Quality, Quality, Quality: How We Rely on It Daily in Pharma

Written by Michael Bronfman, July 21, 2025

At the Guard Rail this week, "Quality, Quality, Quality." If you say it three times, will it appear in the mirror? If only it were that simple. Michael Bronfman from Metis Consulting Services explains why Quality in the pharmaceutical industry is far more than a buzzword; it's the indispensable backbone of every operation, from manufacturing to patient delivery, directly impacting patient trust and organizational success.

In the pharmaceutical industry "Quality" is not just a buzzword. Quality is the foundational structure and overarching support of everything we do—from research and manufacturing to clinical trials and distribution. Quality impacts every tablet, vial, process, and decision. Without Quality, even the most promising therapies can fail to reach patients, or worse, cause harm.

Yet in the midst of fast-paced drug development, regulatory pressure, supply chain challenges, and shifting market demands, quality is sometimes viewed as a checkpoint rather than a driver. That perspective must change. Quality is not just a department. It is not only about compliance. It is a mindset, a system, and a daily responsibility that touches every role in the organization.

In this post, we explore how the pharmaceutical industry depends on quality every day, why it matters more than ever, and how organizations can embed it deeper into their operations and culture.

1. What Does “Quality” Really Mean in Pharma?

When we talk about “Quality” in pharmaceuticals, we are not just referring to whether a pill looks uniform or a report is grammatically correct. We’re talking about:

• Product Quality – Is it safe, effective, and manufactured consistently?

• Process Quality – Are steps followed as designed, and are deviations handled in an appropriate manner?

• Data Quality – Is information accurate, complete, traceable, and reliable?

• Operational Quality – Are systems designed to prevent errors, not just catch them?

• Cultural Quality – Do people across the organization feel responsible for doing things right?

At its core, pharmaceutical quality is about patient trust. The people we serve cannot test the medicine they are taking. They trust that it was developed, manufactured, tested, and delivered to the highest standards.

That is what quality ensures.

2. How Quality Shows Up in Day-to-Day Pharma Operations

Quality may begin with intention, but it is sustained through routine, embedded into every task, decision, and interaction. It plays out in everyday activities across each pharma organization.

a. In Manufacturing: Reproducibility and Consistency

The production of medicines must be highly controlled and repeatable. Operators, engineers, and supervisors rely on validated processes, standard operating procedures (SOPs), in-process controls, and cleanroom environments.Daily decisions including how equipment is cleaned, how materials are labeled, how environmental data is recorded, all impact the final product. Small missteps can trigger costly deviations or batch failures.

That is why good manufacturing practice (GMP) isn’t just a regulation, it is a way of working.

b. In Quality Control Labs: Precision and Documentation

QC labs perform countless tests, including identity, purity, potency, microbial content, dissolution rate, and more. Analysts must work with accuracy, follow detailed methods, calibrate instruments regularly, and maintain thorough documentation.

A single out-of-specification (OOS) result can lead to investigations, delays, and regulatory attention. QC scientists depend on strong systems to ensure integrity in every result. Daily reliance on good documentation practices (GDocP) and lab controls ensures that what we report truly reflects what was tested.

c. In Clinical Trials: Integrity and Subject Protection

Quality is critical in trial design, data collection, monitoring, and safety reporting. Investigators and trial sponsors are entrusted with patient health, and every data point must be collected and reported accurately and faithfully.

Monitors, CRAs, and data managers rely on systems designed to ensure that:

• Protocols are followed

• Adverse events are documented

• Data is clean and verifiable

• Consent is properly obtained

When mistakes happen—or go unreported—the consequences can undermine the entire trial.

d. In Supply Chain and Distribution: Continuity and Control

Medicines must arrive intact, on time, and in the right condition. Cold chain products, for example, are dependent on temperature controls from the warehouse to the doorstep. Quality here involves tracking, inspection, traceability, and having robust deviation response systems. Pharmacovigilance teams need to ensure the right processes are in place to collect and analyze post-marketing safety data.

