Challenge 16

VIDR

Launched Phase 1 awarded Phase 2 awarded Completed

The aim of this Challenge was to develop a virtual platform that modeled infection and the host response to pathogen assault for basic research and would enhance new target development in infectious diseases.

To address this Challenge, the team at University of York, lead by Professor Paul Kaye, have developed an in silico platform to explore a computational model of hosts chronically infected with visceral leishmanisis. This platform uses in silico methods to model infection and the host response in an individual animal to i) reduce the use of animals, ii) model in vivo pathogenesis and protection, and iii) model the potential impact of drugs or vaccines to accelerate the development of treatments to infectious diseases.

This project and its impacts are featured as a case study in the 2019 CRACK IT Review.

10 years of CRACK IT webinar: Virtual labs for non-animal disease research and toxicity testing

In April 2021 we held a webinar about the VIDR Challenge and the in silico platform developed to address the Challenge, as part of our 10 years of CRACK IT celebrations. It featured presentations by Professor Jon Timmis (Co-founder and CTO of Simomics) and Dr Stewart Owen (AstraZeneca). The recording of the webinar is now available online.

Publication

Ashwin H et al. (2019). Tissue and host species-specific transcriptional changes in models of experimental visceral leishmaniasis. Wellcome Open Research. 3:135. doi:10.12688/wellcomeopenres.14867.2.

Presentation at the 2018 CRACK IT Challenges launch event

Professor Paul Kaye and Professor Jon Timmis presented the results of the VIDR Challenge at the 2018 CRACK IT Challenges launch event.

Product launched

Leishmania Virtual Laboratory is a virtual platform that models mouse spleen sections and the host response to chronic infection by leishmania parasites.

Challenge completed

An in silico platform to explore a computational model of hosts chronically infected with visceral leishmanisis. While complex mathematical models are being used to model spread of infection and immune response throughout human populations, it is less common for models to be used to study within host dynamics of infection and response. This platform uses in silico methods to model infection and the host response in an individual animal to i) reduce the use of animals, ii) model in vivo pathogenesis and protection, and iii) model the potential impact of drugs or vaccines to accelerate the development of treatments to infectious diseases.

Publication

Timmis J, Alden K, Andrews P, et al. (2017). Building confidence in quantitative systems pharmacology models: An engineer's guide to exploring the rationale in model design and development. CPT: pharmacometrics & systems pharmacology 6(3): 156-167. doi:10.1002/psp4.12157.

RCUK-CONFAP workshop award

Funding awarded to a run a UK-Brazil Workshop on immune modelling.

Phase 2 awarded

A team led by Professor Paul Kaye, University of York, has been awarded £996,464 to deliver the project: A multiscale model to minimise animal usage in Leishmaniasis drug development.

Phase 1 awarded

Two Phase 1 Awards were made to project teams led by:

  • Professor Tom Freeman, University of Edinburgh, £84,561.
  • Professor Paul Kaye, University of York, £100,000.

Challenge launched

Sponsored by the NC3Rs, the Virtual Infectious Disease Research (VIDR) Challenge aims to develop a virtual platform that models infection and the host response to pathogen assault for basic research and enhances new target development in infectious diseases.

Background

Control of infectious diseases remains a key priority in human and veterinary medicine. The World Health Organisation (WHO) estimates that respiratory and diarrhoeal infections, HIV, tuberculosis, malaria and measles are responsible for 90% of human deaths and a plethora of animal pathogens threaten food security at a time of fast accelerating demand.

Many different species, from non-vertebrates to rodents and non-human primates, are used to study infection, host response and efficacy of drugs and vaccines. Typically, animals are infected with an infectious agent to test therapeutic efficacy, resulting in symptoms of differing severity. A reliable in silico model of infection and the host response would result in the reduced use of animals. Ideally such a model would provide the foundation for future models which would help predict the efficacy of drugs, vaccines and other treatments.

There is a focus across the biosciences on using the availability of large datasets to exploit computational tools to generate results that are not obvious from single experiments. ‘Virtual’ data for infectious diseases includes diverse information relating to the:

  1. Repertoire, sequence, transcription, translation, regulation and function of pathogen and host genes.
  2. Host immune response.
  3. Impact of modulation of pathogen and host functions by genetic modification, drugs or vaccines on the outcome of infection.
  4. Temporal and spatial migration, interactions and activities of pathogen and host cells or their products.

The challenge is how to use this data to develop testable predictions, particularly when such data often reflect an average within a tissue at a specific time interval and may therefore not fully reflect events at a finer spatial and temporal level.

While complex mathematical models are being used to model spread of infection and immune response throughout human populations, it is less common for models to be used to study within-host dynamics of infection and response. The focus of this Challenge is on using in silico methods to model infection and the host response in an individual animal. The aims are to employ in silico methods to:

  1. Reduce the use of animals.
  2. Model in vivo pathogenesis and protection.
  3. Model the potential impact of drugs or vaccines to accelerate the development of treatments to infectious diseases.

3Rs benefits

Animal use in a typical rodent efficacy study for new antibiotics or vaccines can involve approximately 100 animals per candidate. The animals are infected with the pathogen after vaccination or treated with the drug of interest. Untreated controls are always used. The resulting disease in control animals and those in whom the vaccine or drug are ineffective can cause severe suffering. The use of in silico approaches to study disease biology and predict efficacy would reduce the number of animals used.

Phase 1 winners

Project teams led by:

  • Professor Tom Freeman,University of Edinburgh, £84,561.
  • Professor Paul Kaye, University of York, £100,000. 

