Computer-Aided Selective Optimization of Side Activities of Talinolol.
Ontology highlight
ABSTRACT:
SUBMITTER: Hiesinger K
PROVIDER: S-EPMC6580380 | biostudies-literature | 2019 Jun
REPOSITORIES: biostudies-literature
Publications
Computer-Aided Selective Optimization of Side Activities of Talinolol.
Hiesinger Kerstin K Kramer Jan S JS Achenbach Janosch J Moser Daniel D Weber Julia J Wittmann Sandra K SK Morisseau Christophe C Angioni Carlo C Geisslinger Gerd G Kahnt Astrid S AS Kaiser Astrid A Proschak Anna A Steinhilber Dieter D Pogoryelov Denys D Wagner Karen K Hammock Bruce D BD Proschak Ewgenij E
ACS medicinal chemistry letters 20190529 6
Selective optimization of side activities is a valuable source of novel lead structures in drug discovery. In this study, a computer-aided approach was used to deorphanize the pleiotropic cholesterol-lowering effects of the beta-blocker talinolol, which result from the inhibition of the enzyme soluble epoxide hydrolase (sEH). X-ray structure analysis of the sEH in complex with talinolol enables a straightforward optimization of inhibitory potency. The resulting lead structure exhibited <i>in viv ...[more]
Similar Datasets
Project description:Multimaterials deposition, a distinct advantage in bioprinting, overcomes material's limitation in hydrogel-based bioprinting. Multimaterials are deposited in a build/support configuration to improve the structural integrity of three-dimensional bioprinted construct. A combination of rapid cross-linking hydrogel has been chosen for the build/support setup. The bioprinted construct was further chemically cross-linked to ensure a stable construct after print. This paper also proposes a file segmentation and preparation technique to be used in bioprinting for printing freeform structures.
| S-EPMC7294690 | biostudies-literature
Project description:We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of the local binary pattern was used for calculating a feature vector. SVM or XGBoost was trained using the feature vector and its corresponding label. Tree Parzen Estimator (TPE) was used as Bayesian optimization for parameters of SVM and XGBoost. Random search was done for comparison with TPE. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost was 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. Bayesian optimization of SVM and XGBoost parameters was more efficient than random search. Based on observer study, AUC values of two board-certified radiologists were 0.898 and 0.822. The results show that diagnostic accuracy of our CADx system was comparable to that of radiologists with respect to classifying lung nodules.
| S-EPMC5908232 | biostudies-literature
Project description:Chlamydia trachomatis (CT) causes the most prevalent sexually transmitted bacterial disease in the United States. The lack of drug selectivity is one of the main challenges of the current antichlamydial pharmacotherapy. The metabolic needs of CT are controlled, among others, by cylindrical proteases and their chaperones (e.g., ClpX). It has been shown that dihydrothiazepines can disrupt CT-ClpXP. Based on this precedent, we synthesized a dihydrothiazepine library and characterized its antichlamydial activity using a modified semi-high-throughput screening assay. Then, we demonstrated their ability to inhibit ClpX ATPase activity in vitro, supporting ClpX as a target. Further, our lead compound displayed a promising selectivity profile against CT, acceptable cytotoxicity, no mutagenic potential, and good in vitro stability. A two-dimensional quantitative structure-activity relationship (2D QSAR) model was generated as a support tool in the identification of more potent antichlamydial molecules. This study suggests dihydrothiazepines are a promising starting point for the development of new and selective antichlamydial drugs.
| S-EPMC10056257 | biostudies-literature
Project description:Recent clinical trials using antibodies with low toxicity and high efficiency have raised expectations for the development of next-generation protein therapeutics. However, the process of obtaining therapeutic antibodies remains time consuming and empirical. This review summarizes recent progresses in the field of computer-aided antibody development mainly focusing on antibody modeling, which is divided essentially into two parts: (i) modeling the antigen-binding site, also called the complementarity determining regions (CDRs), and (ii) predicting the relative orientations of the variable heavy (V(H)) and light (V(L)) chains. Among the six CDR loops, the greatest challenge is predicting the conformation of CDR-H3, which is the most important in antigen recognition. Further computational methods could be used in drug development based on crystal structures or homology models, including antibody-antigen dockings and energy calculations with approximate potential functions. These methods should guide experimental studies to improve the affinities and physicochemical properties of antibodies. Finally, several successful examples of in silico structure-based antibody designs are reviewed. We also briefly review structure-based antigen or immunogen design, with application to rational vaccine development.
