Screening of Severe Acute Respiratory Syndrome Coronavirus 2 RNA-Dependent RNA Polymerase Inhibitors Using Computational Approach.

The detrimental effect of coronavirus disease 2019 (COVID-19) pandemic has manifested itself as a global crisis. Currently, no specific treatment options are available for COVID-19, so therapeutic interventions to tackle the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection must be urgently established. Therefore, cohesive and multidimensional efforts are required to identify new therapies or investigate the efficacy of small molecules and existing drugs against SARS-CoV-2. Since the RNA-dependent RNA Polymerase (RdRP) of SARS-CoV-2 is a promising therapeutic target, this study addresses the identification of antiviral molecules that can specifically target SARS-CoV-2 RdRP. The computational approach of drug development was used to screen the antiviral molecules from two antiviral libraries (Life Chemicals [LC] and ASINEX) against RdRP. Here, we report six antiviral molecules (F3407-4105, F6523-2250, F6559-0746 from LC and BDG 33693278, BDG 33693315, LAS 34156196 from ASINEX), which show substantial interactions with key amino acid residues of the active site of SARS-CoV-2 RdRP and exhibit higher binding affinity (>7.5 kcalmol-1) than Galidesivir, an Food and Drug Administration-approved inhibitor of the same. Further, molecular dynamics simulation and Molecular Mechanics Poisson-Boltzmann Surface Area results confirmed that identified molecules with RdRP formed higher stable RdRP-inhibitor(s) complex than RdRP-Galidesvir complex. Our findings suggest that these molecules could be potential inhibitors of SARS-CoV-2 RdRP. However, further in vitro and preclinical experiments would be required to validate these potential inhibitors of SARS-CoV-2 protein.

Design, Synthesis, Biological Evaluation, and Computational Studies of Novel Fluorinated Candidates as PI3K Inhibitors: Targeting Fluorophilic Binding Sites.

Highly fluorinated candidates containing anticancer pharmacophores like thiosemicarbazone (5a-e) and its cyclic analogues hydrazineylidenethiazolidine (6a-e), 2-aminothiadiazole (7a-e), and 2-hydrazineylidenethiazolidin-4-one (8a-e) were synthesized, and their cytotoxic activity was assayed against 60 tumor cell lines. Compounds 6c, 7b, and 8b displayed the most potent activity with lower toxic effects on MCF-10a. In vitro phosphatidylinositol 3-kinase (PI3K) enzyme inhibition was performed. Compound 6c displayed half-maximal inhibitory concentration (IC50, µM) values of 5.8, 2.3, and 7.9; compound 7b displayed IC50 values of 19.4, 30.7, and 73.7; and compound 8b displayed IC50 values of 77.5, 53.5, and 121.3 for PI3Kα, ß, and δ, respectively. Moreover, cell cycle progression caused cell cycle arrest at the S phase for compounds 6c and 8b and at G1/S for compound 7b, while apoptosis was induced. In silico studies; molecular docking; physicochemical parameters; and absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis were performed. The results showed that compound 6c is the most potent one with a selectivity index (SI) of 39 and is considered as a latent lead for further optimization of anticancer agents.

3D-QSAR-Based Pharmacophore Modeling, Virtual Screening, and Molecular Docking Studies for Identification of Tubulin Inhibitors with Potential Anticancer Activity.

In this study, we aimed to develop a pharmacophore-based three-dimensional quantitative structure activity relationship (3D-QSAR) for a set including sixty-two cytotoxic quinolines (1-62) as anticancer agents with tubulin inhibitory activity. A total of 279 pharmacophore hypotheses were generated based on the survival score to build QSAR models. A six-point pharmacophore model (AAARRR.1061) was identified as the best model which consisted of three hydrogen bond acceptors (A) and three aromatic ring (R) features. The model showed a high correlation coefficient (R 2 = 0.865), cross-validation coefficient (Q 2 = 0.718), and F value (72.3). The best pharmacophore model was then validated by the Y-Randomization test and ROC-AUC analysis. The generated 3D contour maps were used to reveal the structure activity relationship of the compounds. The IBScreen database was screened against AAARRR.1061, and after calculating ADMET properties, 10 compounds were selected for further docking study. Molecular docking analysis showed that compound STOCK2S-23597 with the highest docking score (-10.948 kcal/mol) had hydrophobic interactions and can form four hydrogen bonds with active site residues.

Permeability prediction in the beyond-Rule-of 5 chemical space: Focus on cyclic hexapeptides.

Cyclic peptides (CPs) are gaining more and more relevance in drug discovery. Since one of their main drawbacks is poor permeability, the discovery of new orally available CP drugs requires computational tools that predict CP permeability in very early drug discovery. In this study we used a literature dataset of 62 cyclic hexapeptides to evaluate the performances of a number of in silico tools based on different computational theory to model and rationalize PAMPA and Caco-2 permeability values. In particular, we submitted the dataset to a) online calculators, b) QSPR strategies, c) a physics-based tool, d) a mixed approach and e) a kinetic method. This latter is an emergent strategy in which a few relevant conformations retrieved from a set of molecular dynamics (MD) simulations by the Markov State Model (MSM) are used to establish the compounds permeability. Both free and commercial software were used. Results were compared with a model based on experimental physicochemical descriptors. All the computational approaches but online calculators performed quite well and show that lipophilicity and not polarity is the main determinant of the investigated event. A second major outcome of the study is that the impact of flexibility on the permeability of the considered dataset cannot be unambiguously assessed. Finally, our comparative analysis, which also included not common applied strategies, allowed a sound evaluation of the pros and cons of the applied computational approaches.

Thyroxine binding to type III iodothyronine deiodinase.

