Transforming life sciences quality management: A Q&A with Gisele Fahmi of Pfizer

This article features a conversation with Gisele Fahmi, director of global digital manufacturing and transformation at Pfizer.

Digital transformation has become a critical aspect of the life sciences quality management industry. As modalities become increasingly complex, efficient data capture, analysis and timely action are needed more than ever. Digital tools have now become essential for streamlining processes and ensuring that medicines reach patients more rapidly and without compromising safety. One key way digital transformation is revolutionizing the industry is through the integration of analytics, which is reshaping the approach to quality management in life sciences.
 

Gisele Fahmi is an experienced digital transformation leader. As director of global digital manufacturing and transformation at Pfizer, she collaborates closely with stakeholders to drive innovation, digitization and effective change management. In a conversation with Sankalp Raviprolu and Raghuram Mutya of ZS, Fahmi discussed the significance of digitization, the value of data and analytics, and her perspective on how the life sciences quality industry will evolve in the future as digital transformation programs generate more data.

ZS: Why has there been such a strong emphasis on digital transformation in life sciences quality management?

Gisele Fahmi: Digitalization’s importance in the life sciences sector has grown exponentially. Historically, its role was vital for enhancing the speed and efficiency with which products reached patients. As our sector has evolved and the complexity of modalities has surged, the need for efficient data capture, analysis and timely action has become more pressing. Digital tools have now become our lifeline, streamlining processes that once consumed significant time. One prime example is how a major pharmaceutical manufacturer employed digital advancements to slash document review times by an astonishing 90%. That sort of efficiency not only expedites processes but ensures that medicines reach patients more rapidly and without compromising on safety.
 

By leveraging digital tools, we’ve seen a significant reduction in the processing and review cycle for deviations in laboratories, further reducing batch release cycle times, eliminating manual entries and bolstering data integrity. A standout example is a transition to digital product specifications paired with laboratory information management system (LIMS) integration, which slashed review times from seven months to just five days. This increasing reliance on digital tools is why there’s such a pronounced emphasis on digitalization in our industry today.

ZS: How has the integration of analytics redefined quality management in the life sciences sector?

GF: Analytics, especially predictive analytics, are reshaping our approach to quality management in life sciences. In the past, our focus was mainly on retrospective data analysis, reacting to issues as they occurred. Now we’re in a position to anticipate and address challenges before they arise, exemplifying the principle of “quality by design” (QbD). To take an historical example, incidents like the Tylenol recall crisis could have been preempted with the analytics capabilities we have today. This proactive approach, backed by tools like key performance predictors, doesn’t just help us foresee issues but actively shapes our strategies for responding to them. Our aim now is not just to solve extant issues but also to predict and mold the future, emphasizing safety and stringent compliance.
 

Further enhancing this proactive stance are tools like control towers, which offer a comprehensive overview across the entire supply chain, from manufacturing to customer delivery, pinpointing quality gaps and enabling timely corrective actions. The emphasis now is on foresight, ensuring quality at every step and consistently delivering on safety and compliance.
 

Firms like ZS are at the forefront of developing such innovative solutions. Digital twins and other risk mitigation tools allow companies to move from concept to value by harnessing the enormous amount of data exhaust coming from digital transformations. This innovation helps both organizations and the industry leverage data, analytics and AI to bring lifesaving drugs to patients when and where they need them.

ZS: You mentioned “quality by design.” Could you elaborate more on this idea, and on how it reshapes distribution in life sciences?
 

DS: Quality by design is a paradigm shift. It promotes a proactive rather than reactive approach to quality management. For instance, in drug development, challenges are anticipated and mitigated at the design phase, ensuring streamlined and steady drug supply. This approach results in fewer recalls, diminished wastage and bolstered trust in product integrity.
 

In distribution, QbD is about ensuring that every step of the distribution process is optimized for quality from the outset. Instead of reacting to quality issues, we design our processes to prevent them. It involves continuous monitoring of shipments—ensuring that products are stored and transported under optimal conditions. It’s about guaranteeing that a product’s efficacy and safety are uncompromised by the time it reaches a patient.

ZS: Continuous monitoring has become pivotal for shipments, hasn’t it?
 

GF: Absolutely. Continuous monitoring is like having a vigilant guardian for shipments. Advanced sensors relay real-time data on variables from temperature to physical shocks, which can be critical for products like vaccines, where even slight deviations can impact efficacy. An alert system ensures that deviations are promptly addressed. AI platforms can improve on that function by forecasting potential disruptions, enhancing the quality and integrity of shipments and preventing shortages.
 

