Design, synthesis, and biological evaluation of dinaciclib and CAN508 hybrids as CDK inhibitors.

The scaffolds of two known CDK inhibitors (CAN508 and dinaciclib) were the starting point for synthesizing two series of pyarazolo[1,5-a]pyrimidines to obtain potent inhibitors with proper selectivity. The study presented four promising compounds; 10d, 10e, 16a, and 16c based on cytotoxic studies. Compound 16a revealed superior activity in the preliminary anticancer screening with GI % = 79.02-99.13 against 15 cancer cell lines at 10 µM from NCI full panel 60 cancer cell lines and was then selected for further investigation. Furthermore, the four compounds revealed good safety profile toward the normal cell lines WI-38. These four compounds were subjected to CDK inhibitory activity against four different isoforms. All of them showed potent inhibition against CDK5/P25 and CDK9/CYCLINT. Compound 10d revealed the best activity against CDK5/P25 (IC50 = 0.063 µM) with proper selectivity index against CDK1 and CDK2. Compound 16c exhibited the highest inhibitory activity against CDK9/CYCLINT (IC50 = 0.074 µM) with good selectivity index against other isoforms. Finally, docking simulations were performed for compounds 10e and 16c accompanied by molecular dynamic simulations to understand their behavior in the active site of the two CDKs with respect to both CAN508 and dinaciclib.

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor.

HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 µM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 µM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.

Utilization of AlphaFold models for drug discovery: Feasibility and challenges. Histone deacetylase 11 as a case study.

Histone deacetylase 11 (HDAC11), an enzyme that cleaves acyl groups from acylated lysine residues, is the sole member of class IV of HDAC family with no reported crystal structure so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms which complicates the conventional template-based homology modeling. AlphaFold is a neural network machine learning approach for predicting the 3D structures of proteins with atomic accuracy even in absence of similar structures. However, the structures predicted by AlphaFold are missing small molecules as ligands and cofactors. In our study, we first optimized the HDAC11 AlphaFold model by adding the catalytic zinc ion followed by assessment of the usability of the model by docking of the selective inhibitor FT895. Minimization of the optimized model in presence of transplanted inhibitors, which have been described as HDAC11 inhibitors, was performed. Four complexes were generated and proved to be stable using three replicas of 50 ns MD simulations and were successfully utilized for docking of the selective inhibitors FT895, MIR002 and SIS17. For SIS17, The most reasonable pose was selected based on structural comparison between HDAC6, HDAC8 and the HDAC11 optimized AlphaFold model. The manually optimized HDAC11 model is thus able to explain the binding behavior of known HDAC11 inhibitors and can be used for further structure-based optimization.

Arylidine extensions of 3-methyl-5-oxo-4,5-dihydro-1H-pyrazol-benzenesulfonamide derivatives: Synthesis, computational simulations and biological evaluation as tumor-associated carbonic anhydrase inhibitors.

Several pyrazole-benzene sulfonamides were reported as human carbonic anhydrase inhibitors. In this research work, a design of Arylidine-extented 5-oxo-pyrazole benzenesulfonamides (4a-i), (8a-d) and (10a-e) were reported based on tail-approach design. Beside the reported synthetic procedures and confirmation by different analytical procedures, a DFT study was employed to confirm the Z- conformer of the synthesized compounds. In vitro biological assay against four different human carbonic anhydrases took place and based on the results, SAR study was illustrated and selectivity indexes were discussed. Compounds 4g and 8a exhibited the best inhibitory activity among the target compounds with values (hCAIX: KI = 71.2 nM, hCAXII: KI = 22.5 nM), (hCAIX: KI = 34.3 nM, hCAXII: KI = 74.3 nM); respectively. Both of them were subjected to cellular assay against two different cancer cell lines with expressing nature to hCA isoforms under both normoxic and hypoxic conditions. Compound 4g showed the highest cytotoxic activity against MCF-7 cancer cell line (IC50 = 4.15 µM under hypoxic conditions and IC50 = 8.59 µM under normoxic conditions) compared to the reference drug doxorubicin under normoxic, (IC50 = 4.34 µM), and hypoxic, (IC50 = 2.23 µM), conditions. Further cellular investigations were employed to study the effect of this compound on the cell cycle of the affected cell line. Finally, molecular docking supported by molecular dynamic simulation was utilized to understand the mechanism of the inhibitory activity of two of these compounds - as representative examples- based on the designed rational.

Novel effective small-molecule inhibitors of protein kinases related to tau pathology in Alzheimer's disease.

Background: Alzheimer's disease (AD) drugs in therapy are limited to acetylcholine esterase inhibitors and memantine. Newly developed drugs against a single target structure have an insufficient effect on symptomatic AD patients. Results: Novel aromatically anellated pyridofuranes have been evaluated for inhibition of AD-relevant protein kinases cdk1, cdk2, gsk-3b and Fyn. Best activities have been found for naphthopyridofuranes with a hydroxyl function as part of the 5-substituent and a hydrogen or halogen substituent in the 8-position. Best results in nanomolar ranges were found for benzopyridofuranes with a 6-hydroxy and a 3-alkoxy substitution or an exclusive 6-alkoxy substituent. Conclusion: First lead compounds were identified inhibiting two to three kinases in nanomolar ranges to be qualified as an innovative approach for AD multitargeting.

A study of the allosteric inhibition of HCV RNA-dependent RNA polymerase and implementing virtual screening for the selection of promising dual-site inhibitors with low resistance potential.

Structure-based pharmacophores were generated and validated using the bioactive conformations of different co-crystallized enzyme-inhibitor complexes for allosteric palm-1 and thumb-2 inhibitors of NS5B. Two pharmacophore models were obtained, one for palm-1 inhibitors with sensitivity = 0.929 and specificity = 0.983, and the other for thumb-2 inhibitors with sensitivity = 1 and specificity = 0.979. In addition, a quantitative structure activity relationship (QSAR) models were developed based on using the values of different scoring functions as descriptors predicting the activity on both allosteric binding sites (palm-1 and thumb-2). QSAR studies revealed good predictive and statistically significant two descriptor models (r2 = .837, r2adjusted = .792 and r2prediction = .688 for palm-1 model and r2 = .927, r2adjusted = .908 and r2prediction = .779 for thumb-2 model). External validation for the QSAR models assured their prediction power with r2ext = .72 and .89 for palm-1 and thumb-2, respectively. Different docking protocols were examined for their validity to predict the correct binding poses of inhibitors inside their respective binding sites. Virtual screening was carried out on ZINC database using the generated pharmacophores, the selected valid docking algorithms and QSAR models to find compounds that could theoretically bind to both sites simultaneously.