(126b) Decision Support System for Route Selection in Pharmaceutical Process Development

Pharmaceutical product development is typically started from medicinal chemistry route, which will be changed synthetically at least once to generate a commercially viable route. Therefore, synthetic route (SR) forms the basis for process development. There is an enormous pressure for process development teams to take right decisions at the right time due to their importance in the downstream of the product life cycle (e.g. process scale up and manufacturing, importance of reaching the market first, capitalising the market and getting the maximum benefit during the ramp-up period). Generally, safety, health and environment (SHE) are the main aspects considered in the chemical process development. However, there are number of other important factors (e.g. chemical feasibility, intellectual property issues, raw material availability, cost of goods, etc) needed to consider in the pharmaceutical process development. Different organisations use their own decision supporting and knowledge management tools for assessing synthetic routes (Butters et al., 2006). The underlying principle behind the decision making is technical and business drivers related to the processes chosen such as corporate culture (e.g. level of risks taking), capabilities (technical, resources and capital), dosage and therapeutic values of the products. Butters et al. (2006) proposed a uniform criteria in developing SRS considering six important factors namely safety, environment, legal aspects, economics, control, and throughout, thus SELECT acronym is formed. However, implementation of SELECT criteria is not delineated adequately. Important features of the SELECT criteria are ability to use this concept throughout the process life cycle, ability to link to product quality lifecycle implementation (PQLI) and the quality by design (QbD) approach. We intend to alleviate the implementation shortcomings found in the Butters et al. (2006) approach using the concepts of QbD, ICH guidelines and PQLI. The main advantages of this approach are the ability to develop heuristics prior to experimental work and to define the knowledge space related to different route options at the beginning. Synthetic route selection (SRS) using the SELECT criteria was first analysed in different layers of data, information and knowledge as shown in Figure 1. Interdependent factors affecting each criterion were identified in the same structure by relating to decision making process. Potential risk sources and how those are linked, were also depicted in the same figure. These interdependent factors assist to make decisions under the data lean environment. Then, this structure was compared with the concepts of criticality, design space and control strategy as proposed in the PQLI strategy. In the highest level, QbD is considered. In the second level, ICH Q8, Q9 and Q10 were used to understand the concepts in QbD approach. Then, at the implementation level, the PQLI concepts (criticality, design and space and control strategy) were used to define the knowledge space for SRS. In order to identify critical, key and non-critical process parameters in SRS knowledge space, probability and consequences of failing those attributes/parameters were considered using frequencies and their impact. A set of metrics is used to assess the scores for different routes, risks associated with them and capabilities to achieve the potential synthetic routes. The interdependencies were identified as depicted in the Figure 1 with similar numbers combining them. For example, toxic hazards under the toxicity are linked to toxicity of the environment. Similar interdependencies related to toxicity could be seen under the legal and control aspects, etc. Consideration of one aspect automatically relates to another aspect of the SRS of this structure. Therefore, relative importance of each aspect is required to consider in this analysis. The most inner layer is the top level of evaluation and will be judged using the available information with respect to different route options. If available information at this layer is not adequate, the next layer of assessment is carried out until a particular criterion can be satisfied with the available knowledge. The requirements of literature review, experimental work or detailed analysis or modelling the phenomena (mechanistic or stochastic modelling) will be increased radially as shown in the Figure 1. Once the ranking was done, risks associated and company's capabilities to attenuate risks in each route are plotted. Superimposing each layer of ranking (i.e. route, risks and capability) different routes are assessed as shown in Figure 2a. This helps to assess the technical and business feasibilities of chosen route options. Route score tells us the knowledge space for each route option. Risk score gives the level of risks associated with each route option. Capability score shows the ability or effort needed to mitigate the risks associated with each route option. By comparing these three scores, we can decide the business case and whether it is worth further investigation. Selecting the best route plays an important role in understanding the science of pharmaceutical PD. From the first principles to heuristics are developed using the relevant data, information and knowledge. At first, use of first principle models will take long time and effort. Competitive pharmaceutical industry needs quick yet reliable approaches to rapid PD. The Qualitative Process Modelling (QPM) has shown significant impact on process understanding (e.g. the BRITEST tool www.britest.co.uk) by visualising the global picture of phenomena using simple pictures, heuristics and logical blocks. Thus, it breaks down the complexity of the problem to a manageable size even to use first principle modelling more effectively. Therefore, the above procedure sets up visualising different aspects of SRS. Further, the proposed approach could effectively be implemented in software suite as the amount of data, information and knowledge required in this exercise is enormous. Thus, an integrated knowledge management tool for SRS is being developed not only for SRS, but also as an organizational knowledge repository. This consists of various components such as databases (e.g. material safety), empirical and theoretical models, data acquisition and control etc. The framework proposed assists to take decisions at high level of SRS. Further, it defines the broader knowledge space based on heuristic knowledge in a team environment. The interdependent decision factors and risk elements associated with them were integrated to the proposed framework. This approach can assess the risks associated with different routes and capabilities of a company in order to implement the potential synthetic routes.