Concepts: Quality-by-Design, from molecule to intestine

SbP brings to the pharma industry a systems-based modelling approach that is well-proven in many other sectors.

It applies high-fidelity mathematical models of a manufacturing process, validated against experimental data, within a global system analysis and optimisation framework in order to explore the design and operational decision space rapidly and effectively.

The approach enables companies to make globally optimal design and operating decisions based on delivering the required key performance indicators (KPIs).


In the pharmaceutical context, a systems-based approach considers the entire manufacture-to-delivery system – from drug substance manufacturing, through drug product manufacturing to delivery – as a single related system.

Systems-based pharmaceutics overview

By considering all key effects simultaneously, it is possible to relate manufacturing decisions directly to drug delivery KPIs. A key target is the reduction of the number of iterations that are currently required between product design and manufacturing process design, subsequent manufacturing steps, bioavailability targets and drug product and process development.

Benefits of a systems-based approach

A key benefit of a systems-based approach is that it provides a formal framework for the capture, generation and management of knowledge, and a means for the effective deployment and exploitation of that knowledge across the organisation. More specific benefits are:

  • End-to-end decision support for pharmaceutical manufacturing R&D. A systems-based approach allows more efficient, effective and rapid exploration of the design space than current practice.
  • Reduction of cost and risk through reduced trial and error analysis. Trial-and-error approaches often are not only expensive, but can fail to identify key effects. Multi-scale systems-based modelling approaches allow rapid and reliable exploration of the decision space.
  • The ability to manage risk by quantifying the impact of uncertainties. Data uncertainty throughout the manufacturing process can be quantified by applying formal mathematical analysis, and areas of risk subject to further, highly-targeted experimentation.
  • Identification of the weakest link in the product-design-to-drug-delivery chain. This in turn identifies the optimal targets for further R&D investment.
  • Enhanced understanding of the process resulting from systematic study of the underlying phenomena and integration of knowledge from different groups provides the ability to make better, more-informed decisions.
  • Improved R&D efficiency. The knowledge generated makes it possible to better integrate R&D with process development and perform fewer, more targeted experiments, reducing time and cost.
  • Faster time-to-market. Improved R&D efficiency coupled with the ability to explore the design space rapidly and effectively accelerates the development and design process, resulting in faster time to market.
  • Better process design and operation. Model-based approaches enable optimisation of process design and operations – for example, the optimisation of equipment sizes or creation of optimal recipes to minimise batch time.