In Silico and In Vivo Experiments Reveal M-CSF Injections Accelerate Regeneration Following Muscle Laceration

2016 ◽  
Vol 45 (3) ◽  
pp. 747-760 ◽  
Author(s):  
Kyle S. Martin ◽  
Christopher D. Kegelman ◽  
Kelley M. Virgilio ◽  
Julianna A. Passipieri ◽  
George J. Christ ◽  
...  
Keyword(s):  
In Silico ◽  
In Vivo Experiments

In Silico Testing and in Vivo Experiments with Closed-Loop Control of Blood Glucose in Diabetes

2008 ◽  
Vol 41 (2) ◽  
pp. 4234-4239 ◽  
Author(s):  
B. Kovatchev ◽  
D.M. Raimondo ◽  
M. Breton ◽  
S. Patek ◽  
C. Cobelli
Keyword(s):  
Blood Glucose ◽  
In Silico ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Loop Control ◽  
In Vivo Experiments

Potential Candidates against COVID-19 Targeting RNA-Dependent RNA Polymerase: A Comprehensive Review

2021 ◽  
Vol 22 ◽  
Author(s):  
Neetu Agrawal ◽  
Ahsas Goyal
Keyword(s):  
Rna Polymerase ◽  
In Silico ◽  
Host Cells ◽  
Rna Dependent Rna Polymerase ◽  
In Vivo Experiments ◽  
Contagious Nature ◽  
In Silico Studies ◽  
Viral Enzyme

: Due to the extremely contagious nature of SARS-COV-2, it presents a significant threat to humans worldwide. A plethora of studies are going on all over the world to discover the drug to fight SARS-COV-2. One of the most promising targets is RNA-dependent RNA polymerase (RdRp), responsible for viral RNA replication in host cells. Since RdRp is a viral enzyme with no host cell homologs, it allows the development of selective SARS-COV-2 RdRp inhibitors. A variety of studies used in silico approaches for virtual screening, molecular docking, and repurposing of already existing drugs and phytochemicals against SARS-COV-2 RdRp. This review focuses on collating compounds possessing the potential to inhibit SARS-COV-2 RdRp based on in silico studies to give medicinal chemists food for thought so that the existing drugs can be repurposed for the control and treatment of ongoing COVID-19 pandemic after performing in vitro and in vivo experiments.

Potential Papain-like Protease Inhibitors against COVID-19: A Comprehensive In Silico Based Review

2021 ◽  
Vol 25 ◽  
Author(s):  
Neetu Agrawal ◽  
Shilpi Pathak ◽  
Ahsas Goyal
Keyword(s):  
In Silico ◽  
Chemical Compounds ◽  
Potential Candidate ◽  
In Vivo Experiments ◽  
Drug Databases ◽  
In Silico Studies ◽  
Approved Drugs ◽  
Fda Approved Drugs

: The entire world has been in a battle against the COVID-19 pandemic since its first appearance in December 2019. Thus researchers are desperately working to find an effective and safe therapeutic agent for its treatment. The multifunctional coronavirus enzyme papain-like protease (PLpro) is a potential target for drug discovery to combat the ongoing pandemic responsible for cleavage of the polypeptide, deISGylation, and suppression of host immune response. The present review collates the in silico studies performed on various FDA-approved drugs, chemical compounds, and phytochemicals from various drug databases and represents the compounds possessing the potential to inhibit PLpro. Thus this review can provide quick access to a potential candidate to medicinal chemists to perform in vitro and in vivo experiments who are thriving to find the effective agents for the treatment of COVID-19.

scholarly journals Direction-Averaged Diffusion-Weighted MRI Signal using different Axisymmetric B-tensor Encoding Schemes “Submitted to Magnetic Resonance in Medicine”

2019 ◽  
Author(s):  
Maryam Afzali ◽  
Santiago Aja-Fernández ◽  
Derek K Jones
Keyword(s):  
Power Law ◽  
In Silico ◽  
Complete Analysis ◽  
Diffusion Weighted Mri ◽  
B Values ◽  
Diffusion Geometry ◽  
In Vivo Experiments ◽  
Diffusion Weighted ◽  
Wave Forms

AbstractPurposeIt has been shown previously that for the conventional Stejskal-Tanner pulsed gradient, or linear tensor encoding (LTE), as well as planar tensor encoding (PTE) and in tissue in which diffusion exhibits a ‘stick-like’ geometry, the diffusion-weighted MRI signal at extremely high b-values follows a power-law. Specifically, the signal decays as a in LTE and 1/b in PTE. Here, the direction-averaged signal for arbitrary diffusion encoding waveforms is considered to establish whether power-law behaviors occur with other encoding wave-forms and for other (non-stick-like) diffusion geometries.MethodsWe consider the signal decay for high b-values for encoding geometries ranging from 2-dimensional planar tensor encoding (PTE), through isotropic or spherical tensor encoding (STE) to linear tensor encoding. When a power-law behavior was suggested, this was tested using in-silico simulations and in-vivo using an ultra-strong gradient (300 mT/m) Connectom scanner.ResultsThe results show that using an axisymmetric b-tensor a power-law only exists for two scenarios: For stick-like geometries, (i) the already-discovered LTE case; and (ii) for pure planar encoding. In this latter case, to first order, the signal decays as 1/b. Our in-silico and in-vivo experiments confirm this 1/b relationship.ConclusionA complete analysis of the power-law dependencies of the diffusion-weighted signal at high b-values has been performed. Only two forms of encoding result in a power-law dependency, pure linear and pure planar tensor encoding and when the diffusion geometry is ‘stick-like’. The different exponents of these encodings could be used to provide independent validation of the presence of stick-like geometries in-vivo.

