Discovery, Multiparametric Optimization, and Solid-State Driven Identification of CHF-6550, a Novel Soft Dual Pharmacology Muscarinic Antagonist and ß2 Agonist (MABA) for the Inhaled Treatment of Respiratory Diseases.

Clinical guidelines for COPD and asthma recommend inhaled ß-adrenergic agonists, muscarinic antagonists, and, for frequent exacerbators, inhaled corticosteroids, with the challenge of combining them into a single device. The MABA (muscarinic antagonist and ß2 agonist) concept has the potential to simplify this complexity while increasing the efficacy of both pharmacologies. In this article, we report the outcome of our solid-state driven back-up program that led to the discovery of the MABA compound CHF-6550. A soft drug approach was applied, aiming at high plasma protein binding and high hepatic clearance, concurrently with an early stage assessment of crystallinity through a dedicated experimental workflow. A new chemotype was identified, the diphenyl hydroxyacetic esters, able to generate crystalline material. Among this class, CHF-6550 demonstrated in vivo efficacy, suitability for dry powder inhaler development, favorable pharmacokinetics, and safety in preclinical settings and was selected as a back-up candidate, fulfilling the desired pharmacological and solid-state profile.

Effective data-driven collective variables for free energy calculations from metadynamics of paths.

A variety of enhanced sampling (ES) methods predict multidimensional free energy landscapes associated with biological and other molecular processes as a function of a few selected collective variables (CVs). The accuracy of these methods is crucially dependent on the ability of the chosen CVs to capture the relevant slow degrees of freedom of the system. For complex processes, finding such CVs is the real challenge. Machine learning (ML) CVs offer, in principle, a solution to handle this problem. However, these methods rely on the availability of high-quality datasets-ideally incorporating information about physical pathways and transition states-which are difficult to access, therefore greatly limiting their domain of application. Here, we demonstrate how these datasets can be generated by means of ES simulations in trajectory space via the metadynamics of paths algorithm. The approach is expected to provide a general and efficient way to generate efficient ML-based CVs for the fast prediction of free energy landscapes in ES simulations. We demonstrate our approach with two numerical examples, a 2D model potential and the isomerization of alanine dipeptide, using deep targeted discriminant analysis as our ML-based CV of choice.

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.

Optimization of M3 Antagonist-PDE4 Inhibitor (MAPI) Dual Pharmacology Molecules for the Treatment of COPD.

Aiming at the inhaled treatment of pulmonary diseases, the optimization process of the previously reported MAPI compound 92a is herein described. The project was focused on overcoming the chemical stability issue and achieving a balanced bronchodilator/anti-inflammatory profile in rats in order to be confident in a clinical effect without having to overdose at one of the biological targets. The chemical strategy was based on fine-tuning of the substitution pattern in the muscarinic and PDE4 structural portions of the dual pharmacology compounds, also making use of the analysis of a proprietary crystal structure in the PDE4 catalytic site. Compound 10f was identified as a chemically stable, potent, and in vivo balanced MAPI lead compound, as assessed in bronchoconstriction and inflammation assays in rats after intratracheal administration. After the in-depth investigation of the pharmacological and solid-state profile, 10f proved to be safe and suitable for development.

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.

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.

Discovery of a Potent, Selective, and Orally Bioavailable Tool Compound for Probing the Role of Lysophosphatidic Acid Type 2 Receptor Antagonists in Fibrotic Disorders.

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal disease characterized by lung fibrosis leading to an irreversible decline of lung function. Current antifibrotic drugs on the market slow down but do not prevent the progression of the disease and are associated with tolerability issues. The involvement of lysophosphatidic acid receptor 2 (LPA2) in IPF is supported by LPA2 knockdown studies. To further validate the role of LPA2 receptors in modulating IPF and potentially other fibrotic processes, a potent and selective LPA2 receptor antagonist with a good pharmacokinetic (PK) profile is needed. Herein, we report the medicinal chemistry exploration that led to the discovery of a new class of highly potent and selective LPA2 antagonists. Among them, compound 58 exhibits excellent potency, selectivity, and oral PK profile, making it a suitable tool for probing the involvement of LPA2 receptors in IPF and other fibrotic processes.

