Enhancing Data Analytic Capabilities in Pharmaceutical Manufacturing

Posted August 16, 2023 | Technology |
Enhancing Data Analytic Capabilities in Pharmaceutical Manufacturing

Providing patients with safe and effective healthcare solutions is the principal goal of every pharmaceutical manufacturing organization. The success of these organizations depends on their ability to maintain SQUIPP: safety, quality, identity, purity, and potency of the products. SQUIPP is closely linked to the effectiveness of way of working and control strategies implemented to ensure the manufactur­ing operations are in control, so the pharmaceutical products that go into the market are safe and effective.

Variations in manufacturing operations, raw materials, and equipment reliability often disturb process per­formance, which can potentially impact SQUIPP, along with product yield (which falls outside of SQUIPP) and quality attributes. In the pharmaceutical and biologics manufacturing industries, the current state of data analytics for process performance monitoring largely involves descriptive analytics, which focuses on what happened, and diagnostic analytics, which helps to understand why something happened.

Descriptive and diagnostic analytics are used to solve manufacturing issues, with the penalty of longer reaction time. The application of these two reactive modes of data analytics is highly matured and widely used for problem solving at Sanofi’s Toronto, Canada, site to support process investigations. However, these methods are used reactively, initiated upon requests from process experts when a process- or product-related issue is identified. As a result, potential nega­tive shifts in process performance and quality attributes may get flagged with delay, sometimes requiring time-consuming investigations.

Thus, the data science team at Sanofi’s Toronto, Canada, site began an initiative to move toward prescriptive analytics. It sought to increase timely process monitor­ing and diagnosis and to move from a reactive to a proactive mode of operation that would enhance data analytics capabilities and increase efficiencies. This initiative aims to: (1) identify process performance shifts proactively by implementing live analytical models to actively detect issues and alert experts and (2) augment data analytics activities to prescriptive models (i.e., to enable data-driven decisions on what actions are needed to avoid an impending negative effect).

The current reactive modes of data analytics (descrip­tive and diagnostic analytics) have successfully helped solve manufacturing performance and product-quality issues. However, identifying the root cause and apply­ing solutions usually happens several impacted batches later. The new prescriptive analytics solution has the potential to reduce that reaction time considerably, which would allow faster recovery of manufacturing operations and avoid potential impacts to the business.

Figure 1 shows an illustrative current state and pro­posed future state. The future state uses knowledge management to perform automated and auton­omous analytics and leverage previous learnings to arrive at a rapid solution. Subject matter experts (SMEs) can then take action to remediate the problem.

Figure 1. Example current vs. future state of investigation  support for manufacturing (for illustration purposes only)
Figure 1. Example current vs. future state of investigation
support for manufacturing (for illustration purposes only)

[For more from the authors on this topic, see: “Sanofi’s Move Toward Prescriptive Data Analytics.”]

Disclaimer: This work was funded by Sanofi. There are no potential conflicts of interest that the authors are aware of. Ramila Peiris and Hossein Sahraei equally contributed to drafting the content, while Olivier Moureau and Natalija Jovanovic provided feedback to improve the content. The authors are Sanofi employees and may hold shares in the company.

About The Author
Hossein Sahraei
Hossein Sahraei is Deputy Director of Process Analytics, MSAT Data Science, at Sanofi, where he leads the implementation of automated prescriptive analytics for proactive yield management across diverse vaccine manufacturing processes. With over 10 years’ experience in data analytics, Dr. Sahraei specializes in translating strategic objectives into actionable plans through data-driven solutions. He also leads technical teams, ensuring efficient… Read More
Ramila Peiris
Ramila Peiris is Head of Data Science at Sanofi, where he provides strategy and vision for data engineering, advanced modeling, and AI solutions. Dr. Peiris has an extensive background in the development and application of data science solutions and has spent over 15 years working in the pharmaceutical, water, and specialty chemicals industries. He is driven by the impact of his work, delivering practical and game-changing solutions, and… Read More
Olivier Moureau
Olivier Moureau is Head of Process Data Science and Digital Transformation, Global MSAT, at Sanofi, where he leads dynamic teams focused on leveraging data and AI for process development, optimization, monitoring, and digital transformation across Sanofi‘s entire manufacturing and supply portfolio. With a background in engineering and statistics, he brings over 18 years’ experience in data science to his role. Previously, Mr. Moureau held… Read More
Natalija Jovanovic
Natalija Jovanovic is Global Head of Digital at Sanofi Vaccines. She leads a global cross-functional team in applying a variety of digital solutions and methods toward an ambitious mission: a world in which no one suffers or dies from a vaccine-preventable disease. Dr. Jovanovic has extensive experience in delivering a wide range of digital solutions at global insurer AIG and as VP of Innovation at Brown Brothers Harriman, a global commercial… Read More