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Epilepsy is a common neurological disorder characterized by abnormal activity of neuronal networks, leading to seizures. The racetam class of anti-seizure medications bind specifically to a membrane protein found in the synaptic vesicles of neurons called synaptic vesicle protein 2 (SV2) A (SV2A). SV2A belongs to an orphan subfamily of the solute carrier 22 organic ion transporter family that also includes SV2B and SV2C. The molecular basis for how anti-seizure medications act on SV2s remains unknown. Here we report cryo-electron microscopy structures of SV2A and SV2B captured in a luminal-occluded conformation complexed with anticonvulsant ligands. The conformation bound by anticonvulsants resembles an inhibited transporter with closed luminal and intracellular gates. Anticonvulsants bind to a highly conserved central site in SV2s. These structures provide blueprints for future drug design and will facilitate future investigations into the biological function of SV2s.
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Stapled peptides are regarded as the promising next-generation therapeutics because of their improved secondary structure, membrane permeability and metabolic stability as compared with the prototype linear peptides. Usually, stapled peptides are obtained by a hydrocarbon stapling technique, anchoring from paired olefin-terminated unnatural amino acids and the consequent ring-closing metathesis (RCM). To investigate the adaptability of the rigid cyclobutane structure in RCM and expand the chemical diversity of hydrocarbon peptide stapling, we herein described the rational design and efficient synthesis of cyclobutane-based conformationally constrained amino acids, termed (E)-1-amino-3-(but-3-en-1-yl)cyclobutane-1-carboxylic acid (E7) and (Z)-1-amino-3-(but-3-en-1-yl)cyclobutane-1-carboxylic acid (Z7). All four combinations including E7-E7, E7-Z7, Z7-Z7 and Z7-E7 were proven to be applicable in RCM-mediated peptide stapling to afford the corresponding geometry-specific stapled peptides. With the aid of the combined quantum and molecular mechanics, the E7-E7 combination was proven to be optimal in both the RCM reaction and helical stabilization. With the spike protein of SARS-CoV-2 as the target, a series of cyclobutane-bearing stapled peptides were obtained. Among them, E7-E7 geometry-specific stapled peptides indeed exhibit higher α-helicity and thus stronger biological activity than canonical hydrocarbon stapled peptides. We believe that this methodology possesses great potential to expand the scope of the existing peptide stapling strategy. These cyclobutane-bearing restricted anchoring residues served as effective supplements for the existing olefin-terminated unnatural amino acids and the resultant geometry-specific hydrocarbon peptide stapling provided more potential for peptide therapeutics.
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Activated coagulation factor XI (FXIa) is a highly attractive antithrombotic target as it contributes to the development and progression of thrombosis but is thought to play only a minor role in hemostasis so that its inhibition may allow for decoupling of antithrombotic efficacy and bleeding time prolongation. Herein, we report our major efforts to identify an orally bioavailable, reversible FXIa inhibitor. Using a protein structure-based de novo design approach, we identified a novel micromolar hit with attractive physicochemical properties. During lead modification, a critical problem was balancing potency and absorption by focusing on the most important interactions of the lead series with FXIa while simultaneously seeking to improve metabolic stability and the cytochrome P450 interaction profile. In clinical trials, the resulting compound from our extensive research program, asundexian (BAY 2433334), proved to possess the desired DMPK properties for once-daily oral dosing, and even more importantly, the initial pharmacological hypothesis was confirmed.
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Factor XIa , Fibrinolíticos , AnticoagulantesRESUMEN
Target 2035, an international federation of biomedical scientists from the public and private sectors, is leveraging 'open' principles to develop a pharmacological tool for every human protein. These tools are important reagents for scientists studying human health and disease and will facilitate the development of new medicines. It is therefore not surprising that pharmaceutical companies are joining Target 2035, contributing both knowledge and reagents to study novel proteins. Here, we present a brief progress update on Target 2035 and highlight some of industry's contributions.
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One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.
