Better Data Management in Process Development

Better Data Management in Process Development


Data management and data integrity are concepts at the core of modern pharmaceutical businesses. Drug development data, raw material data, QC testing data and many more are carefully maintained throughout the product lifecycle and regularly interrogated for product quality insights and potential trend indicators. Handling of in-process data has lagged these other data types somewhat, with widespread connection of equipment SCADA systems to MES or data-lakes only a relatively recent development, and a lot of work is yet to be done before the true value of carefully collecting this data can be realised through widespread automation delivering improved repeatability and greater yields.

Unfortunately, the handling of process data during development is often less structured than for that of established products. Handling this data differently to production data, or storing it in different systems substantially reduces the available or easily accessible process performance information during the early months and years of full-scale manufacturing, missing opportunities for early insights and process improvements. A single system like SmartX from Innopharma Technology, designed from the ground-up to support process data from development to scale-up and manufacturing can contribute substantially to product knowledge and process understanding.


Better Data Management in Process Development


SmartX provides a platform for DoE design and execution ensuring end-to-end data handling and traceability in development, as well as advanced automation of manufacturing processes, supporting the full process lifecycle.

Additionally, SmartX helps to ensure that control logic and parameters are derived in a scale-agnostic way, allowing the easiest possible transition from development to manufacturing scale. This is accomplished through the user-friendly process definition and automation interface, where logical “blocks” representing first-principles-based process-specific control methodologies, carefully designed by relevant process experts, can be dragged and dropped to operate serially or in parallel across multiple process phases, building up the comprehensive logic required for robust automation.

SmartX’s data analytics dashboard further simplifies the process of deriving insights and improving control approaches from the outset of production, allowing painless optimisation and maximised yields.

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Claude Lacey

Claude Lacey leads the Innopharma Technology software development team and has worked in software development for over 20 years.  Prior experience includes software development management in the medical device industry where he led the development of digital pathology software applications.  His areas of expertise include cloud architecture, imaging and data driven systems, UX design, agile and lean thinking.  He has a special interest in IoT, machine learning and computer graphics technologies. Claude holds a BSc in Computer Science and PgDip in Pharmaceutical Business and Technology.

Girish Mallya

Girish Mallya is a Senior Software Developer working at Innopharma Technology with 12 years of professional experience developing image analysis solutions and is responsible for the technical aspects of software R&D projects that involve image analysis/computer vision. Apart from image processing and computer vision, he has experience in C++, Python, MATLAB, Machine learning for image analysis.

Volodymyr Rudiakov

Volodymyr Rudiakov is a Senior Software Developer at Innopharma Technology with 8 years of experience of using C++, Python, JavaScript, NodeJS, Java and different technologies for data exchange, storage and processing, IoT devices and systems integration, and process automation. Overall, more than 20 years of networks specialist and systems integrator experience.

SmartX Process Automation Solution

SmartX - simple and secure handling of data ensures Data Integrity by Design from experimentation and DoE to process data storage. Data storage can be implemented in either a cloud or edge (on-site) model.

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