| S-EPMC3449398 | biostudies-literature
Project description:As one of the most vulnerable cancers of women, the incidence rate of breast cancer in China is increasing at an annual rate of 3%, and the incidence is younger. Therefore, it is necessary to conduct research on the risk of breast cancer, including the cause of disease and the prediction of breast cancer risk based on historical data. Data based statistical learning is an important branch of modern computational intelligence technology. Using machine learning method to predict and judge unknown data provides a new idea for breast cancer diagnosis. In this paper, an improved optimization algorithm (GSP_SVM) is proposed by combining genetic algorithm, particle swarm optimization and simulated annealing with support vector machine algorithm. The results show that the classification accuracy, MCC, AUC and other indicators have reached a very high level. By comparing with other optimization algorithms, it can be seen that this method can provide effective support for decision-making of breast cancer auxiliary diagnosis, thus significantly improving the diagnosis efficiency of medical institutions. Finally, this paper also preliminarily explores the effect of applying this algorithm in detecting and classifying breast cancer in different periods, and discusses the application of this algorithm to multiple classifications by comparing it with other algorithms.
| S-EPMC8287651 | biostudies-literature
Project description:Amongst the chemokine signalling axes involved in cancer, chemokine CXCL12 acting on chemokine receptor CXCR4 is particularly significant since it orchestrates migration of cancer cells in a tissue-specific metastatic process. High CXCR4 tumour expression is associated with poor prognosis of lung, brain, CNS, blood and breast cancers. We have identified a new class of small molecule CXCR4 antagonists based on the use of computational modelling studies in concert with experimental determination of in vitro activity against CXCL12-induced intracellular calcium mobilisation, proliferation and chemotaxis. Molecular modelling proved to be a useful tool in rationalising our observed potencies, as well as informing the direction of the synthetic efforts aimed at producing more potent compounds.
| S-EPMC3800133 | biostudies-literature
Project description:Leishmaniasis are infectious diseases caused by parasites of genus Leishmania that affect affects 12 million people in 98 countries mainly in Africa, Asia, and Latin America. Effective treatments for this disease are urgently needed. In this study, we present a computer-aided approach to investigate a set of 32 recently synthesized chalcone and chalcone-like compounds to act as antileishmanial agents. As a result, nine most promising compounds and three potentially inactive compounds were experimentally evaluated against Leishmania infantum amastigotes and mammalian cells. Four compounds exhibited EC50 in the range of 6.2-10.98?M. In addition, two compounds, LabMol-65 and LabMol-73, exhibited cytotoxicity in macrophages >50?M that resulted in better selectivity compared to standard drug amphotericin B. These two compounds also demonstrated low cytotoxicity and high selectivity towards Vero cells. The results of target fishing followed by homology modeling and docking studies suggest that these chalcone compounds could act in Leishmania because of their interaction with cysteine proteases, such as procathepsin L. Finally, we have provided structural recommendations for designing new antileishmanial chalcones.
| S-EPMC6020026 | biostudies-literature
Project description:Pani heloch (Antidesma montanum) is traditionally used to treat innumerable diseases and is a source of wild vegetables for the management of different pathological conditions. The present study explored the qualitative phytochemicals; quantitative phenol and flavonoid contents; in vitro antioxidant, anti-inflammatory, and thrombolytic effects; and in vivo antipyretic and analgesic properties of the methanol extract of A. montanum leaves in different experimental models. The extract exhibited secondary metabolites including alkaloids, flavonoids, flavanols, phytosterols, cholesterols, phenols, terpenoids, glycosides, fixed oils, emodines, coumarins, resins, and tannins. Besides, Pani heloch showed strong antioxidant activity (IC50 = 99.00 µg/mL), while a moderate percentage of clot lysis (31.56%) in human blood and significant anti-inflammatory activity (p < 0.001) was achieved with the standard. Moreover, the analgesic and antipyretic properties appeared to trigger a significant response (p < 0.001) relative to in the control group. Besides, an in silico study of carpusin revealed favorable protein-binding affinities. Furthermore, the absorption, distribution, metabolism, excretion, and toxicity analysis and toxicological properties of all isolated compounds adopted Lipinski's rule of five for drug-like potential and level of toxicity. Our research unveiled that the methanol extract of A. montanum leaves exhibited secondary metabolites that are a good source for managing inflammation, pyrexia, pain, and cellular toxicity. Computational approaches and further studies are required to identify the possible mechanism which responsible for the biological effects.
