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

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 Case Studies for Practical Food Effect Assessments across BCS/BDDCS Class Compounds using In Silico, In Vitro, and Preclinical In Vivo Data

2012 ◽  
Vol 15 (1) ◽  
pp. 143-158 ◽  
Author(s):  
Tycho Heimbach ◽  
Binfeng Xia ◽  
Tsu-han Lin ◽  
Handan He
Keyword(s):  
Case Studies ◽  
In Silico ◽  
Food Effect

scholarly journals Current model systems for the study of preeclampsia

2018 ◽  
Vol 243 (6) ◽  
pp. 576-585 ◽  
Author(s):  
ML Martinez-Fierro ◽  
GP Hernández-Delgadillo ◽  
V Flores-Morales ◽  
E Cardenas-Vargas ◽  
M Mercado-Reyes ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Endothelial Dysfunction ◽  
In Silico ◽  
Complex Disease ◽  
Model Systems ◽  
Trophoblast Invasion ◽  
Wide Range ◽  
In Silico Models

Preeclampsia (PE) is a pregnancy complex disease, distinguished by high blood pressure and proteinuria, diagnosed after the 20th gestation week. Depending on the values of blood pressure, urine protein concentrations, symptomatology, and onset of disease there is a wide range of phenotypes, from mild forms developing predominantly at the end of pregnancy to severe forms developing in the early stage of pregnancy. In the worst cases severe forms of PE could lead to systemic endothelial dysfunction, eclampsia, and maternal and/or fetal death. Worldwide the fetal morbidity and mortality related to PE is calculated to be around 8% of the total pregnancies. PE still being an enigma regarding its etiology and pathophysiology, in general a deficient trophoblast invasion during placentation at first stage of pregnancy, in combination with maternal conditions are accepted as a cause of endothelial dysfunction, inflammatory alterations and appearance of symptoms. Depending on the PE multifactorial origin, several in vitro, in vivo, and in silico models have been used to evaluate the PE pathophysiology as well as to identify or test biomarkers predicting, diagnosing or prognosing the syndrome. This review focuses on the most common models used for the study of PE, including those related to placental development, abnormal trophoblast invasion, uteroplacental ischemia, angiogenesis, oxygen deregulation, and immune response to maternal–fetal interactions. The advances in mathematical and computational modeling of metabolic network behavior, gene prioritization, the protein–protein interaction network, the genetics of PE, and the PE prediction/classification are discussed. Finally, the potential of these models to enable understanding of PE pathogenesis and to evaluate new preventative and therapeutic approaches in the management of PE are also highlighted. Impact statement This review is important to the field of preeclampsia (PE), because it provides a description of the principal in vitro, in vivo, and in silico models developed for the study of its principal aspects, and to test emerging therapies or biomarkers predicting the syndrome before their evaluation in clinical trials. Despite the current advance, the field still lacking of new methods and original modeling approaches that leads to new knowledge about pathophysiology. The part of in silico models described in this review has not been considered in the previous reports.

scholarly journals Replacement Strategies for Animal Studies in Inhalation Testing

2021 ◽  
Author(s):  
Eleonore Fröhlich
Keyword(s):  
Animal Models ◽  
In Silico ◽  
Particle Deposition ◽  
Drug Testing ◽  
Animal Studies ◽  
Inhalation Exposure ◽  
The Future ◽  
In Silico Models

Testing in animals is mandatory in drug testing and the gold standard for evaluation of toxicity. This situation is expected to change in the future because the 3Rs principle, which stands for replacement, reduction and refinement of the use of animals in science, is reinforced by many countries. On the other hand, technologies for alternatives to animals experiments have increased. The necessity to develop and use of alternatives is influenced by the complexity of the research topic and also by the fact, to which extent the currently used animal models can mimic human physiology and/or exposure. Rodent lung morphology and physiology differs markedly for that of humans and inhalation exposure of the animals are challenging. In vitro and in silico methods can assess important aspects of the in vivo action, namely particle deposition, dissolution, action at and permeation across the respiratory barrier and pharmacokinetics. Out of the numerous homemade in vitro and in silico models some are available commercially or open access. This review discusses limitations of animal models and exposure systems and proposes a panel of in vitro and in silico techniques that, in the future, may replace animal experimentation in inhalation testing.

In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery

2019 ◽  
Vol 25 (31) ◽  
pp. 3292-3305 ◽  
Author(s):  
Harekrishna Roy ◽  
Sisir Nandi
Keyword(s):  
Drug Metabolism ◽  
In Silico ◽  
Physiological Status ◽  
Lead Discovery ◽  
Drug Molecules ◽  
Drug Biotransformation ◽  
In Silico Models ◽  
Precise Simulation

Background: Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly. Methods: To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status. Results: The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound. Conclusion: : It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound–dependent induction of drug-metabolizing enzymes.

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