Flexibility in Drug Product Development: A Perspective

Area of Science:

• Pharmaceutical Sciences
• Drug Development
• Process Engineering

Background:

• Traditional drug product (DP) development is often linear and inflexible, leading to high costs and risks associated with late-stage changes.
• The 'penalty of change' (PoC) discourages pivoting from established development pathways, even when new information suggests a better approach.

Purpose of the Study:

• To propose a more flexible and efficient drug product development paradigm.
• To explore how integrating data sciences and emerging technologies can mitigate the PoC and accelerate development.

Main Methods:

• Review of current and emerging techniques in DP development.
• Discussion of state-of-the-art data sciences, including artificial intelligence (AI).
• Exploration of novel process and measurement technologies, automation, and advanced data analysis.

Main Results:

• A nonlinear DP development approach, combining iterative evaluation, smart clinical studies, and advanced technologies, can significantly reduce the PoC.
• Leveraging AI, novel characterization, and automation enhances flexibility and accelerates the build-measure-learn cycle.
• This integrated approach enables more agile decision-making throughout the R&D process.

Conclusions:

• A nonlinear, technology-driven approach should become the standard for drug product development.
• Enhanced flexibility through iterative evaluation and advanced technologies can streamline the path to market.
• Adoption of these methods promises improved efficiency and reduced R&D costs in pharmaceutical manufacturing.