Benralizumab in the management of rare primary eosinophilic lung diseases.

Background: Eosinophils have a double-edged role in the human body, being essential in important physiologic functions but whose presence is conspicuous in a variety of diseases characterized by a T2 inflammation phenotype. Eosinophils are exquisitely sensitive to corticosteroids, and the latter have, until recently, represented the cornerstone of treatment of eosinophilic diseases. However, most patients remain dependent on oral corticosteroids, with a notable adverse effect burden and experience a chronic relapsing disease that leads to high morbidity and mortality. Treatment prospects have changed with the advent of biologic drugs that target the eosinotropic cytokine interleukin (IL) 5 or its receptor. The success of the latter drugs in severe eosinophilic asthma has paved the way for their use in other, rarer, eosinophilic lung diseases. Recently, mepolizumab, a humanized monoclonal antibody that works against IL-5, was approved for the add-on treatment of relapsing-remitting or refractory eosinophilic granulomatosis with polyangiitis (EGPA) in patients ages ≥ 6 years. Benralizumab, a humanized antibody that binds to the α portion of the IL-5 receptor, is also being tested for its efficacy in EGPA in two clinical trials, after a growing number of case reports and case series supported its use as a steroid-sparing agent in the treatment of EGPA. Methods: In this review, we summarized the scientific literature evaluating the efficacy of benralizumab treatment in patients afflicted with rare primary eosinophilic lung diseases. Results: The literature we found, largely case reports, reported that the use of benralizumab in EGPA, chronic eosinophilic pneumonia (CEP) and allergic bronchopulmonary aspergillosis (ABPA) often led to a depletion of eosinophils, less exacerbations and a decreased systemic corticosteroid burden. No adverse effects were reported. Conclusion: Benralizumab has a prospective role in the treatment of rare eosinophilic lung diseases, which needs to be further elucidated in randomized controlled trials.

Case Report: Severe Eosinophilic Asthma Associated With ANCA-Negative EGPA in a Young Adult Successfully Treated With Benralizumab.

Background: Traditionally, Eosinophilic Granulomatosis with Polyangiitis (EGPA) has been treated with systemic corticosteroids and immunosuppressants. In recent years, therapeutic efforts have been directed towards targeting eosinophils which represent a major player in the pathogenesis of EGPA. In 2017 the Food and Drug Administration (FDA) approved mepolizumab, a humanized monoclonal antibody targeting interleukin 5 (IL-5) which reduces the production and survival of eosinophils, already used to treat severe eosinophilic asthma, for the management of EGPA. Benralizumab is a humanized monoclonal antibody that targets the IL-5 receptor and is indicated in the treatment of severe eosinophilic asthma. Case description: We describe the case of a young female with a positive history of severe eosinophilic asthma associated with EGPA, treated successfully with benralizumab.

Safety of Medical Cannabis in Neuropathic Chronic Pain Management.

Products derived from the plant Cannabis sativa are widely appreciated for their analgesic properties and are employed for the treatment of chronic neuropathic pain. Only nabiximols, a product composed of two extracts containing similar percentages of the two cannabinoids cannabidiol and delta-9-tetrahydrocannabinol, is approved by regulatory authorities for neuropathic pain and spasticity due to multiple sclerosis in many European countries and Canada. It is also included in pharmacovigilance systems monitoring the occurrence of adverse drug reactions. However, it is not the same for the great variety of other cannabis preparations widely used for medical purposes. This creates a situation characterized by insufficient knowledge of the safety of cannabis preparations and the impossibility of establishing a correct risk-benefit profile for their medical use in the treatment of chronic neuropathic pain. With the aim to explore this issue more deeply, we collected data on adverse reactions from published clinical studies reporting the use of cannabis for neuropathic relief.

aquaPELE: A Monte Carlo-Based Algorithm to Sample the Effects of Buried Water Molecules in Proteins.

Water is frequently found inside proteins, carrying out important roles in catalytic reactions or molecular recognition tasks. Therefore, computational models that aim to study protein-ligand interactions usually have to include water effects through explicit or implicit approaches to obtain reliable results. While full explicit models might be too computationally daunting for some applications, implicit models are normally faster but omit some of the most important contributions of water. This is the case of our in-house software, called protein energy landscape exploration (PELE), which uses implicit models to speed up conformational explorations as much as possible; the lack of explicit water sampling, however, limits its model. In this work, we confront this problem with the development of aquaPELE. It is a new algorithm that extends the exploration capabilities while keeping efficiency as it employs a mixed implicit/explicit approach to also take into account the effects of buried water molecules. With an additional Monte Carlo (MC) routine, a set of explicit water molecules is perturbed inside protein cavities and their effects are dynamically adjusted to the current state of the system. As a result, this implementation can be used to predict the principal hydration sites or the rearrangement and displacement of conserved water molecules upon the binding of a ligand. We benchmarked this new tool focusing on estimating ligand binding modes and hydration sites in cavities with important interfacial water molecules, according to crystallographic structures. Results suggest that aquaPELE sets a fast and reliable alternative for molecular recognition studies in systems with a strong water-dependency.

FragPELE: Dynamic Ligand Growing within a Binding Site. A Novel Tool for Hit-To-Lead Drug Design.

