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With the bio/pharmaceutical industry undergoing a significant digital shift, artificial intelligence (AI) is playing an increasingly vital role in achieving operational excellence and bolstering quality maturity. A strategic and well-governed approach to AI adoption is not merely an advantage but a necessity for enhancing efficiency while maintaining rigorous good practices (GxP) compliance, said Vinny Browning, executive director of Quality Assurance, Amgen, who spoke at the 

Parenteral Drug Association (PDA) Regulatory Conference 2025

, occurring Sept. 8–10 in Washington, DC.

In his talk, “Digitization (Artificial Intelligence) for In-House Operational Excellence,” Browning outlined a framework for integrating AI into bio/pharmaceutical manufacturing operations, focusing on strategic foundations, quality management system (QMS) integration, robust risk management, and practical applications (1).

What strategic foundations are necessary for AI adoption in regulated manufacturing?

A fundamental aspect of AI integration is a commitment to continuous learning and adaptation, treating evolving AI guidance like any critical company update, Browning stated in his talk. Companies must critically assess each AI application, distinguishing whether it serves as a job aid—assisting human tasks—or operates autonomously. This distinction profoundly influences the approach to system qualification and validation.

A manufacturer’s strategy for qualifying and validating AI systems must be meticulously documented within standard operating procedures (SOPs) to establish a clear, standard process, Browning emphasized. Crucially, comprehensive training is essential, not just for end-users to grasp basic functionalities, but also for staff who may need to articulate the company’s specific AI processes to auditors or inspectors during regulatory reviews. Browning noted that practicing these explanations will help ensure clarity and confidence in the industry’s digital endeavors.

Effective integration also demands consideration of AI's architectural fit and robust governance within a company’s QMS. Data integrity is paramount, and inconsistencies in data naming or definitions across systems, such as “time” versus “TM”, can undermine AI effectiveness, explained Browning. A clear gating process is necessary, acknowledging that an AI activity initially deemed non-good manufacturing practice (GMP) adherent might later become GMP-relevant, thus requiring specific controls based on identified risks. Continuous monitoring and assessment of AI tools are also essential, he added, with adjustments made if risks or guidelines change, mirroring the approach to computer system software assurance. The broader bio/pharma landscape already shows AI's promise, with initiatives exploring its use in gene writing, CAR-T preclinical development, vaccine research, and circular RNA therapeutics, highlighting its transformative potential across the industry (2,3).

What risk management and practical applications can be applied to ensure manufacturing quality?

Any new AI idea or system intake must undergo a structured risk management framework. This includes defining the idea, conducting a thorough system-level risk assessment that covers security (access control, third-party involvement) and architectural setup (on-site, cloud, remote), and selecting appropriate controls to mediate identified risks. These decisions should be documented in a risk registry. Continuous monitoring of the deployed tool is critical; any change in scope or emergence of new risks necessitates re-evaluation and re-mediation. The ultimate goal is to ensure AI systems are safe, secure, resilient, expandable, integratable, privacy-enhanced, fair (with managed bias), accountable, and transparent, Browning said.

In addition, AI offers significant potential to pull insights and facilitate faster decision making by aggregating data from multiple systems, which drastically reduces the manual effort traditionally required. While specific examples include regulatory surveillance and supplier monitoring, the principle of automating analysis and identifying gaps can be directly applied to manufacturing operations. For instance, Browning explained, AI could streamline annual product reviews by compiling data on deviations, complaints, and change controls, quickly identifying critical issues. Similarly, for periodic reporting, AI can pull information from various systems to create first drafts of reports, such as those related to supplier performance.

These applications create considerable efficiencies, saving numerous hours of manual work, Browning pointed out. He noted, however, that it is imperative that all AI-generated information be human-verified. AI can serve as a powerful tool to support individuals, but it is not meant to replace the essential human oversight for verification and compliance. This "human in the loop" rigor is critical to remain compliant and ensure quality, Browning said.

Browning concluded that there are three core pillars on which the strategic integration of AI into bio/pharma manufacturing quality hinges: training, governance, and oversight. He stressed that investing in staff training, establishing robust governance for data integrity and process assessment, and maintaining stringent human quality oversight are non-negotiable.

As the industry embraces digital transformation, Browning notes the prudency of piloting initiatives rather than attempting to implement AI all at once. Taking it slow allows for adaptability and the willingness to adjust strategies if initial trials are not successful. By meticulously following these principles, the industry can better harness AI to drive operational excellence, enhance quality, and ensure sustained compliance in drug manufacturing endeavors.

1. Browning, V. Digitization (Artificial Intelligence) for In-House Operational Excellence. Presentation at PDA Regulatory Conference 2025, Washington, DC., Sept 8–10, 2025. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://80a7ba3d04f8b71aa576-301909dc4570c350a1649a6d39e3ef3b.ssl.cf1.rackcdn.com//3180120-1718426-001.pdf
2. Dixit, S.; Kumar, A.; Srinivasan, K.; et al. Advancing Genome Editing with Artificial Intelligence: Opportunities, Challenges, and Future Directions. 