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Twenty years after the publication of the first draft of the human genome, our knowledge of the human proteome is still fragmented. The challenge of translating the wealth of new knowledge from genomics into new medicines is that proteins, and not genes, are the primary executers of biological function. Therefore, much of how biology works in health and disease must be understood through the lens of protein function. Accordingly, a subset of human proteins has been at the heart of research interests of scientists over the centuries, and we have accumulated varying degrees of knowledge about approximately 65% of the human proteome. Nevertheless, a large proportion of proteins in the human proteome (â¼35%) remains uncharacterized, and less than 5% of the human proteome has been successfully targeted for drug discovery. This highlights the profound disconnect between our abilities to obtain genetic information and subsequent development of effective medicines. Target 2035 is an international federation of biomedical scientists from the public and private sectors, which aims to address this gap by developing and applying new technologies to create by year 2035 chemogenomic libraries, chemical probes, and/or biological probes for the entire human proteome.
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The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug discovery processes with in silico methods. Significant advances in all three areas are the reason for the regained interest in these models. In this book chapter we review various machine learning (ML) approaches that make use of measured in vitro/in vivo data of many compounds. We put these in context with other digital drug discovery methods and present some application examples.
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Aprendizaje Automático , Algoritmos , Descubrimiento de Drogas , Relación Estructura-Actividad CuantitativaRESUMEN
Despite extensive research on small molecule thrombin inhibitors for oral application in the past decades, only a single double prodrug with very modest oral bioavailability has reached human therapy as a marketed drug. We have undertaken major efforts to identify neutral, non-prodrug inhibitors. Using a holistic analysis of all available internal data, we were able to build computational models and apply these for the selection of a lead series with the highest possibility of achieving oral bioavailability. In our design, we relied on protein structure knowledge to address potency and identified a small window of favorable physicochemical properties to balance absorption and metabolic stability. Protein structure information on the pregnane X receptor helped in overcoming a persistent cytochrome P450 3A4 induction problem. The selected compound series was optimized to a highly potent, neutral, non-prodrug thrombin inhibitor by designing, synthesizing, and testing derivatives. The resulting optimized compound, BAY1217224, has reached first clinical trials, which have confirmed the desired pharmacokinetic properties.
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Anticoagulantes/síntesis química , Diseño de Fármacos , Trombina/antagonistas & inhibidores , Administración Oral , Animales , Anticoagulantes/química , Anticoagulantes/farmacocinética , Anticoagulantes/farmacología , Benzoxazoles/química , Benzoxazoles/metabolismo , Benzoxazoles/farmacología , Sitios de Unión , Citocromo P-450 CYP3A/genética , Citocromo P-450 CYP3A/metabolismo , Semivida , Humanos , Imidazoles/química , Imidazoles/metabolismo , Imidazoles/farmacología , Concentración 50 Inhibidora , Masculino , Simulación del Acoplamiento Molecular , Oxazolidinonas/química , Oxazolidinonas/metabolismo , Oxazolidinonas/farmacología , Receptor X de Pregnano/genética , Receptor X de Pregnano/metabolismo , Ratas , Ratas Wistar , Relación Estructura-Actividad , Trombina/metabolismo , Activación Transcripcional/efectos de los fármacosRESUMEN
Target druggability assessment is an integral part of the early target characterization and selection process in pharmaceutical industry. Here, we investigate a set of five different serine proteases from the blood coagulation cascade. The aim of this study is twofold. Firstly, leveraging the wealth of available in-house high-throughput screening (HTS) data, we analyze HTS hit rates and discuss their predictive value for the development of small molecule (SMOL) candidates. Purely structure-activity relationship (SAR) based druggability ratings are compared with computational protein-structure based druggability assessments. Secondly, we evaluate the impact of using conformational ensembles from molecular dynamics (MD) simulations instead of single static crystal structures as basis for computational druggability assessments. Based on this study, we recommend incorporating molecular dynamics routinely into the early target characterization process, especially if only a single X-ray structure is available.
