scholarly journals EP-1998: In silico modelling of the impact of the fractionation for hypofractionated prostate treatments

2018 ◽  
Vol 127 ◽  
pp. S1086-S1087
Author(s):  
G. Delpon ◽  
J. N'Guessan ◽  
P. Paul-Gilloteaux ◽  
K. Clément-Colmou ◽  
V. Potiron ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Modelling ◽  
The Impact

scholarly journals Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an in silico Trial

2021 ◽  
pp. 193229682110123
Author(s):  
Chiara Roversi ◽  
Martina Vettoretti ◽  
Simone Del Favero ◽  
Andrea Facchinetti ◽  
Pratik Choudhary ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
In Silico ◽  
Open Loop ◽  
Random Errors ◽  
Linear Regression Models ◽  
Reference Case ◽  
Counting Error ◽  
The Impact

Background: In the management of type 1 diabetes (T1D), systematic and random errors in carb-counting can have an adverse effect on glycemic control. In this study, we performed an in silico trial aiming at quantifying the impact of different levels of carb-counting error on glycemic control. Methods: The T1D patient decision simulator was used to simulate 7-day glycemic profiles of 100 adults using open-loop therapy. The simulation was repeated for different values of systematic and random carb-counting errors, generated with Gaussian distribution varying the error mean from -10% to +10% and standard deviation (SD) from 0% to 50%. The effect of the error was evaluated by computing the difference of time inside (∆TIR), above (∆TAR) and below (∆TBR) the target glycemic range (70-180mg/dl) compared to the reference case, that is, absence of error. Finally, 3 linear regression models were developed to mathematically describe how error mean and SD variations result in ∆TIR, ∆TAR, and ∆TBR changes. Results: Random errors globally deteriorate the glycemic control; systematic underestimations lead to, on average, up to 5.2% more TAR than the reference case, while systematic overestimation results in up to 0.8% more TBR. The different time in range metrics were linearly related with error mean and SD ( R2>0.95), with slopes of [Formula: see text], [Formula: see text] for ∆TIR, [Formula: see text], [Formula: see text] for ∆TAR, and [Formula: see text], [Formula: see text] for ∆TBR. Conclusions: The quantification of carb-counting error impact performed in this work may be useful understanding causes of glycemic variability and the impact of possible therapy adjustments or behavior changes in different glucose metrics.

Insight into the impact of EGFR L792Y/F/H mutations on sensitivity to osimertinib: an in silico study

2021 ◽  
Vol 45 (10) ◽  
pp. 4756-4765
Author(s):  
Daoxing Chen ◽  
Liting Zhang ◽  
Yanan Liu ◽  
Jiali Song ◽  
Jingwen Guo ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Study ◽  
The Impact ◽  
Insight Into

EGFR L792Y/F/H mutation makes it difficult for Osimertinib to recognize ATP pockets.

scholarly journals Common Treatment, Common Variant: Evolutionary Prediction of Functional Pharmacogenomic Variants

2021 ◽  
Vol 11 (2) ◽  
pp. 131
Author(s):  
Laura B. Scheinfeldt ◽  
Andrew Brangan ◽  
Dara M. Kusic ◽  
Sudhir Kumar ◽  
Neda Gharani
Keyword(s):  
In Silico ◽  
Drug Efficacy ◽  
Common Variant ◽  
Genomic Research ◽  
Whole Genome Sequencing Data ◽  
Mendelian Disease ◽  
Allele Frequency Distribution ◽  
Sequencing Data ◽  
Patient Race ◽  
The Impact

Pharmacogenomics holds the promise of personalized drug efficacy optimization and drug toxicity minimization. Much of the research conducted to date, however, suffers from an ascertainment bias towards European participants. Here, we leverage publicly available, whole genome sequencing data collected from global populations, evolutionary characteristics, and annotated protein features to construct a new in silico machine learning pharmacogenetic identification method called XGB-PGX. When applied to pharmacogenetic data, XGB-PGX outperformed all existing prediction methods and identified over 2000 new pharmacogenetic variants. While there are modest pharmacogenetic allele frequency distribution differences across global population samples, the most striking distinction is between the relatively rare putatively neutral pharmacogene variants and the relatively common established and newly predicted functional pharamacogenetic variants. Our findings therefore support a focus on individual patient pharmacogenetic testing rather than on clinical presumptions about patient race, ethnicity, or ancestral geographic residence. We further encourage more attention be given to the impact of common variation on drug response and propose a new ‘common treatment, common variant’ perspective for pharmacogenetic prediction that is distinct from the types of variation that underlie complex and Mendelian disease. XGB-PGX has identified many new pharmacovariants that are present across all global communities; however, communities that have been underrepresented in genomic research are likely to benefit the most from XGB-PGX’s in silico predictions.

scholarly journals In-silico modelling of fullerene and fullerene adsorbed by nO2 molecules (n(O2)@Cm with n = 1, 2, 4 and m = 48 and 60) as potential SARS-CoV-2 inhibitors

2021 ◽  
Vol 44 (3) ◽  
Author(s):  
Meziane Brahimi ◽  
Djamila SELLAM ◽  
Afaf Bouchoucha ◽  
Yassamina Arbia ◽  
Hadjer Merazka ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Modelling

In Silico Modelling of Acceptor Materials by End-capped and π-linker modifications for High-Performance Organic Solar Cells: Estimated PCE > 18%

