Chemical Development is a complex and challenging undertaking, involving a large effort from multi-disciplinary teams, sometimes battling Mother Nature, with compressed timelines and limited material for experimentation. There is a broad spectrum of approaches to this challenge, including new lab instruments, use of robotics and automation, outsourcing certain types of development or operations and use of statistical and mechanistic modeling. Companies also experiment to find the best organization structure for this function and frequently separate departments specialize in Analytical (Chemistry) Development, Chemical (Process) Development, Technology Transfer and preparation of Regulatory filings. Collaboration among these groups helps achieve development goals.
Figure 1 is a much simplified graphical representation of the activities involved. There is a large reliance on experiments. Groups involved in process definition and optimization are currently the main users of both statistical and mechanistic modeling. Technology transfer increasingly involves working with external partners remotely. Data search and gather, including data integrity reviews and preparation of regulatory filings, are mostly manual processes. The disparate nature of activities and the needs for specialization make them somewhat siloed, with risks of duplication and dilution of effort. For example, an experimental program may be repeated if the first program missed some key information; or repeated by a CRO to answer new questions that have arisen; or repeated by a CMO in order to accomplish successful tech transfer. None of these data may be harnessed effectively and shared to answer future questions.
Leading companies are changing their approach to chemical development and bringing mechanistic process modeling on stream earlier and more centrally than before. The idea is not new but advances in a range of technologies (see earlier posts) and the momentum of 'Industry 4.0' are helping to fuel the transformation. At a task level, using a model to design the right experiments reduces overall effort. At a project level, the model provides a place to capture the knowledge and reuse it in future. At an organization level, modeling provides a structured, reusable and digital approach to information sharing and retrieval. For example, questions can be answered in real time, without experimentation, interactively when they arise, even live in a meeting or webcon, sparing delays, speculation and doubts, allowing faster progress.
The pieces in Figure 1 are rearranged in a natural way in Figure 2 as a cycle that captures and makes the most of information generated during each chemical development activity, including modeling. Additional items have been added to reflect technologies that are relatively new to Pharma, including continuous manufacturing and feedback process control; opportunities to apply either or both of these in chemical development or full scale manufacturing can be evaluated using a mechanistic process model. Therefore the mechanistic model takes up a central position and is the focal point in the new chemical development process.
It will take some time before Figure 2 reaches its full potential. The throughput of models in chemical development organizations is already increasing as model building tools become easier to use and more prevalent. We're delighted to be able to lead the way with Scale-up Suite.
Figure 2 also includes some great opportunities to automate workflows. We'll discuss some of these in a later post.
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| Figure 1 (click to enlarge): A simplified representation of chemical development today, including the scale and locus of statistical and mechanistic modeling |
Leading companies are changing their approach to chemical development and bringing mechanistic process modeling on stream earlier and more centrally than before. The idea is not new but advances in a range of technologies (see earlier posts) and the momentum of 'Industry 4.0' are helping to fuel the transformation. At a task level, using a model to design the right experiments reduces overall effort. At a project level, the model provides a place to capture the knowledge and reuse it in future. At an organization level, modeling provides a structured, reusable and digital approach to information sharing and retrieval. For example, questions can be answered in real time, without experimentation, interactively when they arise, even live in a meeting or webcon, sparing delays, speculation and doubts, allowing faster progress.
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| Figure 2 (click to enlarge): Future shape of chemical development activities, with mechanistic process models as the focal point for information capture and reuse. |
It will take some time before Figure 2 reaches its full potential. The throughput of models in chemical development organizations is already increasing as model building tools become easier to use and more prevalent. We're delighted to be able to lead the way with Scale-up Suite.
Figure 2 also includes some great opportunities to automate workflows. We'll discuss some of these in a later post.
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