Discovery of Clinical Candidate AZD5462, a Selective Oral Allosteric RXFP1 Agonist for Treatment of Heart Failure.

Optimization of the highly potent and selective, yet metabolically unstable and poorly soluble hRXFP1 agonist AZ7976 led to the identification of the clinical candidate, AZD5462. Assessment of RXFP1-dependent cell signaling demonstrated that AZD5462 activates a highly similar panel of downstream pathways as relaxin H2 but does not modulate relaxin H2-mediated cAMP second messenger responsiveness. The therapeutic potential of AZD5462 was assessed in a translatable cynomolgus monkey heart failure model. Following 8 weeks of treatment with AZD5462, robust improvements in functional cardiac parameters including LVEF were observed at weeks 9, 13, and 17 without changes in heart rate or mean arterial blood pressure. AZD5462 was well tolerated in both rat and cynomolgus monkey and has successfully completed phase I studies in healthy volunteers. In summary, AZD5462 is a small molecule pharmacological mimetic of relaxin H2 signaling at RXFP1 and holds promise as a potential therapeutic approach to treat heart failure patients.

Lipid shape and packing are key for optimal design of pH-sensitive mRNA lipid nanoparticles.

The ionizable-lipid component of RNA-containing nanoparticles controls the pH-dependent behavior necessary for an efficient delivery of the cargo-the so-called endosomal escape. However, it is still an empirical exercise to identify optimally performing lipids. Here, we study two well-known ionizable lipids, DLin-MC3-DMA and DLin-DMA using a combination of experiments, multiscale computer simulations, and electrostatic theory. All-atom molecular dynamics simulations, and experimentally measured polar headgroup pKa values, are used to develop a coarse-grained representation of the lipids, which enables the investigation of the pH-dependent behavior of lipid nanoparticles (LNPs) through Monte Carlo simulations, in the absence and presence of RNA molecules. Our results show that the charge state of the lipids is determined by the interplay between lipid shape and headgroup chemistry, providing an explanation for the similar pH-dependent ionization state observed for lipids with headgroup pKa values about one-pH-unit apart. The pH dependence of lipid ionization is significantly influenced by the presence of RNA, whereby charge neutrality is achieved by imparting a finite and constant charge per lipid at intermediate pH values. The simulation results are experimentally supported by measurements of α-carbon 13C-NMR chemical shifts for eGFP mRNA LNPs of both DLin-MC3-DMA and DLin-DMA at various pH conditions. Further, we evaluate the applicability of a mean-field Poisson-Boltzmann theory to capture these phenomena.

Multiomics analysis of naturally efficacious lipid nanoparticle coronas reveals high-density lipoprotein is necessary for their function.

In terms of lipid nanoparticle (LNP) engineering, the relationship between particle composition, delivery efficacy, and the composition of the biocoronas that form around LNPs, is poorly understood. To explore this we analyze naturally efficacious biocorona compositions using an unbiased screening workflow. First, LNPs are complexed with plasma samples, from individual lean or obese male rats, and then functionally evaluated in vitro. Then, a fast, automated, and miniaturized method retrieves the LNPs with intact biocoronas, and multiomics analysis of the LNP-corona complexes reveals the particle corona content arising from each individual plasma sample. We find that the most efficacious LNP-corona complexes were enriched with high-density lipoprotein (HDL) and, compared to the commonly used corona-biomarker Apolipoprotein E, corona HDL content was a superior predictor of in-vivo activity. Using technically challenging and clinically relevant lipid nanoparticles, these methods reveal a previously unreported role for HDL as a source of ApoE and, form a framework for improving LNP therapeutic efficacy by controlling corona composition.

Machine learning/molecular dynamic protein structure prediction approach to investigate the protein conformational ensemble.

Proteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings show that co-evolutionary analysis coupled with machine-learning techniques improves the precision by providing quantitative distance predictions between pairs of residues. The predicted statistical distance distribution from Multi Sequence Analysis reveals the presence of different local maxima suggesting the flexibility of key residue pairs. Here we investigate the ability of the residue-residue distance prediction to provide insights into the protein conformational ensemble. We combine deep learning approaches with mechanistic modeling to a set of proteins that experimentally showed conformational changes. The predicted protein models were filtered based on energy scores, RMSD clustering, and the centroids selected as the lowest energy structure per cluster. These models were compared to the experimental-Molecular Dynamics (MD) relaxed structure by analyzing the backbone residue torsional distribution and the sidechain orientations. Our pipeline allows to retrieve the experimental structural dynamics experimentally represented by different X-ray conformations for the same sequence as well the conformational space observed with the MD simulations. We show the potential correlation between the experimental structure dynamics and the predicted model ensemble demonstrating the susceptibility of the current state-of-the-art methods in protein folding and dynamics prediction and pointing out the areas of improvement.

