Figure showing the link between different sources of knowledge and QbD applications through knowledge management functions. The knowledge stream begins with the four possible sources and can consist of symbols (numbers, letters), data (symbol collections), information (data in context) and knowledge (applied information). Km uses four different functions to effectively manage the flow of knowledge, to achieve the QbD goals needed to gain an understanding of products and processes. Step 3 – First optimization with DoE: our first asssay detection results have proven that AEX-HPLC is an effective method for separating load variants. We observed four spikes in load variation (Figure 2). As described above, the F protein is prone to self activation (degradation). In Figure 2, the peak value after peak load variation represents the degradation product (impurity) before doE optimization. The impurity could be a mixture of actual product-related impurities resulting from the manufacturing process and contamination of the AEX-CLHP column induced by the test. The column has a positively charged surface that could trigger such degradation. One of the main objectives of our DoE study was therefore to minimize impurities under-induced by the remedy during the subsequent development of the trial.
We created a factor design in case of rupture to evaluate four parameters of the method: column temperature (25 or 40 degrees Celsius), beginning and end pH and complexity of the salt gradient (simple and complex gradients, 0-500 mM of NaCl). The latter involves a slight shift in pH caused by changes in the intensity of ions in the salt gradient. Our simple salt gradient is a typical linear salt gradient, without compensating for this pH drift. We created our complex salt gradient with Agilent`s Buffer Advisor software by adding several additional pH points along the gradient to minimize drift. We tested each condition of the DoE study in triple work (Table 1) and considered the 4 parameters of the method as potential critical method parameters (CMPs). Based on previous experience, we did not consider the other test parameters (for example. B injection volume and flow rate) as critical. Another approach was proposed using patent-based performance metrics to highlight the scientific and technological components of a pharmaceutical company`s knowledge base (considered its “central element”). Patent indicators were correlated with four innovative performance variables: the breadth of the company-related knowledge base, research expenditure, business size and external knowledge flows .68. This can be supplemented by the greatest task of assessing intellectual capital as a whole. A recent study in this area has taken into account human capital (learning and training, experience and know-how, innovation and creation), structural capital (systems and programs, research and development and intellectual property rights) and relational capital (strategic alliances, licenses and agreements, relationships and knowledge about partners, suppliers and customers).