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.

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.

Discovery of Clinical Candidate CHF-6366: A Novel Super-soft Dual Pharmacology Muscarinic Antagonist and ß2 Agonist (MABA) for the Inhaled Treatment of Respiratory Diseases.

The development of molecules embedding two distinct pharmacophores acting as muscarinic antagonists and ß2 agonists (MABAs) promises to be an excellent opportunity to reduce formulation issues and boost efficacy through cross-talk and allosteric interactions. Herein, we report the results of our drug discovery campaign aimed at improving the therapeutic index of a previous MABA series by exploiting the super soft-drug concept. The incorporation of a metabolic liability, stable at the site of administration but undergoing rapid systemic metabolism, to generate poorly active and quickly eliminated fragments was pursued. Our SAR studies yielded MABA 29, which demonstrated a balanced in vivo profile up to 24 h, high instability in plasma and the liver, as well as sustained exposure in the lung. In vitro safety and non-GLP toxicity studies supported the nomination of 29 (CHF-6366) as a clinical candidate, attesting to the successful development of a novel super-soft MABA compound.

Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective.

The dissociation rate (k off) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k off. Next, we discuss the impact of the potential energy function models on the accuracy of calculated k off values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.

S. aureus and IgE-mediated diseases: pilot or copilot? A narrative review.

INTRODUCTION: S. aureus is a major opportunistic pathogen that has been implicated in the pathogenesis of several chronic inflammatory diseases including bronchial asthma, chronic rhinosinusitis with nasal polyps (CRSwNP), chronic spontaneous urticaria (CSU), and atopic dermatitis. S. aureus can induce the production of both polyclonal and specific IgE that can elicit an inflammatory cascade. AREAS COVERED: The link between the sensitization to S. aureus enterotoxins and the severity of several chronic inflammatory diseases is reviewed in detail, as well as its therapeutic implications. EXPERT OPINION: An anti-IgE strategy to inhibit S. aureus enterotoxins would be a valid approach to treat several endotypes of severe asthma, CRSwNP and CSU in which IgE against S. aureus enterotoxins should represent, not only a marker of severity of the diseases but also a target of a treatment.

Indacaterol/glycopyrronium/mometasone fixed dose combination for uncontrolled asthma.

INTRODUCTION: Asthma symptoms can be relieved through a maintenance treatment combining long-acting ß2-agonist and inhaled corticosteroids (LABA/ICS). However, for patients with inadequately controlled asthma, the LABA/ICS combination might not be sufficient, and clinical guidelines recommend the administration of inhaled long-acting muscarinic antagonists (LAMA) as an add-on therapy to better control asthma and improve lung function. For nearly two decades, the only LAMA to be approved on the market has been tiotropium. AREAS COVERED: We reviewed recent clinical studies evaluating the safety and efficacy of LABA/LAMA/ICS fixed dose combinations by searching the PubMed database. Molecular mechanisms and clinical data support the use of a once-daily, single-inhaler fixed dose combination of the LABA/LAMA/ICS indacaterol/glycopyrronium/mometasone (IND/GLY/MF), the first therapy combining three agents in a fixed dose approved in Europe for the treatment of uncontrolled asthma. EXPERT OPINION: IND/GLY/MF was superior to both IND/MF and salmeterol/fluticasone, a well-established LABA/ICS combination improving the lung function in uncontrolled asthma. Moreover, IND/GLY/MF, delivered through the Breezhaler inhaler in a single inhalation, is the first inhaled therapy prescribed alongside a digital companion, a sensor and the Propeller app, allowing for improved treatment adherence, reduced rescue inhaler usage and hospitalizations, increased patient satisfaction and asthma control.

