A medicine may leave the manufacturing site, but the learning does not stop there.
What happens after a product reaches the patient can reveal things not visible during development, validation or routine manufacturing: a packaging issue, a difficult-to-use device, or a recurring complaint that takes on a different meaning alongside manufacturing or quality data.
How can these signals make their way back to the teams that design, manufacture, test and release the product?
We spoke with Dr. Rahul Somani, Head of Global Pharmacovigilance at Alkem, who brings a pharmacovigilance perspective to the discussion and explores how post-market signals connect with manufacturing and quality knowledge.
1. Once a medicine reaches patients, what can manufacturers learn about the product that may not have been evident during development, validation or batch release? Which post-market insights are most relevant for manufacturing and quality teams?
Product approval is not the end of product understanding. It is the point at which a medicine begins to operate in a more complex environment.
Development, process validation and batch release provide confidence that a product can be manufactured consistently and meets predefined quality requirements under established conditions. Once the medicine reaches patients, however, it encounters different climatic zones, distribution networks, storage practices, healthcare settings, administration techniques and patient populations. These variables can reveal aspects of product performance that were not apparent under controlled conditions.
For manufacturing and quality teams, the most relevant post-market insights include:
- Product behaviour throughout its actual shelf life
- Sensitivity to heat, humidity, light, vibration or transportation stress
- Packaging and container-closure performance
- Device usability and dose-delivery reliability
- Reconstitution or administration difficulties
- Changes in appearance, odour, texture or physical integrity
- Unexpected variation in perceived therapeutic performance
- Recurring concerns associated with a particular batch, presentation, market or distribution route
Consider an illustrative example. A tablet may meet its release and stability specifications, yet breakage reports may occur more frequently in a particular climatic region or packaging configuration. That does not prove a manufacturing defect, but it raises a relevant question about interactions among tablet characteristics, packaging, humidity and distribution.
The key lesson is to focus on patterns and common denominators. Similar complaints associated with a common batch, component, packaging configuration or supply route may represent lifecycle knowledge.
2. Manufacturing teams routinely work with batch records, deviations, complaints and process data. What additional insight can adverse-event and pharmacovigilance data provide about how a product is actually performing in the field?
I often describe the distinction this way: manufacturing data tells us how the product was made, whereas pharmacovigilance data tells us what happened when the product was actually used.
A safety report may contain much more than the name of an adverse event. The narrative may describe how the product looked, how it was handled, whether a device functioned as expected, whether the dose was delivered completely, or whether the patient or healthcare professional experienced an unexpected difficulty during administration.
Consider a hypothetical report of an unusually painful injection. On its own, it is non-specific and could relate to administration technique, product temperature, injection site, needle characteristics, formulation or patient sensitivity. If similar reports recur with the same device presentation or batch, particularly alongside difficult activation or incomplete dose delivery, the pattern may become relevant to quality, engineering and manufacturing teams.
The same caution applies to reports of unexpected lack of effect. Disease progression, non-adherence, concomitant medication, incorrect administration, storage after dispensing and other clinical factors must be considered. Nevertheless, if reports cluster around a common batch, market, presentation or distribution period, a multidisciplinary assessment may be warranted.
Pharmacovigilance data should therefore be treated as a hypothesis-generating source of intelligence. It does not, by itself, establish a defect or prove causality. Its value lies in identifying recurring patient and healthcare-professional experiences that may not be visible in batch records or conventional process data.
3. When a market signal points to a possible product issue, how should manufacturers trace it back to its source? How could serialization and Track & Trace data strengthen this process by connecting product-level information across manufacturing, packaging, distribution and the market?
In my experience, the quality of an investigation depends heavily on how clearly the signal is defined at the outset. Before searching for a root cause, the team must establish what was reported, when and where it occurred, which product presentation was involved, and whether reliable batch, lot, expiry and serial-number information is available.
The team should determine whether the concern is clinical, technical or both, and whether plausible patient, administration or distribution explanations exist.
The investigation can then work backwards through the product lifecycle, reviewing as appropriate:
- API, excipient and component lots
- Supplier and material history
- Manufacturing and equipment
- Processing, filling and packaging lines
- In-process controls and laboratory results
- Deviations, maintenance activities and change controls
- Stability data
- Retain and returned samples
- Warehousing and transportation records
- Distribution routes and trading partners
Serialization and Track & Trace can make this process more precise. A single batch may contain packs across several markets and supply routes. Serialization can connect an individual pack with its product code, batch or lot, expiry date and unique serial number.
For example, if several complaints come from the same batch, Track & Trace may show that affected packs passed through the same downstream distribution route, while packs from the same batch supplied through other channels generated no comparable reports. That would direct greater attention to transportation, storage or local handling. Conversely, if the same issue is reported across different markets and independent supply routes but remains associated with one manufacturing or packaging campaign, the investigation may focus more strongly on materials, processing or packaging.
Serialization cannot independently demonstrate a temperature excursion, excessive humidity, vibration or light exposure. Those questions require logistics and environmental data. Track & Trace narrows the field of inquiry; it does not replace scientific investigation or establish causality.
4. Some signals may initially appear too small to warrant action. What has your experience taught you about recognizing an early signal that could point to a wider manufacturing or product-quality issue? What should teams look for before deciding that a signal is simply noise?
