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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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A Rational Hierarchy to Capture Raw Material Attribute Variability in the Pharmaceutical Drug Product Development and

Stephen L Conway1, Kenneth J Rosenberg2, Sutthilug Sotthivirat3

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A new framework for assessing drug product robustness uses a hierarchy of raw material (RM) variations to generate enduring data. This approach improves understanding of attribute-performance relationships, moving beyond historical Quality-by-Design (QbD) limitations.

Keywords:
Drug-excipient interaction(s)Excipient(s)FormulationPhysical characterizationProcess robustnessQuality by design (QbD)

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Area of Science:

  • Pharmaceutical Sciences
  • Chemical Engineering
  • Materials Science

Background:

  • Assessing raw material (RM) variability is crucial for drug product formulation and manufacturing robustness.
  • Existing methods like Quality-by-Design (QbD) have limitations in commercial manufacturing and lifecycle management.
  • Small development datasets struggle to predict real-world variability and attribute-performance relationships.

Purpose of the Study:

  • To propose a holistic framework for exploring RM variations and their impact on drug product robustness.
  • To challenge the proposed framework with common failure modes and demonstrate its effectiveness.
  • To provide a more enduring and valuable approach to robustness assessment compared to historical QbD methods.

Main Methods:

  • Reviewing current practices for exploring RM variations in pharmaceutical development.
  • Developing a holistic framework based on a hierarchy of RM variability.
  • Incorporating higher-ranking RM variations as perturbations in material-conserving experiments.
  • Utilizing case studies to illustrate the framework's application and benefits.

Main Results:

  • Material-conserving experiments incorporating higher-ranking RM variations yield powerful and enduring robustness data.
  • The proposed framework effectively challenges common failure modes.
  • Case studies demonstrate the framework's ability to avoid pitfalls of historical QbD approaches.

Conclusions:

  • The proposed holistic framework offers a superior method for assessing drug product robustness against RM variations.
  • This approach provides valuable data for initial development and lifecycle management, including evaluating changes.
  • Contextualizing robustness data within formulation and process development is key to its successful application.