| S-EPMC7663931 | biostudies-literature
Project description:ObjectiveCustomized Fontan designs, generated by computer-aided design (CAD) and optimized by computational fluid dynamics simulations, can lead to novel, patient-specific Fontan conduits unconstrained by off-the-shelf grafts. The relative contributions of both surgical expertise and CAD to Fontan optimization have not been addressed. In this study, we assessed hemodynamic performance of Fontans designed by both surgeon's unconstrained modeling (SUM) and by CAD.MethodsTen cardiac magnetic resonance imaging datasets were used to create 3-dimensional (3D) models of Fontans. Baseline computational fluid dynamics simulations assessed Fontan indexed power loss (iPL), hepatic flow distribution, and percentage of conduit surface area with abnormally low wall shear stress for venous flow (<1 dyne/cm2). Fontans not meeting thresholds were redesigned using 2 methods: SUM (ie, original venous anatomy without the Fontan was 3D printed and sent to surgeon for Fontan redesign with clay modeling) and CAD (ie, the same 3D geometry was sent to engineers for iterative Fontan redesign guided by computational fluid dynamics). Both groups were blinded to each other's results.ResultsEight Fontans were redesigned by SUM and CAD methods. Both SUM and CAD redesigns met iPL thresholds. SUM had lower iPL, whereas CAD demonstrated balanced hepatic flow distribution and lower wall shear stress percentage. Wall shear stress percentage shared an inverse relationship with iPL, preventing oversized Fontan designs.ConclusionsCustomized Fontan conduits with low iPL can be created by either a surgeon or CAD. CAD can also improve hepatic flow distribution and prevent oversized Fontan designs. Future studies should investigate workflows that combine SUM and CAD to optimize Fontan conduits.
| S-EPMC7888288 | biostudies-literature
Project description:ObjectivesTo compare ultrasonography (US) feature-based radiomics and computer-aided diagnosis (CAD) models for predicting malignancy in thyroid nodules, and to evaluate their utility for thyroid nodule management.MethodsThis prospective study included 262 thyroid nodules obtained between January 2022 and June 2022. All nodules previously underwent standardized US image acquisition, and the nature of the nodules was confirmed by the pathological results. The CAD model exploited two vertical US images of the thyroid nodule to differentiate the lesions. The least absolute shrinkage and operator algorithm (LASSO) was applied to choose radiomics features with excellent predictive properties for building a radiomics model. Ultimately, the area under the receiver operating characteristic curve (AUC) and calibration curves were assessed to compare diagnostic performance between the models. DeLong's test was used to analyze the difference between groups. Both models were used to revise the American College of Radiology Thyroid Imaging Reporting and Data Systems (ACR TI-RADS) to provide biopsy recommendations, and their performance was compared with the original recommendations.ResultsOf the 262 thyroid nodules, 157 were malignant, and the remaining 105 were benign. The diagnostic performance of radiomics, CAD, and ACR TI-RADS models had an AUC of 0.915 (95% confidence interval (CI): 0.881-0.947), 0.814 (95% CI: 0.766-0.863), and 0.849 (95% CI: 0.804-0.894), respectively. DeLong's test showed a statistically significant between the AUC values of models (p < 0.05). Calibration curves showed good agreement in each model. When both models were applied to revise the ACR TI-RADS, our recommendations significantly improved the performance. The revised recommendations based on radiomics and CAD showed an increased sensitivity, accuracy, positive predictive value, and negative predictive value, and decreased unnecessary fine-needle aspiration rates. Furthermore, the radiomics model's improvement scale was more pronounced (33.3-16.7% vs. 33.3-9.7%).ConclusionThe radiomics strategy and CAD system showed good diagnostic performance for discriminating thyroid nodules and could be used to optimize the ACR TI-RADS recommendation, which successfully reduces unnecessary biopsies, especially in the radiomics model.
| S-EPMC10213653 | biostudies-literature