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Funded Investigators:

Dr Davide Tiana, University College Cork, Prof. Stephen O’Brien, MACSI, University of Limerick, Dr Sarah Mitchell, MACSI, University of Limerick, Prof. Michael Vynnycky, MACSI, University of Limerick, Prof. Norma Bargary, MACSI, University of Limerick.

Theme Leaders:
Prof. Harry Van den Akker
University of Limerick
Modelling

Prof. Damien Thompson
University of Limerick

Overall Objective

MODELLING addresses the key challenges by developing new techniques to design and predict behaviour in silico in order to reduce trial-and-error experimentation.

Demonstrating expansion of the State of the Art:

  • Framework to understand and predict multi-component solid forms from first principles
  • Deciphering nanoscale design rules of macromolecular self-assembly
  • Mathematical modelling of physicochemical processes
  • Multi-Scale Simulations of Flow and Crystallisation in Industrial Crystallisers

Computational Fluid Dynamics techniques provide a better understanding of the dynamics of both the local phenomena and processes (by means of completely resolving them, in DNSs) and the overall flow and mixing behaviour of process equipment (by using advanced models).

Key Scientific Expertise

  • Atomic-scale modelling
  • Computer-aided design of experiments
  • Molecule-molecule
  • Molecule-solvent and molecule-surface interactions
  • Complex APIs
  • Molecular formulations
  • Modelling in the fields of fluid mechanics, turbulence, multi-phase flow and transport phenomena
  • Computational fluid dynamics simulations
  • Lattice Boltzmann simulations

Industrial Significance

Modelling provides the central unifying all SSPC pillars of research. By rationally designing from the molecular to materials to macro scale we will dramatically reduce the number of experiments that must be performed to discover and design new molecules, crystals, co-crystals and nanoparticle-enabled deliveries, and to engineer and optimise formulation and processing conditions to deliver new pharma and biopharma solutions.

  • Employ first-principles modelling to quantify solid-state interactions driving stability of crystals & improve rapid informatics-based approaches to predict likely multi-component solid forms & their properties.
  • The platform will push beyond current state of the art by developing new transformative physical models that are sufficiently fast, accurate, and intuitive for broad uptake in drug product manufacture

Example project: framework for understanding and predicting multi-component solid forms from first principles.