3. Bäckel, N.; Hort, S.; Kis, T.; et al. Elaborating the Potential of Artificial Intelligence in Automated CAR-T Cell Manufacturing. 

Articles

The biopharmaceutical industry is increasingly integrating artificial intelligence (AI) to enhance operational excellence and quality maturity. A strategic approach to AI adoption is essential for maintaining efficiency and compliance with good practices (GxP). Key elements include continuous learning, robust governance, and risk management. AI's potential spans gene writing, CAR-T development, and vaccine research. Effective AI integration requires a structured risk management framework, human oversight, and comprehensive training. Piloting initiatives and adapting strategies are recommended for successful AI implementation in drug manufacturing.

AI can offer a strategic blueprint for GxP compliance, risk mitigation, and human-led operational excellence.

PDA 2025: Strategic AI Adoption in Bio/Pharma Manufacturing

Dawn over Washington - with 3 iconic monuments illuminated at sunrise: Lincoln Memorial, Washington Monument and the Capitol Building | Image Credit © mandritoiu - stock.adobe.com

FDA is emphasizing a shift from checkbox compliance to a holistic, quality systems-focused approach in pharmaceutical manufacturing. During the 

, agency officials outlined expectations for companies responding to inspectional findings, from Form FDA 483 observations to post-warning letter remediation, during the session “Sustainable CGMP Remediation Plans and Communication: FDA Updates” (1). Discussions highlighted that compliance is not merely a regulatory burden but a strategic investment that drives operational excellence and strengthens competitive advantage. These themes, along with FDA insights and compliance trends, were widely covered, as seen in articles from pharmtech.com on the conference, including "

PDA Regulatory Conference Features More than 30 FDA Experts

Glenn Wright Highlights AI, FDA Insights at 34th Annual PDA Regulatory Conference

How is FDA compliance evolving for pharmaceutical manufacturers?

FDA officials noted that compliance is inherently focused on quality, science, and patient risk, rather than being a mere paperwork exercise. The agency seeks to foster an "anti-fragile" quality organization that can emerge stronger after problems are found, whether through FDA inspections, internal audits, or other external assessments. This perspective encourages companies to view inspectional findings as a catalyst for strengthening their systems. This aligns with insights exploring how to build resilience in pharma development (2). This transformation requires continuous improvement and a recognition that quality must be built in "by design," a principle echoed in manufacturing regulations and Deming's principles of management. Strategies for a smarter pharma workforce and digital transformation for regulatory training also support continuous improvement (3).

What are FDA's expectations for post-warning letter meetings?

Milind Ganjawala, a Division Director, DDQ, OMQ, OC, CDER, FDA

, stated that a reinspection is guaranteed to occur after a warning letter and will focus on systemic fixes, not just the specific items cited in the warning letter, which are often "a very small snapshot" of potential issues. He emphasized a "holistic view" and a "quality systems improvement approach," encouraging companies with multiple facilities to apply the lessons learned across their entire organization.

To be eligible for a post-warning letter meeting, a facility must have current good manufacturing practices and an Official Action Indicated compliance status from an inspection. The facility must also be current with its fees under the Food, Drug, and Cosmetic Act or be part of a pending application if it is an active pharmaceutical ingredient facility. These meetings are generally specific to 501-type warning letters related to good manufacturing practices violations.

When requesting a meeting, companies must demonstrate "actual implementation" of corrective actions, not just plans, noted Ganjawala, who stressed that progress should be "measurable" and geared toward remediation. He explained that the meeting package should include a robust root cause analysis for each warning letter item, an impact assessment on product quality and other systems, a comprehensive Corrective and Preventive Actions plan with realistic timelines, and evidence of implementation progress. The FDA reviews these requests and typically responds within 30 days, he added.

Denial reasons for meeting requests include missing Corrective and Preventive Actions plans, insufficient documentation of actual progress, a failure to establish a broader quality systems impact, or a lack of retrospective product quality assessment, continued Ganjawala, urging firms to "plan for the long game, not the quick fix."

How can pharmaceutical firms strengthen their 483 responses?

Tara Gooen Bizjak, Associate Director, GMP and Quality Standards, OC, CDER, FDA

, highlighted that a company's response to an FDA Form 483 is critical, serving as the "first impression" of how seriously findings are taken and providing a "visual demonstration of your site’s quality culture." While responding to a 483 is optional, it is invaluable for the FDA's evaluation of an inspection case. The importance of quality culture and FDA trends was a key highlight at the conference (1).

Effective 483 responses should demonstrate a vigilant, prevention-focused approach, rooted in a strong quality system that provides "broader stability" to the organization, continued Gooen Bizjak. The response should show "proactive leadership" and embrace a comprehensive evaluation, including systemic solutions and pertinent risk assessments. She noted that "helpful elements of a response include an executive summary demonstrating leadership’s commitment to quality, comprehensive risk assessment including patient and product impact evaluation, detailed investigation reports," and a diligent root cause analysis for each observation. It should also outline a Corrective and Preventive Actions plan with realistic, measurable milestones and include effectiveness monitoring.

