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Since the invention of the transistor in the 1970’s, Moore’s law has driven immense growth in the Semiconductor industry. The complexity of a typical processor has grown from thousands to tens of billions of transistors on board with kilometers of internal wiring. To meet these demands, Applied Materials has been at the forefront of delivering incredible advances in materials engineering, manufacturing precision and complexity. The pharmaceutical industry is not the semiconductor industry. Raw material variability, regulatory and patient safety considerations, emerging modalities, changing global disease profiles and many other factors contribute to a very different process development and manufacturing profile. However, when compared, we see similarities in the ongoing digital revolution within Pharma and the journey already taken in the semiconductor industry. In particular, the impact of continuous manufacturing, personalized medicine, Machine Learning and Artificial Intelligence all have parallels.

Precision Manufacturing – a framework for approaching operational digitization in the era of Big Data

With biopharma beginning the journey into Big Data, it is finding a lack of clear guidance exists, not just on application but also how to operationalize it to extract repeatable and measurable value. There have been early promising applications, but these have been specific, focused use cases. Since Big Data’s whole tenant is gathering and analyzing the broadest applicable data landscape, addressing implementation best practices across a company network is needed. The industry has some great guidance around QbD, Digital Maturity, Knowledge Management, APC, etc., however, these approach mostly specific topic areas and not how Big Data fits into the Big Picture. Combined with many biopharma companies still working toward better digital maturity, the foundational requirement for Big Data, it’s akin to trying to “build an airplane while in flight”. Biopharma has invested heavily in Big Data technology, AI and ML as well, with many of these projects struggling to deliver the anticipated gains.

Self-Service Analytics in BioPharma Manufacturing: Lessons Learned

The more recent evolution of digital maturity in the world of BioPharma manufacturing has resulted in a substantial push for self-service data analytics. Self-service analytics can loosely be defined as a tool or set of tools that enable non data science trained individuals to query data, evaluate correlations, build predictive models, and generate reports on their own, with minimal IT and/or data scientist support. Self-service analytics is entering a new era with the integration of AI and machine learning which is enabling quick and efficient realization of data driven insights that translate into continuous improvement opportunities. This can then lead to higher yields and improved quality and consistency in manufacturing.

Model-based workflows for process understanding, prediction, monitoring and control of continuous Oral Solid Dose platforms.

Continuous Manufacturing in the pharmaceutical industry provides major advantages over batch. These include more efficient scale-up, reduced energy consumption and the easier incorporation of Process Analytical Techniques for model-based process monitoring, control and real-time release. However, continuous manufacturing brings with it additional layers of process and data management complexity. The monitoring and analysis of multiple streams of PAT and process data in such systems can lead to laborious after-the-fact manual data alignment activities. Further, the development of models for prediction and control of Critical Quality Attributes may require significant consumption of raw materials and experimental time on the continuous line.