Breaking the Aggregation of the Monoclonal Antibody Bevacizumab (Avastin®) by Dexamethasone Phosphate: Insights from Molecular Modelling and Asymmetrical Flow Field-Flow Fractionation

ABSTRACT: Purpose: To investigate the mechanism behind the aggregation breaking properties of dexamethasone phosphate and related corticosteroids on the IgG1 antibody bevacizumab (Avastin®). Methods: An in silico 3D dimer model is developed to identify the bevacizumab-bevacizumab interface, and different corticosteroids are docked onto the model to distinguish preferred binding sites. In silico predictions are validated by in vitro stability studies, where the antibody is stressed in presence or absence of each corticosteroid and formed aggregates are quantified by asymmetrical flow field-flow fractionation. Results: The dimer model features one close crystal contact area: Lys445 on the Fc region interacts with one Fab arm of the second bevacizumab. Docking reveals an interaction between the phosphate group of dexamethasone phosphate and Lys445, while the rest of the molecule is hindering dimer formation. Predictions are confirmed in vitro, demonstrating that dexamethasone phosphate and betamethasone phosphate partly prevent antibody aggregation, whereas triamcinolone acetonide phosphate does not. Conclusions: Results suggest that bevacizumab monomers follow a specific mechanism to form dimers in which a protein-protein interaction hotspot can be distinguished. The dimer formation can be hindered by corticosteroids in a specific way. This approach allows a simple way to stabilize IgG1 antibodie

High-Throughput Prediction of the Impact of Genetic Variability on Drug Sensitivity and Resistance Patterns for Clinically Relevant Epidermal Growth Factor Receptor Mutations from Atomistic Simulations

Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and design new drugs that are effective against resistant variants before they emerge in clinical trials. To this end, we explored a variety of in silico methods, from sequence-based to "state-of-the-art" atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears or the impact of such mutations on drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to cause alterations in drug activity, and they can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on nonequilibrium alchemical free energy calculations show predictive power. The integration of these "state-of-the-art" methods into a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations

In silico pharmacology in drug discovery: methods for binding affinity prediction, identifying protein-protein interaction breakers, and finding targets for small molecules

La présente thèse traite de la conception de médicaments assistée par ordinateur : 1) Prédiction d'affinité de liaison: La « steered molecular dynamics» a permis de distinguer des différences d'affinité d'analogues de substrats de la thymidine kinase 1 du virus de l'herpès simplex aussi faibles que 4.3 μM. 2) Prévention de l'agrégation d'un anticorps: La modélisation par homologie de l'agrégation primaire de bévacizumab, le « docking » et le « scaffold hopping » ont indiqué l'AMP comme prometteur pour prévenir l'agrégation. 3) Inhibiteurs d'une interaction protéine-protéine: 43 inhibiteurs potentiels de la NADPH oxidase 2 (NOX2) ont été identifiés par criblage virtuel. Le docking a suggéré un mode de liaison pour le célastrol, et une analyse d'interaction basée sur GRID a donné des peptides avec des affinités comparables au peptide natif. 4) Découverte de cibles: 80% des cibles de la phénazopyridine identifiées par criblage in silico inverse ont été confirmées.The present thesis addresses different areas of Computer-Aided Drug Design: 1) Binding affinity prediction: Steered molecular dynamics allowed to distinguish affinity differences as small as 4.3 μM for substrate analogues of the Herpes Simplex Virus 1 Thymidine Kinase. 2) Aggregation breakers: Homology modelling of the primary aggregation of the monoclonal antibody bevacizumab as well as docking and scaffold hopping led to the identification of AMP as a promising antibody aggregation breaker. 3) Protein-protein interaction inhibitors: Virtual screening yielded 43 potential inhibitors of a protein-protein interface of the NADPH oxidase 2 (NOX2), docking led to a binding mode for celastrol, and a GRID-based interaction analysis identified peptides with binding affinities comparable to the native peptide. 4) Target discovery: Potential targets for phenazopyridine were identified by inverse in silico screening, of which 80% were confirmed experimentally

Taste Perception: Molecular Recognition of Food Molecules: Chemical Education

Molecular recognition of food molecules by ion channels and G-protein coupled receptors is the basis of taste perception. We explore the chemical nature of dietary molecules, and explore how salty, sour, sweet, bitter, and umami tastes can be explained at a molecular level

High-throughput prediction of the impact of genetic variability on drug sensitivity and resistance patterns for clinically relevant epidermal growth factor receptor mutations from atomistic simulations

Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and design new drugs that are effective against resistant variants before they emerge in clinical trials. To this end, we explored a variety of in silico methods, from sequence-based to “state-of-the-art” atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears or the impact of such mutations on drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to cause alterations in drug activity, and they can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on nonequilibrium alchemical free energy calculations show predictive power. The integration of these “state-of-the-art” methods into a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations.We are indebted to the BioExcel partners, especially Prof. de Groot’s group for helpful discussion on PMX calculations and Prof. Rosa Ma. Badia for help with the PyCOMPSs programming model. This work has been supported by the BioExcel-2: Centre of Excellence for Computational Bio- molecular Research (823830), the Spanish Ministry of Science (RTI2018-096704-B-100 and PID2020-116620GB-I00), and the Instituto de Salud Carlos III−Instituto Nacional de Bioinformática (ISCIII PT 17/0009/0007 cofunded by the Fondo Europeo de Desarrollo Regional). Funding was also provided by the MINECO Severo Ochoa Award of Excellence from the Government of Spain (awarded to IRB Barcelona). M.O. is an ICREA (Institució Catalana de Recerca i Estudis Avancats) Academia researcher. Nostrum Biodiscovery is supported by the Fundación Marcelino Botín (Mind the Gap), CDTI (Neotec grant EXP 00094141/SNEO-20161127), and a Torres Quevedo grant (PTQ2018-009992)Peer Reviewed"Article signat per 12 autors/es: Aristarc Suriñach, Adam Hospital, Yvonne Westermaier, Luis Jordà, Sergi Orozco-Ruiz, Daniel Beltrán, Francesco Colizzi, Pau Andrio, Robert Soliva, Martí Municoy, Josep Lluís Gelpí, and Modesto Orozco*"Postprint (author's final draft

Novel Mechanism for an Old Drug: Phenazopyridine is a Kinase Inhibitor Affecting Autophagy and Cellular Differentiation

Phenazopyridine is a widely used drug against urinary tract pain. The compound has also been shown to enhance neural differentiation of pluripotent stem cells. However, its mechanism of action is not understood. Based on its chemical structure, we hypothesized that phenazopyridine could be a kinase inhibitor. Phenazopyridine was investigated in the following experimental systems: 1) activity of kinases in pluripotent stem cells; 2) binding to recombinant kinases, and 3) functional impact on pluripotent stem cells. Upon addition to pluripotent stem cells, phenazopyridine induced changes in kinase activities, particularly involving Mitogen-Activated Protein Kinases, Cyclin-Dependent Kinases, and AKT pathway kinases. To identify the primary targets of phenazopyridine, we screened its interactions with 401 human kinases. Dose-inhibition curves showed that three of these kinases interacted with phenazopyridine with sub-micromolar binding affinities: cyclin-G-associated kinase, and the two phosphatidylinositol kinases PI4KB and PIP4K2C, the latter being known for participating in pain induction. Docking revealed that phenazopyridine forms strong H-bonds with the hinge region of the ATP-binding pocket of these kinases. As previous studies suggested increased autophagy upon inhibition of the phosphatidyl-inositol/AKT pathway, we also investigated the impact of phenazopyridine on this pathway and found an upregulation. In conclusion, our study demonstrates for the first time that phenazopyridine is a kinase inhibitor, impacting notably phosphatidylinositol kinases involved in nociception

NADPH oxidase (NOX) isoforms are inhibited by celastrol with a dual mode of action

International audienceBACKGROUND Celastrol is one of several bioactive compounds extracted from the medicinal plant Tripterygium wilfordii. Celastrol is used to treat inflammatory conditions, and shows benefits in models of neurodegenerative disease, cancer and arthritis, although its mechanism of action is incompletely understood. EXPERIMENTAL APPROACH Celastrol was tested on human NADPH oxidases (NOXs) using a panel of experiments: production of reactive oxygen species and oxygen consumption by NOX enzymes, xanthine oxidase activity, cell toxicity, phagocyte oxidase subunit translocation, and binding to cytosolic subunits of NOX enzymes. The effect of celastrol was compared with diphenyleneiodonium, an established inhibitor of flavoproteins. KEY RESULTS Low concentrations of celastrol completely inhibited NOX1, NOX2, NOX4 and NOX5 within minutes with concentration-response curves exhibiting higher Hill coefficients and lower IC50 values for NOX1 and NOX2 compared with NOX4 and NOX5, suggesting differences in their mode of action. In a cell-free system, celastrol had an IC50 of 1.24 and 8.4 mM for NOX2 and NOX5, respectively. Cytotoxicity, oxidant scavenging, and inhibition of p47 phox translocation could not account for NOX inhibition. Celastrol bound to a recombinant p47 phox and disrupted the binding of the proline rich region of p22 phox to the tandem SH3 domain of p47 phox and NOXO1, the cytosolic subunits of NOX2 and NOX1, respectively. CONCLUSIONS AND IMPLICATIONS These results demonstrate that celastrol is a potent inhibitor of NOX enzymes in general with increased potency against NOX1 and NOX2. Furthermore, inhibition of NOX1 and NOX2 was mediated via a novel mode of action, namely inhibition of a functional association between cytosolic subunits and the membrane flavocytochrome

Drugit: Crowd-sourcing molecular design of non-peptidic VHL binders

Given the role of human intuition in current drug design efforts, crowd-sourced \u27citizen scientist\u27 games have the potential to greatly expand the pool of potential drug designers. Here, we introduce ‘Drugit\u27, the small molecule design mode of the online ‘citizen science’ game Foldit. We demonstrate its utility for design with a use case to identify novel binders to the von Hippel Lindau E3 ligase. Several thousand molecule suggestions were obtained from players in a series of 10 puzzle rounds. The proposed molecules were then evaluated by in silico methods and by an expert panel and selected candidates were synthesized and tested. One of these molecules, designed by a player, showed dose-dependent shift perturbations in protein-observed NMR experiments. The co-crystal structure in complex with the E3 ligase revealed that the observed binding mode matched in major parts the player’s original idea. The completion of one full design cycle is a proof of concept for the Drugit approach and highlights the potential of involving citizen scientists in early drug discovery