Common pitfalls in 483 responses include minimizing the scope of findings, offering "quick fixes" without thorough root cause analysis, proposing unrealistic timelines, underestimating resource requirements, and poor communication, according to Gooen Bizjak, who cited examples from recent warning letters in which company responses were deemed inadequate due to high defect levels, lack of evidence for analytical method equivalency, failure to identify and implement appropriate Corrective and Preventive Actions from repeated sterility investigations, and an inability to address fundamental deficiencies in the quality unit.

What are key strategies for sustained pharmaceutical compliance?

Both speakers emphasized the importance of a comprehensive approach to quality and compliance. When a facility receives a 483 or a warning letter, it should trigger an examination of other facilities within the company to ensure they are in a sustainable state of compliance. They also suggested looking at publicly available warning letters and 483s to understand potential issues that might apply to one's own operations.

To improve investigations and root cause analysis, a common deficiency cited, companies should focus on sustainable solutions rather than quick fixes. Gooen Bizjak recommended ensuring that a "well-rounded team of subject matter experts" looks at issues to minimize bias in root cause determination and incorporate diverse perspectives. This approach is crucial for effective problem-solving (4). Managing compliance risks also benefits from such a comprehensive view (5).

Regarding organizational design, the FDA expects the quality unit to be independent and not report to production management, according to the presenters. They emphasized that senior management engagement is crucial, demonstrated by their physical presence on the manufacturing floor and their commitment to allocating necessary resources, shifting the mindset of quality from a "cost center" to an "investment." Ultimately, they encouraged firms to take ownership of their quality systems and continuous improvement, rather than relying on FDA to be their quality department. This approach leads to sustained compliance excellence and builds a quality system worthy of patient trust.

Building Resilience and Precision in Pharma Development

Navigating Regulatory Training: Digital Transformation Strategies for a Smarter Pharma Workforce

Effectively Using Contamination Control Strategies

Manufacturer Guidelines for the Secure Destruction of Controlled Substance Pharmaceuticals

The FDA is shifting its focus from mere compliance to a holistic, quality systems-focused approach in pharmaceutical manufacturing. This involves viewing compliance as a strategic investment that enhances operational excellence and competitive advantage. The agency emphasizes building resilient quality organizations that improve after inspections and encourages a comprehensive approach to addressing inspectional findings. Post-warning letter meetings require demonstrated implementation of corrective actions, while effective responses to Form 483 should reflect a strong quality culture. Sustained compliance involves independent quality units, senior management engagement, and a focus on continuous improvement.

FDA experts at PDA 2025 urges pharma to adopt holistic quality systems, strengthening 483 responses & post-warning letter remediation for compliance excellence.

PDA 2025: FDA Experts’ Proactive Strategies for Mastering 483 Responses 

Scientist Managing Prescription Drug Supply Chain | Image Credit: © leowolfert - stock.adobe.com

 in Washington, DC, a session on Sept. 9, 2025 titled “Quality Oversight in a Modern Supply Chain” explored how artificial intelligence (AI), digital governance, and data integrity are shaping quality assurance in pharmaceutical manufacturing. Presentations by 

, director of Data Integrity & Data Governance at 

, director of Data Analytics and Management Review at 

, highlighted both regulatory expectations and practical use cases.

How are regulators guiding AI use in pharmaceutical laboratories?

“Beyond the Bench: AI-Powered Oversight for Chem and Micro Labs,”

 by polling the audience on their AI readiness (1). Responses suggested that while few companies have an approved AI strategy for quality control (QC), most have established governance policies.

“Governance is key,” Gibbons emphasized, noting that “if you don’t have good data in there, you won’t have good data coming out.”

Gibbons also underscored the alignment between United States and European regulators.

“Earlier this year FDA released the draft guidance [on 

‘the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products’

 (2)], which is a great guidance for everyone—very descriptive and it focuses very much on context of use,” he said.

Parallel updates from the European Medicines Agency, including Annex 11 revisions and the new Annex 22, point toward global convergence on AI oversight, Gibbons said (3).

For laboratories, Gibbons outlined AI applications ranging from faster root cause analysis to real-time anomaly detection. He described how natural language processing can accelerate investigations and how virtual witnessing might “improve reliability because the machine is watching, monitoring, and can also allow for early detection of anomalies.”

However, he said explainability remains important.

“If you don’t understand that, can’t trust that model, or explain that model, it’s going to cause a huge problem,” Gibbons said.

And for the most part, Gibbons said generative AI remains off the table for regulated QC activities, for now.

“At this point, I don’t believe there’s any situation where you’re going to let AI loose and just make decisions on its own,” he concluded. “That human supervision piece is key.”

What’s needed to build an AI-ready supply chain?

“Guardians of Quality: Digital Tools and AI in the Era of Complex Supply Networks,”

 extended the conversation from laboratory operations to enterprise-wide supply chains (4).

“Data is the lifeblood of any technology-driven initiative,” Grischeau told the audience, saying that without good data governance, even the most advanced AI systems will fail—but technology alone is not the barrier. “In my situation, usually the data [or] technology isn’t the barrier; a lot of it is process.”