The early stages of drug discovery rely on hit-to-lead programs, where initial hits undergo partial optimization to improve binding affinities for their biological target. This is an expensive and time-consuming process, requiring multiple iterations of trial and error designs, an ideal scenario for applying computer simulation. However, most state-of-the-art modeling techniques fail to provide a fast and reliable answer to the Induced-Fit protein-ligand problem. To aid in this matter, we present FragPELE, a new tool for in silico hit-to-lead drug design, capable of growing a fragment from a bound core while exploring the protein-ligand conformational space. We tested the ability of FragPELE to predict crystallographic data, even in cases where cryptic sub-pockets open because of the presence of particular R-groups. Additionally, we evaluated the potential of the software on growing and scoring five congeneric series from the 2015 FEP+ dataset, comparing them to FEP+, SP and Induced-Fit Glide, and MMGBSA simulations. Results show that FragPELE could be useful not only for finding new cavities and novel binding modes in cases where standard docking tools cannot but also to rank ligand activities in a reasonable amount of time and with acceptable precision.

Monte Carlo simulations using PELE to identify a protein-protein inhibitor binding site and pose.

In silico binding site location and pose prediction for a molecule targeted at a large protein surface is a challenging task. We report a blind test with two peptidomimetic molecules that bind the flu virus hemagglutinin (HA) surface antigen, JNJ7918 and JNJ4796 (recently disclosed in van Dongen et al., Science, 2019, 363). Tests with a series of conventional approaches such as rigid (receptor) docking against available X-ray crystal structures or against an ensemble of structures generated by quick methodologies (NMA, homology modeling) gave mixed results, due to the shallowness and flexibility of the binding site and the sheer size of the target. However, tests with our Monte Carlo platform PELE in two protocols involving either exploration of the whole protein surface (global exploration), or the latter followed by refinement of best solutions (local exploration) yielded remarkably good results by locating the actual binding site and generating binding modes that recovered all native contacts found in the X-ray structures. Thus, the Monte Carlo scheme of PELE seems promising as a quick methodology to overcome the challenge of identifying entirely unknown binding sites and modes for protein-protein disruptors.

PELE-MSM: A Monte Carlo Based Protocol for the Estimation of Absolute Binding Free Energies.

In this study, we present a fully automatic platform based on our Monte Carlo algorithm, the Protein Energy Landscape Exploration method (PELE), for the estimation of absolute protein-ligand binding free energies, one of the most significant challenges in computer aided drug design. Based on a ligand pathway approach, an initial short enhanced sampling simulation is performed to identify reasonable starting positions for more extended sampling. This stepwise approach allows for a significant faster convergence of the free energy estimation using the Markov State Model (MSM) technique. PELE-MSM was applied on four diverse protein and ligand systems, successfully ranking compounds for two systems. Based on the results, current limitations and challenges with physics-based methods in computational structural biology are discussed. Overall, PELE-MSM constitutes a promising step toward computing absolute binding free energies and in their application into drug discovery projects.

Extensive benchmark of rDock as a peptide-protein docking tool.

Peptide-protein interactions are ubiquitous in living cells and essential to a wide range of biological processes, as well as pathologies such as cancer or cardiovascular disease. Yet, obtaining reliable binding mode predictions in peptide-protein docking remains a great challenge for most computational docking programs. The main goal of this study was to assess the performance of the small molecule docking program rDock in comparison to other widely used small molecule docking programs, using 100 peptide-protein systems with peptides ranging from 2 to 12 residues. As we used two large independent benchmark sets previously published for other small-molecule docking programs (AutoDockVina benchmark and LEADSPEP), the performance of rDock could directly be compared to the performances of AutoDockVina, Surflex, GOLD, and Glide, as well as to the peptide docking protocol PIPER-FlexPepDock and the webserver HPepDock. Our benchmark reveals that rDock can dock the 100 peptides with an overall backbone RMSD below 2.5 Å in 58.5% of the cases (76% for the 47 systems of the AutoDockVina benchmark set and 43% for the 53 systems of the LEADSPEP benchmark set). More specifically, rDock docks up to 11-residue peptides with a backbone RMSD below 2.5 Å in 60.75% of the cases. rDock displays higher accuracy than most available small molecule docking programs for 6-10-residue peptides and can sometimes perform similarly to the peptide docking tool, especially at a high level of exhaustiveness (100 or 150 runs). Its performance, as is the case for many other unguided small molecule docking tools, is compromised when the peptides adopt secondary structures upon binding. However, our analyses suggest that rDock could be used for predicting how medium-sized biologically relevant peptides bind to their respective protein targets when the latter bind in an extended mode.

Reliability and validity of active and passive pectoralis minor muscle length measures.

BACKGROUND: Pectoralis minor muscle length is believed to play an important role in shoulder pain and dysfunction. Current clinical procedures for assessing pectoralis minor muscle length may not provide the most useful information for clinical decision making. OBJECTIVE: To establish the reliability and construct validity of a novel technique to measure pectoralis minor muscle length under actively and passively lengthened conditions. DESIGN: Cross-sectional repeated measures. METHODS: Thirty-four healthy adults (age: 23.9, SD=1.6 years; 18 females) participated in this study. Pectoralis minor muscle length was measured on the dominant arm in three length conditions: resting, actively lengthened, and passively lengthened. Based upon availability, two raters, out of a pool of five, used a caliper to measure the distance between the coracoid process and the 4th rib. The average of two pectoralis minor muscle length measures was used for all muscle length conditions and analyses. Intraclass correlation coefficients determined intra-and inter-rater reliability, and measurement error was determined via standard error of measurement and minimal detectable change. Construct validity was assessed by ANOVA to determine differences in muscle length across the three conditions. RESULTS: Our intra- and inter-rater reliability values across all three conditions ranged from 0.84 to 0.92 and from 0.80 to 0.90, respectively. Significant differences (p<0.001) in muscle length were found among all three conditions: rest-active (3.66; SD=1.36cm), rest-passive (4.72, SD=1.41cm), and active-passive (1.06, SD=0.47cm). CONCLUSIONS: The techniques described in this study for measuring pectoralis minor muscle length under resting and actively and passively lengthened conditions have acceptable reliability for clinical decision making.