permeation data that suffer from poor reproducibility. Moreover, this feature frequently occurs when synthetic membranes are used as barriers, in which case biological tissue-associated variability has been removed as an artefact of total variation. The objective of the current study was to evaluate the influence of a full-validation protocol on the performance of a tailor-made array of Franz diffusion cells (GlaxoSmithKline, Harlow, UK) available in our laboratory. To this end, ibuprofen was used as a model hydrophobic drug while synthetic membranes were used as barriers. The parameters investigated included Franz cell dimensions, stirring conditions, membrane type, membrane treatment, temperature regulation and sampling frequency. It was determined that validation dramatically reduced derived data variability as the coefficient of variation for steady-state ibuprofen permeation from a gel formulation was reduced from 25.7% to 5.3% (n = 6). Thus, validation and refinement of the protocol combined with improved operator training can greatly enhance reproducibility in Franz cell experimentation.
Matched MeSH terms: Drug Evaluation, Preclinical/instrumentation*