Development and Benchmarking of Open Force Field v1.0.0-the Parsley Small-Molecule Force Field.

We present a methodology for defining and optimizing a general force field for classical molecular simulations, and we describe its use to derive the Open Force Field 1.0.0 small-molecule force field, codenamed Parsley. Rather than using traditional atom typing, our approach is built on the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism, which handles increases in the diversity and specificity of the force field definition without needlessly increasing the complexity of the specification. Parameters are optimized with the ForceBalance tool, based on reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These quantum reference data are computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. In this initial application of the method, we present essentially a full optimization of all valence parameters and report tests of the resulting force field against compounds and data types outside the training set. These tests show improvements in optimized geometries and conformational energetics and demonstrate that Parsley's accuracy for liquid properties is similar to that of other general force fields, as is accuracy on binding free energies. We find that this initial Parsley force field affords accuracy similar to that of other general force fields when used to calculate relative binding free energies spanning 199 protein-ligand systems. Additionally, the resulting infrastructure allows us to rapidly optimize an entirely new force field with minimal human intervention.

Targeted Free Energy Perturbation Revisited: Accurate Free Energies from Mapped Reference Potentials.

We present an approach that extends the theory of targeted free energy perturbation (TFEP) to calculate free energy differences and free energy surfaces at an accurate quantum mechanical level of theory from a cheaper reference potential. The convergence is accelerated by a mapping function that increases the overlap between the target and the reference distributions. Building on recent work, we show that this map can be learned with a normalizing flow neural network, without requiring simulations with the expensive target potential but only a small number of single-point calculations, and, crucially, avoiding the systematic error that was found previously. We validate the method by numerically evaluating the free energy difference in a system with a double-well potential and by describing the free energy landscape of a simple chemical reaction in the gas phase.

Discovery of M3 Antagonist-PDE4 Inhibitor Dual Pharmacology Molecules for the Treatment of Chronic Obstructive Pulmonary Disease.

In this paper, we report the discovery of dual M3 antagonist-PDE4 inhibitor (MAPI) compounds for the inhaled treatment of pulmonary diseases. The identification of dual compounds was enabled by the intuition that the fusion of a PDE4 scaffold derived from our CHF-6001 series with a muscarinic scaffold through a common linking ring could generate compounds active versus both the transmembrane M3 receptor and the intracellular PDE4 enzyme. Two chemical series characterized by two different muscarinic scaffolds were investigated. SAR optimization was aimed at obtaining M3 nanomolar affinity coupled with nanomolar PDE4 inhibition, which translated into anti-bronchospastic efficacy ex vivo (inhibition of rat trachea contraction) and into anti-inflammatory efficacy in vitro (inhibition of TNFα release). Among the best compounds, compound 92a achieved the goal of demonstrating in vivo efficacy and duration of action in both the bronchoconstriction and inflammation assays in rat after intratracheal administration.

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.

Self-administration of omalizumab: why not? A literature review and expert opinion.

Introduction: Omalizumab is used to treat severe uncontrolled allergic asthma and chronic spontaneous urticaria (CSU), and is approved for self-administration in prefilled syringes. It is thus important to understand the advantages, critical issues, and indications for home administration.Areas covered: The present review summarizes the available evidence on home administration of omalizumab in asthma and CSU to illustrate the advantages derived from self-administration of patients in this setting.Expert opinion: The available data suggest that patients can safely administer biologics at home with suitable training, and that home administration is time saving and cost-effective. The majority of patients with severe asthma or CSU treated with omalizumab are likely to be suitable candidates for self-administration, which can be proposed to anyone that the clinician deems suitable. In addition to clinicians, pharmacists can also play a key role in managing patients who are prescribed home administration. A practical flow chart is proposed on selection of patients and their management during home administration. Self-administration of biologics can be considered as a valid alternative to traditional injections in a clinical setting, and the evidence has shown that no major issues need to be overcome in terms of safety or efficacy.