In silico prediction of the in vitro behavior of polymeric gene delivery vectors

Nanoscale ◽  
2021 ◽  
Author(s):  
Nina Bono ◽  
Bárbara Coloma Smith ◽  
Francesca Moreschi ◽  
Alberto Redaelli ◽  
Alfonso Gautieri ◽  
...  
Keyword(s):  
Gene Delivery ◽  
In Silico ◽  
Screening Tool ◽  
Test Bench ◽  
In Silico Prediction ◽  
Gene Delivery Vectors ◽  
Polymeric Gene Delivery

The authors describe a novel screening tool to test bench non-viral transfectants enabling to foresee the most suitable conditions for the complexation of relevant siRNA-polycation assemblies.

scholarly journals Balancing protection and release of DNA: tools to address a bottleneck of non-viral gene delivery

2009 ◽  
Vol 7 (suppl_1) ◽  
Author(s):  
Christopher L. Grigsby ◽  
Kam W. Leong
Keyword(s):  
Gene Therapy ◽  
Gene Delivery ◽  
Imaging Techniques ◽  
Model Systems ◽  
Viral Gene ◽  
Dna Protection ◽  
Gene Delivery Vectors ◽  
Viral Gene Therapy ◽  
Polymeric Gene Delivery

Engineering polymeric gene-delivery vectors to release an intact DNA payload at the optimal time and subcellular compartment remains a formidable challenge. An ideal vector would provide total protection of complexed DNA from degradation prior to releasing it efficiently near or within the nucleus of a target cell. While optimization of polymer properties, such as molecular weight and charge density, has proved largely inadequate in addressing this challenge, applying polymeric carriers that respond to temperature, light, pH and redox environment to trigger a switch from a tight, protective complex to a more relaxed interaction favouring release at the appropriate time and place has shown promise. Currently, a paucity of gene carriers able to satisfy the contrary requirements of adequate DNA protection and efficient release contributes to the slow progression of non-viral gene therapy towards clinical translation. This review highlights the promising carrier designs that may achieve an optimal balance of DNA protection and release. It also discusses the imaging techniques and three-dimensional in vitro models that can help study these two barriers in the non-viral gene transfer process. Ultimately, efficacious non-viral gene therapy will depend on the combination of intelligent material design, innovative imaging techniques and sophisticated in vitro model systems to facilitate the rational design of polymeric gene-delivery vectors.

In silico prediction, LC-HRMS/MS analysis, and targeted/untargeted data-mining workflow for the profiling of phenylfentanyl in vitro metabolites

Talanta ◽  
2021 ◽  
pp. 122740
Author(s):  
Annagiulia Di Trana ◽  
Pietro Brunetti ◽  
Raffaele Giorgetti ◽  
Enrico Marinelli ◽  
Simona Zaami ◽  
...  
Keyword(s):  
Data Mining ◽  
In Silico ◽  
In Silico Prediction ◽  
Ms Analysis

In vitro screening and in silico prediction of antifungal metabolites from rhizobacterium Achromobacter kerstersii JKP9

2020 ◽  
Vol 202 (10) ◽  
pp. 2855-2864
Author(s):  
Karuppiah Vijay ◽  
Thangarasu Suganya Devi ◽  
Karthikeyan Kirupa Sree ◽  
Abdallah M. Elgorban ◽  
Ponnuchamy Kumar ◽  
...  
Keyword(s):  
In Silico ◽  
In Vitro Screening ◽  
In Silico Prediction ◽  
Antifungal Metabolites

Engineered PEI-piperazinyl nanoparticles as efficient gene delivery vectors: evidence from both in vitro and in vivo studies

RSC Advances ◽  
2012 ◽  
Vol 2 (10) ◽  
pp. 4335 ◽  
Author(s):  
Soma Patnaik ◽  
Ritu Goyal ◽  
Sushil K. Tripathi ◽  
Mohammed Arif ◽  
Kailash C. Gupta
Keyword(s):  
Gene Delivery ◽  
In Vivo Studies ◽  
Gene Delivery Vectors ◽  
Efficient Gene

scholarly journals Meta analysis of variant predictions in congenital adrenal hyperplasia caused by mutations in CYP21A2.

2021 ◽  
Author(s):  
Mayara Jorgens Prado ◽  
Rodrigo Ligabue-Braun ◽  
Arnaldo Zaha ◽  
Maria Lucia Rosa Rossetti ◽  
Amit V Pandey
Keyword(s):  
Congenital Adrenal Hyperplasia ◽  
In Silico ◽  
Meta Analysis ◽  
Genetic Disorders ◽  
In Silico Prediction ◽  
Adrenal Hyperplasia ◽  
Single Nucleotide Variants ◽  
Mild Phenotype ◽  
The Impact

Context: CYP21A2 deficiency represents 95% of congenital adrenal hyperplasia cases (CAH), a group of genetic disorders that affect steroid biosynthesis. The genetic and functional analysis provides critical tools to elucidate complex CAH cases. One of the most accessible tools to infer the pathogenicity of new variants is in silico prediction. Objective: Analyze the performance of in silico prediction tools to categorize missense single nucleotide variants (SNVs) of the CYP21A2. Methods: SNVs of the CYP21A2 characterized in vitro by functional assays were selected to assess the performance of online single and meta predictors. SNVs were tested separately or in combination with the related phenotype (severe or mild CAH form). In total, 103 SNVs of the CYP21A2 (90 pathogenic and 13 neutral) were used to test the performance of 13 single-predictors and four meta-predictors. Results: SNVs associated with the severe phenotypes were well categorized by all tools, with an accuracy between 0.69 (PredictSNP2) and 0.97 (CADD), and Matthews' correlation coefficient (MCC) between 0.49 (PoredicSNP2) and 0.90 (CADD). However, SNVs related to the mild phenotype had more variation, with the accuracy between 0.47 (S3Ds&GO and MAPP) and 0.88 (CADD), and MCC between 0.18 (MAPP) and 0.71 (CADD). Conclusion: From our analysis, we identified four predictors of CYP21A2 pathogenicity with good performance. These results can be used for future analysis to infer the impact of uncharacterized SNVs' in CYP21A2.

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