Delivery of RNA Therapeutics: The Great Endosomal Escape!

Dowdy, Steven F.; Setten, Ryan L.; Cui, Xian-Shu; Jadhav, Satish

doi:10.1089/nat.2022.0004

Cited by 64 publications

(61 citation statements)

References 51 publications

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“…This implies that the molecules have low aqueous solubility and good oral bioavailability (Adianingsih et al, 2022;Ar amburo-G alvez et al, 2022). The Log P also gives information on the cellular membrane permeability and hydrophobic binding to macromolecules such as the target receptors, plasma proteins, metabolizing enzymes, or transporters (Dowdy et al, 2022). Zanamivir and oseltamivir standard neuraminidase drugs have lower Log P scores of 1.013 and -1.317 which tend to experience difficulty in penetrating the lipid bilayer of the cell membrane.…”

Section: Drug Likeness Assessment and Admet Predictionmentioning

confidence: 99%

RETRACTED: In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Abdullahi

Uzairu

Shallangwa

et al. 2022

Heliyon

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Section: Drug Likeness Assessment and Admet Predictionmentioning

confidence: 99%

RETRACTED: In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Abdullahi

Uzairu

Shallangwa

et al. 2022

Heliyon

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“…This implies that the compounds have low aqueous solubility and good oral bioavailability [47,48]. The Log P also gives information on the cellular membrane permeability and hydrophobic binding to macromolecules such as the target receptors, plasma proteins, metabolizing enzymes, or transporters [49]. Oseltamivir as the standard neuraminidase drug has lower log P scores of − 1.317 which tend to experience difficulty in penetrating the lipid bilayer of the cell membrane.…”

Section: Drug-likeness Assessment and Admet Predictionsmentioning

confidence: 99%

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Abdullahi

Uzairu

Shallangwa

et al. 2022

Beni-Suef Univ J Basic Appl Sci

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Background Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA). Results The 2D-QSAR modeling results showed GFA-MLR ($$R_{{\text{train }}}^{2}$$ R train 2 = 0.9192, Q2 = 0.8767, R2adj = 0.8991, RMSE = 0.0959, $$R_{{{\text{test}}}}^{2}$$ R test 2 = 0.8943, $$R_{{{\text{pred}}}}^{2}$$ R pred 2 = 0.7745) and GFA-ANN ($$R_{{\text{train }}}^{2}$$ R train 2 = 0.9227, Q2 = 0.9212, RMSE = 0.0940, $$R_{{{\text{test}}}}^{2}$$ R test 2 = 0.8831, $$R_{{{\text{pred}}}}^{2}$$ R pred 2 = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ($$R_{{\text{train }}}^{2}$$ R train 2 = 0.9620, Q2 = 0.643) and CoMSIA_SED ($$R_{{\text{train }}}^{2}$$ R train 2 = 0.8770, Q2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> − 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents. Conclusion The drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski’s rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

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“…Delivery of RNA oligonucleotide therapeutics into tissues and cells remains the rate-limiting problem to solve before these therapeutic platforms can be utilized to treat wide-spread human disease. 1 , 2 , 3 , 4 Typical antisense oligonucleotides (ASOs) are single-stranded RNAs that sterically block splicing or are, in the case of Gapmer ASOs, a combination of RNA on the ends with an intervening DNA segment in the middle that activates RNase H to cleave the target mRNA. 1 , 2 In contrast, siRNAs are double-stranded RNAs that induce RNA interference (RNAi) responses to knock down the target mRNA.…”

mentioning

confidence: 99%

“… 1 , 2 Compared with native RNA, ASOs are heavily modified and have a full phosphorothioate backbone, where one of the non-bridging oxygen atoms is replaced with a more “hydrophobic” sulfur atom, which results in both a significant increase in metabolic stability and an enhanced escape from endosomes. 1 , 2 , 4 All of the ASO’s 2′ hydroxyls have been replaced with 2′ O-methyl (OMe), 2′ methoxyethyl (MOE) or 2′–4′ locked nucleic acids (LNAs). 1 , 2 ASOs need a targeting domain to concentrate the ASO on the diseased cell type of interest.…”

mentioning

confidence: 99%

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DNA/RNA heteroduplex oligonucleotides: An unanticipated twist in the delivery of ASOs

Setten

Dowdy

2022

Molecular Therapy - Nucleic Acids

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Delivery of RNA Therapeutics: The Great Endosomal Escape!

Cited by 64 publications

References 51 publications

RETRACTED: In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

RETRACTED: In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

DNA/RNA heteroduplex oligonucleotides: An unanticipated twist in the delivery of ASOs

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