scholarly journals Hydrophobic Property of (R)-3 Amidinophenylalanine Inhibitors Contributes to their Inhibition Constants with Thrombin Enzyme

2019 ◽  
Vol 22 (3) ◽  
Author(s):  
Hien Van Nguyen ◽  
Van Thi Bich Pham ◽  
Hao Minh Hoang
Keyword(s):  
Goodness Of Fit ◽  
Blood Clot ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Hydrophobic Property ◽  
Qsar Modeling ◽  
Data Set ◽  
Chemical Structures ◽  
2D Qsar ◽  
Inhibition Constants

Introduction: Thrombin is the key enzyme of fibrin formation in the blood coagulation cascade. Thrombin is released by the hydrolysis of prothrombinase which is generated from factor Xa and factor Va in the presence of calcium ion and phospholipid. The inhibition of thrombin is of therapeutic interest in blood clot treatment. Currently, potent thrombin inhibitors of (R)-3- amidinophenylalanine, derived from benzamidine-containing amino acid, have been developed so far. In order to quantitatively express a relationship between chemical structures and inhibition constants (Ki with thrombin enzyme in a data set of (R)-3-amidinophenylalanine inhibitors), we developed a quantitative structure-activity relationship (QSAR) modeling from a group of 60 (R)-3- amidinophenylalanine inhibitors. Methods: A database containing chemical structures of 60 inhibitors and their Ki values was put into molecular operating environment (MOE) 2008.10 software, and the two-dimensional (2D) physicochemical descriptors were numerically calculated. After removing the irrelevant descriptors, a QSAR modeling was developed from the 2D-descriptors and Ki values by using the partial least squares (PLS) regression method. Results: The results showed that the hydrophobic property, reflected through n-octanol/water partition coefficient (P) of a drug molecule, contributes mainly to Ki values with thrombin.The statistic parameters that give the information about the goodness of fit of a 2D-QSAR model (such as squared correlation coefficient of R2 = 0.791, root mean square error (RMSE) = 0.443, cross-validated Q2 cv = 0.762, and cross-validated RMSEcv = 0.473) were statistically obtained for a training set (60 inhibitors). The R2 and RMSE values were obtained by using a developed model for the testing set (9 inhibitors) ; the total set has statistically significant parameters. Furthermore, the 2D-QSAR modeling was also applied to predict the Ki values of the 69 inhibitors. A linear relationship was found between the experimental and predicted pKi values of the inhibitors. Conclusion: The results support the promising application of established 2D-QSAR modeling in the prediction and design of new (R)-3-amidinophenylalanine candidates in the pharmaceutical industry.  

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

The uncalibrated prothrombinase-induced clotting time test

2010 ◽  
Vol 30 (04) ◽  
pp. 212-216 ◽  
Author(s):  
R. Jovic ◽  
M. Hollenstein ◽  
P. Degiacomi ◽  
M. Gautschi ◽  
A. Ferrández ◽  
...  
Keyword(s):  
Factor Xa ◽  
Heparin Therapy ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Diagnostic Tools ◽  
Direct Thrombin Inhibitors ◽  
Functional Assay ◽  
Clotting Time ◽  
Coagulation Time ◽  
Time Test

SummaryThe activated partial thromboplastin time test (aPTT) represents one of the most commonly used diagnostic tools in order to monitor patients undergoing heparin therapy. Expression of aPTT coagulation time in seconds represents common practice in order to evaluate the integrity of the coagulation cascade. The prolongation of the aPTT thus can indicate whether or not the heparin level is likely to be within therapeutic range. Unfortunately aPTT results are highly variable depending on patient properties, manufacturer, different reagents and instruments among others but most importantly aPTT’s dose response curve to heparin often lacks linearity. Furthermore, aPTT assays are insensitive to drugs such as, for example, low molecular weight heparin (LMWH) and direct factor Xa (FXa) inhibitors among others. On the other hand, the protrombinase-induced clotting time assay (PiCT®) has been show to be a reliable functional assay sensitive to all heparinoids as well as direct thrombin inhibitors (DTIs). So far, the commercially available PiCT assay (Pefakit®-PiCT®, DSM Nutritional Products Ltd. Branch Pentapharm, Basel, Switzerland) is designed to express results in terms of units with the help of specific calibrators, while aPTT results are most commonly expressed as coagulation time in seconds. In this report, we describe the results of a pilot study indicating that the Pefakit PiCT UC assay is superior to the aPTT for the efficient monitoring of patients undergoing UFH therapy; it is also suitable to determine and quantitate the effect of LMWH therapy. This indicates a distinct benefit when using this new approach over the use of aPPT for heparin monitoring.

