QbD brings an integrated science and risk-based approach to the design and quality of pharmaceutical products, providing improved understanding of the interplay between material attributes and process parameters, and how they influence the quality attributes of the finished product.


The International Conference on harmonization of Technical requirements for registration of Pharmaceuticals for human Use (ICH) Q8 (R2) provides an overview of the application of QbD to pharmaceutical development.


The quality of a design depends on how well the product performs against predictions based on the design inputs. Design inputs are knowns. Unknowns which subsequently adversely affect product performance are not criteria against which to assess (with hindsight) the quality of the design (unknown unknowns).


Excipient unknowns fall into several categories and may be unknown to the user, unknown to the excipient manufacturer or unknown to both. Risk assessment requires that unknowns (not unknowable) be addressed with all stakeholders, including the excipient suppliers.


  • The designer can be criticized if the design subsequently falls victim to an excipient effect unknown to the designer but known to the excipient manufacturer, who was not consulted during the design.
  • Effects from an unknown can be likened to a ‘black swan’, a highly unexpected event for a given observer (the designer), which carries large consequences (product failure), and is subject to ex-post rationalization (why did no-one see it coming?).


Pharmaceutically, there tends to be over-reliance on the Certificate of Analysis (CoA), focused on Pharmacopoeial parameters, which may be of limited relevance to determining excipient fitness for purpose in a specific application. Other unspecified excipient attributes may vary uncontrolled in the background, but will be unknown to the user unless discussed with the excipient manufacturer.


Potential CMAs can be identified a priori for the design-critical excipients and confirmed experimentally, which may also identify significant interactions.

  • For example, many excipients are polymeric and specified by a dilute solution apparent viscosity. This reflects an average molecular weight and it is hence important to ask the excipient manufacturers how they meet the viscosity specification.

  • Apparent particle size is another common attribute which can be misleading in a particular application. For example, laser scattering methods are common but their assumption of sphericity is not applicable to most excipients. Laser scattering is dominated by larger particles and may miss multimodal particle size distributions.


Methods of Simulating Material at the edge of Specification

  • Fractionation: e.g. sieve cuts to evaluate the effect of particle size. Milling and granulation are less preferred as other material attributes may be altered.

  • Level of incorporation: e.g. in the case of viscosity, more or less excipient can be added to simulate lots at or outside the viscosity limits.
  • Conditioning: e.g. equilibrating excipient at higher humidity to increase the moisture content, or drying it to decrease the moisture content.
  • Spiking with known concomitants, process aids and additives to simulate extremes of excipient composition.
  • Grade bracketing, where multiple grades are available reflecting ranges of the attribute(s) of interest beyond the original specification limit(s).


A design space can be proposed, which is “the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality”.


QbD also looks beyond the development and asks how quality will be maintained throughout the product lifecycle, via the control strategy, “a planned set of controls, derived from current product- and process-understanding, that assures process performance and product quality”