Science Pool

N-Methyl-D-aspartate receptors (NMDARs) are members of the ionotropic glutamate receptor family and play a crucial role in learning and memory by regulating synaptic plasticity. Activation of NMDARs containing GluN2A, one of the NMDAR subunits, has been recently identified as a promising therapeutic approach for neuropsychiatric diseases such as schizophrenia, depression, and epilepsy.

Identification of a new hit for GluN2A PAMs is however difficult due to the similarity of PAM binding sites between GluN2A and alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPARs), another member of the ionotropic glutamate receptor family.

In this collaborative publication with Takeda, we focus on:

• Identification of an hit compound with moderate AMPAR-binding activity, though a Ca²⁺ influx-based high throughput screening campaign with a compound set including an internal AMPAR-focused compound library
• The strategy using a structure-based drug design (SBDD) approach to minimize the AMPAR-binding activity while improving GluN2A activity
• The use of the potent and brain-penetrable GluN2A-selective positive allosteric modulators GluN2A PAM discovered as in vivo tool exhibiting significant neuroplastic enhancement in the rat hippocampus 24h after oral administration, having potential application for cognitive enhancement in neuropsychiatric diseases

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Pneumonia induced by multidrug resistant (MDR) Acinetobacter baumannii strains is among the most common and deadly forms of healthcare acquired infections. Animal models play a critical role during the preclinical assessment of novel therapeutic approaches, however, only a self-limiting pneumonia with no or limited local bacterial replication is frequently obtained when MDR A. baumannii is the pathogen.

To strengthen and characterise a sustained lung infection model, in this publication, we focus on:

• the ability of intranasal inoculation, intratracheal instillation and oropharyngeal aspiration methods of administration of the bacterial challenge to induce a robust pneumonia in mice
• the characterisation of the infection model obtained after intratracheal instillation and oropharyngeal aspiration in terms of histopathological examination of pulmonary lesions, biomarkers of inflammation and leukocytes cells infiltration after treatment with either vehicle or with the antibiotic tigecycline efficacious into reducing lung bacterial load.

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This article published in Nature describes a first-in-class METTL3 inhibitor for the treatment of acute Myeloid Leukaemia.

The publication covers:

• Development of STM2457 following high-throughput screen
• Selectivity screening across the panel of 45 human methyltransferases
• Crystal structure of METTL3-14 complex with STM2457, part of structure-based drug design strategy for this target
• Compound validation in cellular assays and in vivo efficacy

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Stable expression of recombinant protein therapeutics in CHO cells often relies on random or semi-random genomic integration events which results in a widely heterogenous cell population. This leads to significant effort in clone screening during cell line development in order to identify clones with high expression, growth and product quality. 

In this publication, we focus on:

• the development of two targeted integration systems that express high levels of recombinant protein in CHO cells by: 
  • the installation of rationally designed piggyBac-based chromosomal landing pads
  • the use of site specific recombinases including PhiC31 and CRE 
• demonstration of the functional integration of several donor vectors encoding various therapeutic proteins including two monoclonal antibodies and one Fc-fusion molecule

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In this article, we discuss the role of structure-based virtual screening in drug discovery. 

The publication covers:

• an overview of structural-based virtual screening and the importance of selecting the most appropriate protocol for affinity estimation calculations
• the development of an automated calibration process implemented in a Knime workflow consisting of 4 steps:
  • preparation of a protein test set with structures and models of the target
  • preparation of a compound test set with target-related ligands and decoys
  • automatic test of 24 scoring/rescoring protocols for each target structure and model
  • graphical display of the results
•  demonstration of the application of this tool in setting up an optimal protocol for structure-based virtual screening against Retinoid X Receptor alpha

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This article forms a book chapter in Quantum Mechanics In Drug Discovery which is part of the Methods in Molecular Biology book series.

In this article, we discuss the use of quantum mechanics in understanding receptor-ligand interactions and chemical reactions.

The chapter includes:

• an overview of the use of quantum mechanics methods in drug design and drug discovery
• presentation of the most popular quantum mechanics methods and software packages
• discussion of recent representative applications in drug design and drug discovery

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This article forms a book chapter in Artificial Intelligence in Drug Design.

In this review article, we discuss the use of machine learning approaches in large omics studies

The chapter includes:

• an overview of the typical analysis of an omics dataset using machine learning
• demonstration of how to build a model predicting drug-induced liver injury (DILI) using transcriptomics data
• best practices and pitfalls to consider during initial data exploration and model training through to validation and analysis of the final model

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This article forms a book chapter in Artificial Intelligence in Drug Design.

In this review article, we discuss the use of artificial intelligence, machine learning, and deep learning in computational drug discovery.

The chapter includes:

• an overview of the current state-of-the-art artificial intelligence methods which are applied in drug discovery
• a focus on structure-based and ligand-based virtual screening, library design and high throughput analysis
• further discussion on drug repurposing, de novo design, chemical reactions and synthetic accessibility, ADMET and quantum mechanics
• real life drug design case studies

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This article forms a book chapter in Artificial Intelligence in Drug Design.

In this review article, we discuss artificial intelligence and deep learning, and its application in computational chemistry.

The chapter includes:

• a comparison of deep learning and other machine learning approaches
• the advantages of deep learning including the facile incorporation of multitask learning and the enhancement of generative modelling
• the impact of deep learning on computational chemistry

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This article forms a book chapter in Artificial Intelligence in Drug Design.

In this review article, we provide the latest insights into fighting COVID-19 with technologies such as artificial intelligence.

It includes:

• an overview of the ongoing demand for efficacious drugs to treat potential vaccine-resistant COVID-19 variants in the future
• how identifying and repurposing marketed drugs for the treatment of COVID-19 plays an important role in this process 
• how artificial intelligence can be employed to speed up the drug discovery process by facilitating the selection of potential drug candidates as well as monitoring the pandemic and enabling faster diagnosis in patients
• a focus on the impact and challenges associated with artificial intelligence for drug repurposing of therapies for the treatment of COVD-19

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