Advanced process modelling for Food and Consumer products

Top benefits of modelling and simulation for pharmaceuticals

Modelling and simulation provide many benefits in the design and operation of processes for Food and Consumer products. Here are our top benefits:

Increase productivity and reduce costs associated with the development of new formulations

Model-based techniques provide a means to explore the design space rapidly and reliably to determine the best and most reliable processing options.

Deployment of modelling early on makes it possible to reduce:

  • the time and cost of experimentation
  • the time and cost of process development
  • the operating and capital cost of the installed process.

Improve operational performance and lower production costs

Model-based techniques can help you to improve the operational performance of reaction, crystallization, separation, agglomeration and coating processes, based on high-fidelity information.

Scale-up with confidence: right-first-time commercial designs.

Scale-up is one of the key challenges for the pharmaceutical industry, and is considered by many to be a 'black art'.

Model-based techniques are becoming widely adopted to assist in reliable scale-up from laboratory to industrial scale.

The performance at production scale can now be accurately predicted using techniques that simultaneously take into account chemical phenomena (for example, reaction or crystallization) validated at small scale, and mixing effects calculated using Computational Fluid Dynamics (CFD) models.

Improve process reliability to ensure that results are reproducible every time

Predictive models can be used to determine optimal operating conditions taking into account the variability of external factors such as raw material quality.

Process equipment, operating conditions and operating procedures can be designed to ensure that the process is capable of delivering product of suitable quality for a wide range of variations.

Improve product quality

Model-based techniques allow you to improve product quality in many different ways.

For example, high-fidelity models of a crystallization process can be used to determine the process conditions that will limit the crystal growth rate during certain phases of the batch.

Limiting the growth rate reduces the amount of solvent inclusion in the product crystals, thereby reducing impurities and enhancing quality.

Optimise recipes – for example, to minimise batch time

PSE's gPROMS ModelBuilder was specifically designed as a modelling tool for optimisation of batch processes. This capability is also available in gCRYSTAL and gSOLIDS. A range of techniques can be applied to optimise recipes – for example, to minimise batch time while maintaining quality.

The gPROMS platform's award-winning dynamic modelling and optimisation capabilities, coupled with its powerful TASK language for describing operating procedures, are ideally suited to – for example:

  • maximising product yield
  • minimising batch time
  • minimising production of impurities
  • minimising wastage of costly raw materials

subject to process and material constraints.

Quantify risk accurately and use reliability information to guide experimental programmes

One of the key challenges is to use experimental data effectively in process design.

PSE's data analysis and parameter estimation tools make it possible to generate accurate model parameter information (for example, reaction kinetic parameters) from experimental data so that process and recipe design can be based on accurate predictive information.

PSE's model-based model-targeted experimentation workflow also makes it possible to analyse the uncertainty in the underlying data and guide experimental R&D programmes towards the generation of validated models with quantified uncertainty in their predictions at minimal cost (in terms of the number of experiments needed, the amount of API used, the time required etc.).

Provide an understanding of manufacturing processes.

Many pharma processes, in particular those involving reaction and crystallization, are not well understood.

Understanding the complex interaction between the underlying physical and chemical phenomena is key to achieving many of the other benefits outlined above.

Not only does high-fidelity modelling provide this understanding in a way that cannot be achieved by any other means, but models themselves provide a means to capture knowledge and transfer it across the organisation.

PSE provides an end-to-end modelling platform that allows pharmaceutical organisations to capture their proprietary IP, and that of their collaborators (e.g. academic institutions, consultants, equipment suppliers etc.), in terms of mathematical models spanning the entire lifecycle of a drug product manufacture and its delivery – primary & secondary manufacturing operations, drug delivery systems, pharmacokinetic & pharmacodynamics models.

Move from batch to continuous processing

There are many questions to be answered when moving from batch to continuous processing.

High-fidelity predictive modelling plays an essential role in the move from batch to continuous, by providing reliable quantitative information that helps test viability and determine optimal equipment size, operating conditions and other aspects, and minimises the risk.

High-fidelity models validated against lab or pilot plant data can be used to test every possible operating eventuality in advance of start-up.

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