Grischeau urged companies to invest in process harmonization and standardization. He also pointed to the importance of organizational readiness, including education and change management.

“Making sure that we’re enabling teams across the enterprise and really understanding what is responsible AI and responsible use of AI” is as important as the tools themselves, Grischeau argued.

What are the most practical AI use cases across the pharma supply chain?

Grischeau presented three use cases that illustrate how digital tools are already supporting oversight in complex supply networks.

 AbbVie automated its contract manufacturing organization (CMO) scorecard, which transformed a manual, spreadsheet-driven process into a qualified application that was able to be refreshed nightly. This automation “eliminated the need for manual data processing,” Grischeau said, while ensuring transparency with external partners.

 By digitizing certificates of analysis, the company gained the ability to automate trending and performance monitoring across internal and external manufacturing sites. Human verification remains part of the workflow, but efficiencies and onboarding speed improved dramatically.

 AbbVie has deployed an AI-driven complaint coding system that uses generative AI prompts to classify and risk-rank patient complaints. “We’ve moved from minutes to seconds in coding at intake,” Grischeau said, adding that this has enabled faster escalation of high-priority issues and closer collaboration with CMOs.

Across these examples, Grischeau stressed that compliance and monitoring plans remain central.

“You have to implement a solution, you have to monitor the AI output, you have to make sure that you’re compliant, and you have to make sure that you’re driving continuous improvement,” he said.

What does AI mean for drug discovery, development, and manufacturing?

Collectively, the presentations in the “Quality Oversight in a Modern Supply Chain” session illustrated a dual challenge for the pharmaceutical industry: developing AI strategies that improve efficiency and compliance while maintaining regulatory alignment and human oversight.

For drug discovery and early development, where data integrity is often less formally regulated, these insights reinforce the value of clean, well-governed datasets that can later support downstream manufacturing and QC. In drug development and manufacturing, where reproducibility and regulatory scrutiny are paramount, AI offers opportunities to accelerate investigations, monitor supplier performance, and improve responsiveness to market feedback.

The session also highlighted the convergence of technical and organizational factors. AI adoption is not just about algorithms but also process redesign, workforce readiness, and transparent collaboration across global supply chains.

As the presenters made clear, AI is already reshaping quality oversight in pharma. But whether in the laboratory or across the supply chain, its success depends on governance, explainability, and the irreplaceable role of human judgment.

Beyond the Bench: AI-Powered Oversight for Chem and Micro Labs.

 Presentation at the PDA Regulatory Conference, Washington, DC, Sept. 9, 2025.
2. FDA. 

Draft Guidance for Industry and Other Interested Parties, 

Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products

 (CVM/OII/OCE/CBER/CDRH/CDER/OC/OCMO/OCP, January 2025).
3. Barton, C. 

Stakeholders’ Consultation on GMP Annex 11, Chapter 4, and New Annex 22.

Guardians of Quality: Digital Tools and AI in the Era of Complex Supply Networks.

 Presentation at the PDA Regulatory Conference, Washington, DC, Sept. 9, 2025.

PDA Regulatory Conference

The session at the PDA Regulatory Conference 2025 focused on the integration of AI, digital governance, and data integrity in pharmaceutical quality assurance. Presentations by industry experts highlighted regulatory expectations and practical AI applications in laboratories and supply chains. Key themes included the importance of data governance, process harmonization, and human oversight in AI implementation. AI's role in accelerating investigations, monitoring supplier performance, and enhancing post-market surveillance was emphasized. The convergence of technical and organizational factors was underscored as crucial for successful AI adoption in the pharmaceutical industry.

Charles Gibbons of Lachman Consultants and Michael Grischeau of AbbVie stressed AI governance, data integrity, and human oversight as essential to applying digital tools across labs and supply chains.

AI and Digital Oversight in Pharma Supply Chains: PDA Regulatory Conference Insights

Businessman hand pressing button Training. sign on virtual screen. business Mentoring Learning Education Browsing concept | Image Credit: © NaMong Productions - stock.adobe.com

Optimizing training for performance in the pharmaceutical industry was a key theme at the PDA Regulatory Conference 2025, with discussions centered on moving beyond basic compliance to leveraging digital tools for more relevant and responsive learning. Industry experts Marc Glogovsky and Richard Jaenisch shared insights on the evolution of training methodologies and the potential of new technologies on Monday, September 8, 2025 in the session “Competency-Based Training and Digital Learning,” moderated by PharmTech editorial adivisory board member and 

 co-host Susan Schniepp, fellow, Regulatory Compliance Associates.

Why is training for performance a necessity in regulated industries?

Training is more than a checkbox; it is a legal requirement, said 

Marc Glogovsky, Business Unit Manager of Microbiology at ValSource, Inc

., who emphasized that every person involved in operations must receive job-specific, ongoing training in Good Manufacturing Practices (GMP), delivered by qualified individuals. Unfortunately, training sometimes devolves into rote exercises lacking relevance. As Glogovsky observed, employees question modules like “lockout tagout” that have little practical connection to their duties. Effective training, he stressed, ensures personnel are “fit for purpose.”

