Aug 052014

Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact



Identifying target product profile (TPP). TPP has been defined as a “prospective and dynamic summary of the quality characteristics of a drug product that ideally will be achieved to ensure that the desired quality, and thus the safety and efficacy, of a drug product is realized”. This includes dosage form and route of administration, dosage form strength(s), therapeutic moiety release or delivery and pharmacokinetic characteristics (e.g., dissolution and aerodynamic performance) appropriate to the drug product dosage form being developed and drug product-quality criteria (e.g., sterility and purity) appropriate for the intended marketed product. The concept of TPP in this form and its application is novel in the QbD paradigm.

Identifying CQAs. Once TPP has been identified, the next step is to identify the relevant CQAs. A CQA has been defined as “a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality”10. Identification of CQAs is done through risk assessment as per the ICH guidance Q9 . Prior product knowledge, such as the accumulated laboratory, nonclinical and clinical experience with a specific product-quality attribute, is key in making these risk assessments. Such knowledge may also include relevant data from similar molecules and data from literature references. Taken together, this information provides a rationale for relating the CQA to product safety and efficacy. The outcome of the risk assessment would be a list of CQAs ranked in order of importance. Use of robust risk assessment methods for identification of CQAs is novel to the QbD paradigm.

Defining product design space. After CQAs for a product have been identified, the next step is to define the product design space (that is, specifications for in-process, drug substance and drug product attributes). These specifications are established based on several sources of information that link the attributes to the safety and efficacy of the product, including, but not limited to, the following:

  • Clinical design space
  • Nonclinical studies with the product, such as binding assays, in vivo assays and in vitro cell-based assays
  • Clinical and nonclinical studies with similar platform products
  • Published literature on other similar products
  • Process capability with respect to the variability observed in the manufactured lots

The difference between the actual experience in the clinic and the specifications set for the product would depend on our level of understanding of the impact that the CQA under consideration can have on the safety and efficacy of the product. For example, taking host cell proteins as a CQA, it is common to propose a specification that is considerably broader than the clinical experience. This is possible because of a greater ability to use data from other platform molecules to justify the broader specifications. On the other hand, in the case of an impurity that is unique to the product, the specifications would rely solely on clinical and nonclinical studies.

In QbD, an improved understanding of the linkages between the CQA and safety and efficacy of the product is required. QbD has brought a realization of the importance of the analytical, nonclinical and animal studies in establishing these linkages and has led to the creation of novel approaches.

Defining process design space. The overall approach toward process characterization involves three key steps. First, risk analysis is performed to identify parameters for process characterization. Second, studies are designed using design of experiments (DOE), such that the data are amenable for use in understanding and defining the design space. And third, the studies are executed and the results analyzed to determine the importance of the parameters as well as their role in establishing design space.

Failure mode and effects analysis (FMEA) is commonly used to assess the potential degree of risk for every operating parameter in a systematic manner and to prioritize the activities, such as experiments, necessary to understand the impact of these parameters on overall process performance. A team consisting of representatives from process development, manufacturing and other relevant disciplines performs an assessment to determine severity, occurrence and detection. The severity score measures the seriousness of a particular failure and is based on an estimate of the severity of the potential failure effect at a local or process level and the potential failure effect at end product use or patient level. Occurrence and detection scores are based on an excursion (manufacturing deviation) outside the operating range that results in the identified failure. Although the occurrence score measures how frequently the failure might occur, the detection score indicates the probability of timely detection and correction of the excursion or the probability of detection before end product use. All three scores are multiplied to provide a risk priority number (RPN) and the RPN scores are then ranked to identify the parameters with a high enough risk to merit process characterization.  FMEA outcome for a process chromatography step in a biotech process. RPN scores are calculated and operating parameters with an RPN score >50 are characterized using a qualified scaled-down model. For the case study presented here, these include gradient slope, temperature, flow rate, product loading, end of pool collection, buffer A pH, start of pool collection, volume of wash 1, buffer B pH, buffer C pH and bed height. Process characterization focused on parameters such as temperature, that have a high impact on the process (severity = 6), occur frequently in the manufacturing plant (occurrence = 6) and are difficult to quickly correct if detected (detection = 7). In contrast, parameters such as equilibration volume, with a low impact on the process (severity = 3), low occurrence (occurrence = 2) and a limited ability to detect and correct (detection = 5), were not examined in process characterization.

I liked this pic

this is the check" class="grayscale



Sorry, the comment form is closed at this time.


Get every new post on this blog delivered to your Inbox.

Join other followers: