Free energies at QM accuracy from force fields via multimap targeted estimation.

Accurate predictions of ligand binding affinities would greatly accelerate the first stages of drug discovery campaigns. However, using highly accurate interatomic potentials based on quantum mechanics (QM) in free energy methods has been so far largely unfeasible due to their prohibitive computational cost. Here, we present an efficient method to compute QM free energies from simulations using cheap reference potentials, such as force fields (FFs). This task has traditionally been out of reach due to the slow convergence of computing the correction from the FF to the QM potential. To overcome this bottleneck, we generalize targeted free energy methods to employ multiple maps-implemented with normalizing flow neural networks (NNs)-that maximize the overlap between the distributions. Critically, the method requires neither a separate expensive training phase for the NNs nor samples from the QM potential. We further propose a one-epoch learning policy to efficiently avoid overfitting, and we combine our approach with enhanced sampling strategies to overcome the pervasive problem of poor convergence due to slow degrees of freedom. On the drug-like molecules in the HiPen dataset, the method accelerates the calculation of the free energy difference of switching from an FF to a DFTB3 potential by three orders of magnitude compared to standard free energy perturbation and by a factor of eight compared to previously published nonequilibrium calculations. Our results suggest that our method, in combination with efficient QM/MM calculations, may be used in lead optimization campaigns in drug discovery and to study protein-ligand molecular recognition processes.

Determination of the quality of lipoproteins by Raman spectroscopy in obese and healthy subjects.

Lipoproteins (LPs) are multimolecular complexes of lipids and proteins responsible for transporting fatty acids, cholesterol, and micronutrients (carotenoids) through the body. The quantification of triglycerides and cholesterol carried by lipoproteins is a leading clinical parameter to assess the increased risk of cardiovascular events. However, in recent times, the study of the overall "quality" of lipoproteins, defined by their biochemical composition and oxidation state, has emerged as necessary to improve the definition of the cardiovascular risk. In this work, we present Raman spectroscopy (RS) as an effective method to immediately detect the functional groups relative to the principal biochemical components and the level of unsaturated lipids present in LPs. Furthermore, we show how RS can reveal the differences in the biochemical composition and oxidation state of LPs extracted from a cohort of obese patients (Ob) and a control group of healthy subjects (HC). In particular, RS revealed how low-density lipoproteins (LDLs) from obese patients are enriched in triglycerides and more oxidized than those from the control group, while high-density lipoproteins (HDLs) from Ob patients were depleted in cholesterol and phospholipids. RS analysis also allowed the study of the relationship between the levels of carotenoids present in the different classes of LPs highlighting how this parameter depends on the disease severity. Overall, these results demonstrated that RS is a viable approach for quickly and effectively gaining information on LPs' biochemical composition and oxidation state, providing an immediate measure of their quality. Besides, RS further proved the role of LPs in obesity and metabolic dysfunctions.

Unbinding Kinetics of Muscarinic M3 Receptor Antagonists Explained by Metadynamics Simulations.

The residence time (RT), the time for which a drug remains bound to its biological target, is a critical parameter for drug design. The prediction of this key kinetic property has been proven to be challenging and computationally demanding in the framework of atomistic simulations. In the present work, we setup and applied two distinct metadynamics protocols to estimate the RTs of muscarinic M3 receptor antagonists. In the first method, derived from the conformational flooding approach, the kinetics of unbinding is retrieved from a physics-based parameter known as the acceleration factor α (i.e., the running average over time of the potential deposited in the bound state). Such an approach is expected to recover the absolute RT value for a compound of interest. In the second method, known as the tMETA-D approach, a qualitative estimation of the RT is given by the time of simulation required to drive the ligand from the binding site to the solvent bulk. This approach has been developed to reproduce the change of experimental RTs for compounds targeting the same target. Our analysis shows that both computational protocols are able to rank compounds in agreement with their experimental RTs. Quantitative structure-kinetics relationship (SKR) models can be identified and employed to predict the impact of a chemical modification on the experimental RT once a calibration study has been performed.

Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations.

The initial phases of drug discovery - in silico drug design - could benefit from first principle Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) simulations in explicit solvent, yet many applications are currently limited by the short time scales that this approach can cover. Developing scalable first principle QM/MM MD interfaces fully exploiting current exascale machines - so far an unmet and crucial goal - will help overcome this problem, opening the way to the study of the thermodynamics and kinetics of ligand binding to protein with first principle accuracy. Here, taking two relevant case studies involving the interactions of ligands with rather large enzymes, we showcase the use of our recently developed massively scalable Multiscale Modeling in Computational Chemistry (MiMiC) QM/MM framework (currently using DFT to describe the QM region) to investigate reactions and ligand binding in enzymes of pharmacological relevance. We also demonstrate for the first time strong scaling of MiMiC-QM/MM MD simulations with parallel efficiency of ∼70% up to >80,000 cores. Thus, among many others, the MiMiC interface represents a promising candidate toward exascale applications by combining machine learning with statistical mechanics based algorithms tailored for exascale supercomputers.

A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar.

Identifying a reduced set of collective variables is critical for understanding atomistic simulations and accelerating them through enhanced sampling techniques. Recently, several methods have been proposed to learn these variables directly from atomistic data. Depending on the type of data available, the learning process can be framed as dimensionality reduction, classification of metastable states, or identification of slow modes. Here, we present mlcolvar, a Python library that simplifies the construction of these variables and their use in the context of enhanced sampling through a contributed interface to the PLUMED software. The library is organized modularly to facilitate the extension and cross-contamination of these methodologies. In this spirit, we developed a general multi-task learning framework in which multiple objective functions and data from different simulations can be combined to improve the collective variables. The library's versatility is demonstrated through simple examples that are prototypical of realistic scenarios.

Acute myocardial infarction and large coronary thrombosis in a patient with COVID-19.

This is a case report of a 60-year-old male, without any cardiovascular risk factor and no cardiac history admitted to hospital with a diagnosis of interstitial pneumonia caused by coronavirus disease 2019 (COVID-19). After 7 days, the blood tests showed a significant rise of inflammatory and procoagulant markers, along with a relevant elevation of high-sensitivity Troponin I. Electrocardiogram and transthoracic echocardiogram (TTE) were consistent with a diagnosis of infero-posterolateral acute myocardial infarction and the patient was transferred to the isolated Cath Lab for primary percutaneous coronary intervention (PCI). The angiography showed an acute massive thrombosis of a dominant right coronary artery without clear evidence of atherosclerosis. Despite the optimal pharmacological therapies and different PCI techniques, the final TIMI flow was 0/1 and after 3 hr the clinical condition evolved in cardiac arrest for pulseless electric activity. Acute coronary syndrome-ST-elevation myocardial infarction is a relevant complication of COVID-19. Due to high levels of proinflammatory mediators, diffuse coronary thrombosis could occur even in patients without cardiac history or comorbidities. This clinical case suggests that coronary thrombosis in COVID-19 patients may be unresponsive to optimal pharmacological (GP IIb-IIIa infusion) and mechanical treatment (PCI).

Discovery of a novel class of inhaled dual pharmacology muscarinic antagonist and ß2 agonist (MABA) for the treatment of chronic obstructive pulmonary disease (COPD).

The targeting of both the muscarinic and ß-adrenergic pathways is a well validated therapeutic approach for the treatment of chronic obstructive pulmonary disease (COPD). In this communication we report our effort to incorporate two pharmacologies into a single chemical entity, whose characteristic must be suitable for a once daily inhaled administration. Contextually, we aimed at a locally acting therapy with limited systemic absorption to minimize side effects. Our lung-tailored design of bifunctional compounds that combine the muscarinic and ß-adrenergic pharmacologies by the elaboration of the muscarinic inhibitor 7, successfully led to the potent, pharmacologically balanced muscarinic antagonist and ß2 agonist (MABA) 13.