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Industria Farmacéutica , Serina Proteasas/metabolismo , Inhibidores de Serina Proteinasa/farmacología , Bibliotecas de Moléculas Pequeñas/farmacología , Ensayos Analíticos de Alto Rendimiento , Humanos , Simulación de Dinámica Molecular , Inhibidores de Serina Proteinasa/química , Bibliotecas de Moléculas Pequeñas/química , Relación Estructura-ActividadRESUMEN
Over the past two decades, an in silico absorption, distribution, metabolism, and excretion (ADMET) platform has been created at Bayer Pharma with the goal to generate models for a variety of pharmacokinetic and physicochemical endpoints in early drug discovery. These tools are accessible to all scientists within the company and can be a useful in assisting with the selection and design of novel leads, as well as the process of lead optimization. Here. we discuss the development of machine-learning (ML) approaches with special emphasis on data, descriptors, and algorithms. We show that high company internal data quality and tailored descriptors, as well as a thorough understanding of the experimental endpoints, are essential to the utility of our models. We discuss the recent impact of deep neural networks and show selected application examples.
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Aprendizaje Automático , Farmacocinética , Animales , Simulación por Computador , Humanos , Absorción Intestinal , Modelos Teóricos , Preparaciones Farmacéuticas/metabolismoRESUMEN
We herein report the first thorough analysis of the structure-permeability relationship of semipeptidic macrocycles. In total, 47 macrocycles were synthesized using a hybrid solid-phase/solution strategy, and then their passive and cellular permeability was assessed using the parallel artificial membrane permeability assay (PAMPA) and Caco-2 assay, respectively. The results indicate that semipeptidic macrocycles generally possess high passive permeability based on the PAMPA, yet their cellular permeability is governed by efflux, as reported in the Caco-2 assay. Structural variations led to tractable structure-permeability and structure-efflux relationships, wherein the linker length, stereoinversion, N-methylation, and peptoids site-specifically impact the permeability and efflux. Extensive nuclear magnetic resonance, molecular dynamics, and ensemble-based three-dimensional polar surface area (3D-PSA) studies showed that ensemble-based 3D-PSA is a good predictor of passive permeability.
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Compuestos Macrocíclicos/química , Compuestos Macrocíclicos/metabolismo , Péptidos/química , Células CACO-2 , Humanos , Membranas Artificiales , PermeabilidadRESUMEN
Oral administration of drug products is a strict requirement in many medical indications. Therefore, bioavailability prediction models are of high importance for prioritization of compound candidates in the drug discovery process. However, oral exposure and bioavailability are difficult to predict, as they are the result of various highly complex factors and/or processes influenced by the physicochemical properties of a compound, such as solubility, lipophilicity, or charge state, as well as by interactions with the organism, for instance, metabolism or membrane permeation. In this study, we assess whether it is possible to predict intravenous (iv) or oral drug exposure and oral bioavailability in rats. As input parameters, we use (i) six experimentally determined in vitro and physicochemical endpoints, namely, membrane permeation, free fraction, metabolic stability, solubility, pKa value, and lipophilicity; (ii) the outputs of six in silico absorption, distribution, metabolism, and excretion models trained on the same endpoints, or (iii) the chemical structure encoded as fingerprints or simplified molecular input line entry system strings. The underlying data set for the models is an unprecedented collection of almost 1900 data points with high-quality in vivo experiments performed in rats. We find that drug exposure after iv administration can be predicted similarly well using hybrid models with in vitro- or in silico-predicted endpoints as inputs, with fold change errors (FCE) of 2.28 and 2.08, respectively. The FCEs for exposure after oral administration are higher, and here, the prediction from in vitro inputs performs significantly better in comparison to in silico-based models with FCEs of 3.49 and 2.40, respectively, most probably reflecting the higher complexity of oral bioavailability. Simplifying the prediction task to a binary alert for low oral bioavailability, based only on chemical structure, we achieve accuracy and precision close to 70%.