Oral Mucosa Model for Electrochemotherapy Treatment of Dog Mouth Cancer: Ex Vivo, In Silico, and In Vivo Experiments

2017 ◽  
Vol 42 (3) ◽  
pp. 297-304 ◽  
Author(s):  
Daniela O. H. Suzuki ◽  
José A. Berkenbrock ◽  
Marisa J. S. Frederico ◽  
Fátima R. M. B. Silva ◽  
Marcelo M. M. Rangel
Keyword(s):  
Oral Mucosa ◽  
In Silico ◽  
Ex Vivo ◽  
In Vivo Experiments

Harnessing tumor immunity with chemotherapy: Mathematical modeling for decision-making in combinatorial regimen with immune-oncology drugs.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14095-e14095
Author(s):  
Vesna Cuplov ◽  
Guillaume Sicard ◽  
Dominique Barbolosi ◽  
Joseph Ciccolini ◽  
Fabrice Barlesi
Keyword(s):  
Mathematical Modeling ◽  
T Cells ◽  
In Silico ◽  
Checkpoint Inhibitors ◽  
Cytotoxic T Cells ◽  
Model Parameters ◽  
In Vivo Experiments ◽  
The Impact

e14095 Background: Combining chemotherapy and immune checkpoint inhibitors (ICI) is challenging due to the near-infinite choice of dosing, scheduling and sequencing between drugs. The aim of this work is to develop a phenomenological model that describes the synergistic effect between cytotoxics and immune check point inhibitors in patients with cancer. Methods: Inspired from literature, we have developed an integrative mathematical model that includes tumor cells, cytotoxic T cells (CTLs) and regulatory T cells (TREGs) plus pharmacokinetics (PK) inputs. Loss in tumor mass is due to combined effect of direct chemotherapy-induced cytotoxicity and CTLs immune response, which is in turn inhibited by the tumor and mitigated by TREGs in the tumor micro-environment. The model describes as well the impact of chemotherapy-induced lymphodepletion on immune tolerance, whereas ICIs protect CTLs against tumor inhibition. Identification of model’s parameters and simulations of various scheduling were performed using Mlxplore software and a Python standalone code. In vitro and in vivo experiments using lung cancer models generate experimental data to adjust model parameters. Results: Complex interplays between cytotoxics and immune cells were best described by a 10-parameters model so as to ensure better identifiability. PK/PD relationships were integrated using compartmental modeling. In silico simulations show how changes in dosing and scheduling impact efficacy endpoints, an observation in line with data from the literature. Ongoing in vitro and in vivo experiments with pemetrexed-cisplatin doublet and anti-PD1 pembrolizumab help optimizing the model’s parameters in a self-learning loop. Conclusions: This work is at the frontier between mathematical modeling and experimental therapeutics with ICIs. In silico modeling and simulations could help narrow down the treatment choices and define optimal combinations prior to running clinical trials. Such model will help identify optimal dosing and scheduling, so as to achieve better synergism and efficacy.

scholarly journals An in silico approach combined with in vivo experiments enables the identification of a new protein whose overexpression can compensate for specific respiratory defects in Saccharomyces cerevisiae

2011 ◽  
Vol 5 (1) ◽  
pp. 173 ◽  
Author(s):  
Annie Glatigny ◽  
Lise Mathieu ◽  
Christopher J Herbert ◽  
Geneviève Dujardin ◽  
Brigitte Meunier ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
In Silico ◽  
In Vivo Experiments ◽  
New Protein

scholarly journals Immunoinformatics Analysis and In-silico Design of Multi- Epitopes Vaccine Against Lassa Virus

2021 ◽  
Author(s):  
Akinyemi Ademola Omoniyi ◽  
Samuel Sunday Adebisi ◽  
Sunday Abraham Musa ◽  
James Oliver Nzalak ◽  
Barnabas Danborno ◽  
...  
Keyword(s):  
In Silico ◽  
Lassa Virus ◽  
Virus Infections ◽  
African Countries ◽  
E Coli ◽  
In Vivo Experiments ◽  
Viral Haemorrhagic Fever ◽  
Chimeric Vaccine

Abstract Lassa virus, an arenavirus, represents the most prevalent human pathogen causing viral haemorrhagic fever. It is endemic in Nigeria and other West African countries. Despite the high burden of the disease, limited treatments are available and no approved vaccine for the prevention of this disease is available. In this study, an immunoinformatics approach was used to predict response of B and T cells from the Lassa virus proteome (GPC, NP, L and Z). The designed chimeric vaccine was modeled, refined, validated and docked with the RIG-I receptor. The docked complex of vaccine-RIG-I was subjected to dynamic stability test and the results suggest that the complex is stable. Validation of the final vaccine construct was done through in silico cloning using E. coli as host. A CAI value of 0.99 suggests that the vaccine construct expressed properly in the host. Immune simulation predicted significantly high levels of IgG1, T-helper, T-cytotoxic cells, INF-γ and IL-2. This theoretical study suggests infection control by creating an effective immunological memory against Lassa virus infections. However, both in vitro and in vivo experiments are needed to validate the immunogenicity and safety of the chimeric vaccine.

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