Simple Index to Assess the Calibration Quality of Safety Performance Functions Based on Multiple Goodness-of-Fit Metrics

2021 ◽  
pp. 036119812110088
Author(s):  
Raul E. Avelar ◽  
Karen Dixon ◽  
Boniphace Kutela ◽  
Sam Klump ◽  
Beth Wemple ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Synthetic Data ◽  
Calibration Procedure ◽  
Safety Performance ◽  
Absolute Deviation ◽  
Data Set ◽  
Safety Database ◽  
Simple Index ◽  
Safety Performance Functions

The calibration of safety performance functions (SPFs) is a mechanism included in the Highway Safety Manual (HSM) to adjust SPFs in the HSM for use in intended jurisdictions. Critically, the quality of the calibration procedure must be assessed before using the calibrated SPFs. Multiple resources to aid practitioners in calibrating SPFs have been developed in the years following the publication of the HSM 1st edition. Similarly, the literature suggests multiple ways to assess the goodness-of-fit (GOF) of a calibrated SPF to a data set from a given jurisdiction. This paper uses the calibration results of multiple intersection SPFs to a large Mississippi safety database to examine the relations between multiple GOF metrics. The goal is to develop a sensible single index that leverages the joint information from multiple GOF metrics to assess overall quality of calibration. A factor analysis applied to the calibration results revealed three underlying factors explaining 76% of the variability in the data. From these results, the authors developed an index and performed a sensitivity analysis. The key metrics were found to be, in descending order: the deviation of the cumulative residual (CURE) plot from the 95% confidence area, the mean absolute deviation, the modified R-squared, and the value of the calibration factor. This paper also presents comparisons between the index and alternative scoring strategies, as well as an effort to verify the results using synthetic data. The developed index is recommended to comprehensively assess the quality of the calibrated intersection SPFs.

scholarly journals Dark energy survey internal consistency tests of the joint cosmological probes analysis with posterior predictive distributions

2021 ◽  
Vol 503 (2) ◽  
pp. 2688-2705
Author(s):  
C Doux ◽  
E Baxter ◽  
P Lemos ◽  
C Chang ◽  
A Alarcon ◽  
...  
Keyword(s):  
Dark Energy ◽  
Internal Consistency ◽  
Goodness Of Fit ◽  
Small Scale ◽  
P Value ◽  
Posterior Predictive Distribution ◽  
Data Set ◽  
Dark Energy Survey ◽  
Data Vector ◽  
Energy Survey

ABSTRACT Beyond ΛCDM, physics or systematic errors may cause subsets of a cosmological data set to appear inconsistent when analysed assuming ΛCDM. We present an application of internal consistency tests to measurements from the Dark Energy Survey Year 1 (DES Y1) joint probes analysis. Our analysis relies on computing the posterior predictive distribution (PPD) for these data under the assumption of ΛCDM. We find that the DES Y1 data have an acceptable goodness of fit to ΛCDM, with a probability of finding a worse fit by random chance of p = 0.046. Using numerical PPD tests, supplemented by graphical checks, we show that most of the data vector appears completely consistent with expectations, although we observe a small tension between large- and small-scale measurements. A small part (roughly 1.5 per cent) of the data vector shows an unusually large departure from expectations; excluding this part of the data has negligible impact on cosmological constraints, but does significantly improve the p-value to 0.10. The methodology developed here will be applied to test the consistency of DES Year 3 joint probes data sets.

The Haemocompatibility of Biomaterials In Vitro: Investigations on the Mechanism of the Whole-blood Clot Formation Test

1992 ◽  
Vol 20 (3) ◽  
pp. 390-395 ◽  
Author(s):  
Thomas Groth ◽  
Katrin Derdau ◽  
Frank Strietzel ◽  
Frank Foerster ◽  
Hartmut Wolf
Keyword(s):  
Whole Blood ◽  
Blood Clotting ◽  
Blood Clot ◽  
Formation Rate ◽  
Clot Formation ◽  
Coagulation Cascade ◽  
Contact Activation ◽  
Human Fibrinogen ◽  
Static Conditions

Twenty years ago Imai & Nose introduced a whole-blood clotting test for the estimation of haemocompatibility of biomaterials in vitro In our paper a modification of this assay is described and the mechanism of clot formation further elucidated. It was found that neither the inhibition of platelet function nor the removal of platelets from blood significantly changed the clot formation rate on glass and polyvinyl chloride in comparison to the rate tor whole blood. Scanning electron microscopy demonstrated that platelets were not involved in clot formation near the blood/biomaterial interface. Thus, it was concluded that the system of contact activation of the coagulation cascade dominates during clot formation under static conditions. The latter conclusion was supported by the fact that preadsorption of human serum albumin or human fibrinogen onto the glass plates used, decreased the clot formation rate in the same manner.