This isn’t just theoretical. In 2024, there were 27 FDA 483s citing training deficiencies among 561 total for drug products, Glogovsky noted, a trend seen year after year. “If you're not giving them the proper tools, the proper training… you’re going to get some type of half-assed response… not in line with what the [GMP] expectations are,” he stated.

What are the common challenges facing traditional training programs?

Both speakers acknowledged major obstacles in traditional training. Glogovsky cited the overwhelming number of regulations across GMP, good clinical practice, good distribution practice, etc., skill gaps caused by evolving roles, rapid tech shifts, and high employee turnover. In-person training often strains resources, leads to disengagement, and can cause “burnout” for instructors and learners alike, he added. As Glogovsky joked, “If you give me training at 10:00 on a Monday, it's going to be a good day; you give that to me at 4:59 on a Friday, good luck.”

Richard Jaenisch, Director of Education and Outreach at Open Biopharma Research and Training Institute

, echoed these points, stressing the “human factor,” noting that people switch organizations, and systems’ needs change swiftly.

How can digital transformation elevate learning management systems?

Digital learning management systems (LMSs)—from 

—are central to modernizing training. Jaenisch explained that these systems, accelerated by the COVID-19 pandemic, simplify audit readiness, allow easy tracking, and enable learning “anywhere, anytime.” Interactive features like quizzes within videos and global accessibility are advancing continuously. Micro-certifications are also becoming standard for validating workforce skills.

What roles do VR and AR play in practical skill development?

Virtual Reality (VR) and Augmented Reality (AR) transform hands-on skills training, said Glogovsky, who found VR “eye opening” for cleanroom work, letting staff see how actions alter airflow in a way impossible to replicate in person. Jaenisch described VR as invaluable for onboarding, reducing risk and costs, and supplying critical feedback on techniques like aseptic manipulation.

AR is a game-changer for “digital work instruction,” they both agreed. With AR, protocols and schematics overlay live equipment, and trainees can practice virtually using digital twins of facilities—essential for complex, unfamiliar tasks and equipment.

Is gamification an effective strategy for enhancing engagement?

Gamification applies game mechanics—points, badges, leaderboards—to increase engagement. Glogovsky noted it can “be quite engaging,” turning even reticent operators competitive. Yet Jaenisch cautioned that gamification doesn’t motivate everyone, joking that “Some people don't need no stinking badges” and explaining that misapplied gamification may favor “addiction over utility,” distracting from true learning. Extrinsic motivators like financial incentives and time off often work better, and gamified elements need purposeful, personalized integration.

How can generative AI revolutionize training content and delivery?

Generative artificial intelligence (AI) is reshaping workforce training, said Jaenisch, who called it one of the “two big fundamental changes” in digital learning. Key AI applications that he noted include:

Content summaries: AI rewrites standard operatice procedures (SOPs) or training content for clarity and accessibility, especially for non-native English speakers.

Quiz generation/assessment: Automated, adaptive quizzes measure understanding instantly.

Personalized learning: AI fine-tunes curricula to individual needs.

24/7 feedback: Intelligent tutoring systems can support learners in real-time, offering tailored guidance.

At Open Biopharma, digital interactive SOPs use AI to analyze quiz answers deeply, giving specific feedback, even for partially correct responses, and tracking progress in detail. Glogovsky also noted AI’s usefulness for generating training visuals and materials.

What are the barriers to adopting new digital training tools?

Despite digital potential, adoption is challenging, according to Jaenisch, who pointed to the high costs of LMS and AI tools—an LMS can run tens of thousands of dollars, while sophisticated generative AI can cost hundreds of thousands to millions, he added. Implementing these systems demands robust data infrastructure, regulatory clarity (often lacking or inconsistent between the US and EU), and organizational readiness for change.

What does the future hold for biomanufacturing training?

Looking ahead, biomanufacturing training is moving toward a “unified, intelligent learning ecosystem.” Centralized LMS platforms, integrated VR/AR for skills building, carefully applied gamification, and micro-certifications will define future models. Overcoming obstacles hinges on phased pilots, commitment to compliance and talent growth, security by design, and ongoing measurement.

A digital-first mindset—rooted in compliance, ethics, and relentless improvement—will grow a skilled workforce. Glogovsky advocated, “I would like to see training done every day—don’t just slam someone with 75 days of training into one week.” Breaking training into daily sessions supports mentoring, sharing, and transparency. Jaenisch’s group plans daily 20-minute sessions with generative AI, enabling continuous learning and real-time progress tracking for both individuals and teams.

Optimizing training in the pharmaceutical industry is crucial for compliance and performance, with digital tools enhancing learning experiences. Industry experts emphasize the need for job-specific, ongoing training to meet Good Manufacturing Practices (GMP) standards. Traditional training faces challenges like resource strain and disengagement, while digital learning management systems (LMSs) offer flexibility and audit readiness. Virtual Reality (VR) and Augmented Reality (AR) enhance practical skills training, and generative AI personalizes learning. Despite high costs and regulatory challenges, a digital-first approach promises a unified, intelligent learning ecosystem for biomanufacturing.