Novel Chemical Series of 5-Lipoxygenase-Activating Protein Inhibitors for Treatment of Coronary Artery Disease.

5-Lipoxygenase (5-LO)-activating protein (FLAP) inhibitors have proven to attenuate 5-LO pathway activity and leukotriene production in human clinical trials. However, previous clinical candidates have been discontinued and the link between FLAP inhibition and outcome in inflammatory diseases remains to be established. We here describe a novel series of FLAP inhibitors identified from a screen of 10k compounds and the medicinal chemistry strategies undertaken to progress this series. Compound 4i showed good overall properties and a pIC50 hWBfree of 8.1 and an lipophilic ligand efficiency of 5.2. Target engagement for 4i was established in dogs using ex vivo measurement of leukotriene B4 (LTB4) levels in blood with good correlation to in vitro potency. A predicted human dose of 280 mg b.i.d. suggests a wide margin to any identified in vitro off-target effects and sufficient exposure to achieve an 80% reduction of LTB4 levels in humans. Compound 4i is progressed to preclinical in vivo safety studies.

Discovery and Early Clinical Development of an Inhibitor of 5-Lipoxygenase Activating Protein (AZD5718) for Treatment of Coronary Artery Disease.

5-Lipoxygenase activating protein (FLAP) inhibitors attenuate 5-lipoxygenase pathway activity and reduce the production of proinflammatory and vasoactive leukotrienes. As such, they are hypothesized to have therapeutic benefit for the treatment of diseases that involve chronic inflammation including coronary artery disease. Herein, we disclose the medicinal chemistry discovery and the early clinical development of the FLAP inhibitor AZD5718 (12). Multiparameter optimization included securing adequate potency in human whole blood, navigation away from Ames mutagenic amine fragments while balancing metabolic stability and PK properties allowing for clinically relevant exposures after oral dosing. The superior safety profile of AZD5718 compared to earlier frontrunner compounds allowed us to perform a phase 1 clinical study in which AZD5718 demonstrated a dose dependent and greater than 90% suppression of leukotriene production over 24 h. Currently, AZD5718 is evaluated in a phase 2a study for treatment of coronary artery disease.

Exploring Binding Mechanisms in Nuclear Hormone Receptors by Monte Carlo and X-ray-derived Motions.

In this study, we performed an extensive exploration of the ligand entry mechanism for members of the steroid nuclear hormone receptor family (androgen receptor, estrogen receptor α, glucocorticoid receptor, mineralocorticoid receptor, and progesterone receptor) and their endogenous ligands. The exploration revealed a shared entry path through the helix 3, 7, and 11 regions. Examination of the x-ray structures of the receptor-ligand complexes further showed two distinct folds of the helix 6-7 region, classified as "open" and "closed", which could potentially affect ligand binding. To improve sampling of the helix 6-7 loop, we incorporated motion modes based on principal component analysis of existing crystal structures of the receptors and applied them to the protein-ligand sampling. A detailed comparison with the anisotropic network model (an elastic network model) highlights the importance of flexibility in the entrance region. While the binding (interaction) energy of individual simulations can be used to score different ligands, extensive sampling further allows us to predict absolute binding free energies and analyze reaction kinetics using Markov state models and Perron-cluster cluster analysis, respectively. The predicted relative binding free energies for three ligands binding to the progesterone receptor are in very good agreement with experimental results and the Perron-cluster cluster analysis highlighted the importance of a peripheral binding site. Our analysis revealed that the flexibility of the helix 3, 7, and 11 regions represents the most important factor for ligand binding. Furthermore, the hydrophobicity of the ligand influences the transition between the peripheral and the active binding site.

Intramolecular Hydrogen Bond Expectations in Medicinal Chemistry.