Overview of the SAMPL6 pKa challenge: evaluating small molecule microscopic and macroscopic pKa predictions.

The prediction of acid dissociation constants (pKa) is a prerequisite for predicting many other properties of a small molecule, such as its protein-ligand binding affinity, distribution coefficient (log D), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 pKa Challenge was the first time that a separate challenge was conducted for evaluating pKa predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises. This challenge was motivated by significant inaccuracies observed in prior physical property prediction challenges, such as the SAMPL5 log D Challenge, caused by protonation state and pKa prediction issues. The goal of the pKa challenge was to assess the performance of contemporary pKa prediction methods for drug-like molecules. The challenge set was composed of 24 small molecules that resembled fragments of kinase inhibitors, a number of which were multiprotic. Eleven research groups contributed blind predictions for a total of 37 pKa distinct prediction methods. In addition to blinded submissions, four widely used pKa prediction methods were included in the analysis as reference methods. Collecting both microscopic and macroscopic pKa predictions allowed in-depth evaluation of pKa prediction performance. This article highlights deficiencies of typical pKa prediction evaluation approaches when the distinction between microscopic and macroscopic pKas is ignored; in particular, we suggest more stringent evaluation criteria for microscopic and macroscopic pKa predictions guided by the available experimental data. Top-performing submissions for macroscopic pKa predictions achieved RMSE of 0.7-1.0 pKa units and included both quantum chemical and empirical approaches, where the total number of extra or missing macroscopic pKas predicted by these submissions were fewer than 8 for 24 molecules. A large number of submissions had RMSE spanning 1-3 pKa units. Molecules with sulfur-containing heterocycles or iodo and bromo groups were less accurately predicted on average considering all methods evaluated. For a subset of molecules, we utilized experimentally-determined microstates based on NMR to evaluate the dominant tautomer predictions for each macroscopic state. Prediction of dominant tautomers was a major source of error for microscopic pKa predictions, especially errors in charged tautomers. The degree of inaccuracy in pKa predictions observed in this challenge is detrimental to the protein-ligand binding affinity predictions due to errors in dominant protonation state predictions and the calculation of free energy corrections for multiple protonation states. Underestimation of ligand pKa by 1 unit can lead to errors in binding free energy errors up to 1.2 kcal/mol. The SAMPL6 pKa Challenge demonstrated the need for improving pKa prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules.

Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge.

The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the octanol-water partition coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 p[Formula: see text] Challenge, which asked participants to predict p[Formula: see text] values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of octanol-water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental octanol-water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 ± 0.13, 0.48 ± 0.06, 0.47 ± 0.05, and 0.50 ± 0.06, respectively.

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations.

Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange-while displaying very small variance-can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

Preserving air pollution forest archives accessible through dendrochemistry.

Plants are continuously exposed to human air pollution, absorbing pollutants in their tissues. Trees can store pollutants in wood, in the annual growth rings, retaining traces of pollutants in the environment. Information on past pollution events are archived by trees, which dendrochemistry, a dendrochronological science combined with chemistry, is able to access. Many authors have suggested that trees could complement the conventional environmental monitoring: a forest archive of pollution events. However, the implications of trees occurrence in polluted areas on planning and management have not yet been discussed. In this article, we investigate whether forest archives exist and whether they should be integrated into the network of existing monitoring stations. We use a case study, the Veneto region of Italy, one of the most polluted areas in Europe, to examine the occurrence of trees around 28 industrial plants retrieved from a European pollution register. We propose planning actions to develop the latent potential of these forest archives for environmental monitoring, which society may benefit. We follow three steps: (a) assessing the cover and composition of tree canopies around the industrial plants, (b) inventorying the existing artificial air monitoring stations in order to discover whether pollutants around the industrial plants are already monitored, (c) assessing land use patterns in order to identify which are the receptors of air pollution and enhance the forest archive in the future. These spatial analyses are conducted in a 1-km radius buffer with the industrial plant as the centre. Results show that forest archives are available, with cover and composition suitable for dendrochemistry studies. Artificial monitoring stations are too far from industrial plants or have been installed recently, unable to provide historical data. Trees are an alternative source of pollution data. Receptors of air pollution include a diversity of urban, rural and agricultural lands, where forest archives can be managed and conserved through a variety of actions. Environmental protection agencies should value these trees, preserving them and accessing the records held in this forest archive. Similar inventories must be promoted in other industrialised regions of the world even at larger scales. Studies like this one should also be incorporated into landscape or urban planning processes.