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Descubrimiento de Drogas/métodos , Hepatocitos/metabolismo , Preparaciones Farmacéuticas/metabolismo , Administración Oral , Animales , Disponibilidad Biológica , Células CACO-2 , Simulación por Computador , Humanos , Aprendizaje Automático , Masculino , Modelos Biológicos , Permeabilidad , Preparaciones Farmacéuticas/química , Ratas , Ratas Wistar , Albúmina Sérica/metabolismo , SolubilidadRESUMEN
Pharmaceutical companies often refer to 'screening their library' when performing high-throughput screening (HTS) on a corporate compound collection to identify lead structures for small-molecule drug discovery programs. Characteristics of such a library, including the size, chemical space covered, and physicochemical properties, often determine the success of a screening campaign. Therefore, strategies to maintain and enhance the overall quality of screening collections are crucial to stay competitive and to cope with the 'novelty erosion' that is observed gradually. The Next Generation Library Initiative (NGLI), the enhancement of Bayer's HTS collection by 500000 newly designed compounds within 5 years, is addressing exactly this challenge. Here, we describe this collaborative project, which involves all internal medicinal chemists in a crowd-sourcing approach, as well as selected external partners, to reach this ambitious goal.
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Descubrimiento de Drogas , Ensayos Analíticos de Alto Rendimiento , Bibliotecas de Moléculas Pequeñas , Industria Farmacéutica , Control de CalidadRESUMEN
Prediction of compound properties from structure via quantitative structure-activity relationship and machine-learning approaches is an important computational chemistry task in small-molecule drug research. Though many such properties are dependent on three-dimensional structures or even conformer ensembles, the majority of models are based on descriptors derived from two-dimensional structures. Here we present results from a thorough benchmark study of force field, semiempirical, and density functional methods for the calculation of conformer energies in the gas phase and water solvation as a foundation for the correct identification of relevant low-energy conformers. We find that the tight-binding ansatz GFN-xTB shows the lowest error metrics and highest correlation to the benchmark PBE0-D3(BJ)/def2-TZVP in the gas phase for the computationally fast methods and that in solvent OPLS3 becomes comparable in performance. MMFF94, AM1, and DFTB+ perform worse, whereas the performance-optimized but far more expensive functional PBEh-3c yields energies almost perfectly correlated to the benchmark and should be used whenever affordable. On the basis of our findings, we have implemented a reliable and fast protocol for the identification of low-energy conformers of drug-like molecules in water that can be used for the quantification of strain energy and entropy contributions to target binding as well as for the derivation of conformer-ensemble-dependent molecular descriptors.
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Gases/química , Informática/métodos , Aprendizaje Automático , Agua/química , Descubrimiento de Drogas , Modelos Moleculares , Conformación Molecular , Preparaciones Farmacéuticas/química , Relación Estructura-Actividad Cuantitativa , Solventes/química , TermodinámicaRESUMEN
Computational chemistry within the pharmaceutical industry has grown into a field that proactively contributes to many aspects of drug design, including target selection and lead identification and optimization. While methodological advancements have been key to this development, organizational developments have been crucial to our success as well. In particular, the interaction between computational and medicinal chemistry and the integration of computational chemistry into the entire drug discovery process have been invaluable. Over the past ten years we have shaped and developed a highly efficient computational chemistry group for small-molecule drug discovery at Bayer HealthCare that has significantly impacted the clinical development pipeline. In this article we describe the setup and tasks of the computational group and discuss external collaborations. We explain what we have found to be the most valuable and productive methods and discuss future directions for computational chemistry method development. We share this information with the hope of igniting interesting discussions around this topic.