Design strategies centered on intramolecular hydrogen bonds are sometime used in drug discovery, but their general applicability has not been addressed beyond scattered examples or circumstantial evidence. A total of 1053 matched molecular pairs where only one of the two molecules is able to form an intramolecular hydrogen bond via monatomic transformations have been identified across the ChEMBL database. These pairs were used to investigate the effect of intramolecular hydrogen bonds on biological activity. While cases of extreme, conflicting variation of effect emerge, the mean biological activity difference for a pair is close to zero and does not exceed ±0.5 log biological activity for over 50% of the analyzed sample.

Binding thermodynamics discriminates fragments from druglike compounds: a thermodynamic description of fragment-based drug discovery.

Small is beautiful - reducing the size and complexity of chemical starting points for drug design allows better sampling of chemical space, reveals the most energetically important interactions within protein-binding sites and can lead to improvements in the physicochemical properties of the final drug. The impact of fragment-based drug discovery (FBDD) on recent drug discovery projects and our improved knowledge of the structural and thermodynamic details of ligand binding has prompted us to explore the relationships between ligand-binding thermodynamics and FBDD. Information on binding thermodynamics can give insights into the contributions to protein-ligand interactions and could therefore be used to prioritise compounds with a high degree of specificity in forming key interactions.

A comprehensive company database analysis of biological assay variability.

Analysis of data from various compounds measured in diverse biological assays is a central part of drug discovery research projects. However, no systematic overview of the variability in biological assays has been published and judgments on assay quality and robustness of data are often based on personal belief and experience within the drug discovery community. To address this we performed a reproducibility analysis of all biological assays at AstraZeneca between 2005 and 2014. We found an average experimental uncertainty of less than a twofold difference and no technologies or assay types had higher variability than others. This work suggests that robust data can be obtained from the most commonly applied biological assays.

Binding Mode and Induced Fit Predictions for Prospective Computational Drug Design.

Computer-aided drug design plays an important role in medicinal chemistry to obtain insights into molecular mechanisms and to prioritize design strategies. Although significant improvement has been made in structure based design, it still remains a key challenge to accurately model and predict induced fit mechanisms. Most of the current available techniques either do not provide sufficient protein conformational sampling or are too computationally demanding to fit an industrial setting. The current study presents a systematic and exhaustive investigation of predicting binding modes for a range of systems using PELE (Protein Energy Landscape Exploration), an efficient and fast protein-ligand sampling algorithm. The systems analyzed (cytochrome P, kinase, protease, and nuclear hormone receptor) exhibit different complexities of ligand induced fit mechanisms and protein dynamics. The results are compared with results from classical molecular dynamics simulations and (induced fit) docking. This study shows that ligand induced side chain rearrangements and smaller to medium backbone movements are captured well in PELE. Large secondary structure rearrangements, however, remain challenging for all employed techniques. Relevant binding modes (ligand heavy atom RMSD < 1.0 Å) can be obtained by the PELE method within a few hours of simulation, positioning PELE as a tool applicable for rapid drug design cycles.

Ligand Binding Thermodynamics in Drug Discovery: Still a Hot Tip?

The use of ligand binding thermodynamics has been proposed as a potential success factor to accelerate drug discovery. However, despite the intuitive appeal of optimizing binding enthalpy, a number of factors complicate routine use of thermodynamic data. On a macroscopic level, a range of experimental parameters including temperature and buffer choice significantly influence the observed thermodynamic signatures. On a microscopic level, solute effects, structural flexibility, and cooperativity lead to nonlinear changes in enthalpy. This multifactorial character hides essential enthalpy contributions of intermolecular contacts, making them experimentally nonobservable. In this perspective, we present three case studies, reflect on some key factors affecting thermodynamic signatures, and investigate their relation to the hydrophobic effect, enthalpy-entropy compensation, lipophilic ligand efficiency, and promiscuity. The studies highlight that enthalpy and entropy cannot be used as direct end points but can together with calculations increase our understanding of ligand binding and identify interesting outliers that do not behave as expected.

Computational prediction of drug solubility in fasted simulated and aspirated human intestinal fluid.

