The philosophy underlying TransQST is that improved Translation from nonclinical to human safety during clinical trials can be achieved with novel Quantitative Systems Toxicology models.

To achieve this ambitious goal, the TransQST partnership will focus on liver, kidney, cardiovascular and gastrointestinal-immune systems, common target organs for drug-induced injury in order to:

  • Build on existing PB-PK/PD models to define systemic as well as specific organ/cell exposure to drugs and metabolites in a holistic fashion.
  • Develop SYSTEMS models for drug-induced organ damage across the four target organs.
  • Integrate PB-PK/PD models and output from SYSTEMS models into quantitative systems toxicology (QST) models.
  • Test the models using selected compounds with nonclinical and human data.
  • Form a unique and unprecedented public-private partnership that leverage industry data and practical experience with public expertise in mechanistic work as well as modelling across scales of complexity
Objective 1: Develop an open (for consortium members), focused and sustainable knowledge database to build system toxicology models.

We will develop data harmonization and mining tools to provide single-point access to ’omics type data, molecular network information and any models used or developed in the project. The knowledge management platform will be based on open-source tools, previous IMI investments and existing web services will be used wherever possible. Human and pre-clinical data that assists an expanded knowledge of mechanistic toxicity will be identified and curated and existing ontologies will be used to support harmonisation of data across public and private resources. Tool development will be associated with long-term maintained resources and platforms whenever possible to ensure sustainability.

Objective 2: Provide a clearer understanding of the translational confidence from non-clinical species to human.

Transfer of knowledge along the different phases of drug development is fundamental to improve the understanding of the safety of new medicines. In particular, cross-species extrapolation between different laboratory animals and translation to first-in-human trials is challenging because of the uncertain comparability of physiological processes. To improve the situation, the applicants will use established and novel approaches of physiologically-based pharmacokinetic (PBPK) modelling that allows translation of mechanistic knowledge from one species to another by specifically considering physiological and biochemical interspecies differences, based on model parameter domains of a target species into the PBPK model of a reference species, in both in vitro and in vivo systems. These simulations consider activities of relevant enzymes and transporters, species-specific physiology, plasma protein binding, enzyme and transport kinetics. Using our already established database of mouse and human pharmacokinetics and toxicity data, the accuracy of cross-species modelling will be determined. In exactly the same manner, we will take account of species differences in susceptibility to drug-induced disease. Moreover, drug toxicity networks (developed by identifying the relevant modules in protein interaction networks involving drug targets and toxicity-associated proteins) will be integrated into the knowledge platform to support the development of quantitative models of pathways related to the development of adverse events. Pathway similarity across species will be assessed using network alignment algorithms to evaluate divergences to toxicological insults. All work will be underpinned by knowledge of the physiological phenotype of test systems so that results can be explained in a robust translational context.

Objective 3: Support key risk assessment decisions, such as safety margin, clinical monitoring and reversibility, using mechanistic and quantitative modelling.

An important and as yet unaccomplished task in drug safety research is to predict concentrations of pharmaceutical compounds in tissues. For this purpose, the applicants will develop and apply integrated PBPK, spatio-temporal and metabolic models. These techniques will, for example, predict if a compound accumulates in specific cell types of a human tissue and whether the tissue concentrations exceed levels where adverse effects can be expected. We will utilise the DDMoRe interoperability framework (where possible) and other approaches (e.g. Lua scripting) to access existing PBPK models available within consortium partners. Plasma and organ/cell concentration data, as well as quantitative data from mechanism-based biomarkers, will then be linked to organ-specific systems biology models and logic-based modelling will be used to model the complex processes of drug toxicity and recovery following cessation of drug exposure. This will allow improved understanding of both reversible and irreversible clinical outcomes.

Objective 4: Provide improved methods for visualising and analysing complex high content data to support drug safety assessment.

The experience of some of the partners in developing recognized user-friendly software in the field is a guarantee of the accomplishment of this objective. These tools allow integration of different types of data onto networks and we can leverage a variety of network analysis tools available in the community.

Objective 5: Help inform regulatory decisions by providing evidence supporting the usefulness of QST modelling to support safety risk assessment.

We will test the human relevance of the network associations and models that are developed through the use of normal and drug-perturbed clinical samples and correlation with translatable biomarker data. It is essential that there is a common understanding and endorsement of the translational models that will be developed, and the purpose they are fit for, by academics, industry and drug regulators.

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