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Biología Computacional , Bases de Datos Factuales , Diseño de Fármacos , Industria Farmacéutica , Ensayos Analíticos de Alto Rendimiento , Ligandos , Proteínas/química , Proteínas/metabolismoRESUMEN
Aldosterone regulates sodium homeostasis by activating the mineralocorticoid receptor (MR), a member of the nuclear receptor superfamily. Hyperaldosteronism leads todeleterious effects on the kidney, blood vessels, and heart. Although steroidal antagonists such as spironolactone and eplerenone are clinically useful for the treatment of cardiovascular diseases, they are associated with several side effects. Finerenone, a novel nonsteroidal MR antagonist, is presently being evaluated in two clinical phase IIb trials. Here, we characterized the molecular mechanisms of action of finerenone and spironolactone at several key steps of the MR signaling pathway. Molecular modeling and mutagenesis approaches allowed identification of Ser-810 and Ala-773 as key residues for the high MR selectivity of finerenone. Moreover, we showed that, in contrast to spironolactone, which activates the S810L mutant MR responsible for a severe form of early onset hypertension, finerenone displays strict antagonistic properties. Aldosterone-dependent phosphorylation and degradation of MR are inhibited by both finerenone and spironolactone. However, automated quantification of MR subcellular distribution demonstrated that finerenone delays aldosterone-induced nuclear accumulation of MR more efficiently than spironolactone. Finally, chromatin immunoprecipitation assays revealed that, as opposed to spironolactone, finerenone inhibits MR, steroid receptor coactivator-1, and RNA polymerase II binding at the regulatory sequence of the SCNN1A gene and also remarkably reduces basal MR and steroid receptor coactivator-1 recruitment, unraveling a specific and unrecognized inactivating mechanism on MR signaling. Overall, our data demonstrate that the highly potent and selective MR antagonist finerenone specifically impairs several critical steps of the MR signaling pathway and therefore represents a promising new generation MR antagonist.
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Aldosterona/farmacología , Naftiridinas/farmacología , Coactivador 1 de Receptor Nuclear/metabolismo , Receptores de Mineralocorticoides/metabolismo , Transporte Activo de Núcleo Celular/efectos de los fármacos , Western Blotting , Línea Celular , Núcleo Celular/efectos de los fármacos , Núcleo Celular/metabolismo , Inmunoprecipitación de Cromatina , Relación Dosis-Respuesta a Droga , Regulación hacia Abajo/efectos de los fármacos , Canales Epiteliales de Sodio/genética , Células HEK293 , Humanos , Cinética , Microscopía Fluorescente , Mutación , Regiones Promotoras Genéticas/genética , Unión Proteica/efectos de los fármacos , Receptores de Mineralocorticoides/genética , Transducción de Señal/efectos de los fármacos , Espironolactona/farmacología , Activación Transcripcional/efectos de los fármacosRESUMEN
In a unique collaboration between a software company and a pharmaceutical company, we were able to develop a new in silico pKa prediction tool with outstanding prediction quality. An existing pKa prediction method from Simulations Plus based on artificial neural network ensembles (ANNE), microstates analysis, and literature data was retrained with a large homogeneous data set of drug-like molecules from Bayer. The new model was thus built with curated sets of â¼14,000 literature pKa values (â¼11,000 compounds, representing literature chemical space) and â¼19,500 pKa values experimentally determined at Bayer Pharma (â¼16,000 compounds, representing industry chemical space). Model validation was performed with several test sets consisting of a total of â¼31,000 new pKa values measured at Bayer. For the largest and most difficult test set with >16,000 pKa values that were not used for training, the original model achieved a mean absolute error (MAE) of 0.72, root-mean-square error (RMSE) of 0.94, and squared correlation coefficient (R(2)) of 0.87. The new model achieves significantly improved prediction statistics, with MAE = 0.50, RMSE = 0.67, and R(2) = 0.93. It is commercially available as part of the Simulations Plus ADMET Predictor release 7.0. Good predictions are only of value when delivered effectively to those who can use them. The new pKa prediction model has been integrated into Pipeline Pilot and the PharmacophorInformatics (PIx) platform used by scientists at Bayer Pharma. Different output formats allow customized application by medicinal chemists, physical chemists, and computational chemists.
