The design, synthesis, and in vitro activities of a series of potent and selective small-molecule inhibitors of caspase-3 are described. From extended tethering, a salicylic acid fragment was identified as having binding affinity for the S(4) pocket of caspase-3. X-ray crystallography and molecular modeling of the initial tethering hit resulted in the synthesis of 4, which reversibly inhibited caspase-3 with a K(i) = 40 nM. Further optimization led to the identification of a series of potent and selective inhibitors with K(i) values in the 20-50 nM range. One of the most potent compounds in this series, 66b, inhibited caspase-3 with a K(i) = 20 nM and selectivity of 8-500-fold for caspase-3 vs a panel of seven caspases (1, 2, and 4-8). A high-resolution X-ray cocrystal structure of 4 and 66b supports the predicted binding modes of our compounds with caspase-3.
"All of the above studies are available on different nuclei and BioMed Research International different caspase-3 PDB IDs, but till now no study is available on pyrrolo[3,4-c]quinoline-1,3-diones using PDB ID: 1GFW. Generally, potent caspase-3 inhibitors reported till date are peptides in nature    . However, such inhibitors often acquire poor cell permeability and low metabolic stability . "
[Show abstract][Hide abstract] ABSTRACT: Neurodegenerative disorders are major consequences of excessive apoptosis caused by a proteolytic enzyme known as caspase-3. Therefore, caspase-3 inhibition has become a validated therapeutic approach for neurodegenerative disorders. We performed pharmacophore modeling on some synthetic derivatives of caspase-3 inhibitors (pyrrolo[3,4-c]quinoline-1,3-diones) using PHASE 3.0. This resulted in the common pharmacophore hypothesis AAHRR.6 which might be responsible for the biological activity: two aromatic rings (R) mainly in the quinoline nucleus, one hydrophobic (H) group (CH3), and two acceptor (A) groups (-C=O). After identifying a valid hypothesis, we also developed an atom-based 3D-QSAR model applying the PLS algorithm. The developed model was statistically robust (q (2) = 0.53; pred_r (2) = 0.80). Additionally, we have performed molecular docking studies, cross-validated our results, and gained a deeper insight into its molecular recognition process. Our developed model may serve as a query tool for future virtual screening and drug designing for this particular target.
BioMed Research International 09/2013; 2013:306081. DOI:10.1155/2013/306081 · 2.71 Impact Factor
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