On the cusp of a profound transformation, the bio/pharmaceutical industry is driven by an exponential surge in data generation and the sophisticated capabilities of artificial intelligence. With approximately 400 exabytes of data, equivalent to 18,000 trillion books, produced globally each day—much of it unstructured—the challenge and opportunity for leveraging this information are immense, said 

, co-founder and chief scientific officer, Aizon, at the 

 occurring Sept. 8–10 in Washington, DC.

Data Governance to Ensure Regulatory Compliance, Data Protection, and Future Readiness for Advanced Use Cases

,” Manzano pointed out that this data deluge, combined with the widespread availability of computing power and data storage, is fueling an artificial intelligence (AI) renaissance that promises to redefine drug discovery, development, and manufacturing. AI is proving to be a scientifically validated tool, even playing a crucial role in Nobel Prize-winning research in chemistry and physics in 2024, demonstrating its capacity to solve complex problems and model intricate biological processes (1). Understanding and strategically implementing data-driven AI is no longer optional but an imperative for enhancing safety, quality, and efficiency, ultimately benefiting the patient.

How can bio/pharma operations maximize data value?

At its core, AI is a mathematical tool, Manzano explained in his talk. However, AI’s efficacy hinges entirely on the quality and management of data. Data in the pharmaceutical context extend far beyond traditional numbers and lists, encompassing diverse unstructured images, sounds, and continuous values collected from various systems.

Regulators are keenly aware of this, frequently referencing data and metadata in AI guidelines, emphasizing their critical role in transforming raw information into actionable knowledge. This focus is not without reason, Manzano said, emphasizing that more than 25% of warning letters issued by FDA since 2019 have cited data accuracy issues, a complex problem that continues to challenge the industry. This concern is further underscored by 

® Group highlighting common FDA 483 findings and strategies to prevent regulatory non-compliance, often rooted in data management deficiencies (2). For bio/pharmaceutical companies, ensuring robust data integrity and compliance is paramount, as regulatory actions can significantly impede progress and public trust.

The journey to effective AI implementation is heavily weighted toward data preparation, consuming an estimated 80% of an AI project's time. This rigorous preparation ensures that data are findable, accessible, interoperable, and reusable, known as the FAIR principles, which are essential for quality AI outcomes. As more data is integrated, the potential for knowledge extraction and sophisticated AI models increases, yet this also adds layers of complexity to data and model management, Manzano said in his talk.

What AI-driven innovations are the industry seeing in bio/pharmaceutical manufacturing and development?

The practical applications of AI across the bio/pharmaceutical lifecycle are profound. Consider the management of human plasma, a highly variable raw material for which donations cannot be refused and establishing full product genealogy is exceptionally difficult, Manzano highlighted. AI can be applied here to integrate batch record information with data from other systems, such as programmable logic controllers and online controls, which transforms these diverse data into knowledge that can guarantee expected yields. Through AI, crucial process variables, such as pH in plasma fractionation, can be identified and adjusted to achieve target outcomes. This capability moves beyond trial-and-error, enabling predictive models that recommend optimal process parameters based on raw material characteristics and desired yields (3,4).

Beyond manufacturing, AI is also advancing quality control in novel therapies. For instance, the Parenteral Drug Association, in collaboration with the European Medicines Agency, is utilizing AI for quality control in advanced therapy medicinal products, allowing for the prediction of batch success an hour in advance (5). This collaboration provides critical lead time for decision making without replacing human labor or traditional lab testing, enhancing efficiency and patient safety for those awaiting these crucial treatments, Manzano said.

"Everyone in this room, we are working for pharma, but we are [also] working for patients, so we have to [always] have in mind that everything we do is because there is a patient waiting,” he emphasized in his talk.

The industry's embrace of digitalization is progressing, Manzano concluded, pointing to a recent survey indicating an average maturity score of 3.5 out of 5. That is a notable increase from 2.6 in 2019. While challenges such as cybersecurity compliance and budget constraints remain top concerns for the industry, the perceived benefits are clear, Manzano asserts: respondents anticipate significant improvements in process efficiency and quality through digitalization, especially in areas such as production, research, and quality control. AI, when applied with scientific rigor and supported by high-quality, well-managed data, offers a significant opportunity to navigate the inherent complexities and variability of bio/pharmaceutical operations, ensuring that the ultimate focus remains on the patient.

1. Longhi, S.; Ventura, S.; Macedo-Ribeiro, S.; et al. When Artificial Intelligence Meets Protein Research [Version 1; Peer Review: 2 Approved, 1 Approved with Reservations]. 