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Simulación por Computador , Bases de Datos Factuales , Modelos Químicos , Algoritmos , Biología Computacional , Minería de Datos , Informática , Redes Neurales de la Computación , Valor Predictivo de las Pruebas , Relación Estructura-ActividadRESUMEN
Correctly ranking compounds according to their computed relative binding affinities will be of great value for decision making in the lead optimization phase of industrial drug discovery. However, the performance of existing computationally demanding binding free energy calculation methods in this context is largely unknown. We analyzed the performance of the molecular mechanics continuum solvent, the linear interaction energy (LIE), and the thermodynamic integration (TI) approach for three sets of compounds from industrial lead optimization projects. The data sets pose challenges typical for this early stage of drug discovery. None of the methods was sufficiently predictive when applied out of the box without considering these challenges. Detailed investigations of failures revealed critical points that are essential for good binding free energy predictions. When data set-specific features were considered accordingly, predictions valuable for lead optimization could be obtained for all approaches but LIE. Our findings lead to clear recommendations for when to use which of the above approaches. Our findings also stress the important role of expert knowledge in this process, not least for estimating the accuracy of prediction results by TI, using indicators such as the size and chemical structure of exchanged groups and the statistical error in the predictions. Such knowledge will be invaluable when it comes to the question which of the TI results can be trusted for decision making.
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Lead optimization of a high-throughput screening hit led to the rapid identification of aminopyrimidine ZKâ 304709, a multitargeted CDK and VEGF-R inhibitor that displayed a promising preclinical profile. Nevertheless, ZKâ 304709 failed in phaseâ I studies due to dose-limited absorption and high inter-patient variability, which was attributed to limited aqueous solubility and off-target activity against carbonic anhydrases. Further lead optimization efforts to address the off-target activity profile finally resulted in the introduction of a sulfoximine group, which is still a rather unusual approach in medicinal chemistry. However, the sulfoximine series of compounds quickly revealed very interesting properties, culminating in the identification of the nanomolar pan-CDK inhibitor BAYâ 1000394, which is currently being investigated in phaseâ I clinical trials.
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Antineoplásicos/farmacología , Quinasas Ciclina-Dependientes/antagonistas & inhibidores , Descubrimiento de Drogas , Neoplasias Experimentales/tratamiento farmacológico , Inhibidores de Proteínas Quinasas/farmacología , Pirimidinas/farmacología , Sulfóxidos/farmacología , Neoplasias del Cuello Uterino/tratamiento farmacológico , Animales , Antineoplásicos/administración & dosificación , Antineoplásicos/síntesis química , Quinasas Ciclina-Dependientes/metabolismo , Relación Dosis-Respuesta a Droga , Femenino , Células HeLa , Ensayos Analíticos de Alto Rendimiento , Humanos , Ratones , Modelos Moleculares , Estructura Molecular , Peso Molecular , Neoplasias Experimentales/enzimología , Neoplasias Experimentales/metabolismo , Inhibidores de Proteínas Quinasas/administración & dosificación , Inhibidores de Proteínas Quinasas/síntesis química , Pirimidinas/administración & dosificación , Pirimidinas/síntesis química , Ratas , Relación Estructura-Actividad , Sulfóxidos/administración & dosificación , Sulfóxidos/síntesis química , Neoplasias del Cuello Uterino/enzimología , Neoplasias del Cuello Uterino/metabolismoRESUMEN
Aldosterone is a hormone that exerts manifold deleterious effects on the kidneys, blood vessels, and heart which can lead to pathophysiological consequences. Inhibition of the mineralocorticoid receptor (MR) is a proven therapeutic concept for the management of associated diseases. Use of the currently marketed MR antagonists spironolactone and eplerenone is restricted, however, due to a lack of selectivity in spironolactone and the lower potency and efficacy of eplerenone. Several pharmaceutical companies have implemented programs to identify drugs that overcome the known liabilities of steroidal MR antagonists. Herein we disclose an extended SAR exploration starting from cyano-1,4-dihydropyridines that were identified by high-throughput screening. Our efforts led to the identification of a dihydronaphthyridine, BAY 94-8862, which is a potent, selective, and orally available nonsteroidal MR antagonist currently under investigation in a clinical phaseâ II trial.