Three-dimensional quantitative structure-activity relationship and docking studies in a series of anthocyanin derivatives as cytochrome P450 3A4 inhibitors

Resonance structure contributions, flexibility, and frontier molecular orbitals (HOMO-LUMO) of pelargonidin, cyanidin, and delphinidin throughout the conformational space: application to antioxidant and antimutagenic activities

This research refers to the study and understanding of the conformational space of the positive-charged anthocyanidin structures in relation with the known chemical reactivities and bioactivities of these compounds. Therefore, the planar (P) and nonplanar (Z) conformers of the three hydroxylated anthocyanidins pelargonidin, cyanidin, and delphinidin were analyzed throughout the conformational space at the B3LYP/6-311 ++ G** level of theory. The outcome displayed eleven new conformers for pelargonidin, fifty-four for cyanidin, and thirty-one for delphinidin. Positive-charged quinoidal structures showed a significant statistical weight in the conformational space, thus coexisting simultaneously with other resonance structures, such that under certain reaction conditions, the anthocyanidins behave as positive-charged quinoidal structures instead of oxonium salts. The calculations of the permanent dipole moment and the polarizability showed relationships with the quantity and arrangement of hydroxyls in the structure. In addition, theoretical calculations were used to analyze the frontier molecular orbitals (HOMO-LUMO) of the three anthocyanidins. The novel conception of this work lies in the fact that dipole moment, polarizability, and HOMO-LUMO values were related to the reactivity/bioactivity of these three anthocyanidins. HOMO-LUMO energy gaps were useful to explain the antioxidant activity, while the percent atom contributions to HOMO were appropriate to demonstrate the antimutagenic activity as enzyme inhibitors, as well as the steric and electrostatic requirements to form the pharmacophore. Delphinidin was the strongest antioxidant anthocyanidin, and pelargonidin the best anthocyanidin with antimutagenic activity.

Computer-Aided (In Silico) Modeling of Cytochrome P450-Mediated Food–Drug Interactions (FDI)

Modifications of the activity of Cytochrome 450 (CYP) enzymes by compounds in food might impair medical treatments. These CYP-mediated food–drug interactions (FDI) play a major role in drug clearance in the intestine and liver. Inter-individual variation in both CYP expression and structure is an important determinant of FDI. Traditional targeted approaches have highlighted a limited number of dietary inhibitors and single-nucleotide variations (SNVs), each determining personal CYP activity and inhibition. These approaches are costly in time, money and labor. Here, we review computational tools and databases that are already available and are relevant to predicting CYP-mediated FDIs. Computer-aided approaches such as protein–ligand interaction modeling and the virtual screening of big data narrow down hundreds of thousands of items in databanks to a few putative targets, to which the research resources could be further directed. Structure-based methods are used to explore the structural nature of the interaction between compounds and CYP enzymes. However, while collections of chemical, biochemical and genetic data are available today and call for the implementation of big-data approaches, ligand-based machine-learning approaches for virtual screening are still scarcely used for FDI studies. This review of CYP-mediated FDIs promises to attract scientists and the general public.

Molecular modeling approaches to address drug-metabolizing enzymes (DMEs) mediated chemoresistance: a review

Resistance against clinically approved anticancer drugs is the main roadblock in cancer treatment. Drug metabolizing enzymes (DMEs) that are capable of metabolizing a variety of xenobiotic get overexpressed in malignant cells, therefore, catalyzing drug inactivation. As evident from the literature reports, the levels of DMEs increase in cancer cells that ultimately lead to drug inactivation followed by drug resistance. To puzzle out this issue, several strategies inclusive of analog designing, prodrug designing, and inhibitor designing have been forged. On that front, the implementation of computational tools can be considered a fascinating approach to address the problem of chemoresistance. Various research groups have adopted different molecular modeling tools for the investigation of DMEs mediated toxicity problems. However, the utilization of these in-silico tools in maneuvering the DME mediated chemoresistance is least considered and yet to be explored. These tools can be employed in the designing of such chemotherapeutic agents that are devoid of the resistance problem. The current review canvasses various molecular modeling approaches that can be implemented to address this issue. Special focus was laid on the development of specific inhibitors of DMEs. Additionally, the strategies to bypass the DMEs mediated drug metabolism were also contemplated in this report that includes analogs and pro-drugs designing. Different strategies discussed in the review will be beneficial in designing novel chemotherapeutic agents that depreciate the resistance problem.

