scholarly journals Prediction of protein targets of kinetin using in silico and in vitro methods: a case study on spinach seed germination mechanism

2015 ◽  
Vol 8 (3) ◽  
pp. 95-105 ◽  
Author(s):  
Sivakumar Prasanth Kumar ◽  
Vilas R. Parmar ◽  
Yogesh T. Jasrai ◽  
Himanshu A. Pandya
Keyword(s):  
Seed Germination ◽  
In Silico ◽  
Protein Targets ◽  
In Vitro Methods

scholarly journals Combining species specific in vitro & in silico models to predict in vivo food effect in a preclinical stage – case study of Venetoclax

2021 ◽  
pp. 105840
Author(s):  
Laura J. Henze ◽  
Niklas J. Koehl ◽  
Joseph P. O'Shea ◽  
René Holm ◽  
Maria Vertzoni ◽  
...  
Keyword(s):  
In Silico ◽  
Food Effect ◽  
Preclinical Stage ◽  
Species Specific ◽  
In Silico Models

Semen Cuscutae-Fructus Lycii improves spermatogenic dysfunction by repairing the blood-testis barrier in rats according to in silico and in vitro methods

2021 ◽  
pp. 114022
Author(s):  
Yutian Zhu ◽  
Lei Dong ◽  
Jingshang Wang ◽  
Qiuning Liu ◽  
Hongxuan Tong ◽  
...  
Keyword(s):  
In Silico ◽  
In Vitro Methods ◽  
Fructus Lycii ◽  
Blood Testis Barrier

Evaluation of toxicological aspects of three new benzoxazole compounds with sunscreen photophysical properties using in silico and in vitro methods

2021 ◽  
pp. 105300
Author(s):  
Jâmeson Ferreira da Silva ◽  
Dione Silva Corrêa ◽  
Érico Leite Campos ◽  
Giovana Zamprônio Leite ◽  
João Denis Medeiros de Oliveira ◽  
...  
Keyword(s):  
In Silico ◽  
Photophysical Properties ◽  
In Vitro Methods

scholarly journals Computational and micro-analytical techniques to study the in vitro and in silico models of novel therapeutic drugs

2016 ◽  
Author(s):  
◽  
Njabulo Joyfull Gumede
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Pharmacophore Model ◽  
Binding Affinities ◽  
Ic50 Values ◽  
Database Screening ◽  
Pharmacophore Models ◽  
Novel Therapeutic

In drug discovery and development projects, metabolism of new chemical entities (NCEs) is a major contributing factor for the withdrawal of drug candidates, a major concern for other chemical industries where chemical-biological interactions are involved. NCEs interact with a target macro-molecule to stimulate a pharmacological or toxic response, known as pharmacodynamics (PD) effect or through the Adsorption, Distribution, Metabolism, and Excretion (ADME) process, triggered when a bio-macromolecule interacts with a therapeutic drug. Therefore, the drug discovery process is important because 75% of diseases known to human kind are not all cured by therapeutics currently available in the market. This is attributed to the lack of knowledge of the function of targets and their therapeutic use in order to design therapeutics that would trigger their pharmacological responses. Accordingly, the focus of this work is to develop cost saving strategies for medicinal chemists involved with drug discovery projects. Therefore, studying the synergy between in silico and in vitro approaches maybe useful in the discovery of novel therapeutic compounds and their biological activities. In this work, in silico methods such as structure-based and ligand-based approaches were used in the design of the pharmacophore model, database screening and flexible docking methods. Specifically, this work is presented by the following case studies: The first involved molecular docking studies to predict the binding modes of catechin enantiomer to human serum albumin (HSA) interaction; the second involved the use of docking methods to predict the binding affinities and enantioselectivity of the interaction of warfarin enantiomers to HSA. the third case study involved a combined computational strategy in order to generate information on a diverse set of steroidal and non-steroidal CYP17A1 inhibitors obtained from literature with known experimental IC50 values. Finally, the fourth case study involved the prediction of the site of metabolisms (SOMs) of probe substrates to Cytochrome P450 metabolic enzymes CYP 3A4, 2D6, and 2C9 making use of P450 module from Schrödinger suite for ADME/Tox prediction. The results of case study I were promising as they were able to provide clues to the factors that drive the synergy between experimental kinetic parameters and computational thermodynamics parameters to explain the interaction between drug enantiomers and thetarget protein. These parameters were correlated/converted and used to estimate the pseudo enantioselectivity of catechin enantiomer to HSA. This approach of combining docking methodology with docking post-processing methods such as MM-GBSA proved to be vital in estimating the correct pseudo binding affinities of a protein-ligand complexes. The enantioselectivity for enantiomers of catechin to HSA were 1,60 and 1,25 for site I and site II respectively. The results of case study II validates and verifies the preparation of ligands and accounting for tautomers at physiological pH, as well as conformational changes prior to and during docking with a flexible protein. The log KS = 5.43 and log KR = 5.34 for warfarin enantiomer-HSA interaction and the enantioselectivity (ES = KS/KR) of 1.23 were close to the experimental results and hence referred to as experimental-like affinity constants which validated and verified their applicability to predict protein-ligand binding affinities. In case study III, a 3D-QSAR pharmacophore model was developed by using 98 known CYP17A1 inhibitors from the literature with known experimental IC50 values. The starting compounds were diverse which included steroidal and non-steroidal inhibitors. The resulting pharmacophore models were trained with 69 molecules and 19 test set ligands. The best pharmacophore models were selected based on the regression coefficient for a best fit model with R2 (ranging from 0.85-0.99) & Q2 (ranging from 0.80-0.99) for both the training and test sets respectively, using Partial Least Squares (PLS) regression. On the other hand, the best pharmacophore model selected was further used for a database screening of novel inhibitors and the prediction of their CYP17A1 inhibition. The hits obtained from the database searches were further subjected to a virtual screening workflow docked to CYP17A1 enzyme in order to predict the binding mode and their binding affinities. The resulting poses from the virtual screening workflow were subjected to Induced Fit Docking workflow to account for protein flexibility during docking. The resulting docking poses were examined and ranked ordered according to the docking scores (a measure of affinity). Finally, the resulting hits designed from an updated model from case study III were further synthesized in an external organic chemistry laboratory and the synthetic protocols as well as spectroscopic data for structure elucidation forms part of the provisional patent specification. A provisional patent specification has been filed (RSA Pat. Appln. 2015/ 07849). The case studies performed in this thesis have enabled the discovery of non-steroidal CYP17A1 inhibitors.