At every link in the chain, people are relying on downstream and upstream decisions being right. Without Quality controls, the entire system is weakened.

3. The Cost of Getting It Wrong

Poor quality does not just affect regulators, it affects patients, reputations, and long-term performance.

a. Product Recalls and Patient Harm

Recalls caused by contamination, mislabeling, or potency failures can lead to serious health consequences. Even when no harm occurs, public confidence is shaken.

b. Regulatory Sanctions 

FDA warning letters, import bans, and 483 observations can stall product launches, impact revenue and create lengthy remediation projects.

c. Operational Disruption

When quality is not built into operations, deviations pile up. Investigations slow production. Resources are spent reacting instead of being invested in improvement.

d. Reputational Damage

In today’s digital world, news travels fast. One viral news story about a faulty product can damage years of trust. That is why companies must invest in Quality, not just for Compliance, but for continuity, credibility, and care.

4. Building a Strong Quality Culture

While systems and processes are essential, culture is the glue that binds them. A true culture of quality means that:

• Employees speak up when something seems off

• People understand why a step matters, not just that it’s required

• Quality is seen as part of everyone’s job, not just the quality department

Here are a few ways companies can build and reinforce this culture: . 1. 1What Is Digital Trust? How Can Businesses Build It Among Consumers? - TechPinas.

a. Leadership Visibility

When senior leaders consistently speak about quality, walk the plant floor, and ask questions about processes—not just KPIs—it sends a message. Leadership must be visible in quality moments.

b. Training and Empowerment

Training must go beyond “check-the-box” compliance. Employees need to understand the real-world implications of their roles. When people understand why steps matter, they are more likely to follow them and improve them.

c. Encouraging Reporting

Blame-free reporting systems allow early detection of issues. Employees should be rewarded—not punished—for catching mistakes or raising concerns.

d. Celebrate Good Quality Behaviors

Recognizing teams that catch near-misses, close CAPAs effectively, or improve a process builds pride in doing things right.

5. Quality Is Everyone’s Job

It is easy to think of quality as something owned by QA, QC, or regulatory affairs. But in reality, quality lives in every department:

• R&D scientists who document their experiments in detail

• Manufacturing operators who double-check materials before use

• Procurement teams that verify supplier quality

• Pharmacovigilance staff who track and respond to safety trends

• IT teams that validate systems that store critical data

When every person sees their work as contributing to product quality and patient safety, the entire organization becomes stronger.

6. Adapting Quality in a Changing Industry

The pharma landscape is evolving. 
Companies are managing:

• Biologics and cell therapies with complex cold chain needs

• Decentralized clinical trials with remote monitoring

• Personalized medicine requiring tight data control

• New manufacturing technologies like continuous production

• These changes bring new risks and new responsibilities for quality teams. The core principles stay the same, but systems must adapt. Now more than ever, quality needs to be proactive, integrated, and forward-looking.

That means:

• Updating quality systems to reflect modern workflows
  

• Collaborating cross-functionally to anticipate quality risks
  

• Investing in systems that improve visibility and traceability
  

• Ensuring scalability without sacrificing control

7. Final Thoughts: Why We Say “Quality” Three Times

The title of this post—”Quality, Quality,Quality”—is more than repetition. It reflects a truth: In pharma, we don’t rely on quality once, but repeatedly, at every step, every day. We trust that the lab test was done right. That the materials were labeled correctly. That the study was run ethically. That the distribution center kept the product within spec.

That our colleagues did their part, just as we do ours. Quality is not something we check at the end. It is something we build into the beginning, carry through the middle, and protect at the finish.

So when we say “Quality, Quality, Quality,” it is because that is how many times we depend on it—per step, per process, per product.

At Metis Consulting Services, we do not just talk about quality; we help you build it into your organization’s DNA. Our experts understand the unique challenges of the pharmaceutical industry and can help you:

• Optimize your quality systems to meet evolving regulatory demands.

• Foster a proactive quality culture where every employee feels empowered and responsible.

• Enhance operational efficiency by integrating quality across all departments.

