“Dual Anta-Inhibitors” of the A2A Adenosine Receptor and Casein Kinase CK1delta: Synthesis, Biological Evaluation, and Molecular Modeling Studies

Spinaci, Andrea; Buccioni, Michela; Catarzi, Daniela; Cui, Chang; Colotta, Vittoria; Ben, Diego Dal; Cescon, Eleonora; Francucci, Beatrice; Grieco, Ilenia; Lambertucci, Catia; Marucci, Gabriella; Bassani, Davide; Pavan, Matteo; Varano, Flavia; Federico, Stephanie; Spalluto, Giampiero; Moro, Stefano; Volpini, Rosaria

doi:10.3390/ph16020167

Cited by 6 publications

(4 citation statements)

References 77 publications

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“…As already mentioned, molecular docking is extensively used in the early phases of drug discovery, from hit identification up to lead optimization [69]. It is applied both to identify novel chemical candidates for pharmacological testing and to help rationalize experimental data to a molecular level.…”

Section: Molecular Dockingmentioning

confidence: 99%

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

Bassani

Moro

2023

Molecules

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The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of “computer-aided drug design” (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

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Section: Molecular Dockingmentioning

confidence: 99%

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

Bassani

Moro

2023

Molecules

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“…Additionally, there is an enhancement of cytokines in proinflammatory response [ 31 , 32 ]. In a recent paper, a set of di- and tri-substituted adenine derivatives was obtained and studied for their capability to inhibit the CK1δ enzyme and to antagonize adenosine receptors (ARs) [ 33 ]. Some of these compounds acted as dual anta-inhibitors, demonstrating a suitable CK1δ inhibitory activity combined with a high binding affinity, especially for the A 2A AR.…”

Section: Resultsmentioning

confidence: 99%

Dual Anta-Inhibitors Targeting Protein Kinase CK1δ and A2A Adenosine Receptor Useful in Neurodegenerative Disorders

et al. 2023

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Currently, the number of patients with neurodegenerative pathologies is estimated at over one million, with consequences also on the economic level. Several factors contribute to their development, including overexpression of A2A adenosine receptors (A2AAR) in microglial cells and up-regulation and post-translational alterations of some casein kinases (CK), among them, CK-1δ. The aim of the work was to study the activity of A2AAR and CK1δ in neurodegeneration using in-house synthesized A2A/CK1δ dual anta-inhibitors and to evaluate their intestinal absorption. Experiments were performed on N13 microglial cells, which were treated with a proinflammatory CK cocktail to simulate an inflammatory state typical of neurodegenerative diseases. Results showed that the dual anta-inhibitors have the ability to counteract the inflammatory state, even if compound 2 is more active than compound 1. In addition, compound 2 displayed an important antioxidant effect similar to the reference compound ZM241385. Since many known kinase inhibitors are very often unable to cross lipid bilayer membranes, the ability of A2A/CK1δ double anta-inhibitors to cross the intestinal barrier was investigated by an everted gut sac assay. HPLC analysis revealed that both compounds are able to cross the intestinal barrier, making them promising candidates for oral therapy.

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“…In the small-molecule drug development scenario, − where ML is applied mainly to predict a desired chemical, pharmaceutical, or physical property, the data points represent the chemical compounds themselves. These molecules are encoded in a series of features, which can span from the simple physicochemical descriptors to more complex representations such as pharmacophore fingerprints , or molecular graphs .…”

Section: Introductionmentioning

confidence: 99%

Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon

Bassani,

Brigo,

Andrews-Morger

2023

Chem. Res. Toxicol.

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In silico approaches have acquired a towering role in pharmaceutical research and development, allowing laboratories all around the world to design, create, and optimize novel molecular entities with unprecedented efficiency. From a toxicological perspective, computational methods have guided the choices of medicinal chemists toward compounds displaying improved safety profiles. Even if the recent advances in the field are significant, many challenges remain active in the on-target and off-target prediction fields. Machine learning methods have shown their ability to identify molecules with safety concerns. However, they strongly depend on the abundance and diversity of data used for their training. Sharing such information among pharmaceutical companies remains extremely limited due to confidentiality reasons, but in this scenario, a recent concept named "federated learning" can help overcome such concerns. Within this framework, it is possible for companies to contribute to the training of common machine learning algorithms, using, but not sharing, their proprietary data. Very recently, Lhasa Limited organized a hackathon involving several industrial partners in order to assess the performance of their federated learning platform, called "Effiris". In this paper, we share our experience as Roche in participating in such an event, evaluating the performance of the federated algorithms and comparing them with those coming from our in-house-only machine learning models. Our aim is to highlight the advantages of federated learning and its intrinsic limitations and also suggest some points for potential improvements in the method.

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“Dual Anta-Inhibitors” of the A2A Adenosine Receptor and Casein Kinase CK1delta: Synthesis, Biological Evaluation, and Molecular Modeling Studies

Cited by 6 publications

References 77 publications

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

Dual Anta-Inhibitors Targeting Protein Kinase CK1δ and A2A Adenosine Receptor Useful in Neurodegenerative Disorders

Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon

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