scholarly journals AMPing Up the Search: A Structural and Functional Repository of Antimicrobial Peptides for Biofilm Studies, and a Case Study of Its Application to Corynebacterium striatum, an Emerging Pathogen

2021 ◽  
Vol 11 ◽  
Author(s):  
Shreeya Mhade ◽  
Stutee Panse ◽  
Gandhar Tendulkar ◽  
Rohit Awate ◽  
Yatindrapravanan Narasimhan ◽  
...  
Keyword(s):  
Antimicrobial Peptides ◽  
In Silico ◽  
Emerging Pathogen ◽  
Preference Scale ◽  
Modeling Tools ◽  
Interaction Models ◽  
Corynebacterium Striatum

Antimicrobial peptides (AMPs) have been recognized for their ability to target processes important for biofilm formation. Given the vast array of AMPs, identifying potential anti-biofilm candidates remains a significant challenge, and prompts the need for preliminary in silico investigations prior to extensive in vitro and in vivo studies. We have developed Biofilm-AMP (B-AMP), a curated 3D structural and functional repository of AMPs relevant to biofilm studies. In its current version, B-AMP contains predicted 3D structural models of 5544 AMPs (from the DRAMP database) developed using a suite of molecular modeling tools. The repository supports a user-friendly search, using source, name, DRAMP ID, and PepID (unique to B-AMP). Further, AMPs are annotated to existing biofilm literature, consisting of a vast library of over 10,000 articles, enhancing the functional capabilities of B-AMP. To provide an example of the usability of B-AMP, we use the sortase C biofilm target of the emerging pathogen Corynebacterium striatum as a case study. For this, 100 structural AMP models from B-AMP were subject to in silico protein-peptide molecular docking against the catalytic site residues of the C. striatum sortase C protein. Based on docking scores and interacting residues, we suggest a preference scale using which candidate AMPs could be taken up for further in silico, in vitro and in vivo testing. The 3D protein-peptide interaction models and preference scale are available in B-AMP. B-AMP is a comprehensive structural and functional repository of AMPs, and will serve as a starting point for future studies exploring AMPs for biofilm studies. B-AMP is freely available to the community at https://b-amp.karishmakaushiklab.com and will be regularly updated with AMP structures, interaction models with potential biofilm targets, and annotations to biofilm literature.

ABSORPTION AND METABOLISM OF ASTRAGALI RADIX DECOCTION: IN SILICO, IN VITRO, AND A CASE STUDY IN VIVO

2006 ◽  
Vol 34 (6) ◽  
pp. 913-924 ◽  
Author(s):  
Feng Xu ◽  
Yue Zhang ◽  
Shengyuan Xiao ◽  
Xiaowei Lu ◽  
Donghui Yang ◽  
...  
Keyword(s):  
In Silico ◽  
Astragali Radix

scholarly journals Comparative Assessment of In Vitro and In Silico Methods for Aerodynamic Characterization of Powders for Inhalation

Pharmaceutics ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1831
Author(s):  
Jelisaveta Ignjatović ◽  
Tijana Šušteršič ◽  
Aleksandar Bodić ◽  
Sandra Cvijić ◽  
Jelena Đuriš ◽  
...  
Keyword(s):  
In Silico ◽  
Aerodynamic Performance ◽  
Dry Powders ◽  
In Vitro Methods ◽  
Discrete Phase Modeling ◽  
Discrete Phase ◽  
Computational Fluid Dynamics Cfd ◽  
In Silico Methods

In vitro assessment of dry powders for inhalation (DPIs) aerodynamic performance is an inevitable test in DPI development. However, contemporary trends in drug development also implicate the use of in silico methods, e.g., computational fluid dynamics (CFD) coupled with discrete phase modeling (DPM). The aim of this study was to compare the designed CFD-DPM outcomes with the results of three in vitro methods for aerodynamic assessment of solid lipid microparticle DPIs. The model was able to simulate particle-to-wall sticking and estimate fractions of particles that stick or bounce off the inhaler’s wall; however, we observed notable differences between the in silico and in vitro results. The predicted emitted fractions (EFs) were comparable to the in vitro determined EFs, whereas the predicted fine particle fractions (FPFs) were generally lower than the corresponding in vitro values. In addition, CFD-DPM predicted higher mass median aerodynamic diameter (MMAD) in comparison to the in vitro values. The outcomes of different in vitro methods also diverged, implying that these methods are not interchangeable. Overall, our results support the utility of CFD-DPM in the DPI development, but highlight the need for additional improvements in these models to capture all the key processes influencing aerodynamic performance of specific DPIs.

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