RETRACTED ARTICLE: Intranasal delivery of quercetin-loaded mucoadhesive nanoemulsion for treatment of cerebral ischaemia
Authors: Md Aftab Alam
Mohammad Ashafaq
Niyaz Ahmad
Atta Abbas Naqvi
Rehan Abdur Rub
Rizwan Ahmad
Farhan Jalees Ahmad
Journal: Artificial Cells Nanomedicine and Biotechnology (2018)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: Quercetin; UPLC-ESI-Q-TOF-MS/MS method validation; brain pharmacokinetics and pharmacodynamics; cerebral ischaemia; mucoadhesive nanoemulsion.
Affiliations: Department of Pharmaceutics, College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University (Formerly University of Dammam), Dammam, Kingdom of Saudi Arabia;, Department of Pharmaceutical Chemistry, College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University (Formerly University of Dammam), Dammam, Kingdom of Saudi Arabia;, Department of Natural Products and Alternative Medicine, College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University (Formerly University of Dammam), Dammam, Kingdom of Saudi Arabia;, Department of Pharmacy Practice, College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University (Formerly University of Dammam), Dammam, Kingdom of Saudi Arabia;, Department of Pharmaceutics, School of Medical and Allied Sciences, Galgotias University, Greater Noida, India;, Neuroscience and Toxicology Unit, College of Pharmacy, Jazan University, Jazan, Saudi Arabia;, Nanomedicine Lab, Department of Pharmaceutics, Faculty of Pharmacy, Jamia Hamdard, New Delhi, India

Abstract:  Background: Quercetin (QUR), as an antioxidant flavonoid, exhibits potential role in the amelioration of cerebral ischaemia; however, poor solubility as well as oral absorption results low serum and tissue levels for this drug. Purpose of the study: To enhance bioavailability, this study aims to prepare QUR nanoemulsions and administer via non-invasive nasal route in order to evaluate the drug targeting in brain. Methods: Quercetin mucoadhesive nanoemulsion (QMNE) was prepared (ionic gelation method) and optimized using various parameters, that is, particle size, entrapment efficiency, zeta potential and ex vivo permeation study. Results: The results observed for optimized QMNE were as follows: mean globule size (91.63 ± 4.36 nm), zeta potential (-17.26 ± 1.04 mV), drug content (99.84 ± 0.34%) and viscosity (121 ± 13 cp). To evaluate the extent of bioavailability for QMNE via post-intranasal (i.n.) administration, Ultra performance liquid chromatography-mass spectroscopy (UPLC-ESI-Q-TOF-MS/MS)-based bioanalytical method was developed and validated for pharmacokinetics, biodistribution, brain-targeting efficiency (9333.33 ± 39.39%) and brain drug-targeting potential (2181.83 ± 5.69%) which revealed enhanced QUR brain bioavailability as compared to intravenous administration (i.v.). Furthermore, improved neurobehavioral activity (locomotor and grip strength), histopathology and reduced infarction volume effects were observed in middle cerebral artery occlusion (MCAO)-induced cerebral ischemic rats model after i.n. administration of QMNE. Conclusion: This study supports a significant role for QMNE in terms of high brain-targeting potential and formulation efficiency due to ease of access and effective targeting in brain. 

Retracted: Yes

PubPeer comments:
 (a) The authors presented some operations for preference relations, which are actually very confusing. For example, let $$(p_{12,alpha}, q_{12,alpha}) =(0.3, s_8) qquad (p_{12,beta}, q_{12,beta}) =(0.8, s_8)$$ According to the operations introduced in the context, $$(p_{12,alpha}, q_{12,alpha}) + (p_{12,beta}, q_{12,beta}) = (1.1, s_8)$$ $$(p_{12,alpha}, q_{12,alpha}) - (p_{12,beta}, q_{12,beta}) = (-0.5, s_8)$$ Both of the outputs are meaningless because the preference values are supposed to be in $[0,1]$. ![file] (b) The authors presented **Algorithm 1** to cluster experts into subgroups. However, **Algorithm1 is not an algorithm in the usual sense.** For example, it even does not define a subgroup. To illustrate this point, let $lambda = 0.85$. Suppose that there are three experts $alpha$, $beta$, and $gamma$, and $$rho_{alphabeta} = 0.9qquad rho_{betagamma} = 0.9qquad rho_{alphagamma} = 0.1$$ **Step 2 of Algorithm 1** implies that $alpha$, $beta$ are grouped and $beta$, $gamma$ are grouped. However, Algorithm 1 does not specify whether or not $alpha$ and $gamma$ should be grouped. This is a crucial issue for a clustering procedure. In this simple example, if $alpha$ and $gamma$ are also grouped, it is possible that all the experts may form a single group with Algorithm 1. On the contrary, if $alpha$ and $gamma$ are not grouped, it is clear the output of Algorithm 1 is not unique. In general, the number of subgroups may also vary with different starting points. Can the authors kindly address these issues? ![file] ,