Computational Fluorine Scanning Using Free-Energy Perturbation.

We present perturbative fluorine scanning, a computational fluorine scanning approach using free-energy perturbation. This method can be applied to molecular dynamics simulations of a single compound and make predictions for the best binders out of numerous fluorinated analogues. We tested the method on nine test systems: renin, DPP4, menin, P38, factor Xa, CDK2, AKT, JAK2, and androgen receptor. The predictions were in excellent agreement with more rigorous alchemical free-energy calculations and in good agreement with experimental data for most of the test systems. However, the agreement with experiment was very poor in some of the test systems, and this highlights the need for improved force fields in addition to accurate treatment of tautomeric and protonation states. The method is of particular interest due to the wide use of fluorine in medicinal chemistry to improve binding affinity and ADME properties. The promising results on this test case suggest that perturbative fluorine scanning will be a useful addition to the available arsenal of free-energy methods.

Ileal interposition coupled with duodenal diverted sleeve gastrectomy versus standard medical treatment in type 2 diabetes mellitus obese patients: long-term results of a case-control study.

BACKGROUND: Randomized controlled trials have demonstrated that bariatric surgery is effective in obtaining remission of type 2 diabetes mellitus (T2DM) in obese patients, yet no data exist in the literature from prospective studies with ileal interposition with duodenal diversion sleeve gastrectomy (II-DD-SG). The aim of this case-control study is to investigate if II-DD-SG is superior to medical treatment in T2DM obese patients. METHODS: Thirty obese patients (BMI > 30) affected by T2DM were recruited for surgery (II-DD-SG) between 2008 and 2011 and were matched with an equal control group which received standard medical treatment. Anthropometric measures, glucose metabolism, cardiovascular risk factors were determined baseline and during follow-up. The primary end point was T2DM remission; reduction of body weight, BMI, and cardiovascular risk factors were secondary end-points. RESULTS: Shortly after II-DD-SG, normalization of glucose plasma levels and glycated hemoglobin was observed followed by a significant decrease in body weight and BMI. At one-year follow-up, insulin resistance strongly declined as did insulin plasma levels. Complete remission was observed in 26 patients (86%); 2 (6.6%) had partial remission, and two (6.6%) were still diabetic. After 5 years, 17 of 25 patients on follow-up (68%) showed complete remission of T2DM and 56% had remission of cardiovascular risk factors. Only two patients receiving medical treatment showed complete remission of T2DM (p < 0.0001 versus II-DD-SG). No significant changes of anthropometric parameters and lipid metabolism were recorded. CONCLUSIONS: II-DD-SG is an effective surgical procedure, able to induce complete and prolonged remission of T2DM in obese patients as opposed to medical treatment.

Overview of the SAMPL6 host-guest binding affinity prediction challenge.

Accurately predicting the binding affinities of small organic molecules to biological macromolecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from ten participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host-guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host-guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host-guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states), may be required to further enhance predictive accuracy.

Long-term effects of inhaled corticosteroids on sputum bacterial and viral loads in COPD.

