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: K_I = 71.2 nM, hCAXII: K_I = 22.5 nM), (hCAIX: K_I = 34.3 nM, hCAXII: K_I = 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.