Refinement of Computational Access to Molecular Physicochemical Properties: From Ro5 to bRo5.

There is a need of computational tools to rank bRo5 drug candidates in the very early phases of drug discovery when chemical matter is unavailable. In this study, we selected three compounds: (a) a Ro5 drug (Pomalidomide), (b) a bRo5 orally available drug (Saquinavir), and (c) a polar PROTAC (CMP 98) to focus on computational access to physicochemical properties. To provide a benchmark, the three compounds were first experimentally characterized for their lipophilicity, polarity, IMHBs, and chameleonicity. To reproduce the experimental information content, we generated conformer ensembles with conformational sampling and molecular dynamics in both water and nonpolar solvents. Then we calculated Rgyr, 3D PSA, and IMHB number. An innovative pool of strategies for data analysis was then provided. Overall, we report a contribution to close the gap between experimental and computational methods for characterizing bRo5 physicochemical properties.

Discovery of Potential Antiviral Compounds against Hendra Virus by Targeting Its Receptor-Binding Protein (G) Using Computational Approaches.

Hendra virus (HeV) belongs to the paramyxoviridae family of viruses which is associated with the respiratory distress, neurological illness, and potential fatality of the affected individuals. So far, no competitive approved therapeutic substance is available for HeV. For that reason, the current research work was conducted to propose some novel compounds, by adopting a Computer Aided Drug Discovery approach, which could be used to combat HeV. The G attachment Glycoprotein (Ggp) of HeV was selected to achieve the primary objective of this study, as this protein makes the entry of HeV possible in the host cells. Briefly, a library of 6000 antiviral compounds was screened for potential drug-like properties, followed by the molecular docking of short-listed compounds with the Protein Data Bank (PDB) structure of Ggp. Docked complexes of top two hits, having maximum binding affinities with the active sites of Ggp, were further considered for molecular dynamic simulations of 200 ns to elucidate the results of molecular docking analysis. MD simulations and Molecular Mechanics Energies combined with the Generalized Born and Surface Area (MMGBSA) or Poisson-Boltzmann and Surface Area (MMPBSA) revealed that both docked complexes are stable in nature. Furthermore, the same methodology was used between lead compounds and HeV Ggp in complex with its functional receptor in human, Ephrin-B2. Surprisingly, no major differences were found in the results, which demonstrates that our identified compounds can also perform their action even when the Ggp is attached to the Ephrin-B2 ligand. Therefore, in light of all of these results, we strongly suggest that compounds (S)-5-(benzylcarbamoyl)-1-(2-(4-methyl-2-phenylpiperazin-1-yl)-2-oxoethyl)-6-oxo-3,6-dihydropyridin-1-ium-3-ide and 5-(cyclohexylcarbamoyl)-1-(2-((2-(3-fluorophenyl)-2-methylpropyl)amino)-2-oxoethyl)-6-oxo-3,6-dihydropyridin-1-ium-3-ide could be considered as potential therapeutic agents against HeV; however, further in vitro and in vivo experiments are required to validate this study.

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.

Binding Mode Prediction and Virtual Screening Applications by Covalent Docking.

The mechanism of action of covalent drugs involves the formation of a bond between their electrophilic warhead group and a nucleophilic residue of the protein target. The recent advances in covalent drug discovery have accelerated the development of computational tools for the design and characterization of covalent binders. Covalent docking algorithms can predict the binding mode of covalent ligands by modeling the bonds and interactions formed at the reaction site. Their scoring functions can estimate the relative binding affinity of ligands towards the target of interest, thus allowing virtual screening of compound libraries. However, most of the scoring schemes have no specific terms for the bond formation, and therefore it prevents the direct comparison of warheads with different intrinsic reactivity. Herein, we describe a protocol for the binding mode prediction of covalent ligands, a typical virtual screening of compound sets with a single warhead chemistry, and an alternative approach to screen libraries that include various warhead types, as applied in recently validated studies.

Expression of NOTCH receptors and ligands and prognosis of hepatocellular carcinoma.