Inhaled corticosteroid-containing medications reduce the frequency of COPD exacerbations (mainly infectious in origin) while paradoxically increasing the risk of other respiratory infections. The aim was to determine the effects of inhaled corticosteroids on airway microbial load in COPD patients and evaluate the influence of the underlying inflammatory profile on airway colonisation and microbiome.This is a proof-of-concept prospective, randomised, open-label, blinded endpoint study. Sixty patients with stable moderate COPD were randomised to receive one inhalation twice daily of either a combination of salmeterol 50 µg plus fluticasone propionate 500 µg or salmeterol 50 µg for 12 months. The primary outcome was the change of sputum bacterial loads over the course of treatment.Compared with salmeterol, 1-year treatment with salmeterol plus fluticasone was associated with a significant increase in sputum bacterial load (p=0.005), modification of sputum microbial composition and increased airway load of potentially pathogenic bacteria. The increased bacterial load was observed only in inhaled corticosteroid-treated patients with lower baseline sputum or blood eosinophil (≤2%) levels but not in patients with higher baseline eosinophils.Long-term inhaled corticosteroid treatment affects bacterial load in stable COPD. Lower eosinophil counts are associated with increased airway bacterial load.

Metadynamics Simulations Distinguish Short- and Long-Residence-Time Inhibitors of Cyclin-Dependent Kinase 8.

The duration of drug efficacy in vivo is a key aspect primarily addressed during the lead optimization phase of drug discovery. Hence, the availability of robust computational approaches that can predict the residence time of a compound at its target would accelerate candidate selection. Nowadays the theoretical prediction of this parameter is still very challenging. Starting from methods reported in the literature, we set up and validated a new metadynamics (META-D)-based protocol that was used to rank the experimental residence times of 10 arylpyrazole cyclin-dependent kinase 8 (CDK8) inhibitors for which target-bound X-ray structures are available. The application of reported methods based on the detection of the escape from the first free energy well gave a poor correlation with the experimental values. Our protocol evaluates the energetics of the whole unbinding process, accounting for multiple intermediates and transition states. Using seven collective variables (CVs) encoding both roto-translational and conformational motions of the ligand, a history-dependent biasing potential is deposited as a sum of constant-height Gaussian functions until the ligand reaches an unbound state. The time required to achieve this state is proportional to the integral of the deposited potential over the CV hyperspace. Average values of this time, for replicated META-D simulations, provided an accurate classification of CDK8 inhibitors spanning short, medium, and long residence times.

Phenomenology of COPD: interpreting phenotypes with the ECLIPSE study.

The Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE) study was a large 3-year observational multicentre international study aimed at defining COPD phenotypes and identifying biomarkers and/or genetic parameters that help to predict disease progression. The study has contributed to a better understanding of COPD heterogeneity, with the characterization of clinically important subtypes/phenotypes of patients, such as the frequent exacerbators or patient with persistent systemic inflammation, who may have different prognosis or treatment requirements. Because of the big amount of information that is starting to be produced from metabolomic, proteomic and genomic approaches, one of the biggest challenges is the integration of data in a biological prospective such as clinical prognosis and response to medicinal products. In this article we highlight some of the progress in phenotyping the heterogeneity of the disease that have been made thanks to the analyses of this longitudinal study.

CHF6001 I: a novel highly potent and selective phosphodiesterase 4 inhibitor with robust anti-inflammatory activity and suitable for topical pulmonary administration.