Phase 2 winner

Project team led by:

Full Challenge information

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Leishmaniasis is a disease caused by the protozoan parasites Leishmania which is spread by sand flies throughout tropical and subtropical countries. There are approximately 12 million chronic cases worldwide with roughly 2 million new clinical cases each year. Around 40,000 people die each year from the most severe form, visceral leishmaniasis. Only a handful of drugs exist to treat leishmaniasis, most cause serious side effects and drug resistance is a growing problem. No vaccines are yet available for use in humans.

To address the VIDR Challenge and as part of a global effort to improve the treatment for patients with leishmaniasis, the team have developed a virtual laboratory that contains computational models, alongside experimental data, to discover how existing drugs may be used more effectively, to identify new drug targets and to develop or repurpose biological therapies.

The LeishSim Virtual Laboratory is an in silico platform to explore a computational model of hosts chronically infected with visceral leishmanisis. While complex mathematical models are being used to model spread of infection and immune response throughout human populations, it is less common for models to be used to study within host dynamics of infection and response.

This platform models infection and the host response in an individual animal to:

  • Reduce the number of animals used in the development of treatments.
  • Model in vivo pathogenesis and protection.
  • Model the potential impact of drugs or vaccines to accelerate the development of treatments to infectious diseases.

The LeishSim Virtual Laboratory provides a framework for this approach to be used for other diseases and the team are currently developing its application for use with drug development for sarcoidosis.

 

Leishmania Virtual Laboratory

A virtual laboratory platform that models mouse spleen sections and the host response to chronic infection by leishmania parasites. This platform models infection and the host response in an individual animal to reduce the number of animals used in the development of treatments.

The platform allows the user to explore the model structure, evidence for its design, and results from a series of knockout experiments that identify the potential of new drug targets, combining model, simulation, results, analysis, evidencing and arguments into a single web-based tool.

Features

Animated data visualisation

Results from in silico knockout experiments can be visualised and animated on the model network structure highlighting which aspects of the model are statistically significantly changed.

Network based representations

The user can explore a network-based representation of the model structure by selecting the cellular and molecular species they are interested in.

View evidence


The model is constructed from hundreds of lines of code. Each is viewable with the ability for supporting evidence to be attached to model concepts. Evidence is then viewable within the virtual laboratory.

A suite of products based on the platform technology developed to address the VIDR Challenge and delivering 3Rs impacts across a range of applications are also available from Simomics. These include:

Virtual Fish EcoToxicology Laboratory (VFETL)

Developed in partnership with AstraZeneca and the University of York, this cloud-based virtual laboratory provides a platform to explore the eco-toxicological effects of legacy and new drugs. A suite of mathematical models have been integrated to reduce the need for unnecessary in vitro fish trials for new drugs and enable scientists to cost effectively prioritise legacy drugs for environmental impact assessments, saving hundreds of fish per avoided trial without compromising environmental protection.

VFETL provides transparent data analysis and visualization, with automated decision making based on EU regulation to determine environmental risk assessment (ERA) requirements, with auditable reasoning in the form of flowcharts and argument diagrams. VFETL has the ability to screen out ~70% of legacy APIs from needing a particular ERA fish study based on risk, resulting in cost savings and reduced fish trials.

Features

Data analysis and visualisation: sophisticated data analysis and visualisation for eco-toxicology data and a suite of mathematical models.

Transparency: transparent and auditable calculations, tracking data provenance to display where each data point originated from and how it was calculated.

Decision support: supporting complex data-based decision making based on embedded rules, with auto-generated argumentation diagrams to visualise and explain the path taken in a way that is transparent to users or regulators.

EcoPharmacoVigilance (EPV) Risk Visualisation Tool

The EcoPharmacoVigilance (EPV) Risk Visualisation Tool provides internal or public facing transparency on the relative risk for measured environmental concentrations (MECs) of active pharmaceutical ingredients (APIs) and chemicals found in the environment.
EPV shows MECs of substances from around the world and visualises their relative environmental risk based on their Predicted No Effect Concentration (PNEC). EPV demonstrates which substances pose a potential risk, or do not based on their MEC and PNEC, reducing unnecessary environmental risk assessments and animal studies for legacy drugs and allowing users to focus on and prioritise higher risk substances.

Features

Engaging risk visualisation: EPV visualises MEC data for substances found in water samples from around the world and their relative environmental risk on an interactive map and dashboard with company specific branding.

Interactive substance information: the substance, PNEC, risk and MEC information displayed, which can be embedded into the tool for easy deployment on a website, can be filtered by substance, protection goal, environmental compartment or location.

Transparent data sources: citation information for the original scientific literature can be found for each MEC data point reflected on the map, by selecting a point of interest.

Manufacturing Effluent Risk Modelling & Assessment System (MERMAS)

Simomics are currently developing a new software tool for release in 2021, in collaboration with the University of York, to support pharmaceutical manufacturing environmental and antimicrobial resistance (AMR) risk assessments for effluent releases that can contain active pharmaceutical ingredients (APIs) and antimicrobials. MERMAS guides users to more accurately predict potential environmental, human and antimicrobial resistance risks from pharmaceuticals manufacturing, reusing existing data including animal study data where possible for 3Rs benefits.
MERMAS supports pharmaceutical companies and suppliers in confidently enforcing and demonstrating compliance with safe limits at their manufacturing sites.

Features

More accurate safe limit calculation: guiding users in how to do safe limit and PNEC calculations effectively, reusing existing study data where possible for 3Rs benefits.

Proactive scenario planning: capturing company specific and/or regional policy limits, and allowing users to do rapid ‘what if’ scenario planning for individual or multiple sites prior to production changes, with scenario comparison and best practice guidance to reduce environmental risk.

Reporting and transparency: streamlining supplier assessments for sites to demonstrate that safe environmental and AMR limits are being considered and met for effluent releases with auditable and transparent centralised reporting.

 

You can find out more about these products on the Simomics website.