This practice covers process design, which is integral to process development as well as post-development process optimization. It is focused on practical implementation and experimental development of process understanding.


The principles in this practice are applicable to both drug substance and drug product processes. For drug products, formulation development and process development are interrelated and therefore the process design will incorporate knowledge from the formulation development. Which is apply during development of a new process or the improvement or redesign of an existing one, or both.


In the desired state of a process, all sources of variation are to be defined and controlled, and end product variation is to be minimal. That implies that critical product attributes are controlled to target for all individual units of a product. As a result the process is capable of consistently supplying, unit to unit and batch to batch, desired quality.


Risk Assessment and Mitigation

Products and manufacturing processes should be designed to minimize variation. Therefore, process design is a means to mitigate the risk of having product units with varying quality. The process design requires the use of formal risk evaluation methodologies and mitigation assessments.


Continuous Improvement

Process design starts with the identification of first design options that reflect the desired process state and the desired product attributes.

Evaluation of the first and all following design options should follow an iterative process of design improvement.

Design improvement is continued post-launch (continuous improvement) to support management of process quality throughout the product lifecycle.

Continuous process design improvement includes:

  • Initiation of the design process based on information about product structure, composition, desired quality attributes, and so forth,
  • Definition of initial design concepts based on institutional knowledge, intuition, experience, first principles, and so forth,
  • Generation of design options,
  • Identification of feasible design options from development studies,
  • Detailed process development, and
  • Design review and learning from experience from development or implementation, or both, where quality risk management principles and methodology are applied on each step, and information and learning is fed-back and fed-forward between all steps.


Process Fitness for Purpose

Process fitness should be established regarding:

  • Product characteristics, product quality definition.
  • Process characteristics, for example, unit operation quality.
  • Process systems (for example, control system, mea-surement system).
  • System components (for example, design elements, modules, interfaces).
  • Commercial fitness for purpose.


Intrinsic Performance Assessment

Processes should be designed with intrinsic process assessments and control systems that are integral components of the manufacturing operations. This approach is fundamentally different from conventional design approaches that rely on separation of process from process output assessment, for example, by sampling, averaging, and off-line testing.


Manufacturing Strategy

There is a mutual relationship between the development of the manufacturing process and the risk mitigation strategy for a given product, as the process is designed to deliver the product with desired attributes.

The design of the manufacturing process should form part of the risk mitigation strategy for a product. For example, the risks to the patient for a low dose/high potency drug will be different from a high dose drug, and therefore the manufacturing process designed in each case will reflect those differences.

Where a process is scaled-up, product quality and process robustness can be assured by measuring the in-process material attributes and critical quality attributes, rather than the machine parameters and using these to ensure end product quality.


Data Collection and Formal Experimental Design

Experimental design tools (such as Design of Experiments (DoE)) are used to ensure that data is collected through-out the design space in a manner that minimizes the necessary experimental load and maximizes the information extracted about the process. Several cycles of such experimental work, each focusing more closely on the likely operating area, maybe required to establish initial production process conditions.


Multivariate Tools

Multivariate tools are used to generate predicted values for the critical quality attributes, to generate values for factors directly or indirectly linked to process condition, or to generate qualitative information about material. Multivariate tools can be used to under-stand and control process and product variability.


Process Analyzers

In-, on-, at-line process analytical tools are used for rapid measurements which can be used to evaluate material attributes and process performance and enable process control.


Process Control

The combination of univariate and multivariate data derived in real-time from the process is used to evaluate effect son process critical quality attributes. These in turn are used to evaluate the necessary process parametric settings to ensure both the desired process trajectory and end product quality or desired state. This feedback loop, and any associated feed-forward and feed-back of data from stage-to-stage, comprises the process control.


So we can say that, this standard brings together a number of different aspects including risk management, continuous improvement, intrinsic performance assessment, manufacturing strategy, data collection, formal experimental design, multivariate tools, process analyzers, and process control.