Computational Modeling on Binding Interactions of Cyclodextrin s with the Human Multidrug Resistance P-glycoprotein Toward Efficient Drug-delivery System Applications.

BACKGROUND: Herein, molecular docking approaches and DFT ab initio simulations were combined for the first time, to study the key interactions of cyclodextrins (CDs: α-CD, ß-CD, and γ-CD) family with potential pharmacological relevance and the multidrug resistance P-gp protein toward efficient drug-delivery applications. The treatment of neurological disorders and cancer therapy where the multiple drug-resistance phenomenon mediated by the P-gp protein constitutes the fundamental cause of unsuccessful therapies. OBJECTIVES: To understand more about the CD docking mechanism and the P-gp. METHODS: In order to achieve the main goal, the computational docking process was used. The observed docking-mechanism of the CDs on the P-gp was fundamentally based on hybrid backbone/side-chain hydrophobic interactions,and also hybrid electrostatic/side-chain interactions of the CD-ligands' OHmotifs with acceptor and donor characteristics, which might theoretically cause local perturbations in the TMD/P-gp inter-residues network, influencing ligand extrusion through the blood-brain barrier. P-gp residues were conformationally favored. Despite the structural differences, all the cyclodextrins exhibit very close Gibbs free binding energy values (or affinity) by the P-gp binding site (transmembrane domains - TMDs). RESULT: The obtained theoretical docking-mechanism of the CDs on the P-gp was fundamentally based on hybrid backbone/side-chain hydrophobic interactions, and also hybrid electrostatic/side-chain interactions of the OH-motifs of the CD-ligands with acceptor and donor properties which theoretically could induce allosteric local-perturbations in the TMDs-inter-residues network of P-gp modulating to the CD-ligand extrusion from the blood-brain-barrier (or cancer cells). CONCLUSION: Finally, these theoretical results open new horizons for evaluating new nanotherapeutic drugs with potential pharmacological relevance for efficient drug-delivery applications and precision nanomedicine.

Targeting Beta-Blocker Drug-Drug Interactions with Fibrinogen Blood Plasma Protein: A Computational and Experimental Study.

In this work, one of the most prevalent polypharmacology drug-drug interaction events that occurs between two widely used beta-blocker drugs-i.e., acebutolol and propranolol-with the most abundant blood plasma fibrinogen protein was evaluated. Towards that end, molecular docking and Density Functional Theory (DFT) calculations were used as complementary tools. A fibrinogen crystallographic validation for the three best ranked binding-sites shows 100% of conformationally favored residues with total absence of restricted flexibility. From those three sites, results on both the binding-site druggability and ligand transport analysis-based free energy trajectories pointed out the most preferred biophysical environment site for drug-drug interactions. Furthermore, the total affinity for the stabilization of the drug-drug complexes was mostly influenced by steric energy contributions, based mainly on multiple hydrophobic contacts with critical residues (THR22: P and SER50: Q) in such best-ranked site. Additionally, the DFT calculations revealed that the beta-blocker drug-drug complexes have a spontaneous thermodynamic stabilization following the same affinity order obtained in the docking simulations, without covalent-bond formation between both interacting beta-blockers in the best-ranked site. Lastly, experimental ultrasound density and velocity measurements were performed and allowed us to validate and corroborate the computational obtained results.

MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors.

Recently, it has been suggested that the mitochondrial oligomycin A-sensitive F0-ATPase subunit is an uncoupling channel linked to apoptotic cell death, and as such, the toxicological inhibition of mitochondrial F0-ATP hydrolase can be an interesting mitotoxicity-based therapy under pathological conditions. In addition, carbon nanotubes (CNTs) have been shown to offer higher selectivity like mitotoxic-targeting nanoparticles. In this work, linear and nonlinear classification algorithms on structure-toxicity relationships with artificial neural network (ANN) models were set up using the fractal dimensions calculated from CNTs as a source of supramolecular chemical information. The potential ability of CNT-family members to induce mitochondrial toxicity-based inhibition of the mitochondrial H+-F0F1-ATPase from in vitro assays was predicted. The attained experimental data suggest that CNTs have a strong ability to inhibit the F0-ATPase active-binding site following the order oxidized-CNT (CNT-COOH > CNT-OH) > pristine-CNT and mimicking the oligomycin A mitotoxicity behavior. Meanwhile, the performance of the ANN models was found to be improved by including different nonlinear combinations of the calculated fractal scanning electron microscopy (SEM) nanodescriptors, leading to models with excellent internal accuracy and predictivity on external data to classify correctly CNT-mitotoxic and nonmitotoxic with specificity (Sp > 98.9%) and sensitivity (Sn > 99.0%) from ANN models compared with linear approaches (LNN) with Sp ≈ Sn > 95.5%. Finally, the present study can contribute toward the rational design of carbon nanomaterials and opens new opportunities toward mitochondrial nanotoxicology-based in silico models.

Looking for New Inhibitors for the Epidermal Growth Factor Receptor.

Epidermal Growth Factor Receptor (EGFR) is still the main target of the Head and Neck Squamous Cell Cancer (HNSCC) because its overexpression has been detected in more than 90% of this type of cancer. This overexpression is usually linked with more aggressive disease, increased resistance to chemotherapy and radiotherapy, increased metastasis, inhibition of apoptosis, promotion of neoplastic angiogenesis, and, finally, poor prognosis and decreased survival. Due to this reason, the main target in the search of new drugs and inhibitors candidates is to downturn this overexpression. Quantitative Structure-Activity Relationship (QSAR) is one of the most widely used approaches while looking for new and more active inhibitors drugs. In this contest, a lot of authors used this technique, combined with others, to find new drugs or enhance the activity of well-known inhibitors. In this paper, on one hand, we will review the most important QSAR approaches developed in the last fifteen years, spacing from classical 1D approaches until more sophisticated 3D; the first paper is dated 2003 while the last one is from 2017. On the other hand, we will present a completely new QSAR approach aimed at the prediction of new EGFR inhibitors drugs. The model presented here has been developed over a dataset consisting of more than 1000 compounds using various molecular descriptors calculated with the DRAGON 7.0© software.

Cetuximab and the Head and Neck Squamous Cell Cancer.

The Head and Neck Squamous Cell Cancer (HNSCC) is the most common type of head and neck cancer (more than 90%), and all over the world more than a half million people have been developing this cancer in the last years. This type of cancer is usually marked by a poor prognosis with a really significant morbidity and mortality. Cetuximab received early favor as an exciting and promising new therapy with relatively mild side effect, and due to this, received authorization in 2004 from the European Medicines Agency (EMA) and in 2006 from the Food and Drug Association (FDA) for the treatment of patients with squamous cell cancer of the head and neck in combination with radiation therapy for locally advanced disease. In this work we will review the application and the efficacy of the Cetuximab in the treatment of the HNSCC.