ZS: How pivotal is life sciences quality management In preventing drug shortages?

GF: Drug shortages often stem from unforeseen disruptions—whether related to raw material scarcities or manufacturing glitches. Quality management, equipped with real-time monitoring and predictive analytics, solves such challenges. For instance, during the COVID-19 pandemic, many companies adeptly managed vaccine ingredients supply, mitigating potential shortages. By diversifying suppliers, refining production schedules and optimizing storage conditions, we can ensure an uninterrupted supply of crucial medications.
 

ZS: The focus on quality in life sciences is essential to shorter lead times and turnarounds. How do digital advancements help achieve this?
 

GF: The essence of life sciences revolves around timely deliveries, often impacting patients’ lives. Digital advancements, notably advanced analytics, have revolutionized our approach. They enable real-time tracking, prompt decision-making and instantaneous adjustments. For instance, when a batch faces a complication, we no longer wait for end-of-line checks; we address it immediately. This proactive approach drastically reduces delays and enhances delivery speeds.
 

By continuously logging and analyzing test results, we’ve refined our review and approval cycles, reducing the overall manufacturing cycle time. Such digital tools not only streamline processes but also spotlight inefficiencies, ensuring a continuous cycle of improvement. Direct implications to supply operations include reducing lead times, expediting time to market and enhancing launch efficiency. Additionally, by leveraging digital twin capabilities, we’re able to model and predict potential risks, providing another layer of efficiency and risk management to our operations.
 

ZS: How does life sciences quality management maintain consistency and relevance across the diverse range of therapeutic areas and modalities?
 

GF: Navigating the diverse landscape of therapeutic areas is a key challenge. Demands vary between addressing the distinct needs of oncology, neurology or even the specific logistics of vein-to-vein modalities. However, the overarching principle of quality management remains the same: to ensure product efficacy, safety and regulatory compliance. Advanced analytics and AI tools grant us the adaptability we need. While the strategies and tactics may differ, these tools ensure that the standard of product quality and safety remains consistent across all domains.
 

ZS: How does the transformative impact of generative AI fit into the realm of quality management in life sciences?
 

GF: Generative AI, with its transformative capabilities, is making an indelible mark in quality management. Its strength lies in its ability to simulate a plethora of scenarios, offering unparalleled insights into challenges in product quality or supply chain nuances. Such advanced foresight allows us to develop strategies proactively, bolstering our supply chains’ resilience. Predicting batch releases is an illustrative application. AI trained with historical batch- and sample-testing data can offer predictions about a batch’s potential for approval or rejection. Akin to having a crystal ball, this ability to anticipate results ensures that life sciences organizations are always a step ahead of those approvals.
 

Consider the application of generative AI in environmental monitoring. I’m starting to see the industry leverage generative AI to analyze deviations against historical benchmarks, auto-generating tags and drafting investigative reports with little human intervention. Such methods will improve efficiency and drastically reduce our investigation cycle times. An endeavor that once took 30 days could now be accomplished in just six business days—an 80% acceleration.
 

Another of generative AI’s exciting prospects lies in its potential application within regulatory responses. Imagine harnessing the power of AI to assist in drafting biological license applications and new drug applications. With the depth and breadth of data these applications require, generative AI could synthesize vast amounts of information, ensuring that submissions are comprehensive, accurate and tailored to regulatory requirements. This function not only promises improved efficiency but also a higher likelihood of first-time approval, streamlining the drug development and release processes.
 

ZS: Looking into the future, as life sciences continue to evolve, what do you envision as the next frontier in quality management and how can organizations prepare for it?
 

GF: The future is undeniably exciting. As life sciences evolve, I envision a more interconnected ecosystem, where real-time data from various sources converge for holistic quality management. With the integration of the internet of things, blockchain for traceability and advanced AI models, quality management will be even more proactive. Imagine a world where every instrument, device and even consumable is interconnected, creating a living, breathing ecosystem of data. AI will transcend its role as an analyzer to become a decision-maker, optimizing processes in real time. Predictive analytics will shift to prescriptive analytics, not just forecasting the future but actively molding it. Production lines might self-adjust, and drug formulations might auto-optimize based on patient feedback. Quality management will no longer be a checkpoint—it will be an omnipresent force, continuously shaping the landscape to ensure unyielding excellence.
 

ZS: We greatly appreciate your perspective. Thanks so much for joining this conversation.

GF: Thank you for having me!