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Flexibility in Drug Product Development: A Perspective.

Yash Kapoor¹, Robert F Meyer¹, Heidi M Ferguson¹

¹Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States.

Molecular Pharmaceutics

|June 24, 2021

Summary

Drug product development can be revolutionized by adopting a nonlinear approach. Integrating data sciences and advanced technologies reduces the penalty of change, making drug development more flexible and efficient.

Keywords:

advanced characterization data mining flexibility modeling smart clinical trials

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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.