This study examined the pharmacologic characterization of CHF6001 [(S)-3,5-dichloro-4-(2-(3-(cyclopropylmethoxy)-4-(difluoromethoxy)phenyl)-2-(3-(cyclopropylmethoxy)-4-(methylsulfonamido)benzoyloxy)ethyl)pyridine 1-oxide], a novel phosphodiesterase (PDE)4 inhibitor designed for treating pulmonary inflammatory diseases via inhaled administration. CHF6001 was 7- and 923-fold more potent than roflumilast and cilomilast, respectively, in inhibiting PDE4 enzymatic activity (IC50 = 0.026 ± 0.006 nM). CHF6001 inhibited PDE4 isoforms A-D with equal potency, showed an elevated ratio of high-affinity rolipram binding site versus low-affinity rolipram binding site (i.e., >40) and displayed >20,000-fold selectivity versus PDE4 compared with a panel of PDEs. CHF6001 effectively inhibited (subnanomolar IC50 values) the release of tumor necrosis factor-α from human peripheral blood mononuclear cells, human acute monocytic leukemia cell line macrophages (THP-1), and rodent macrophages (RAW264.7 and NR8383). Moreover, CHF6001 potently inhibited the activation of oxidative burst in neutrophils and eosinophils, neutrophil chemotaxis, and the release of interferon-γ from CD4(+) T cells. In all these functional assays, CHF6001 was more potent than previously described PDE4 inhibitors, including roflumilast, UK-500,001 [2-(3,4-difluorophenoxy)-5-fluoro-N-((1S,4S)-4-(2-hydroxy-5-methylbenzamido)cyclohexyl)nicotinamide], and cilomilast, and it was comparable to GSK256066 [6-((3-(dimethylcarbamoyl)phenyl)sulfonyl)-4-((3-methoxyphenyl)amino)-8-methylquinoline-3-carboxamide]. When administered intratracheally to rats as a micronized dry powder, CHF6001 inhibited liposaccharide-induced pulmonary neutrophilia (ED50 = 0.205 µmol/kg) and leukocyte infiltration (ED50 = 0.188 µmol/kg) with an efficacy comparable to a high dose of budesonide (1 µmol/kg i.p.). In sum, CHF6001 has the potential to be an effective topical treatment of conditions associated with pulmonary inflammation, including asthma and chronic obstructive pulmonary disease.

Osteopetrosis: a Case of 'Hostile Chest' Associated with Symptomatic Aortic Valve Stenosis.

Osteopetrosis is a heterogeneous group of heritable conditions in which there is a defect in bone resorption by osteoclasts. It is associated with an increased skeletal mass due to abnormally dense, but brittle, bones. Osteopetrosis varies greatly in severity, and fracture treatment remains a matter of controversy due to altered responses to fixation and the risk of osteomyelitis. The fate of sternotomy in this condition is unknown, and osteopetrosis could represent a situation of 'hostile chest'. Here, the case is described of a patient with osteopetrosis and concomitant symptomatic aortic valve stenosis and coronary artery disease.

Asthma under/misdiagnosis in primary care setting: an observational community-based study in Italy.

BACKGROUND: Published data suggest that asthma is significantly under/misdiagnosed. The present community-based study performed in Italy aims at investigating the level of asthma under/misdiagnosis among patients referring to the General Practitioner (GP) for respiratory symptoms and undergoing Inhaled corticosteroids. METHODS: A sub-analysis of a previously published observational cross-sectional study has been provided. It included subjects registered in the GP databases with at least three prescriptions of inhaled or nebulised corticosteroids during the 12 months preceding the start of the study. All subjects, independently of the diagnosis, were invited to visit their GP's office for a standardised interview and to fill the European Community Respiratory Health Survey (ECRHS) questionnaire. RESULTS: The studies involved 540 GPs in most of the Italian regions and 2090 subjects (mean age 54.9 years, 54.1 % females) were enrolled. Among them 991 cases of physician-diagnosed asthma were observed while 1099 subjects received a diagnosis other than asthma (chronic obstructive pulmonary disease, chronic upper respiratory tract infections etc.). Among the lasts, the ECRHS questionnaire was suggestive for asthma diagnosis in 365 subjects (33.2 %). CONCLUSIONS: The data suggest that there is still a large under/misdiagnosis of asthma in the Italian primary care setting, despite the spread of GINA guidelines nearly 20 years before this study. A validated tool like the ECRHS questionnaire has detected a considerable proportion of potentially asthmatic patients who should be addressed to lung function assessment to confirm the diagnosis. Further educational efforts directed to the GPs are needed to improve their diagnosis of asthma (SAM104964).