Pyridazine Derivatives as New Antidiabetic Agents: Synthesis, In‐Vitro α‐Amylase Inhibitory Activity, Molecular Docking and Molecular Dynamics Simulations Studies

Abstract This study involved the synthesis, characterization, and assessment of fourteen pyridazine analogs (designated as 1 – 14 ) to investigate their efficacy in inhibiting the α‐amylase enzyme for potential diabetes treatment using an in vitro approach. Additionally, in silico molecular docking and molecular dynamic (MD) simulations were conducted to assess the inhibitory properties of these analogs. Physicochemical and pharmacokinetic properties of the fourteen pyridazine analogs were predicted using the DataWarrior tool. Results indicated that all tested compounds demonstrated significant α‐amylase inhibitory activity, with IC 50 values ranging from 81.28±0.00 to 1623.54±2.67 μM compared to the standard drug acarbose (IC 50 =220.42±36.40 μM). Notably, compounds 8 and 12 exhibited the most potent α‐amylase inhibitory activity, with IC 50 values of 81.28±0.00 μM and 200.60±34.65 μM, respectively. Molecular docking analysis revealed binding energies ranging from −7.53 to −5.77 kcal/mol and inhibition constants ranging from 3.00 to 58.96 μM, with compounds 9 , 7 , 8 , 5 , and 3 demonstrating the best binding energies. Subsequent MD simulation analyses indicated that all five compound formed stable complexes after 100 ns MD simulation. Consequently, these compounds hold promise as potential α‐amylase inhibitors pending successful clinical validation.

Authors

Institutions

Publication Details

Journal
ChemistrySelect
Published
2024-09-09
DOI
https://doi.org/10.1002/slct.202401557
Citations
5
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.81

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Pyridazine Derivatives as New Antidiabetic Agents: Synthesis, In‐Vitro α‐Amylase Inhibitory Activity, Molecular Docking and Molecular Dynamics Simulations Studies

Afaf Oulmidi, Khalid Karrouchi, My El Abbés Faouzi, Youness Boukharsa et al.
5 citations
ChemistrySelect
Computational Drug Discovery Methods
1.81
article

Pyridazine Derivatives as New Antidiabetic Agents: Synthesis, In‐Vitro α‐Amylase Inhibitory Activity, Molecular Docking and Molecular Dynamics Simulations Studies

Afaf Oulmidi, Khalid Karrouchi, My El Abbés Faouzi, Youness Boukharsa, Ali Alsalme, Mustafa Alhaji Isa, Karima Sayah, Somia Shehzadi, M’hammed Ansar
article en
5 citations

Abstract

Abstract This study involved the synthesis, characterization, and assessment of fourteen pyridazine analogs (designated as 1 – 14 ) to investigate their efficacy in inhibiting the α‐amylase enzyme for potential diabetes treatment using an in vitro approach. Additionally, in silico molecular docking and molecular dynamic (MD) simulations were conducted to assess the inhibitory properties of these analogs. Physicochemical and pharmacokinetic properties of the fourteen pyridazine analogs were predicted using the DataWarrior tool. Results indicated that all tested compounds demonstrated significant α‐amylase inhibitory activity, with IC 50 values ranging from 81.28±0.00 to 1623.54±2.67 μM compared to the standard drug acarbose (IC 50 =220.42±36.40 μM). Notably, compounds 8 and 12 exhibited the most potent α‐amylase inhibitory activity, with IC 50 values of 81.28±0.00 μM and 200.60±34.65 μM, respectively. Molecular docking analysis revealed binding energies ranging from −7.53 to −5.77 kcal/mol and inhibition constants ranging from 3.00 to 58.96 μM, with compounds 9 , 7 , 8 , 5 , and 3 demonstrating the best binding energies. Subsequent MD simulation analyses indicated that all five compound formed stable complexes after 100 ns MD simulation. Consequently, these compounds hold promise as potential α‐amylase inhibitors pending successful clinical validation.

ChemistrySelectVol. 9(34)
Mohammed V University (MA), University of Lahore (PK), University of Johannesburg (ZA), King Saud University (SA), Instituts Supérieurs des Professions Infirmières et Techniques de Santé (MA), University of Maiduguri (NG), UCLouvain (BE)
King Saud University
Good health and well-being
Openalex Percentile: Top 13%
Computational Drug Discovery Methods
1.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.