In silico approaches, and in vitro and in vivo experiments to predict induction of drug metabolism

2003 ◽  
Vol 16 (7) ◽  
pp. 423 ◽  
Author(s):  
C. Handschin ◽  
M. Podvinec ◽  
U.A. Meyer
Keyword(s):  
Drug Metabolism ◽  
In Silico ◽  
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.

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.

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 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.

scholarly journals New Clamp-PID Algorithm for Automated Glucose Clamps Improves Clamp Quality

2021 ◽  
pp. 193229682199151
Author(s):  
Carsten Benesch ◽  
Mareike Kuhlenkötter ◽  
Leszek Nosek ◽  
Tim Heise
Keyword(s):  
Clinical Study ◽  
In Silico ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Pid Algorithm ◽  
In Vivo Experiments ◽  
Absolute Control

Background: In automated glucose clamp experiments, blood glucose (BG) concentrations are kept close to a predefined target level using variable glucose infusion rates (GIRs) determined by implemented algorithms. Clamp quality (ie, the ability to keep BG close to target) highly depends on the quality of these algorithms. We developed a new Clamp algorithm based on the proportional-integral-derivative (PID) approach and compared clamp quality between this and the established Biostator (BS) algorithm. Methods: In numerical simulations, the PID-based algorithm was optimized in silico. The optimized Clamp-PID algorithm was tested in in vitro experiments and finally validated in vivo in a small ( n = 5) clinical study. Results: In silico, in vitro, and in vivo experiments showed better clamp quality for the new Clamp-PID algorithm compared with the BS algorithm: precision and absolute control deviation (ACD) decreased from 3.7% to 1.1% and from 2.9 mg/dL to 0.6 mg/dL, respectively, in the numerical simulation. The in vitro validation demonstrated reductions in precision (from 3.3% ± 0.1% (mean ± SD) to 1.4% ± 0.4%) and in ACD (from 2.3 mg/dL ± 0.4 mg/dL to 0.8 mg/dL ± 0.2 mg/dL), respectively. In the clinical study, precision and ACD improved from 6.5% ± 1.3% to 4.0% ± 1.1% and from 3.6 mg/dL ± 0.9 mg/dL to 2.2 mg/dl ± 0.6 mg/dl, respectively. The quality parameter utility did not change. Conclusions: The new Clamp-PID algorithm improves the clamp quality parameters precision and ACD versus the BS algorithm.

scholarly journals qNMR quantification and in silico analysis of isobrucein B and neosergeolide from Picrolemma sprucei as potential inhibitors of SARS-CoV-2 protease (3CLpro) and RNA-dependent RNA polymerase (RdRp) and pharmacokinetic and toxicological properties

2021 ◽  
Vol 10 (16) ◽  
pp. e69101623220
Author(s):  
Marcos Túlio da Silva ◽  
Matheus Gabriel de Oliveira ◽  
José Realino de Paula ◽  
Vinicius Barreto da Silva ◽  
Kidney de Oliveira Gomes Neves ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Medicinal Plant ◽  
In Silico ◽  
Active Sites ◽  
In Silico Analysis ◽  
In Vivo Experiments ◽  
Inhibitory Potential ◽  
Potential Inhibitors

Objective: To quantify the quassinoids of P. sprucei, a medicinal plant that is native to the Amazon region, using qNMR and investigate the inhibitory potential of isobrucein B and neosergeolide on the 3CLpro and RdRp targets of SARS-CoV-2 through in silico approaches. Methods: the quantification was performed in a fraction (F2-F3) enriched with the quassinoids isobrucein B and neosergeolide using the PULCON method. In silico assays were performed using molecular docking to assess interactions and binding affinity between neosergeolide and isobrucein B ligands with SARS-CoV-2 3CLpro and RdRp targets, and online servers were used to estimate pharmacokinetic and toxicity. Results: It was possible to determine the quantity of the two quassinoids isobrucein B and neosergeolide in the F2-F3 fraction (769.6 mg), which were present in significant amounts in the PsMeOH extract (5.46%). The results of the docking analysis, based on the crystallized structures of RdRp and 3CLpro, indicated that isobrucein B and neosergeolide are potential inhibitors of the two proteins evaluated, as well as showing the importance of hydrogen bonding and pi (π) interactions for the active sites foreseen for each target. Conclusion: The results suggest that P. sprucei quassinoids may interact with 3CLpro and RdRp targets. In vitro and in vivo experiments are needed to confirm the results of molecular docking and investigate the risks of using P. sprucei as a medicinal plant against COVID-19.

scholarly journals A Report on Multi-Target Anti-Inflammatory Properties of Phytoconstituents from Monochoria hastata (Family: Pontederiaceae)

Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7397
Author(s):  
Md Mazedul Haq ◽  
Md Arifur Rahman Chowdhury ◽  
Hilal Tayara ◽  
Ibrahim Abdelbaky ◽  
Md Shariful Islam ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Analysis ◽  
Control Group ◽  
Dependent Manner ◽  
In Vivo Experiments ◽  
Microbial Infections ◽  
Anti Inflammatory ◽  
Silico Analysis

This study aims to investigate the potential analgesic properties of the crude extract of Monochoria hastata (MH) leaves using in vivo experiments and in silico analysis. The extract, in a dose-dependent manner, exhibited a moderate analgesic property (~54% pain inhibition in acetic acid-induced writhing test), which is significant (** p < 0.001) as compared to the control group. The complex inflammatory mechanism involves diverse pathways and they are inter-connected. Therefore, multiple inflammatory modulator proteins were selected as the target for in silico analysis. Computational analysis suggests that all the selected targets had different degrees of interaction with the phytochemicals from the extract. Rutin (RU), protocatechuic acid (PA), vanillic acid (VA), and ferulic acid (FA) could regulate multiple targets with a robust efficiency. None of the compounds showed selectivity to Cyclooxygenase-2 (COX-2). However, regulation of COX and lipoxygenase (LOX) cascade by PA can reduce non-steroidal analgesic drugs (NSAIDs)-related side effects, including asthma. RU showed robust regulation of cytokine-mediated pathways like RAS/MAPK and PI3K/NF-kB by inhibition of EGFR and IKBα (IKK), which may prevent multi-organ failure due to cytokine storm in several microbial infections, for example, SARS-CoV-2. Further investigation, using in vivo and in vitro experiments, can be conducted to develop multi-target anti-inflammatory drugs using the isolated compounds from the extract.

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