Hypoxia in tumors contributes to chemotherapy resistance, worsened by acidosis driven by carbonic anhydrases (hCA IX and XII). Targeting these enzymes can mitigate acidosis, thus enhancing tumor sensitivity to cytotoxic drugs. Herein, novel 4-(pyrazolyl)benzenesulfonamide ureas (SH7a-t) were developed and evaluated for their inhibitory activity against hCA IX and XII. They showed promising results (hCA IX: KI = 15.9-67.6 nM, hCA XII: KI = 16.7-65.7 nM). Particularly, SH7s demonstrated outstanding activity (KIs = 15.9 nM for hCA IX and 55.2 nM for hCA XII) and minimal off-target kinase inhibition over a panel of 258 kinases. In NCI anticancer screening, SH7s exhibited broad-spectrum activity with an effective growth inhibition full panel GI50 (MG-MID) value of 3.5 μM and a subpanel GI50 (MG-MID) range of 2.4-6.3 μM. Furthermore, SH7s enhanced the efficacy of Taxol and 5-fluorouracil in cotreatment regimens under hypoxic conditions in HCT-116 colorectal cancer cells, indicating its potential as a promising anticancer agent.

Class I histone deacetylases (HDACs) are considered promising targets in current cancer research. To obtain subtype-selective and potent HDAC inhibitors, we used the aminobenzamide scaffold as the zinc-binding group and prepared new derivatives with a pyrazole ring as the linking group. The synthesized compounds were analyzed in vitro using an enzymatic assay against HDAC1, -2, and -3. Compounds 12b, 15b, and 15i were found to be potent HDAC1 inhibitors, also in comparison to the reference compounds entinostat and tacedinaline, with IC50 values of 0.93, 0.22, and 0.68 μM, respectively. The best compounds were measured for their cellular effect and target engagement in acute myeloid leukemia (AML) cells. In addition, we studied the interaction of the compounds with HDAC subtypes using docking and molecular dynamic simulations. In summary, we have developed a new chemotype of HDAC1 inhibitors that can be used for further structure-based optimization.

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.

AlphaFold is an artificial intelligence approach for predicting the 3D structures of proteins with atomic accuracy. One challenge that limits the use of AlphaFold models for drug discovery is the correct prediction of folding in the absence of ligands and cofactors, which compromises their direct use. We have previously described the optimization and use of the HDAC11-AlphaFold model for the docking of selective inhibitors such as FT895 and SIS17. Based on the predicted binding mode of FT895 in the optimized HDAC11 AlphaFold model, a new scaffold for HDAC11 inhibitors was designed, and the resulting compounds were tested in vitro against various HDAC isoforms. Compound 5a proved to be the most active compound with an IC50 of 365 nM and was able to selectively inhibit HDAC11. 5a also showed promising activity with an EC50 of 3.6 µM on neuroblastoma cells. Furthermore, we supported our study by comparative docking and MD simulations.

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.

In a fragment-based approach using NMR spectroscopy, benzyloxyacetohydroxamic acid-derived inhibitors of the bacterial deacetylase LpxC bearing a substituent to target the uridine diphosphate-binding site of the enzyme were developed. By appending privileged fragments via a suitable linker, potent LpxC inhibitors with promising antibacterial activities could be obtained, like the one-digit nanomolar LpxC inhibitor (S)-13j [Ki (EcLpxC C63A) = 9.5 nM; Ki (PaLpxC): 5.6 nM]. To rationalize the observed structure-activity relationships, molecular docking and molecular dynamics studies were performed. Initial in vitro absorption-distribution-metabolism-excretion-toxicity (ADMET) studies of the most potent compounds have paved the way for multiparameter optimization of our newly developed isoserine-based amides.

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.

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.

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 (r(2)=.837, r(adjusted)(2)=.792 and r(prediction)(2)=.688 for palm-1 model and r(2)=.927, r(adjusted)(2)=.908 and r(prediction)(2)=.779 for thumb-2 model). External validation for the QSAR models assured their prediction power with r(ext)(2)=.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.

Novel tropane-based compounds were synthesized exhibiting antiproliferative properties against HepG2 and MCF7 carcinoma cell lines.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

3‐(2,4‐Dichlorophenyl)‐7‐[(2,4‐dichlorophenyl)methylidene]‐11‐methyl‐4‐phenyl‐4,5,11‐triazatricyclo[6.2.1.0*2,6*]undec‐5‐ene (3) was synthesized in a stereoselective manner. Single crystal X‐ray study of 3 revealed that the structure belongs to the triclinic system with space group . The structure is further stabilized by an intermolecular CH…π interaction. Molecular mechanics force field (MM+), followed by either semi‐empirical AM1 or PM3, was used to calculate the optimized geometrical parameters of 3. The determined theoretical geometry parameters were found to be in good agreement with the parameters obtained by a single‐crystal study. Vibrational frequencies and gauge‐independent atomic orbital, 1H‐NMR and 13C‐NMR chemical shifts were computed at B3LYP/6‐31G(d) level of theory. Moreover, the molecular electrostatic potential of 3 has been calculated exhibiting that the most electrophilic site of 3 is the N‐3 atom; however, the nucleophilic site is distributed on the H atoms of dichlorophenyl groups. The frontier molecular orbitals of 3 have been determined by the same technique.