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Tytuł:
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QSAR analysis of sodium glucose co-transporter 2 (SGLT2) inhibitors for anti-hyperglycaemic lead development.
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Autorzy:
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Gandhi A; Department of Chemistry, Government College of Arts and Science, Aurangabad, Maharashtra, India.
Masand V; Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, India.
Zaki MEA; Department of Chemistry, College of Science, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia.
Al-Hussain SA; Department of Chemistry, College of Science, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia.
Ghorbal AB; Department of Mathematics and Statistics, College of Sciences, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia.
Chapolikar A; Department of Chemistry, Government College of Arts and Science, Aurangabad, Maharashtra, India.
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Źródło:
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SAR and QSAR in environmental research [SAR QSAR Environ Res] 2021 Sep; Vol. 32 (9), pp. 731-744.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Publication: 2002- : London : Taylor & Francis
Original Publication: Reading, Berkshire; New York, NY, USA : Gordon and Breach Science Publishers, 1993-
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MeSH Terms:
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Quantitative Structure-Activity Relationship*
Sodium-Glucose Transporter 2 Inhibitors/*chemistry
Sodium-Glucose Transporter 2 Inhibitors/*pharmacology
Databases, Chemical ; Glucosides/chemistry ; Glucosides/pharmacology ; Linear Models
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Contributed Indexing:
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Keywords: QSAR; SGLT2; T1DM; T2DM; hyperglycaemia
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Substance Nomenclature:
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0 (Glucosides)
0 (Sodium-Glucose Transporter 2 Inhibitors)
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Entry Date(s):
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Date Created: 20210908 Date Completed: 20210927 Latest Revision: 20210927
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Update Code:
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20240105
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DOI:
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10.1080/1062936X.2021.1971295
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PMID:
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34494464
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QSAR (Quantitative Structure Activity Relationship) modelling was performed on a dataset of 90 sodium-dependent glucose cotransporter 2 (SGLT2) inhibitors. The quantitative and explicative evaluations revealed some of the subtle and distinguished structural features that are responsible for the inhibitory potency of these compounds against SGLT2, such as less possible number of ring carbons at 8 Å from the lipophilic atoms in the molecule (fringClipo8A) and more possible value for the sum of the partial charges of the lipophilic atoms present within seven bonds from the donor atoms (lipo_don_7Bc). Multivariate GA-MLR (genetic algorithm-multi linear regression) and thorough validation methodology out-turned a statistically robust QSAR model with a very high predictability shown from various statistical parameters. A QSAR model with r 2 = 0.83, F = 51.54, Q 2 LOO = 0.79, Q 2 LMO = 0.79, CCC cv = 0.88, Q 2 F n = 0.76-0.81, r 2 ext = 0.77, CCC ext = 0.85, and with RMSE tr < RMSE cv was proposed. This QSAR model will assist synthetic chemists in the development of the SGLT2 inhibitors as the antidiabetic leads.