New Year, Same Challenges: Pharma’s Ongoing Struggle with FDA 483s

Quality and Regulations 2025 eBook August 2025.
3. IDBS. Enhancing Process Performance with AI in Bioprocessing. Blog Post. May 22, 2025. 

https://www.idbs.com/knowledge-base/enhancing-process-performance-with-ai-in-bioprocessing/


4. Cheng, Y.; Bi,X.; Xu, Y.; et al. Artificial Intelligence Technologies in Bioprocess: Opportunities and Challenges. 


5. Manzano, T. PDA Europe and EMA Unite. 

https://www.pda.org/pda-letter-portal/home/full-article/pda-europe-and-ema-unite

The bio/pharmaceutical industry is undergoing a transformation driven by data generation and AI capabilities. With vast amounts of unstructured data produced daily, AI is becoming essential for drug discovery, development, and manufacturing. Effective AI implementation requires rigorous data preparation, adhering to FAIR principles. AI applications in manufacturing and quality control are enhancing efficiency and patient safety. Despite challenges like cybersecurity and budget constraints, digitalization is advancing, with significant improvements anticipated in process efficiency and quality. The industry's focus remains on benefiting patients through these technological advancements.

Bothe data integrity and quality of data are critical for drug discovery, manufacturing efficiency, regulatory compliance, and patient safety.

PDA 2025: How Data-Driven AI Transforms Quality Control & Development

Glenn Wright previews the 34th PDA/FDA Regulatory Conference, spotlighting AI, quality culture, FDA trends, and collaboration shaping industry progress.

PDA President and CEO Glenn Wright highlights the significance of the 34th Annual PDA Joint Regulatory Conference, noting his long-standing connection to the event: “I was actually chair of this conference when it was year number five… I've been involved with PDA for 36 years.” He praises the alliance between industry and regulators, emphasizing that “we really have kind of a nice industry-FDA collaborative conference.”

This year’s agenda captures emerging trends, including artificial intelligence (AI) in manufacturing and compliance. Wright shares his enthusiasm: “I always talk about the tidal wave of AI and data analytics that's gonna be hitting the industry. It really is good for the patient. It really means we have more control of the processes.” Attendees can also expect sessions on quality culture, effective remediation, and the critical roles of CDMOs and CMOs in modern manufacturing.

The conference remains a vital source for regulatory updates. “Some really great updates from FDA, really talking about trends that they're seeing, which is really important for the industry as a whole to understand what's going on out there,” notes Wright. He concludes with optimism, adding “We're just really excited. It's gonna be, I think, a hugely successful conference again for year number 34.”

Check out all our 

*Editor’s Note: This transcript is a direct, unedited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.


Glenn Wright, I'm the president and CEO of PDA.

Well, I'm really excited about this year's conference, our 34th regulatory conference. I can remember, quite some time ago, I was quite new to the industry and I was actually chair of this conference when it was year number five. So that should tell you I've been involved with this conference for quite a long time.

I've been involved with PDA for 36 years. You know, every year this conference surprises me because there's always exciting things that are going on in the industry and it kind of captures that, as well as with the heavy FDA involvement. We really have kind of a nice industry FDA collaborative conference, and this year, the topics are diverse. I mean, we're gonna be talking about AI in manufacturing, which I am really excited about. We're also gonna be talking about AI in auditing. I just got done doing a presentation on that topic. It's really incredible when you think about what AI may do in that whole auditing area in regard to looking at data, looking for anomalies, trying to find challenges that a company may have or an area may have, you know, so that's gonna be kind of exciting to me 'cause we see that AI space just continuing to grow. And then just AI in manufacturing, right, because again, you know, if we're thinking about product quality, once we get AI in manufacturing—and it's really ramping up, right. So I think I always talk about the tidal wave of AI and data analytics that's gonna be hitting the industry. It really is good for the patient. It really means we have more control of the processes. So that's just one small area I'm excited about.

I'm also excited about the talks on quality culture and really how to implement quality culture. I think that's fantastic. And there's a couple talks on remediation. So, you know, once you've had challenges and now you're gonna have to remediate those challenges, how do you go about that? How do you develop your plan? Some exciting things there. Again, it covers so many things, supply chain is on there, supply chain disruption, some discussions around use of contract services, so CDMOs and laboratories, and that's really important for the industry now as we see continued increases in the use of CDMOs and CMOs. They're so important because they have the technology that we need a lot of times, and so as new products get more and more complex, a lot of times those CDMOs and CMOs are just critical for that operation. But how do you manage that and how do you make sure you've got enough control over your contracted operations? So, some discussions on that, which I think are gonna be really exciting. I always like group topics, around the pre-approval inspections and what can you kind of expect in pre-approval inspections and what are the trends? So, we're gonna have some topics on that.

And of course, as we do every year, some really great updates from FDA, really talking about trends that they're seeing, which is really important for the industry as a whole to understand what's going on out there, what are they seeing, because when we think about FDA, you know, we have to remember that they see everything. They're the ones that can really help industry by letting us know what kind of trends are going on, what are they seeing as they go out and do inspections or what they see during submission reviews.

Another great topic this year is gonna be on post-approval changes, which, you know, for me being in regulatory for many, many years, is really exciting because we always kind of forget how important the post-approval change process is, how we need to navigate that carefully. You know, it can take years when you think globally about making a change and getting it approved by all the different countries. So that whole PAC (post-approval change) process, I'm really excited to see that topic on this year's agenda as well.

