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.

New classes of potent heparanase inhibitors from ligand-based virtual screening.

Heparanase is a validated target in cancer therapy and a potential target for several inflammatory pathologies. A ligand-based virtual screening of commercial libraries was performed to expand the chemical space of small-molecule inhibitors. The screening was based on similarity with known inhibitors and was performed in several runs, starting from literature compounds and progressing through newly discovered inhibitors. Among the fifty-five tested compounds, nineteen had IC50 values lower than 5 µM and some showed remarkable potencies. Importantly, tere- and isophthalamides derivatives belong to new structural classes of heparanase inhibitors and some of them showed enzyme affinities (61 and 63, IC50 = 0.32 and 0.12 µM, respectively) similar to those of the most potent small-molecule inhibitors reported so far. Docking studies provided a comprehensive binding hypothesis shared by compounds with significant structural diversity. The most potent inhibitors reduced cell invasiveness and inhibited the expression of proangiogenic factors in tumour cell lines.

Deep Learning to Unveil Correlations between Urban Landscape and Population Health.

The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in pollution exposure and a more sedentary lifestyle. Healthcare providers deal with increasing new challenges, and thanks to fast-developing big data technologies, they can be faced with systems that provide direct support to citizens. In this context, within the EU-funded Participatory Urban Living for Sustainable Environments (PULSE) project, we are implementing a data analytic platform designed to provide public health decision makers with advanced approaches, to jointly analyze maps and geospatial information with healthcare and air pollution data. In this paper we describe a component of such platforms, which couples deep learning analysis of urban geospatial images with healthcare indexes collected by the 500 Cities project. By applying a pre-learned deep Neural Network architecture, satellite images of New York City are analyzed and latent feature variables are extracted. These features are used to derive clusters, which are correlated with healthcare indicators by means of a multivariate classification model. Thanks to this pipeline, it is possible to show that, in New York City, health care indexes are significantly correlated to the urban landscape. This pipeline can serve as a basis to ease urban planning, since the same interventions can be organized on similar areas, even if geographically distant.

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.

Kinetic analysis and molecular modeling of the inhibition mechanism of roneparstat (SST0001) on human heparanase.

Heparanase is a ß-d-glucuronidase which cleaves heparan sulfate chains in the extracellular matrix and on cellular membranes. A dysregulated heparanase activity is intimately associated with cell invasion, tumor metastasis and angiogenesis, making heparanase an attractive target for the development of anticancer therapies. SST0001 (roneparstat; Sigma-Tau Research Switzerland S.A.) is a non-anticoagulant 100% N-acetylated and glycol-split heparin acting as a potent heparanase inhibitor, currently in phase I in advanced multiple myeloma. Herein, the kinetics of heparanase inhibition by roneparstat is reported. The analysis of dose-inhibition curves confirmed the high potency of roneparstat (IC50 ≈ 3 nM) and showed, at higher concentrations, a Hill coefficient consistent with the engagement of two molecules of inhibitor. A homology model of human heparanase GS3 construct was built and used for docking experiments with inhibitor fragments. The model has high structural similarity with the recently reported crystal structure of human heparanase. Different interaction schemes are proposed, which support the hypothesis of a complex binding mechanism involving the recruitment of one or multiple roneparstat chains, depending on its concentration. In particular, docking solutions were obtained in which (i) a single roneparstat molecule interacts with both heparin-binding domains (HBDs) of heparanase or (ii) two fragments of roneparstat interact with either HBD-1 or HBD-2, consistent with the possibility of different inhibitor:enzyme binding stoichiometries. This study provides unique insights into the mode of action of roneparstat as well as clues of its interaction with heparanase at a molecular level, which could be exploited to design novel potential inhibitor molecules.

Exploiting Free-Energy Minima to Design Novel EphA2 Protein-Protein Antagonists: From Simulation to Experiment and Return.

The free-energy surface (FES) of protein-ligand binding contains information useful for drug design. Here we show how to exploit a free-energy minimum of a protein-ligand complex identified by metadynamics simulations to design a new EphA2 antagonist with improved inhibitory potency.

Comparative Analysis of Virtual Screening Approaches in the Search for Novel EphA2 Receptor Antagonists.