Aim: To elucidate potential prognostic significance of NOTCH receptor and ligand expression in hepatocellular carcinoma. Materials & methods: NOTCH receptors and ligands were divided into increased and decreased expression groups by X-tile program. The association between NOTCH receptors/ligands and prognosis was analyzed by Kaplan-Meier method and log-rank test. Gene set enrichment analysis was performed to explore NOTCH receptors/ligands-related pathways via gsea-3.0. Results: DLL3 and DLL4 were independent prognostic factors for overall survival. Further studies showed that only DLL3 was significantly associated with tumor, node, metastasis stage. Gene set enrichment analysis analysis demonstrated that retinol metabolism, drug metabolism cytochrome P450 and tryptophan metabolism were significantly enriched in DLL3 expression phenotype. Conclusion: We demonstrate that DLL3 may be a prognostic biomarker in hepatocellular carcinoma.

Applying a Chemogeographic Strategy for Natural Product Discovery from the Marine Cyanobacterium Moorena bouillonii.

The tropical marine cyanobacterium Moorena bouillonii occupies a large geographic range across the Indian and Western Tropical Pacific Oceans and is a prolific producer of structurally unique and biologically active natural products. An ensemble of computational approaches, including the creation of the ORCA (Objective Relational Comparative Analysis) pipeline for flexible MS1 feature detection and multivariate analyses, were used to analyze various M. bouillonii samples. The observed chemogeographic patterns suggested the production of regionally specific natural products by M. bouillonii. Analyzing the drivers of these chemogeographic patterns allowed for the identification, targeted isolation, and structure elucidation of a regionally specific natural product, doscadenamide A (1). Analyses of MS2 fragmentation patterns further revealed this natural product to be part of an extensive family of herein annotated, proposed natural structural analogs (doscadenamides B-J, 2-10); the ensemble of structures reflect a combinatorial biosynthesis using nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) components. Compound 1 displayed synergistic in vitro cancer cell cytotoxicity when administered with lipopolysaccharide (LPS). These discoveries illustrate the utility in leveraging chemogeographic patterns for prioritizing natural product discovery efforts.

Computational design of oligosaccharide producing levansucrase from Bacillus licheniformis RN-01 to improve its thermostability for production of levan-type fructooligosaccharides from sucrose.

Levansucrase catalyzes production of levan and levan-type fructooligosaccharides (LFOs) with potential applications in food and pharmaceutical industries such as prebiotics and anti-tumor agents. Previous study found that Y246S mutant of Bacillus licheniformis RN-01 levansucrase (oligosaccharide producing levansucrase, OPL) could effectively produce LFOs but its thermostability is limited at high temperature. In this study, molecular dynamics (MD) and computational protein design were used to create mutants with higher thermostability than OPL by rigidifying highly flexible residues on enzyme surface. MD results show that highly flexible residues suitable for design are K82, N83, D179, and Q308. Two approaches were employed to improve their interactions by allowing them to be amino acids that could potentially form favorable interactions with their neighboring residues or natural amino acids except G, P and C. Flexibilities of designed residues of K82H, N83R, Q308S and K82H/N83R mutants are lower than those of OPL. Experimental results show that characteristics and product patterns of designed mutants are relatively similar to those of OPL. K82H/N83R mutant has higher thermostability than OPL with 1.7-fold increase in t1/2. Circular dichroism result suggests that designed mutations do not drastically affect secondary structures. This study shows how computational technique can engineer enzyme for thermostability improvement.

How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs.

The early and late development of new anticancer drugs, small molecules or peptides can be slowed down by some issues such as poor selectivity for the target or poor ADME properties. Computer-aided drug design (CADD) and target drug delivery (TDD) techniques, although apparently far from each other, are two research fields that can give a significant contribution to overcome these problems. Their combination may provide mechanistic understanding resulting in a synergy that makes possible the rational design of novel anticancer based therapies. Herein, we aim to discuss selected applications, some also from our research experience, in the fields of anticancer small organic drugs and peptides.

Helicity of Peptoid Ions in the Gas Phase.