PURPOSE: To develop predictive models of apparent solubility (Sapp) of lipophilic drugs in fasted state simulated intestinal fluid (FaSSIF) and aspirated human intestinal fluid (HIF). METHODS: Measured Sapp values in FaSSIF, HIF and phosphate buffer pH 6.5 (PhBpH6.5) for 86 lipophilic drugs were compiled and divided into training (Tr) and test (Te) sets. Projection to latent structure (PLS) models were developed through variable selection of calculated molecular descriptors. Experimentally determined properties were included to investigate their contribution to the predictions. RESULTS: Modest relationships between Sapp in PhBpH6.5 and FaSSIF (R(2) = 0.61) or HIF (R(2) = 0.62) were found. As expected, there was a stronger correlation obtained between FaSSIF and HIF (R(2) = 0.78). Computational models were developed using calculated descriptors alone (FaSSIF, R(2) = 0.69 and RMSEte of 0.77; HIF, R(2) = 0.84 and RMSEte of 0.81). Accuracy improved when solubility in PhBpH6.5 was added as a descriptor (FaSSIF, R(2) = 0.76 and RMSETe of 0.65; HIF, R(2) = 0.86 and RMSETe of 0.69), whereas no improvement was seen when melting point (Tm) or logDpH 6.5 were included in the models. CONCLUSION: Computational models were developed, that reliably predicted Sapp of lipophilic compounds in intestinal fluid, from molecular structures alone. If experimentally determined pH-dependent solubility values were available, this further improved the accuracy of the predictions.

Discovery of AZD6642, an inhibitor of 5-lipoxygenase activating protein (FLAP) for the treatment of inflammatory diseases.

A drug discovery program in search of novel 5-lipoxygenase activating protein (FLAP) inhibitors focused on driving a reduction in lipophilicity with maintained or increased ligand lipophilic efficiency (LLE) compared to previously reported compounds led to the discovery of AZD6642 (15b). Introduction of a hydrophilic tetrahydrofuran (THF) ring at the stereogenic central carbon atom led to a significant shift in physicochemical property space. The structure-activity relationship exploration and optimization of DMPK properties leading to this compound are described in addition to pharmacokinetic analysis and an investigation of the pharmacokinetic (PK)-pharmacodynamic (PD) relationship based on ex vivo leukotriene B4 (LTB4) levels in dog. AZD6642 shows high specific potency and low lipophilicity, resulting in a selective and metabolically stable profile. On the basis of initial PK/PD relation measured, a low dose to human was predicted.

A serendipitously identified novel small molecule procoagulant compound giving rise to a high-throughput screening assay based on human plasma.

INTRODUCTION: Oral treatment is lacking for haemophilia, the rare bleeding disorders, and some severe forms of von Willebrand's disease. We have serendipitously identified a small molecule procoagulant compound (AZ10047130). This publication describes some characteristics of AZ10047130 and a systematic search for novel hits using a, human plasma-based, high-throughput screening (HTS) assay. MATERIAL AND METHODS: Coagulation, thrombin generation, chromogenic assays and surface plasmon resonance (SPR) experiments were used to characterise AZ10047130. A 1536-well formatted human plasma coagulation assay for HTS was developed. RESULTS: In the plasma clot assay (re-calcified plasma with low tissue factor) AZ10047130 shortened time to coagulation with an EC50 value of 3.9 µM (assay concentration). AZ10047130 was similarly effective in immunodepleted human and haemophilia A plasmas. SPR and chromogenic substrate experiments indicated that AZ10047130 binds to the heparin binding site of several coagulation factors. The HTS screened in excess of one million compounds. It generated some hits belonging to the same pharmacophore as AZ10047130 but also some entirely novel hits. CONCLUSION: These novel small molecule procoagulant compounds may serve as templates for discovery of oral procoagulant drugs.

Discovery of agonists of cannabinoid receptor 1 with restricted central nervous system penetration aimed for treatment of gastroesophageal reflux disease.

Agonists of the cannabinoid receptor 1 (CB1) have been suggested as possible treatments for a range of medical disorders including gastroesophageal reflux disease (GERD). While centrally acting cannabinoid agonists are known to produce psychotropic effects, it has been suggested that the CB1 receptors in the periphery could play a significant role in reducing reflux. A moderately potent and highly lipophilic series of 2-aminobenzamides was identified through focused screening of GPCR libraries. Development of this series focused on improving potency and efficacy at the CB1 receptor, reducing lipophilicity and limiting the central nervous system (CNS) exposure while maintaining good oral absorption. Improvement of the series led to compounds having excellent potency at the CB1 receptor and high levels of agonism, good physical and pharmacokinetic properties, and low penetration into the CNS. A range of compounds demonstrated a dose-dependent inhibition of transient lower esophageal sphincter relaxations in a dog model.

