Identifying the mechanism of eriosematin E from Eriosema chinense Vogel. for its antidiarrhoeal potential against Shigella flexneri-induced diarrhoea using in vitro, in vivo and in silico models

2020 ◽  
Vol 149 ◽  
pp. 104582
Author(s):  
Komal M. Parmar ◽  
Saurabh K. Sinha ◽  
Rupali S. Prasad ◽  
Mohit S. Jogi ◽  
Damiki Laloo ◽  
...  
Keyword(s):  
In Silico ◽  
Shigella Flexneri ◽  
In Silico Models

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.

AhR Agonist Activity Confirmation of Polyhalogenated Carbazoles (PHCZs) Using an Integration of in Vitro, in Vivo, and in Silico Models

2019 ◽  
Vol 53 (24) ◽  
pp. 14716-14723 ◽  
Author(s):  
Chenyang Ji ◽  
Chao Shen ◽  
Yixi Zhou ◽  
Kongyang Zhu ◽  
Zhe Sun ◽  
...  
Keyword(s):  
In Silico ◽  
Agonist Activity ◽  
In Silico Models

scholarly journals Challenges and Opportunities for Integrating In Silico Models and Adverse Outcomes Pathways to Set and Relate New Biomarkers

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3549
Author(s):  
Carlos E. Matos dos Santos ◽  
Raul Ghiraldelli Miranda ◽  
Danielle Palma de Oliveira ◽  
Daniel Junqueira Dorta
Keyword(s):  
In Silico ◽  
Ex Vivo ◽  
Adverse Outcomes ◽  
Adverse Outcome Pathway ◽  
Biological Organization ◽  
Toxicological Effects ◽  
New Biomarkers ◽  
In Silico Models

The Adverse Outcome Pathway (AOP) framework has been considered the most innovative tool to collect, organize, and evaluate relevant information on the toxicological effects of chemicals, facilitating the establishment of links between molecular events and adverse outcomes at the critical level of biological organization. Considering the combination of the high volume of toxicological and ecotoxicological data produced and the application of artificial intelligence algorithms from the last few years, not only can higher mechanistic interpretability be reached with new in silico models, but also a potential increase in predictivity in hazard assessments and the identification of new potential biomarkers can be achieved. The current paper aims to discuss some potential challenges and ways of integrating in silico models and AOPs to predict toxicological effects and to set and relate new biomarkers for defined purposes. With the use of the AOP framework to organize the ecotoxicological, toxicological, and structural data generated from in chemico, in vitro, ex vivo, in vivo, and population studies, it is expected that the generated biological and chemical construct will improve its application, establishing a knowledge platform to set and relate new biomarkers by key event relationships (KERs).

scholarly journals The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development

Pharmaceutics ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 704
Author(s):  
Zhengying Zhou ◽  
Jinwei Zhu ◽  
Muhan Jiang ◽  
Lan Sang ◽  
Kun Hao ◽  
...  
Keyword(s):  
Drug Development ◽  
In Silico ◽  
In Vitro Models ◽  
Clinical Settings ◽  
Culture Methods ◽  
Physiologically Based Pharmacokinetic ◽  
Induced Pluripotent ◽  
In Silico Models

Human-derived in vitro models can provide high-throughput efficacy and toxicity data without a species gap in drug development. Challenges are still encountered regarding the full utilisation of massive data in clinical settings. The lack of translated methods hinders the reliable prediction of clinical outcomes. Therefore, in this study, in silico models were proposed to tackle these obstacles from in vitro to in vivo translation, and the current major cell culture methods were introduced, such as human-induced pluripotent stem cells (hiPSCs), 3D cells, organoids, and microphysiological systems (MPS). Furthermore, the role and applications of several in silico models were summarised, including the physiologically based pharmacokinetic model (PBPK), pharmacokinetic/pharmacodynamic model (PK/PD), quantitative systems pharmacology model (QSP), and virtual clinical trials. These credible translation cases will provide templates for subsequent in vitro to in vivo translation. We believe that synergising high-quality in vitro data with existing models can better guide drug development and clinical use.

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