In silico Design of Novel N-hydrosulfonylbenzamides inhibitors of dengue RNA-dependent RNA polymerase showing favorable predicted pharmacokinetic profiles

Background: In recent years, there has been a growing interest in Denv NS5 inhibition, with several reported RdRp inhibitors such as sulfonylbenzamides, non-nucleo-side inhibitors without any 3D-QSAR pharmacophore (PH4) available. In this context, we report here, in silico design and virtual evaluation of novel sulfonylbenzamides Denv RdRp inhibitors with favorable predicted pharmacokinetic profile. Methods: By using in situ modifications of the crystal structure of 5-(5-(3-hydroxyprop-1-yn-1-yl)thiophen-2-yl)-4- methoxy-2-methyl-N-(methylsulfonyl) benzamide (EHB)-RdRp complex (PDB entry 5HMZ), 3D models of RdRp-EHBx complexes were prepared for a training set of 18 EHBs with experimentally determined inhibitory potencies (half-maximal inhibitory concentrations IC50exp). In the search for active conformation of the EHB1-18, linear QSAR model was prepared, which correlated computed gas phase enthalpies of formation ∆∆HMM of RdRp-EHBx complexes with the IC50exp. Further, considering the solvent effect and entropy changes upon ligand binding resulted in a superior QSAR model correlating computed complexation Gibbs free energies (∆∆Gcom). The successive pharmacophore model (PH4) generated from the active conformations of EHBs served as a virtual screening tool of novel analogs included in a virtual combinatorial library (VCL) of compounds with scaffolds restricted to phenyl. The VCL filtered by the Lipinski’s rule-of-five was screened by the PH4 model to identify new EHB analogs. Results: Gas phase QSAR model: -log10(IC50exp) = p IC50exp =-0.1403 x ∆∆HMM _ 7.0879, R2 = 0.73; superior aqueous phase QSAR model: p IC50exp = -0.2036 x ∆∆Gcom + 7.4974, R2 = 0.81 and PH4 pharmacophore model: p IC50exp = 1.0001 x p IC50pre -0.0017, R2 = 0.97. The VCL of more than 30 million EHBs was filtered down to 125,915 analogs Lipinski’s rule. The five-point PH4 screening retained 329 new and potent EHBs with predicted inhibitory potencies p IC50pre up to 30 times lower than that of EHB1 (IC50exp = 23nM). Predicted pharmacokinetic profile of the new analogs showed enhanced cell membrane permeability and high human oral absorption compared to the alone drug to treat dengue virus. Conclusions: Combined use of QSAR models, which considered binding of the EHBs to RdRp, pharmacophore model and ADME properties helped to recognize bound active conformation of the sulfonylbenzamide inhibitors, permitted in silico screening of VCL of compounds sharing sulfonylbenzamide scaffold and identify new analogs with predicted high inhibitory potencies and favorable pharmacokinetic profiles. Keywords: ADME properties prediction, Dengue, 3-(5-ethynylthiophen-2-yl)-N-hydrosulfonylbenzamides, in silico screening, RNA-dependent RNA polymerase.

Conformational analysis and molecular design of anthranilic acid derivatives as partial agonists of the Farnesoid X Receptor (FXR) with favorable predicted pharmacokinetic profiles

We report here virtual design of new anthranilic acid derivatives (AAD) identified as potent partial Farnesoid X recep-tor (FXR) agonists with favorable predicted pharmacokinetic profiles. By in situ modification of the crystal structure (PDB ID: 3OLF) of FXR complex with a benzimidazole-based partial agonistic ligand, 3D models of 17 FXR:AADx complexes with known observed activity (EC50exp) were prepared to establish a quantitative structure–activity (QSAR) model and linear correla-tion between relative Gibbs free energy (GFE) of receptor-ligand complex formation (Gcom) and EC50exp: pEC50exp = -0,1146 Gcom + 8,175 (#); R2 = 0.98. A 3D QSAR pharmacophore model (PH4) derived from the QSAR directed our effort to design novel AAD analogs. During the design, an initial virtual library of 94501 AAD was focused down to 33134 drug-like compounds and finally, PH4 screened to identify 100 promising compounds. Theoretical EC50 (EC50pre) values of all analogs compounds were predicted by means of equation (#) and their pharmacokinetics (ADME) profiles were computed. More than 12 putative AADs display EC50pre 300 times superior to that of the reported most active training set inhibitor AAD1.

