Oil-in-Water Nanosized Emulsions for Drug Delivery and Targeting. Tamilvanan Shunmugaperumal

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Flowchart 2.1. Typical steps involved for the new drug products during their formulation development stage as per the quality by design (QbD) approach of formulation by design (FbD).

      This traditional framework has certain drawbacks. Any minor changes made in input materials and processes (including equipment) for anticipated variability are empirical and addressed via the OFAT experimental approach. This development practice is not cost‐effective and results in incomplete product and process understandings, which in turn leads to restrictive (or fixed) manufacturing processes that are unable to compensate for the regular variability in input materials, processes, manufacturing equipment, and laboratory instrumentation (Debevec et al. 2018). As mentioned earlier, the QbT approach also requires extensive testing to comply with restrictive FDA‐approved specifications (Yu 2008).

For these reasons, traditional industry practices are often extraordinarily expensive and time consuming and present an overwhelming burden to regulatory agencies for reviewing multiple chemistry, manufacturing, and control (CMC) supplements. Another major drawback of the traditional approach is that it does not differentiate products with regard to complexity (for example, APIs with high potency and/or narrow therapeutic indices) and associated level of quality and health risks to manufacturers and consumers, respectively (Yu 2008). These drawbacks of “minimal” or “traditional approach” and need for higher regulatory flexibility via an “enhanced approach” prompted a paradigm shift in the industry practice [ICH Q8(R2) guideline 2009].

      The need for transition from traditional QbT to an enhanced approach was formally communicated through an ICH Q8 guidance published in May 2006, which emphasized that “quality cannot be tested into products, rather it should be built into products by design” (FDA Guidance for Industry 2006).

      These innovative frameworks are fully reflected in current regulatory guidance on QbD and PAT [FDA Guidance for Industry 2004; ICH Q8(R2) guideline 2009] and are encouraged for industry practice.

      Both QbD and PAT share common goals of providing a rapid and science‐ and risk‐based road map for product development and economically effective strategies for process monitoring and analytical testing. The QbD strategy involves an end‐to‐end integration of six key elements, which are quality target product profile (QTPP), risk assessments related to process and product design, DOEs, design space, control strategy, and continuous process improvement. Each of these elements is essential for product development, manufacturing, and quality assurance. Through parallel evolution of QbD and PAT, it is now well established that developing a robust formulation and manufacturing process requires a thorough understanding of inter‐relationships between material attributes (MAs), processing parameters (PPs), and dependent product quality attributes at each stage of product development. In this regard, pharmaceutical, chemical, and engineering industries, research institutions, and regulatory agencies worldwide have significantly contributed through joint collaborations, workshops, conference presentations, and publications. More recently, a 5‐year pilot QbD program (March 2011–April 2016) was launched between the FDA and the EMEA (EMA 2017).

      In this pilot program, the subject matter experts from both agencies had thoroughly exchanged their viewpoints to allow a joint evaluation of QbD elements in an effort to harmonize agencies’ expectation for regulatory submissions worldwide. As a result of this pilot program, both the FDA and the EMEA have mutually agreed on several pertinent topics and level of details required in a regulatory submission (EMA 2014).

These include risk assessments related to process and product design, DOEs, and design space, which are briefly summarized below. In QbD‐based product development, the MAs and PPs are initially identified from prior scientific knowledge using risk assessment techniques. One of commonly utilized risk assessment tools is failure mode effect analysis (FMEA). In this approach, a risk priority number (RPN) is calculated for each factor that can impact the CQAs of the product. The RPN is calculated by multiplying numerical rankings (e.g., 1–5 or 1–10) ascribed to three risk components (severity of harm, probability of occurrence, and detectability) associated with each factor, i.e.,

(2.1)

      The RPN values can be presented in a tabular format or as a Pareto plot for a quantitative display of relative risk rank order. It needs to be emphasized that all the MAs and PPs that can potentially affect CQAs are considered as part of the initial risk analysis; however, only a subset of these attributes and parameters are selected for development studies as warranted by the outcome of risk analysis (Badawy et al. 2016).

      This QbD element has originated from the Pareto principle. As a rule of thumb, about 80% of the problems originate from roughly 20% of the factors identified, that is why Pareto concept is sometimes referred to as 80/20 rule (Orloff 2011).

      Subsequently, the combinations and interactions of identified subset of MAs and PPs are studied as part of the product development through DOE, which is regarded as a “toolkit” component of a QbD approach. The collective outcome from formulation and process design DOEs is utilized in finalizing the list of critical material attributes (CMAs) and CPPs, thereby establishing a design space and an overall control strategy. Based on current ICH Q8(R2), a design space is defined as the “multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality” [ICH Q8(R2) guideline 2009].

A generalized scheme representing the evolution of QbD and multidimensional combination and interaction of critical input variables (CMAs and CPPs) on CQAs of an output product is given in Flowchart 2.2. CMAs of input materials СКАЧАТЬ