2021 ◽  
pp. 113555
Author(s):  
Muhammad Yasir Mehboob ◽  
Rania Zaier ◽  
Riaz Hussain ◽  
Muhammad Adnan ◽  
Malik Muhammad Asif Iqbal ◽  
...  
Keyword(s):  
Solar Cells ◽  
Organic Solar Cells ◽  
In Silico ◽  
High Performance ◽  
In Silico Modelling

scholarly journals In silico modelling of hormone response elements

2006 ◽  
Vol 7 (Suppl 4) ◽  
pp. S27 ◽  
Author(s):  
Maria Stepanova ◽  
Feng Lin ◽  
Valerie Lin
Keyword(s):  
In Silico ◽  
Hormone Response ◽  
Response Elements ◽  
Hormone Response Elements ◽  
In Silico Modelling

scholarly journals Phycobilins as Potent Food Bioactive Broad-Spectrum Inhibitors Against Proteases of SARS-CoV-2 and Other Coronaviruses: A Preliminary Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Brahmaiah Pendyala ◽  
Ankit Patras ◽  
Chandravanu Dash
Keyword(s):  
Binding Affinity ◽  
Broad Spectrum ◽  
In Silico ◽  
Research Area ◽  
Therapeutic Drug ◽  
Inhibitor Activity ◽  
Current Vaccine ◽  
Mutant Strains ◽  
The Impact

In the 21st century, we have witnessed three coronavirus outbreaks: SARS in 2003, MERS in 2012, and the ongoing pandemic coronavirus disease 2019 (COVID-19). The search for efficient vaccines and development and repurposing of therapeutic drugs are the major approaches in the COVID-19 pandemic research area. There are concerns about the evolution of mutant strains (e.g., VUI – 202012/01, a mutant coronavirus in the United Kingdom), which can potentially reduce the impact of the current vaccine and therapeutic drug development trials. One promising approach to counter the mutant strains is the “development of effective broad-spectrum antiviral drugs” against coronaviruses. This study scientifically investigates potent food bioactive broad-spectrum antiviral compounds by targeting main protease (Mpro) and papain-like protease (PLpro) proteases of coronaviruses (CoVs) using in silico and in vitro approaches. The results reveal that phycocyanobilin (PCB) shows potential inhibitor activity against both proteases. PCB had the best binding affinity to Mpro and PLpro with IC50 values of 71 and 62 μm, respectively. Also, in silico studies with Mpro and PLpro enzymes of other human and animal CoVs indicate broad-spectrum inhibitor activity of the PCB. As with PCB, other phycobilins, such as phycourobilin (PUB), phycoerythrobilin (PEB), and phycoviolobilin (PVB) show similar binding affinity to SARS-CoV-2 Mpro and PLpro.

scholarly journals Synthesis, Antileishmanial Activity and in silico Studies of Aminoguanidine Hydrazones (AGH) and Thiosemicarbazones (TSC) Against Leishmania chagasi Amastigotes

2021 ◽  
Vol 17 ◽  
Author(s):  
Thiago M. de Aquino ◽  
Paulo H. B. França ◽  
Érica E. E. S. Rodrigues ◽  
Igor J. S. Nascimento ◽  
Paulo F. S. Santos-Júnior ◽  
...  
Keyword(s):  
In Silico ◽  
Biological Activities ◽  
Leishmania Species ◽  
Antileishmanial Activity ◽  
Trypanothione Reductase ◽  
Aromatic Substituent ◽  
In Silico Studies ◽  
Ic50 Values ◽  
The Impact

Background: Leishmaniasis is a worldwide health problem, highly endemic in developing countries. Among the four main clinical forms of the disease, visceral leishmaniasis is the most severe, fatal in 95% of cases. The undesired side-effects from first-line chemotherapy and the reported drug resistance search for effective drugs that can replace or supplement those currently used an urgent need. Aminoguanidine hydrazones (AGH's) have been explored for exhibiting a diverse spectrum of biological activities, in particular the antileishmanial activity of MGBG. The bioisosteres thiosemicarbazones (TSC's) offer a similar biological activity diversity, including antiprotozoal effects against Leishmania species and Trypanosoma cruzi. Objective: Considering the impact of leishmaniasis worldwide, this work aimed to design, synthesize, and perform a screening upon L. chagasi amastigotes and for the cytotoxicity of the small "in-house" library of both AGH and TSC derivatives and their structurally-related compounds. Method: A set of AGH's (3-7), TSC's (9, 10), and semicarbazones (11) were initially synthesized. Subsequently, different semi-constrained analogs were designed and also prepared, including thiazolidines (12), dihydrothiazines (13), imidazolines (15), pyrimidines (16, 18) azines (19, 20), and benzotriazepinones (23-25). All intermediates and target compounds were obtained with satisfactory yields and exhibited spectral data consistent with their structures. All final compounds were evaluated against L. chagasi amastigotes and J774.A1 cell line. Molecular docking was performed towards trypanothione reductase using GOLD® software. Result: The AGH's 3i, 4a, and 5d, and the TSC's 9i, 9k, and 9o were selected as valuable hits. These compounds presented antileishmanial activity compared with pentamidine, showing IC50 values ranged from 0.6 to 7.27 μM, maximal effects up to 55.3%, and satisfactory SI values (ranged from 11 to 87). On the other hand, most of the resulting semi-constrained analogs were found cytotoxic or presented reduced antileishmanial activity. In general, TSC class is more promising than its isosteric AGH analogs, and the beneficial aromatic substituent effects are not similar in both series. In silico studies have suggested that these hits are capable of inhibiting the trypanothione reductase from the amastigote forms. Conclusion: The promising antileishmanial activity of three AGH’s and three TSC’s was characterized. These compounds presented antileishmanial activity compared with PTD, showing IC50 values ranged from 0.6 to 7.27 μM, and satisfactory SI values. Further pharmacological assays involving other Leishmania strains are under progress, which will help to choose the best hits for in vivo experiments.

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