Inhibition of Thrombin-induced Neuronal Cell Death by Recombinant Thrombomodulin and E5510, a Synthetic Thrombin Receptor Signaling Inhibitor

1999 ◽  
Vol 82 (09) ◽  
pp. 1071-1077 ◽  
Author(s):  
Krishna Pada Sarker ◽  
Kazuhiro Abeyama ◽  
Jun-ichiro Nishi ◽  
Masanori Nakata ◽  
Takeshi Tokioka ◽  
...  
Keyword(s):  
Cell Death ◽  
Neuronal Cell ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Thrombin Receptor ◽  
Functional Domain ◽  
Dependent Manner ◽  
Nuclear Condensation ◽  
Human Neuroblastoma ◽  
Recombinant Thrombomodulin

SummaryThrombin, a serine protease generated by the activation of the blood coagulation cascade following vessel injury, converts fibrinogen to fibrin, activates platelets and several coagulation factors, and plays a pivotal role in thrombosis and haemostasis. Thrombin acts as a mitogen and apoptosis inducer in a dose-dependent fashion. We have previously shown that thrombin caused proliferation of vascular smooth muscle cells (VSMCs). Here, we show that a low concentration of thrombin caused proliferation of mouse neuroblastoma (Neuro-2a) and human neuroblastoma (NB-1) cells, while higher concentrations affected cell viability in a time-dependent manner. Similar effects were observed when thrombin receptor agonist peptide (SFLLRNPNDKYEPF, TRAP) was applied. The dying cells showed nuclear condensation and fragmentation, suggesting that cell death occurred by apoptosis. The extent to which thrombin induced cell death was significantly attenuated by recombinant thrombomodulin (rTM), or by a minimum functional domain of TM, termed E456. Furthermore, a synthetic compound that inhibits signaling from the thrombin receptor, 4-cyano-5,5-bis (4-methoxyphenyl)-4-pentanoic acid (E5510), and the antioxidant N-acetyl L-cysteine (NAC), efficiently prevented thrombin-induced Neuro-2a cell death. Thus, thrombin inhibitors and antioxidant appear to neutralize thrombin toxicity.

scholarly journals Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) task

2010 ◽  
Vol 2 (2) ◽  
pp. 38-51 ◽  
Author(s):  
Marc Halbrügge
Keyword(s):  
Goodness Of Fit ◽  
Model Development ◽  
Cognitive Model ◽  
Training Data ◽  
Sequence Matching ◽  
Data Set ◽  
Depth Analysis ◽  
Stocks And Flows ◽  
Matching Techniques

Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) taskThis paper describes the creation of a cognitive model submitted to the ‘Dynamic Stocks and Flows’ (DSF) modeling challenge. This challenge aims at comparing computational cognitive models for human behavior during an open ended control task. Participants in the modeling competition were provided with a simulation environment and training data for benchmarking their models while the actual specification of the competition task was withheld. To meet this challenge, the cognitive model described here was designed and optimized for generalizability. Only two simple assumptions about human problem solving were used to explain the empirical findings of the training data. In-depth analysis of the data set prior to the development of the model led to the dismissal of correlations or other parametric statistics as goodness-of-fit indicators. A new statistical measurement based on rank orders and sequence matching techniques is being proposed instead. This measurement, when being applied to the human sample, also identifies clusters of subjects that use different strategies for the task. The acceptability of the fits achieved by the model is verified using permutation tests.

scholarly journals Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2322 ◽  
Author(s):  
Saw Simeon ◽  
Nuttapat Anuwongcharoen ◽  
Watshara Shoombuatong ◽  
Aijaz Ahmad Malik ◽  
Virapong Prachayasittikul ◽  
...  
Keyword(s):  
Hydrogen Bonding ◽  
Molecular Docking ◽  
Binding Pocket ◽  
Binding Energies ◽  
Acetylcholinesterase Inhibition ◽  
Qsar Modeling ◽  
Qsar Study ◽  
Data Set ◽  
Ache Inhibitors ◽  
Redundant Data

Alzheimer’s disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential for cognition and memory. A large non-redundant data set of 2,570 compounds with reported IC50values against AChE was obtained from ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 different data splits using random forest. Generated models affordedR2, ${Q}_{\mathrm{CV }}^{2}$ and ${Q}_{\mathrm{Ext}}^{2}$ values in ranges of 0.66–0.93, 0.55–0.79 and 0.56–0.81 for the training set, 10-fold cross-validated set and external set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it affordedR2, ${Q}_{\mathrm{CV }}^{2}$ and ${Q}_{\mathrm{Ext}}^{2}$ values of 0.92 ± 0.01, 0.78 ± 0.06 and 0.78 ± 0.05, respectively. Furthermore, Y-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover, Kennard–Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed that compounds13,5and28exhibited the lowest binding energies of −12.2, −12.0 and −12.0 kcal/mol, respectively, against human AChE, which is modulated by hydrogen bonding,π–πstacking and hydrophobic interaction inside the binding pocket. These information may be used as guidelines for the design of novel and robust AChE inhibitors.

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