Science Pool

Chikungunya virus, which is transmitted by mosquitoes, can cause disabling chronic arthritis. There are currently no medicines for prophylaxis of Chikungunya, or other viruses transmitted by Aedes mosquitoes, such as Dengue and Zika. Potential therapeutics have been identified, but to perform a successful chemoprophylaxis trial during a short Chikungunya outbreak requires an identified at-risk population. We examined the potential for application of a household transmission model, as used in testing prophylactic drugs against respiratory viruses, included influenza and COVID-19.

In this poster we:

• Set out the evidence for multiple Chikungunya infections occurring per household
• Describe how we are validating the secondary household infection rate
• Show how this could be used in future clinical trials to demonstrate prophylactic efficacy against chikungunya virus infection and other Aedes-mosquito-borne viruses

Read our poster to learn more about our research!

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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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The Covid-19 pandemic is a stark reminder that infectious diseases and their sometimes devastating effects will always have to be reckoned with. However, beyond Covid-19 a second global health crisis is emerging, and it is imperative that we act now to prepare for the increasing development of antimicrobial resistance (AMR) towards our currently existing arsenal of antibiotics.

The key to de-risking and expediting the development and approval of new antimicrobials is a detailed understanding of the relationship between the fate of the antimicrobial compound in the body: pharmacokinetics (PK), and the impact of exposure to the compound on the target microbe: pharmacodynamics (PD). This understanding is essential for development of optimal human dosing regimens, maximising efficacy and minimising the emergence of resistance. Only with this understanding will we mitigate the risk of clinical trial failure, and ultimately extending the clinical utility of a new antimicrobial in the face of increasing AMR.

Among the several non-clinical in vivo and in vitro models of infection which provide complementary information to understand this PK/PD relationship, the in vitro Hollow Fibre Infection Model (HFIM) is the most versatile. Evotec’s comprehensive HFIM capabilities combined with its drug development expertise and unique EvostrAIn™ collection of microbial pathogens provide a versatile in vitro PK/PD platform to de-risk and accelerate the development of antibacterial, antifungal and antiviral compounds.

The Hollow Fibre Infection System consists of two principle compartments:

1. a central reservoir and associated tubing, which constitutes a circulating system, and
2. a hollow fibre cartridge consisting of thousands of hollow permeable capillaries, or fibres, housing the bacteria, fungi or virus of interest

The HFIM is a dynamic model that is able to simulate almost any given concentration-time profile for an antimicrobial compound or combination of compounds, without the constraints of animal PK. The containment of the bacterial in the peripheral compartment of the hollow fibre cartridge means the system is able to simulate PK profiles with no bacterial cell washout. It is also suitable for simulated dose range fractionation studies and can determine resistance prevention exposure for any simulated PK profile. Combining the ability to run long-duration experiments with multiple drug infusion profiles means that the HFIM is especially well suited to the development of antimicrobial combination therapy against slowly replicating bacteria such as Mycobacterium tuberculosis, a view which is supported by the European Medicines Agency (EMA).

When should the Hollow Fibre Infection Model be used in the drug discovery process?

• Early screening of compounds can de-risk future HFIM experiments.
• Studies at the pre-clinical stage (pre-IND) will help to improve understanding of the PK/PD relationship. This is particularly important if an animal model is not available, or if the animal can’t tolerate the inoculum levels that need to be tested for example in resistance studies.
• During clinical development the HFIM can help to inform trial design. Hollow fibre studies can be performed based on human exposure data to address discrepancies between clinical and pre-clinical findings.
• Even after drug approval the HFIM can be used to expand the label or optimise the dosing regimens.

Evotec has established HFIM capabilities in state-of-the-art containment level 2 facilities, with a growing team of dedicated Hollow Fibre specialists. Providing full microbiological support to projects and aided by specialist bioanalytical and mathematical modelling and simulation teams, Evotec can offer a bespoke in vitro PK/PD service tailored to advance individual antimicrobial development programmes.

Learn more about Evotec's Hollow Fibre Infection Model by downloading our white paper

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