• Mitigate risks and ensure product integrity from development to bedside.

Don't let quality be a checkpoint—make it a driver of your success.

Contact Metis Consulting Services today at Hello@Metisconsultingservices.com to schedule an appointment or visit our website at: https://www.metisconsultingservices.com/contact

 To discuss how we can help you build a solid foundation of quality.

Welcome Back to the Metis Blog

Written by Li-Anne Roswell Mufson

We've been on Hiatus for a few months, generally having a "Make-Over." We have been doing some fixing up and growing within our company, looking at the blog as well, and at last, we are back! 

We now have a title for this Blog Space.

We are calling it...Drum Roll, please, "The Guard Rail." TADA

Why?

In this Blog Space, we discuss topics of interest to people in our industry and what is happening here at our company. At Metis, we offer services in

 *Audits/Inspection Readiness, *Consulting Services,*Corporate Training,*Data Management,*Pharmacovigilance,*Quality Management, and *REMS.*

We aim to help organizations keep up with regulatory conditions and industry best practices to ensure patient safety and access to effective therapies.

Metis has passionate, experienced consultants who do this work because we care about keeping the focus on the patients.  Our goal is to keep them safe and get them access to effective therapies. We have the most significant impact by helping organizations keep up with global regulatory conditions, Risk Mitigation, and industry best practices. And we see the most crucial part of our mission as keeping the patient first, for us and for our clients.

In other words, we work on the industry Guard Rails. 

Our industry needs guard rails. Nowhere is this more evident than in the recent surge of AI use in Life Sciences, which, not so coincidentally, is our topic for today. 

We say "new" in quotes because AI is not new; the concept has been around since the 50s, but with the eruption of ChatGpt and generative AI, the use of AI in Life Sciences is exploding.  

Today, we want to discuss the intersection of artificial intelligence (AI) and life sciences. While AI is not a new concept, the recent eruption of generative AI and ChatGpt has led to explosive growth in the use of AI in life sciences. This growth is exciting but raises concerns about ethical considerations and responsible deployment.

The ethical deployment of AI is crucial to ensure patient safety and to uphold the highest ethical standards in the pharmaceutical industry. We need to balance technological advancements with ethical considerations. AI has tremendous potential to optimize treatments and unlock profound insights from complex datasets. However, it also poses unique challenges, including data privacy, algorithmic biases, and responsible use of patient information. As Steve Thompson put it so well when he was Metis CEO Michelleanne Bradley's guest on the Queens of Quality Podcast Bonus episode S2.5 E1, "Although this is exciting, we have to act ethically, responsibly…with a multidisciplinary approach..it all sounds really good and fascinating.. but (Michelleanne), you mentioned Guard Rails; we have got to put these things in place to ensure we are applying this technology ethically and responsibly."

In that episode and the others in this bonus series, Michelleanne clearly agrees. AI may be the "shiniest tool in the shed," but that doesn't mean we should leap into using it everywhere without putting guard rails in place. We aim to help organizations keep up with regulatory conditions and industry best practices to ensure patient safety and access to effective therapies.

Check out Queens of Quality podcast episode S2.5E1QoQ

To ensure the ethical deployment of AI in life sciences, we must commit to continuous improvement, transparent model development, and the mitigation of biases. A diverse team of multidisciplinary experts, including scientists, ethicists, data analysts, industry professionals, and more, should work collaboratively to align technological advancements with ethical principles.

The ethical implications of relying on AI for critical decision-making demand meticulous attention and algorithm oversight. Fairness, transparency, accountability, and privacy principles should be non-negotiable in every facet of AI development, deployment, and regulation. Michelleanne quoted Michael Crichton to remind us in Jurassic Park by way of his character Dr. Ian Malcolm, played by Jeff Goldblum, in the movie, "Your scientists were so preoccupied with whether they could, they didn't stop to think if they should." We need to ask ourselves this question. 

We can only harness AI's potential to improve patient outcomes and drive innovation by embracing an ethically conscious approach. However, we must uphold the highest ethical standards in the pharmaceutical industry. 