Peptoids are attractive substitutes for peptides in several research areas, especially when they adopt a helical structure. The chain-size evolution of the secondary structure of the widely studied (S)-N-1-phenylethyl peptoids is here analyzed by means of the ion mobility mass spectrometry technique increasingly used as a powerful analytical tool and is further supported by theoretical modeling. We conclude that the helical shape of the peptoids prevailing in solution is lost in the gas phase by the need to screen the positive charge borne by the peptoid even though the collisional cross sections are close to the values expected for helical systems. We further illustrate that trend line analyses predicting molecular shapes from fits of the size evolution of cross sections can be very misleading since they critically depend on the range of polymerization degrees under study.

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.

Mining large databases to find new leads with low similarity to known actives: application to find new DPP-IV inhibitors.

Aim: Fragment-based drug design or bioisosteric replacement is used to find new actives with low (or no) similarity to existing ones but requires the synthesis of nonexisting compounds to prove their predicted bioactivity. Protein-ligand docking or pharmacophore screening are alternatives but they can become computationally expensive when applied to very large databases such as ZINC. Therefore, fast strategies are necessary to find new leads in such databases. Materials & methods: We designed a computational strategy to find lead molecules with very low (or no) similarity to existing actives and applied it to DPP-IV. Results: The bioactivity assays confirm that this strategy finds new leads for DPP-IV inhibitors. Conclusion: This computational strategy reduces the time of finding new lead molecules.

Efficient Corrections for DFT Noncovalent Interactions Based on Ensemble Learning Models.

Machine learning has exhibited powerful capabilities in many areas. However, machine learning models are mostly database dependent, requiring a new model if the database changes. Therefore, a universal model is highly desired to accommodate the widest variety of databases. Fortunately, this universality may be achieved by ensemble learning, which can integrate multiple learners to meet the demands of diversified databases. Therefore, we propose a general procedure for learning ensemble establishment based on noncovalent interactions (NCIs) databases. Additionally, accurate NCI computation is quite demanding for first-principles methods, for which a competent machine learning model can be an efficient solution to obtain high NCI accuracy with minimal computational resources. In regard to these aspects, multiple schemes of ensemble learning models (Bagging, Boosting, and Stacking frameworks), are explored in this study. The models are based on various low levels of density functional theory (DFT) calculations for the benchmark databases S66, S22, and X40. All NCIs computed by the DFT calculations can be improved to high-level accuracy (root-mean-square error RMSE = 0.22 kcal/mol in contrast to CCSD(T)/CBS benchmark) by established ensemble learning models. Compared with single machine learning models, ensemble models show better accuracy (RMSE of the best model is further lowered by ∼25%), robustness and goodness-of-fit according to evaluation parameters suggested by the OECD. Among ensemble learning models, heterogeneous Stacking ensemble models show the most valuable application potential. The standardized procedure of constructing learning ensembles has been well utilized on several NCI data sets, and this procedure may also be applicable for other chemical databases.

Development of a New Inhaler for High-Efficiency Dispersion of Spray-Dried Powders Using Computational Fluid Dynamics (CFD) Modeling.

Computational fluid dynamics (CFD) modeling offers a powerful tool for the development of drug delivery devices using a first principles approach but has been underutilized in the development of pharmaceutical inhalers. The objective of this study was to develop quantitative correlations for predicting the aerosolization behavior of a newly proposed dry powder inhaler (DPI). The dose aerosolization and containment (DAC) unit DPI utilizes inlet and outlet air orifices designed to maximize the dispersion of spray-dried powders, typically with low air volumes (~ 10 mL) and relatively low airflow rates (~ 3 L/min). Five DAC unit geometries with varying orifice outlet sizes, configurations, and protrusion distances were considered. Aerosolization experiments were performed using cascade impaction to determine mean device emitted dose (ED) and mass median aerodynamic diameter (MMAD). Concurrent CFD simulations were conducted to predict both flow field-based and particle-based dispersion parameters that captured different measures of turbulence. Strong quantitative correlations were established between multiple measures of turbulence and the experimentally observed aerosolization metrics of ED and MMAD. As expected, increasing turbulence produced increased ED with best case values reaching 85% of loaded dose. Surprisingly, decreasing turbulence produced an advantageous decrease in MMAD with values as low as approximately 1.6 µm, which is in contrast with previous studies. In conclusion, CFD provided valuable insights into the performance of the DAC unit DPI as a new device including a two-stage aerosolization process offering multiple avenues for future enhancements.