The EphA2 receptor and its ephrin-A1 ligand form a key cell communication system, which has been found overexpressed in many cancer types and involved in tumor growth. Recent medicinal chemistry efforts have identified bile acid derivatives as low micromolar binders of the EphA2 receptor. However, these compounds suffer from poor physicochemical properties, hampering their use in vivo. The identification of compounds able to disrupt the EphA2-ephrin-A1 complex lacking the bile acid scaffold may lead to new pharmacological tools suitable for in vivo studies. To identify the most promising virtual screening (VS) protocol aimed at finding novel EphA2 antagonists, we investigated the ability of both ligand-based and structure-based approaches to retrieve known EphA2 antagonists from libraries of decoys with similar molecular properties. While ligand-based VSs were conducted using UniPR129 and ephrin-A1 ligand as reference structures, structure-based VSs were performed with Glide, using the X-ray structure of the EphA2 receptor/ephrin-A1 complex. A comparison of enrichment factors showed that ligand-based approaches outperformed the structure-based ones, suggesting ligand-based methods using the G-H loop of ephrin-A1 ligand as template as the most promising protocols to search for novel EphA2 antagonists.

Synthesis, structure and inhibitory activity of a stereoisomer of oseltamivir carboxylate.

A stereodivergent plan is presented leading to all eight stereoisomers of oseltamivir carboxylate (OC). Key chemical manoeuvers are (1) a three-component vinylogous Mukaiyama-Mannich reaction, which sets the whole carbon skeleton and heteroatom substituents, and (2) an intramolecular, silylative Mukaiyama aldol reaction, which creates the targeted carbocycle. The viability of the plan was demonstrated by the first total synthesis of 4-epi-oseltamivir carboxylate (6), accessed in 15 steps from glyceraldehyde, o-anisidine and pyrrole siloxydiene precursors. Compound 6 inhibits influenza A virus strains H1N1 and H3N2 at the µM level, about 150 000-fold less than the OC reference, testifying that the stereodisposition of the C4 acetamido function is key for enzyme recognition. Guided by in-depth structural evaluation including NMR solution studies, molecular mechanics simulations, docking analyses and X-ray crystallography, rationalization of the biological verdict was established.

Combining ligand- and structure-based approaches for the discovery of new inhibitors of the EPHA2-ephrin-A1 interaction.

The EPH receptor A2 (EPHA2) represents an attractive anticancer target. With the aim to identify novel EPHA2 receptor antagonists, a virtual screening campaign, combining shape-similarity and docking calculations, was conducted on a set of commercially available compounds. A combined score, taking into account both ligand- and structure-based results, was then used to identify the most promising candidates. Two compounds, selected among the best-ranked ones, were identified as EPHA2 receptor antagonists with micromolar affinity.

Synthesis and characterization of new bivalent agents as melatonin- and histamine H3-ligands.

Melatonin is an endogenous molecule involved in many pathophysiological processes. In addition to the control of circadian rhythms, its antioxidant and neuroprotective properties have been widely described. Thus far, different bivalent compounds composed by a melatonin molecule linked to another neuroprotective agent were synthesized and tested for their ability to block neurodegenerative processes in vitro and in vivo. To identify a novel class of potential neuroprotective compounds, we prepared a series of bivalent ligands, in which a prototypic melatonergic ligand is connected to an imidazole-based H3 receptor antagonist through a flexible linker. Four imidazolyl-alkyloxy-anilinoethylamide derivatives, characterized by linkers of different length, were synthesized and their binding affinity for human MT1, MT2 and H3 receptor subtypes was evaluated. Among the tested compounds, 14c and 14d, bearing a pentyl and a hexyl linker, respectively, were able to bind to all receptor subtypes at micromolar concentrations and represent the first bivalent melatonergic/histaminergic ligands reported so far. These preliminary results, based on binding affinity evaluation, pave the way for the future development of new dual-acting compounds targeting both melatonin and histamine receptors, which could represent promising therapeutic agents for the treatment of neurodegenerative pathologies.

Caught between a ROCK and a hard place: current challenges in structure-based drug design.

The discipline of structure-based drug design (SBDD) is several decades old and it is tempting to think that the proliferation of experimental structures for many drug targets might make computer-aided drug design (CADD) straightforward. However, this is far from true. In this review, we illustrate some of the challenges that CADD scientists face every day in their work, even now. We use Rho-associated protein kinase (ROCK), and public domain structures and data, as an example to illustrate some of the challenges we have experienced during our project targeting this protein. We hope that this will help to prevent unrealistic expectations of what CADD can accomplish and to educate non-CADD scientists regarding the challenges still facing their CADD colleagues.

Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities.

INTRODUCTION: A proper nutrition is essential for human life. Recently, special attention on this topic has been given in relation to three health statuses: obesity, malnutrition and specific diseases that can be related to food or treated with specific diets. Mobile technology is often used to assist users that wish to regulate their eating habits, and identifying which fields of application have been explored the most by the app developers and which main functionalities have been adopted can be useful in view of future app developments. METHODS: We selected 322 articles mentioning nutrition support apps through a literature database search, all of which have undergone an initial screening. After the exclusion of papers that were already reviews, not presenting apps or not focused on nutrition, not relevant or not developed for human subjects, 100 papers were selected for subsequent analyses that aimed at identifying the main treated conditions, outcome measures and functionalities implemented in the Apps. RESULTS: Of the selected studies, 33 focus on specific diseases, 24 on obesity, 2 on malnutrition and 41 on other targets (e.g., weight/diet control). Type 2 diabetes is the most targeted disease, followed by gestational diabetes, hypertension, colorectal cancer and CVDs which all were targeted by more than one app. Most Apps include self-monitoring and coaching functionalities, educational content and artificial intelligence (AI) tools are slightly less common, whereas counseling, gamification and questionnaires are the least implemented. Body weight and calories/nutrients were the most common general outcome measures, while glycated hemoglobin (HbA1c) was the most common clinical outcome. No statistically significant differences in the effectiveness of the different functionalities were found. CONCLUSION: The use of mobile technology to improve nutrition has been widely explored in the last years, especially for weight control and specific diseases like diabetes; however, other food-related conditions such as Irritable Bowel Diseases appear to be less targeted by newly developed smartphone apps and their related studies. All different kinds of functionalities appear to be equally effective, but further specific studies are needed to confirm the results.

Discovery and Optimization of Pyridazinones as PI3Kδ Selective Inhibitors for Administration by Inhalation.

A hit-to-lead campaign pursuing the identification of novel inhalant small-molecule phosphatidylinositol 3-kinase (PI3K) inhibitors for the treatment of inflammatory respiratory diseases is disclosed. A synthetically versatile pyridazin-3(2H)-one scaffold was designed, and three exit vectors on the core moiety were used to explore chemical diversity and optimize pharmacological and absorption, distribution, metabolism, and excretion (ADME) properties. Desired modulation of PI3Kδ selectivity and cellular potency as well as ADME properties in view of administration by inhalation was achieved. Intratracheal administration of lead compound 26 resulted in a promising pharmacokinetic profile, thus demonstrating that the optimization strategy of in vitro profiles successfully translated to an in vivo setting.

Structure-based virtual screening of MT2 melatonin receptor: influence of template choice and structural refinement.

Developing GPCR homology models for structure-based virtual screening requires the choice of a suitable template and refinement of binding site residues. We explored this systematically for the MT2 melatonin receptor, with the aim to build a receptor homology model that is optimized for the enrichment of active melatoninergic ligands. A set of 12 MT2 melatonin receptor models was built using different GPCR X-ray structural templates and submitted to a virtual screening campaign on a set of compounds composed of 29 known melatonin receptor ligands and 2560 drug-like decoys. To evaluate the effect of including a priori information in receptor models, 12 representative melatonin receptor ligands were placed into the MT2 receptor models in poses consistent with known mutagenesis data and with assessed pharmacophore models. The receptor structures were then adapted to the ligands by induced-fit docking. Most of the 144 ligand-adapted MT2 receptor models showed significant improvements in screening enrichments compared to the unrefined homology models, with some template/refinement combinations giving excellent enrichment factors. The discriminating ability of the models was further tested on the 29 active ligands plus a set of 21 inactive or low-affinity compounds from the same chemical classes. Rotameric states of side chains for some residues, presumed to be involved in the binding process, were correlated with screening effectiveness, suggesting the existence of specific receptor conformations able to recognize active compounds. The top MT2 receptor model was able to identify 24 of 29 active ligands among the first 2% of the screened database. This work provides insights into the use of refined GPCR homology models for virtual screening.

Homology models of melatonin receptors: challenges and recent advances.