Novel Approach for Characterizing Propofol Binding Affinities to Serum Albumins from Different Species

The interaction between the main carrier (serum albumin, SA) of endogenous and exogenous compounds in the bloodstream of different species (human, bovine, canine, rat, rabbit, and sheep) and a general anesthetic agent (propofol, PR) was investigated using an experimental technique (high-performance liquid chromatography) and computational methods (molecular docking, molecular dynamics, sequence, and phylogenetic analyses). The obtained results revealed the differences in the PR binding affinity to various homologous forms of this protein with reliable statistics (R ² = 0.9 and p-value < 0.005), correlating with the evolutionary relationships among SAs from different species. Additionally, the protein conformational changes (root-mean-square deviation ≈ 1.0 Å) and amino acid conservation of binding sites in protein domains were detected, contributing to the SA-PR binding modes. Overall, the outcomes from this study might provide a novel methodology to assess protein-ligand interactions and to gain some interesting insights into drug pharmacokinetics and pharmacodynamics to explain its variations among different species.

Nanoemulsions for "Nose-to-Brain" Drug Delivery

The blood–brain barrier (BBB) plays a fundamental role in protecting the brain from toxic substances and therefore also controls and restricts the entry of therapeutic agents. The nasal administration of drugs using the nose-to-brain pathway allows direct drug targeting into the brain, avoiding the first-pass effect and bypassing the BBB. Through the nasal route, the drug can access the brain directly along the trigeminal and olfactory nerves, which are located in the upper part of the nasal cavity. Nanoemulsions are formulations belonging to the field of nanomedicine. They consist of emulsions (commonly oil in water) stabilized by one or more surfactants—and eventually co-surfactants—delivered in droplets of small dimensions (sizes of 100–300 nm or less) with a high surface area. A mucoadhesive polymer such as chitosan can be added to the formulation to impair rapid nasal clearance. Nanoemulsions represent promising formulations to deliver drugs directly into the brain through the intranasal route. Therefore, they can be used as a possible alternative to oral administration, avoiding problems such as low solubility in water, poor bioavailability, enzymatic degradation and slow onset of action. This review focuses the present situation in literature regarding the use of nanoemulsions for nose-to-brain targeting, with particular attention to recent publications. Nasal nanoemulsions appear to be effective, non-invasive and safe drug delivery systems to achieve brain targeting for the treatment of neurological diseases.

Artificial neural network cascade identifies multi-P450 inhibitors in natural compounds

Substantial evidence has shown that most exogenous substances are metabolized by multiple cytochrome P450 (P450) enzymes instead of by merely one P450 isoform. Thus, multi-P450 inhibition leads to greater drug-drug interaction risk than specific P450 inhibition. Herein, we innovatively established an artificial neural network cascade (NNC) model composed of 23 cascaded networks in a ladder-like framework to identify potential multi-P450 inhibitors among natural compounds by integrating 12 molecular descriptors into a P450 inhibition score (PIS). Experimental data reporting in vitro inhibition of five P450 isoforms (CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4) were obtained for 8,148 compounds from the Cytochrome P450 Inhibitors Database (CPID). The results indicate significant positive correlation between the PIS values and the number of inhibited P450 isoforms (Spearman’s ρ = 0.684, p < 0.0001). Thus, a higher PIS indicates a greater possibility for a chemical to inhibit the enzyme activity of at least three P450 isoforms. Ten-fold cross-validation of the NNC model suggested an accuracy of 78.7% for identifying whether a compound is a multi-P450 inhibitor or not. Using our NNC model, 22.2% of the approximately 160,000 natural compounds in TCM Database@Taiwan were identified as potential multi-P450 inhibitors. Furthermore, chemical similarity calculations suggested that the prevailing parent structures of natural multi-P450 inhibitors were alkaloids. Our findings show that dissection of chemical structure contributes to confident identification of natural multi-P450 inhibitors and provides a feasible method for virtually evaluating multi-P450 inhibition risk for a known structure.