 In today's fast-evolving landscape, the convergence of Artificial Intelligence (AI) and Life Sciences heralds immense possibilities and intricate ethical quandaries. Understanding the interplay between AI and ethical considerations is essential for professionals entrenched in the Regulatory, Life Sciences, and Pharmaceutical industries. This understanding will steer this transformative journey.

The Current State and Future of AI in Life Sciences

AI is a beacon of innovation in the Life Sciences domain. Its capabilities span from streamlining drug discovery processes to enhancing patient care through predictive analytics. The current landscape paints a promising future where AI optimizes treatments and unlocks profound insights from complex datasets. It is vital to be watchful with our Data Management Systems. Link to DMS

Ethical Dilemmas in AI

However, ethical dilemmas accompany this rapid advancement. Issues such as data privacy, algorithmic biases, and the responsible use of patient information cast a critical spotlight on the ethical deployment of AI in the pharmaceutical realm. We know that patients will suffer if we remove the human element from the equation altogether OR if we don't work to ferret out the human biases inherent in human-provided data and other challenges. Our industry will suffer, too, and we may be throttled to a stop or even move backward before we can move forward.

The Importance of Continuous Improvement in AI

Continuous improvement lies at the core of ethical AI deployment. It needs ongoing algorithm refinement, transparent model development, and bias mitigation. A commitment to oversight ensures that AI systems evolve to become more accurate, fair, and reliable. Nobody can move forward if we do things as we have always done. Check out this episode of Queens of Quality podcast: QoQ 2.5E2

Challenges of Creating Synthetic Patients

Developing synthetic patients and digital replicas of actual patients for research and analysis poses unique challenges. Ensuring these models accurately represent diverse demographics and medical conditions while avoiding biases demands collaborative efforts and stringent ethical considerations. Unaided by AI, humans have done some very unkind things to one another in the name of science while perpetuating harmful biases. The 1932 Tuskegee Airmen syphilis study and the New Zealand 1966 cervical cancer study, to name two, have provided models of unethical practice for a generation of researchers. We need to be sure we don't exacerbate those biases by plugging them into AI.

The Role of AI in Risk Management

AI serves a pivotal role in risk management within the Pharmaceutical industry. Its applications span from early detection of health risks to optimizing clinical trials and facilitating regulatory compliance. However, the ethical implications of relying on AI for critical decision-making demand meticulous attention.

Importance of Multidisciplinary Teams in AI

The ethical deployment of AI in Life Sciences necessitates collaborative efforts by people with diverse expertise. Multidisciplinary teams, comprising scientists, ethicists, data analysts, and industry professionals, bring varied perspectives essential for aligning technological advancements with ethical principles. By our very nature, none of us can "see" our own biases. Michelleanne likens our job to an almost anthropological approach here. The more diverse Multidisciplinary Teams we have working on this, the less likely those biases will slip through to the algorithms unchallenged.  

Ethical Considerations in AI

Above all, ethical considerations are the guiding compass for AI integration in the Pharmaceutical industry. Fairness, transparency, accountability, and privacy principles should be non-negotiable in every facet of AI development, deployment, and regulation. We need algorithm oversight, and the FDA will require industry experts to advise on this. 

The synergy between AI and Life Sciences presents unparalleled opportunities for the Pharmaceutical industry. However, this convergence demands a delicate equilibrium between innovation and ethics. Balancing technological advancements with ethical considerations is not just a choice but a responsibility that shapes the future of healthcare. Only by embracing an ethically conscious approach can the potential of AI be harnessed to improve patient outcomes, drive innovation, and uphold the highest ethical standards in the Pharmaceutical industry.

So how can we do this? What is the mechanism for maintaining this balance?

We need people from all the diverse specialties, Data people, Clinical people, etc., to get involved now. We also need an Algorithm Review Board, like the IRB, to get engaged before we find ourselves dealing with a catastrophe that causes a reactive regulatory response.  

Let's put out the fires before they start.

If you want to become part of this solution, contact us at hello@Metisconsultingservices.com to join the conversation.