Structure-Based Design of Tetrahydroisoquinoline-Based Hydroxamic Acid Derivatives Inhibiting Human Histone Deacetylase 8

We have designed new human histone deacetylase 8 (HDAC8) inhibitors using structure-based molecular design. 3D models of HDAC8–inhibitor complexes were prepared by in situ modification of the crystal structure of HDAC8 co-crystallized with the hydroxamic acid suberoylanilide (SAHA) and a training set (TS) of tetrahydroisoquinoline-based hydroxamic acid derivatives (DAHTs) with known inhibitory potencies. A QSAR model was elaborated for the TS yielding a linear correlation between the computed Gibbs free energies (GFE) of HDAC8–DAHTs complexation (∆∆Gcom) and observed half-maximal enzyme inhibitory concentrations (IC50exp). From this QSAR model a 3D-QSAR pharmacophore (PH4) was generated. Structural information derived from the 3D model and breakdown of computed HDAC8–DAHTs interaction energies up to individual active site residue contributions helped us to design new more potent HDAC8 inhibitors. We obtained a reasonable agreement ∆∆Gcom and values: (pIC50exp = – 0.0376 × ∆∆Gcom + 7.4605, R2 = 0.89). Similar agreement was established for the PH4 model (pIC50exp = 0.8769 × + 0.7854, R2 = 0.87). A comparative analysis of the contributions of active site residues guided the choice of fragments used in designing a virtual combinatorial library (VCL) of DAHT analogs. The VCL of more than 17 thousand DAHTs was screened by the PH4 and furnished 229 new DAHTs. The best-designed analog displayed predicted inhibitory potency up to 110 times higher than that of DAHT1 (IC50exp = 0.047 µM). Predicted pharmacokinetic profiles of the new analogs were compared to current per oral anticancer compounds. This computational approach, which combines molecular modelling, pharmacophore generation, analysis of HDAC8–DAHTs interaction energies and virtual screening of a combinatorial library of DAHTs resulted in a set of proposed new HDAC8 inhibitors. It can thus direct medicinal chemists in their search for new anticancer agents.

Computational analysis by molecular docking of thirty alkaloid compounds from medicinal plants as potent inhibitors of SARS-CoV-2 main protease

Year 2020 has been highly affected by the COVID-19 outbreak. The urgent need for a potent and effective drug for the treatment of this malignancy put pressure on researchers and scientists worldwide to develop a potential drug or a vaccine to resist SARS-CoV-2 virus. We report in this paper the assessment of the efficiency of thirty alkaloid compounds derived from African medicinal plants against the SARS-CoV-2 main protease through molecular docking and bioinformatics approaches. The results revealed four potential inhibitors (ligands 18, 21, 23 and 24) with 12.26 kcal/mol being the highest binding energy. Additionally, in silico drug-likeness and ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) properties for the four ligands showed a good predicted therapeutic profile of druggability, and fully obey the Lipinski's rule of five as well.

Quantum and Structural Molecular Fragment models used to predict anti-inflammatory activity

In this paper, we predict the anti-inflammatory activity of a series of 26 structures of N-arylanthranilic acid. So, Quantitatve Structure-Activity Relationship (QSAR) method remains the focus of many studies aimed at modeling and prediction of physicochemical properties or biological activities of molecule. Two models was used: quantum model and Structural Molecular Fragment (SMF) model. In the first model, semi-empirical (AM1) approach was used to calculate the quantum chemical descriptors using GAUSSIAN 09 package and the others chemical descriptors were calculated with chemaxon package. In the second model, Structural Molecular Fragment were generated by I.S.I.D.A (In Silico Design and Data Analysis). Our two models were built by using a Multiple Linear Regression Analysis (MLR).The concluded QSAR models reflected that the drugs activity was mainly attributed to quantum chemical descriptors with the statistical analysis of multiple R-squared equal to 0.9898 v.s 0.9077 for the Structural Molecular Fragment developed in I.S.I.D.A. Keywords: N-arylanthranilic acids, anti-inflammatory activity, quantum descriptors, Structural Molecular Fragment.

HELICAL STRUCTURE OF POLYUNSATURATED FATTY ACIDS: GAUSSIAN G4 THERMODYNAMIC FUNCTIONS

Molecular modeling of lipids has been hampered by the size of these complex, biologically important molecules. Yet, understanding the structure and energy (enthalpy) of large molecules is critical to identifying their function in chemical equilibrium and transition state theory. In this work, we use both experimental data and G4 computed results, to show that cis polyunsaturated lipids have helical conformers. We present linear functions for the enthalpy of formation ΔfH°298 and the Gibbs free energy of formation ΔfG°298 as a function of n, where n is the number of carbon atoms in a linear carboxylic acid chain. Taking ΔfH°298 of a saturated acid as a starting point, we add the enthalpy of hydrogenation ΔhydH°298 at appropriate locations on the carbon chain to model polyunsaturated fatty acids. For example, taking eicosanoic acid (C20) as a saturated starting point, we add four enthalpies of cis-dehydrogenation (ΔhydH°298) to obtain arachidonic acid (eicosa-5Z,8Z,11Z,14Z-tetraenoic acid). We compare Gaussian-4 computational results, to show evidence of helical structure. We conclude that fatty acids can have helical conformers facilitating a broad range of biological functions. Keywords: G4 Calculations, Helix, Lipid, Molecular Structure, Thermochemistry