scholarly journals Translocation of gold nanoparticles across the lung epithelial tissue barrier: Combining in vitro and in silico methods to substitute in vivo experiments

2015 ◽  
Vol 12 (1) ◽  
Author(s):  
Gerald Bachler ◽  
Sabrina Losert ◽  
Yuki Umehara ◽  
Natalie von Goetz ◽  
Laura Rodriguez-Lorenzo ◽  
...  
Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2505
Author(s):  
Raheem Remtulla ◽  
Sanjoy Kumar Das ◽  
Leonard A. Levin

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.


Shock ◽  
2020 ◽  
Vol 53 (5) ◽  
pp. 605-615
Author(s):  
Joseph E. Rupert ◽  
Daenique H. A. Jengelley ◽  
Teresa A. Zimmers

2018 ◽  
Vol 25 (28) ◽  
pp. 3286-3318 ◽  
Author(s):  
Kaja Bergant ◽  
Matej Janezic ◽  
Andrej Perdih

Background: The family of DNA topoisomerases comprises a group of enzymes that catalyse the induction of topological changes to DNA. These enzymes play a role in the cell replication machinery and are, therefore, important targets for anticancer drugs - with human DNA topoisomerase IIα being one of the most prominent. Active compounds targeting this enzyme are classified into two groups with diverse mechanisms of action: DNA poisons act by stabilizing a covalent cleavage complex between DNA and the topoisomerase enzyme, transforming it into a cellular toxin, while the second diverse group of catalytic inhibitors, provides novel inhibition avenues for tackling this enzyme due to frequent occurrence of side effects observed during the DNA poison therapy. Methods: Based on a comprehensive literature search we present an overview of available bioassays and in silico methods in the identification of human DNA topoisomerase IIα inhibitors. Results and Conclusion: A comprehensive outline of the available methods and approaches that explore in detail the in vitro mechanistic and functional aspects of the topoisomerase IIα inhibition of both topo IIα inhibitor groups is presented. The utilized in vitro cell-based assays and in vivo studies to further explore the validated topo IIα inhibitors in subsequent preclinical stages of the drug discovery are discussed. The potential of in silico methods in topoisomerase IIα inhibitor discovery is outlined. A list of practical guidelines was compiled to aid new as well experienced researchers in how to optimally approach the design of targeted inhibitors and validation in the preclinical drug development stages.


Author(s):  
Neetu Agrawal ◽  
Ahsas Goyal

: 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.


Author(s):  
Neetu Agrawal ◽  
Shilpi Pathak ◽  
Ahsas Goyal

: 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.


Author(s):  
Sachin M. Mendhi ◽  
Manoj S. Ghoti ◽  
Mandar A. Thool ◽  
Rinkesh M. Tekade

This article deals with the in – silico techniques for predicting the toxicity of chemical compounds. Toxicology is the branch of biology that deals with the study of adverse effect of chemical substances on the living organisms and the practice of treating and preventing such adverse effects. Predicting toxicity of a new drug to be produced is the first aim of preclinical trials. It is achieved by in-silico methods. There are several in - silico technique softwares which are used for the prediction of ADME and hence toxicity of drugs. In – silico methods involves the use of various softwares to calculate and then predict the toxicity of a compound by first determining its structural and pharmacokinetic and pharmacodynamic properties and then it correlates this information with already existing drugs and molecules and thus gives us conclusion. The article focuses on QSAR and its techniques, HQSAR, several other methods like structural alerts and rule-based models, chemical category and read across model, dose and time response model, virtual ligand screening, docking, 3D pharmacophore mapping, simulation approaches, PKPD models and several other approaches like bioinformatics. After reviewing and studying various in silico techniques the conclusion comes out to be that, in-silico methods of predictive toxicology are more better than in-vitro and in-vivo methods since they are much more safe (as animals are not harmed), economic, fast and accurate w.r.to, results/output in predicting toxicity of compounds by computational methods and hence are widely used in the production of new drug for accessing its toxicity


2018 ◽  
Vol 52 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Praveen Kumar Pasala ◽  
Ramesh Alluri ◽  
Sri Chandana Mavulati ◽  
Raghu Prasad Mailavaram ◽  
Khasim Shaik ◽  
...  

2009 ◽  
Vol 98 (12) ◽  
pp. 4429-4468 ◽  
Author(s):  
Jurgen Mensch ◽  
Julen Oyarzabal ◽  
Claire Mackie ◽  
Patrick Augustijns

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14095-e14095
Author(s):  
Vesna Cuplov ◽  
Guillaume Sicard ◽  
Dominique Barbolosi ◽  
Joseph Ciccolini ◽  
Fabrice Barlesi

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.


Sign in / Sign up

Export Citation Format

Share Document