Pharmacodynamic-pharmacokinetic integration as a guide to medicinal chemistry.

A primary objective of pharmacokinetic-pharmacodynamic (PKPD) reasoning is to identify key in vivo drug and system proper¬ties, enabling prediction of the magnitude and time course of drug responses under physiological and pathological conditions in animals and man. Since the pharmacological response generated by a drug is highly dependent on the actual system used to study its action, knowledge about its potency and efficacy at a given concentration or dose is insufficient to obtain a proper understanding of its pharmacodynamic profile. Hence, the output of PKPD activities extends beyond the provision of quantitative measures (models) of results, to the design of future protocols. Furthermore, because PKPD integrates DMPK (e.g. clearance) and pharmacology (e.g. potency),it provides an anchor point for compound selection, and, as such, should be viewed as an important weapon in medicinal chemistry. Here we outline key PK concepts relevant to PD, and then consider real-life experiments to illustrate the importance to the medicinal chemist of data obtained by PKPD. Useful assumptions and potential pitfalls are described, providing a holistic view of the plethora of determinants behind in vitro-in vivo correlations. By condensing complexity to simplicity, there are not only consequences for experimental design, and for the ranking and design of compounds, but it is also possible to make important predictions such as the impact of changes in drug potency and kinetics. In short, by using quantitative methods to tease apart pharmacodynamic complexities such as temporal differences and changes in plasma protein binding, it is possible to target the changes necessary for improving a compound's profile.

Impact of input parameters on the prediction of hepatic plasma clearance using the well-stirred model.

The in vitro metabolic stability assays are indispensable for screening the metabolic liability of new chemical entities (NCEs) in drug discovery. Intrinsic clearance (CL(int)) values from liver microsomes and/or hepatocytes are frequently used to assess metabolic stability as well as to quantitatively predict in vivo hepatic plasma clearance (CL(H)). An often used approximation is the so called well-stirred model which has gained widespread use. The applications of the well-stirred model are typically dependent on several measured parameters and hence with potential for error-propagation. Despite widespread use, it was recently suggested that the well-stirred model in some circumstances has been misused for in vitro in vivo extrapolation (IVIVE). In this work, we follow up that discussion and present a retrospective analysis of IVIVE for hepatic clearance prediction from in vitro metabolic stability data. We focus on the impact of input parameters on the well stirred model; in particular comparing "reference model" (with all experimentally determined values as input parameters) versus simplified models (with incomplete input parameters in the models). Based on a systematic comparative analysis and model comparison using datasets of diverse drug-like compounds and NCEs from rat and human, we conclude that simplified models, disregarding binding data, may be sufficiently good for IVIVE evaluation and compound ranking at early stage for cost-effective screening. Factors that can influence prediction accuracy are discussed, including in vitro intrinsic clearance (CL(int)) and in vivo CL(int) scaling factor used, non-specific binding to microsomes (fu(m)), blood to plasma ratio (C(B)/C(P)) and in particular fraction unbound in plasma (fu). In particular, the fu discrepancies between literature data and in-house values and between two different compound concentrations 1 and 10 µM are exemplified and its potential impact on prediction performance is demonstrated using a simulation example.

High-throughput pKa screening and prediction amenable for ADME profiling.

Recent technological advances have made it possible for several new pK(a) assays to be used in drug screening. In this review, a critical overview is provided of current new methodologies for high-throughput screening and prediction of pK(a). Typical applications of using pK(a )constants and charge state for absorption, distribution, metabolism and excretion (ADME) profiling and quantitative structure-activity relationship modelling complements the methodological comparisons and discussions. The experimental methods discussed include high-throughput screening of pK(a) by multiplexed capillary with ultraviolet absorbance detection on a 96-capillary format instrument, capillary electrophoresis and mass spectrometry (CEMS) based on sample pooling, determination of pK(a) by pH gradient high-performance liquid chromatography, and measurement of pK(a) by a mixed-buffer liner pH gradient system. Comparisons of the different experimental assays are made with emphasis on the newly developed CEMS method. The current status and recent progress in computational approaches to pK(a) prediction are also discussed. In particular, the accuracy limits of simple fragment-based approaches as well as quantum mechanical methods are addressed. Examples of pK(a) prediction from in-house drug candidates as well as commercially available drug molecules are shown and an outline is provided for how drug discovery companies can integrate experiments with computational approaches for increased applications for ADME profiling.