Iodothyronine deiodinases (Dios) are important selenoproteins that control the concentration of the active thyroid hormone (TH) triiodothyronine through regioselective deiodination. The X-ray structure of a truncated monomer of Type III Dio (Dio3), which deiodinates TH inner rings through a selenocysteine (Sec) residue, revealed a thioredoxin-fold catalytic domain supplemented with an unstructured Ω-loop. Loop dynamics are driven by interactions of the conserved Trp207 with solvent in multi-microsecond molecular dynamics simulations of the Dio3 thioredoxin(Trx)-fold domain. Hydrogen bonding interactions of Glu200 with residues conserved across the Dio family anchor the loop's N-terminus to the active site Ser-Cys-Thr-Sec sequence. A key long-lived loop conformation coincides with the opening of a cryptic pocket that accommodates thyroxine (T4) through an I⋯Se halogen bond to Sec170 and the amino acid group with a polar cleft. The Dio3-T4 complex is stabilized by an I⋯O halogen bond between an outer ring iodine and Asp211, consistent with Dio3 selectivity for inner ring deiodination. Non-conservation of residues, such as Asp211, in other Dio types in the flexible portion of the loop sequence suggests a mechanism for regioselectivity through Dio type-specific loop conformations. Cys168 is proposed to attack the selenenyl iodide intermediate to regenerate Dio3 based upon structural comparison with related Trx-fold proteins.

Chemistry, Biological Activities and In Silico Bioprospection of Sterols and Triterpenes from Mexican Columnar Cactaceae.

The Cactaceae family is an important source of triterpenes and sterols. The wide uses of those plants include food, gathering, medicinal, and live fences. Several studies have led to the isolation and characterization of many bioactive compounds. This review is focused on the chemistry and biological properties of sterols and triterpenes isolated mainly from some species with columnar and arborescent growth forms of Mexican Cactaceae. Regarding the biological properties of those compounds, apart from a few cases, their molecular mechanisms displayed are not still fully understand. To contribute to the above, computational chemistry tools have given a boost to traditional methods used in natural products research, allowing a more comprehensive exploration of chemistry and biological activities of isolated compounds and extracts. From this information an in silico bioprospection was carried out. The results suggest that sterols and triterpenoids present in Cactaceae have interesting substitution patterns that allow them to interact with some bio targets related to inflammation, metabolic diseases, and neurodegenerative processes. Thus, they should be considered as attractive leads for the development of drugs for the management of chronic degenerative diseases.

Identification of novel inhibitors for TNFα, TNFR1 and TNFα-TNFR1 complex using pharmacophore-based approaches.

BACKGROUND: Tumor necrosis factor α (TNFα) is a multifunctional cytokine with a potent pro-inflammatory effect. It is a validated therapeutic target molecule for several disorders related to autoimmunity and inflammation. TNFα-TNF receptor-1 (TNFR1) signaling contributes to the pathological processes of these disorders. The current study is focused on finding novel small molecules that can directly bind to TNFα and/or TNFR1, preventing the interaction between TNFα or TNFR1, and regulating downstream signaling pathways. METHODS: Cheminformatics pipeline (pharmacophore modeling, virtual screening, molecular docking and in silico ADMET analysis) was used to screen for novel TNFα and TNFR1 inhibitors in the Zinc database. The pharmacophore-based models were generated to screen for the best drug like compounds in the Zinc database. RESULTS: The 39, 37 and 45 best hit molecules were mapped with the core pharmacophore features of TNFα, TNFR1, and the TNFα-TNFR1 complex respectively. They were further evaluated by molecular docking, protein-ligand interactions and in silico ADMET studies. The molecular docking analysis revealed the binding energies of TNFα, TNFR1 and the TNFα-TNFR1 complex, the basis of which was used to select the top five best binding energy compounds. Furthermore, in silico ADMET studies clearly revealed that all 15 compounds (ZINC09609430, ZINC49467549, ZINC13113075, ZINC39907639, ZINC25251930, ZINC02968981, ZINC09544246, ZINC58047088, ZINC72021182, ZINC08704414, ZINC05462670, ZINC35681945, ZINC23553920, ZINC05328058, and ZINC17206695) satisfied the Lipinski rule of five and had no toxicity. CONCLUSIONS: The new selective TNFα, TNFR1 and TNFα-TNFR1 complex inhibitors can serve as anti-inflammatory agents and are promising candidates for further research.

Timescales of developmental toxicity impacting on research and needs for intervention.

Much progress has happened in understanding developmental vulnerability to preventable environmental hazards. Along with the improved insight, the perspective has widened, and developmental toxicity now involves latent effects that can result in delayed adverse effects in adults or at old age and additional effects that can be transgenerationally transferred to future generations. Although epidemiology and toxicology to an increasing degree are exploring the adverse effects from developmental exposures in human beings, the improved documentation has resulted in little progress in protection, and few environmental chemicals are currently regulated to protect against developmental toxicity, whether it be neurotoxicity, endocrine disruption or other adverse outcome. The desire to obtain a high degree of certainty and verification of the evidence used for decision-making must be weighed against the costs and necessary duration of research, as well as the long-term costs to human health because of delayed protection of vulnerable early-life stages of human development and, possibly, future generations. Although two-generation toxicology tests may be useful for initial test purposes, other rapidly emerging tools need to be seriously considered from computational chemistry and metabolomics to CLARITY-BPA-type designs, big data and population record linkage approaches that will allow efficient generation of new insight; epigenetic mechanisms may necessitate a set of additional regulatory tests to reveal such effects. As reflected by the Prenatal Programming and Toxicity (PPTOX) VI conference, the current scientific understanding and the timescales involved require an intensified approach to protect against preventable adverse health effects that can harm the next generation and generations to come. While further research is needed, the main emphasis should be on research translation and timely public health intervention to avoid serious, irreversible and perhaps transgenerational harm.