You know, I think the biggest takeaways from this year's conference, I think they're probably gonna come from the FDA speakers as they talk about some of the things they're seeing, some of the challenges they're seeing. That's always kind of the case. I think in this conference, we hear really great things from industry, kind of some practical approaches on how to do things, how to implement things, and that's really great, but there's always those great nuggets that come out of the agency in regard to things they're seeing and the expectations they have. And, so I think, those two areas… really excited to see what comes out. 

I never quite know, you kind of got to be there, you got to go through the presentations to understand everything. So, I think, on the back end of this, I'm sure I'll have some, nuggets that we've gained from the conference, but we're just really excited. It's gonna be, I think, a hugely successful conference again for year number 34. Again, FDA is heavily engaged in this conference, as is industry. And we've got folks all the way from heads of manufacturing to heads of quality talking—all kinds of folks from the agency, so I really think it's gonna be a great conference.

Videos

Glenn Wright previews the 34th PDA Regulatory Conference, spotlighting AI, quality culture, FDA trends, and collaboration shaping industry progress.

3d render. Pharmaceutical manufacture background with glass bottles with clear liquid on automatic conveyor line. COVID-19 mRNA vaccine production platform. | Image Credit: © wacomka - stock.adobe.com

PMMI, The Association for Packaging and Processing Technologies,

 has announced some of the unique features being made available to attendees at 

 to be held from Sept. 29 through Oct. 1, 2025, commemorating 30 years of the event’s history (1).

Pack Expo Las Vegas 2025 will debut guided floor tours tailored to automation and sustainability, aimed at decision-makers and brand owners seeking curated insights.

The show emphasizes key trends—automation, AI, sustainability, and workforce development—spanning over 1 million square feet and drawing 35,000 attendees.

PMMI continues building long-term industry engagement, recognizing more than 100 companies from the original 1995 show and announcing five future Pack Expo events through 2027.

Curated experiences are being offered in the form of two new, guided show floor tours. The Pack Expo Discovery Tours, which will be sponsored by 

 are geared toward first-time buyers and senior-level decision-makers in automation solutions, according to a press release (1). The Pack Expo Sustainability Tour is intended for consumer packaged goods brand owners and is sponsored by 

The Sustainability Tour follows on the heels of Sustainability Central, a show floor destination launched with the 2023 edition, which offers educational, expert-led sessions (1). Another feature, the Logistics Pavilion, was also created for the 2023 show in response to demand for logistics solutions.

“Pack Expo Las Vegas has always been more than a trade show—it’s where the future of packaging and processing takes shape,” 

 PMMI president and CEO, said in the press release (1). “For 30 years, we’ve been proud to drive innovation, foster collaboration, and spotlight the technologies shaping tomorrow’s supply chains. As we look ahead, we remain focused on the forces transforming our industry: automation, AI [artificial intelligence], sustainability, and workforce development.”

PMMI said Pack Expo Las Vegas 2025 is expected to draw 35,000 attendees, from more than 40 vertical markets, to a show space that spans in excess of 1 million net square feet.

“As the industry accelerates toward smarter, more sustainable operations, Pack Expo Las Vegas remains the one place where it all comes together—technology, insight, and community,” 

 vice president, trade shows, PMMI, said in the release (1). “This year’s show reflects the momentum we’re seeing across the market, with record participation and unmatched opportunities to learn, connect, and grow.”

As provided by a link in the press release, in advance of the Las Vegas show PMMI is recognizing 

 that took part in the inaugural event in 1995, and which plan to exhibit once again in 2025.

® covered numerous product updates and unveilings presented at the international edition of Pack Expo in Chicago in November 2024: 

 displayed a product from Descon Conveyor Systems; and 

 for medical device packaging applications (2–4).

Dates for five other Pack Expos following the Las Vegas show, to be held over the next two years, are currently announced: Pack Expo East, in Philadelphia from Feb. 17–19, 2026; Expo Pack Mexico, in Mexico City from June 2–5, 2026; Pack Expo International, in Chicago from Oct. 18–21, 2026; Pack Expo Southeast, in Atlanta from April 12–14, 2027; and Expo Pack Guadalajara, from June 15–17, 2027 (5).

Pack Expo Las Vegas Celebrates 30 Years of Innovation.

 Press Release. July 21, 2025.
2. Hapman. 

Hapman to Showcase Material Handling Solutions at Pack Expo 2024.

 Press Release. Oct. 30, 2024.
3. Pacteon Group. 

Pacteon Exhibits New Packaging Automation Solutions at Pack Expo International 2024.

 Press Release. Sept. 9, 2024.
4. TekniPlex Healthcare. 

At Pack Expo, TekniPlex Healthcare to Debut Its Strongest-Ever Reinforced Paper for Medical Device Packaging Applications.

 Press Release. Sept. 26, 2024.
5. PMMI. 

The trade show is expected to draw 35,000 attendees to a show floor netting more than 1 million square feet.

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