Toward the Discovery of a Novel Class of Leads for High Altitude Disorders by Virtual Screening and Molecular Dynamics Approaches Targeting Carbonic Anhydrase

Exploring the Pharmacological Potential of Carrageenan Disaccharides as Antitumor Agents: An In Silico Approach

Carrageenans have demonstrated enhanced antitumor activity upon depolymerization into disaccharides. However, the pharmacological viability of these disaccharides and their mechanisms of antitumor action remains to be fully elucidated. This study aimed to employ computational tools to investigate the pharmacological properties and molecular targets pertinent to cancer of the disaccharides derived from the primary carrageenans. Analyses of pharmacological properties predicted by the pkCSM and SwissADME servers indicated that the disaccharides possess a favorable pharmacokinetic profile, although they encounter permeability challenges primarily due to their high polarity and low lipophilicity. Target prediction using SwissTarget and PPB2 identified five carbonic anhydrases, which are also targets of oncology drugs, as common targets for the disaccharides. Molecular docking performed with AutoDock Vina revealed that the binding energies of the disaccharides with carbonic anhydrases were comparable to or greater than those of existing drugs that target these lyases. Notably, six of the complexes formed exhibited interactions between the disaccharides and the zinc cofactor, which represents a primary mechanism of inhibition for these targets. Furthermore, molecular dynamics simulations conducted using GROMACS demonstrated a stable interaction between the disaccharides and carbonic anhydrases. These findings offer new insights into the pharmacological properties and mechanisms of action of carrageenan-derived disaccharides, highlighting their potential for further exploration in clinical trials and experimental studies.

Synthesis, In Vivo Anticonvulsant Activity Evaluation and In Silico Studies of Some Quinazolin-4(3H)-One Derivatives

Two series, "a" and "b", each consisting of nine chemical compounds, with 2,3-disubstituted quinazolin-4(3H)-one scaffold, were synthesized and evaluated for their anticonvulsant activity. They were investigated as dual potential positive allosteric modulators of the GABAA receptor at the benzodiazepine binding site and inhibitors of carbonic anhydrase II. Quinazolin-4(3H)-one derivatives were evaluated in vivo (D1-3 = 50, 100, 150 mg/kg, administered intraperitoneally) using the pentylenetetrazole (PTZ)-induced seizure model in mice, with phenobarbital and diazepam, as reference anticonvulsant agents. The in silico studies suggested the compounds act as anticonvulsants by binding on the allosteric site of GABAA receptor and not by inhibiting the carbonic anhydrase II, because the ligands-carbonic anhydrase II predicted complexes were unstable in the molecular dynamics simulations. The mechanism targeting GABAA receptor was confirmed through the in vivo flumazenil antagonism assay. The pentylenetetrazole experimental anticonvulsant model indicated that the tested compounds, 1a-9a and 1b-9b, present a potential anticonvulsant activity. The evaluation, considering the percentage of protection against PTZ, latency until the onset of the first seizure, and reduction in the number of seizures, revealed more favorable results for the "b" series, particularly for compound 8b.

Discovery of Nanomolar Inhibitors for Human Dihydroorotate Dehydrogenase Using Structure-Based Drug Discovery Methods

We used a structure-based drug discovery approach to identify novel inhibitors of human dihydroorotate dehydrogenase (DHODH), which is a therapeutic target for treating cancer and autoimmune and inflammatory diseases. In the case of acute myeloid leukemia, no previously discovered DHODH inhibitors have yet succeeded in this clinical application. Thus, there remains a strong need for new inhibitors that could be used as alternatives to the current standard-of-care. Our goal was to identify novel inhibitors of DHODH. We implemented prefiltering steps to omit PAINS and Lipinski violators at the earliest stages of this project. This enriched compounds in the data set that had a higher potential of favorable oral druggability. Guided by Glide SP docking scores, we found 20 structurally unique compounds from the ChemBridge EXPRESS-pick library that inhibited DHODH with IC50, DHODH values between 91 nM and 2.7 μM. Ten of these compounds reduced MOLM-13 cell viability with IC50, MOLM-13 values between 2.3 and 50.6 μM. Compound 16 (IC50, DHODH = 91 nM) inhibited DHODH more potently than the known DHODH inhibitor, teriflunomide (IC50, DHODH = 130 nM), during biochemical characterizations and presented a promising scaffold for future hit-to-lead optimization efforts. Compound 17 (IC50, MOLM-13 = 2.3 μM) was most successful at reducing survival in MOLM-13 cell lines compared with our other hits. The discovered compounds represent excellent starting points for the development and optimization of novel DHODH inhibitors.

Evaluating the ability of some natural phenolic acids to target the main protease and AAK1 in SARS COV-2

Researchers are constantly searching for drugs to combat the coronavirus pandemic caused by SARS-CoV-2, which has lasted for over two years. Natural compounds such as phenolic acids are being tested against Mpro and AAK1, which are key players in the SARS-CoV-2 life cycle. This research work aims to study the ability of a panel of natural phenolic acids to inhibit the virus's multiplication directly through Mpro and indirectly by affecting the adaptor-associated protein kinase-1 (AAK1). Pharmacophore mapping, molecular docking, and dynamic studies were conducted over 50 ns and 100 ns on a panel of 39 natural phenolic acids. Rosmarinic acid (16) on the Mpro receptor (- 16.33 kcal/mol) and tannic acid (17) on the AAK1 receptor (- 17.15 kcal/mol) exhibited the best docking energy against both receptors. These favourable docking score values were found to be superior to those of the co-crystallized ligands. Preclinical and clinical research is required before using them simultaneously to halt the COVID-19 life cycle in a synergistic manner.

Progress of Section "Biochemistry" in 2022

Of more than 16,400 papers published in 2022 in International Journal of Molecular Sciences [...].

Dependence on linkers' flexibility designed for benzenesulfonamides targeting discovery of novel hCA IX inhibitors as potent anticancer agents

Herein we reported the design and synthesis of two series comprising twenty-two benzenesulfonamides that integrate the s-triazine moiety. Target compounds successfully suppressed the hCA IX, with IC₅₀ ranging from 28.6 to 871 nM. Compounds 5d, 11b, 5b, and 7b were the most active analogues, which inhibited hCA IX isoform in the low nanomolar range (K_I = 28.6, 31.9, 33.4, and 36.6 nM, respectively). Furthermore, they were assessed for their cytotoxic activity against a panel of 60 cancer cell lines following US-NCI protocol. According to five-dose assay, 13c showed significant anticancer activity than 5c with GI₅₀-MID values of 25.08 and 189.01 µM, respectively. Additionally, 13c's effects on wound healing, cell cycle disruption, and apoptosis induction in NCI-H460 cancer cells were examined. Further, docking studies combined with molecular dynamic simulation showed a stable complex with high binding affinity of 5d to hCA IX, exploiting a favourable H-bond and lipophilic interactions.HIGHLIGHTSCarbonic anhydrase (CA) inhibitors comprising rigid and flexible linkers were developed.Compound 5d is the most potent CA IX inhibitor in the study (IC50: 28.6 nM).Compounds 5c and 13c displayed the greatest antiproliferative activity towards 60 cell lines.Compound 13c exposed constructive outcomes on normal cell lines, metastasis, and wound healing.Molecular docking and molecular dynamics (MDs) simulation was utilised to study binding mode.

Applications of machine learning in computer-aided drug discovery

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.