Melatonin exerts many of its actions through the activation of two G protein-coupled receptors (GPCRs), named MT1 and MT2. So far, a number of different MT1 and MT2 receptor homology models, built either from the prototypic structure of rhodopsin or from recently solved X-ray structures of druggable GPCRs, have been proposed. These receptor models differ in the binding modes hypothesized for melatonin and melatonergic ligands, with distinct patterns of ligand-receptor interactions and putative bioactive conformations of ligands. The receptor models will be described, and they will be discussed in light of the available information from mutagenesis experiments and ligand-based pharmacophore models. The ability of these ligand-receptor complexes to rationalize structure-activity relationships of known series of melatonergic compounds will be commented upon.

Synthesis and structure-activity relationships of amino acid conjugates of cholanic acid as antagonists of the EphA2 receptor.

The Eph-ephrin system plays a critical role in tumor growth and vascular functions during carcinogenesis. We had previously identified cholanic acid as a competitive and reversible EphA2 antagonist able to disrupt EphA2-ephrinA1 interaction and to inhibit EphA2 activation in prostate cancer cells. Herein, we report the synthesis and biological evaluation of a set of cholanic acid derivatives obtained by conjugation of its carboxyl group with a panel of naturally occurring amino acids with the aim to improve EphA2 receptor inhibition. Structure-activity relationships indicate that conjugation of cholanic acid with linear amino acids of small size leads to effective EphA2 antagonists whereas the introduction of aromatic amino acids reduces the potency in displacement studies. The b-alanine derivative 4 was able to disrupt EphA2-ephrinA1 interaction in the micromolar range and to dose-dependently inhibit EphA2 activation on PC3 cells. These findings may help the design of novel EphA2 antagonists active on cancer cell lines.

A comparative analysis of the effects of containment policies on the epidemiological manifestation of the COVID-19 pandemic across nine European countries.

The COVID-19 pandemic has been a catastrophic event that has seriously endangered the world's population. Governments have largely been unprepared to deal with such an unprecedented calamity, partially due to the lack of sufficient or adequately fine-grained data necessary for forecasting the pandemic's evolution. To fill this gap, researchers worldwide have been collecting data about different aspects of COVID-19's evolution and government responses to them so as to provide the foundation for informative models and tools that can be used to mitigate the current pandemic and possibly prevent future ones. Indeed, since the early stages of the pandemic, a number of research initiatives were launched with this goal, including the PERISCOPE (Pan-European Response to the ImpactS of COVID-19 and future Pandemics and Epidemics) Project, funded by the European Commission. PERISCOPE aims to investigate the broad socio-economic and behavioral impacts of the COVID-19 pandemic, with the goal of making Europe more resilient and prepared for future large-scale risks. The purpose of this study, carried out as part of the PERISCOPE project, is to provide a first European-level analysis of the effect of government policies on the spread of the virus. To do so, we assessed the relationship between a novel index, the Policy Intensity Index, and four epidemiological variables collected by the European Centre for Disease Control and Prevention, and then applied a comprehensive Pan-European population model based on Multilevel Vector Autoregression. This model aims at identifying effects that are common to some European countries while treating country-specific policies as covariates, explaining the different evolution of the pandemic in nine selected countries due to data availability: Spain, France, Netherlands, Latvia, Slovenia, Greece, Ireland, Cyprus, Estonia. Results show that specific policies' effectiveness tend to vary consistently within the different countries, although in general policies related to Health Monitoring and Health Resources are the most effective for all countries.

Application of an "inhalation by design" approach to the identification and in-vitro evaluation of novel purine based PI3Kδ inhibitors.

PI3Kδ is a lipid kinase which plays a key role in airway inflammatory conditions. Accordingly, the inhibition of PI3Kδ can be considered a valuable strategy for the treatment of chronic respiratory diseases such as Asthma and Chronic obstructive pulmonary disease (COPD). In this work, we describe our efforts to identify new PI3Kδ inhibitors following an "inhalation by design" strategy. Starting from the identification of a purine scaffold, we carried out a preliminary SAR expansion which led to the identification of a new hit characterized by a high enzymatic potency and moderate PI3Kδ selectivity. A subsequent optimization led to novel purine based derivatives with favorable in vitro ADME profiles, which might represent promising starting points for future development of new inhaled drug candidates.

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review.

BACKGROUND: Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous. OBJECTIVE: This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS. METHODS: We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics. RESULTS: Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only. CONCLUSION: This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.