Meaningful signals do not always begin with large numbers. Sometimes the first indication is a single technically unusual report or a small group of observations that only become meaningful when considered together.
When assessing a small or emerging signal, I look particularly for specificity, repetition, clustering, technical plausibility, potential patient impact and convergence across data sources.
Reports may use different terminology yet share a common batch, component supplier, packaging format, manufacturing line, market or distribution period. A common, non-specific event may have limited manufacturing relevance, whereas repeated distinctive problems such as device activation failure or incomplete dose delivery may matter even when numbers are small.
Technical plausibility matters. The team should ask whether a scientifically credible mechanism links the observation to a formulation characteristic, material attribute, process parameter, packaging component, device feature or storage condition.
The strongest early signals often arise when evidence streams converge. For example:
- Pharmacovigilance identifies several reports of incomplete dose delivery.
- Quality records show a modest increase in technically similar device complaints.
- Manufacturing data shows a subtle shift in an assembly parameter, although results remain within specification.
- Supplier information identifies a recent component lot change.
None may be conclusive independently. Together, they form a technically coherent pattern that warrants investigation. Potential patient impact should also influence escalation. A rare report suggesting contamination, loss of sterility, significant underdosing, incorrect dosing or failure of a critical therapy may require prompt attention even without a numerical trend.
The relevant question is not simply whether the number of reports is small, but whether the observation demonstrates sufficient specificity, recurrence, technical plausibility, patient impact or cross-source convergence to justify further assessment.
5. How effectively are post-market learnings being fed back into manufacturing today? Where do you see the biggest gaps between pharmacovigilance, quality, manufacturing and process-development teams, and what could be done differently?
In my experience, the industry does not lack post-market data. The greater challenge is connecting and interpreting it across organizational and functional boundaries.
Pharmacovigilance, quality, manufacturing, process development, supply chain and regulatory affairs often operate in different systems and terminology. Pharmacovigilance analyses clinical cases and safety signals; Quality focuses on complaints, investigations and CAPA; Manufacturing monitors processes, equipment and batch performance; and Process Development and Manufacturing Science and Technology teams focus on formulation knowledge, technology transfer and process robustness.
Each function may see only part of the same issue. Pharmacovigilance may observe a recurring patient experience, Quality a small increase in complaints, Manufacturing a minor process shift within limits, and Supply Chain a concentration of reports in one region. Together, they may reveal a meaningful pattern.
The biggest gap is often the absence of a systematic mechanism for cross-functional signal convergence. A structured, risk-based review should bring together:
- Pharmacovigilance
- Quality Assurance
- Manufacturing
- Manufacturing Science and Technology
- Process Development
- Supply Chain
- Medical or Clinical functions
- Regulatory Affairs
- Packaging, device and engineering specialists
This does not mean every adverse event should be referred to manufacturing. Clearly defined criteria should identify cases with possible technical or product-quality relevance, including:
- Batch-associated adverse events
- Device malfunction or dose-delivery concerns
- Visible or physical product changes
- Suspected contamination
- Reconstitution or administration difficulties
- Unexpected lack-of-effect clusters
- Adverse events accompanied by product complaints
- Market-specific or distribution-route-specific patterns
Common terminology is critical. A patient may say, “It did not work,” while a manufacturing scientist needs to know whether this means activation failure, incomplete dose delivery, administration difficulty, non-adherence or lack of therapeutic response.
The opportunity is better integration of data and expertise across the lifecycle, with clear ownership and decision-making, consistent with knowledge management, quality risk management and continual improvement within the Pharmaceutical Quality System.
6. When a recurring market signal is identified, how should it influence the way manufacturers think about CAPA, change control, process monitoring and continued process verification? Can post-market experience sometimes challenge assumptions that were accepted during validation?
I consider post-market experience to be an important source of lifecycle product and process knowledge. When an investigation identifies credible evidence of a relationship between a recurring market signal and product or process performance, that learning should be incorporated into the Pharmaceutical Quality System.
CAPA should address the established root cause and relevant contributing factors rather than simply correcting visible symptoms. Depending on the investigation, actions may involve:
- Process parameters or operating ranges
- Equipment controls and maintenance
- Raw materials or components
- Supplier controls
- Packaging or container-closure systems
- Device design or assembly processes
- Transportation and storage arrangements
- Analytical or in-process controls
- Instructions for use
- Training or monitoring requirements
Suppose recurring market complaints suggest that a packaging component may be more vulnerable under high-humidity distribution conditions. Subject to a product-specific investigation, an appropriate response might include reassessing component specifications, packaging controls, supplier requirements, transportation arrangements and monitoring measures.
CAPA effectiveness should be considered at two levels:
- Was the technical corrective or preventive action implemented as intended and shown to be effective?
- Did the corresponding post-market complaint or safety pattern decrease or disappear?
Post-market evidence should also inform process monitoring and continued process verification. If commercial experience suggests greater sensitivity to a material attribute or process parameter, monitoring may need closer trending, stratification or revised criteria.
Post-market experience can also challenge assumptions accepted during validation. Validation demonstrates that a process can consistently produce acceptable products within defined conditions. It does not mean that every interaction among materials, process parameters, packaging, distribution and clinical use has been permanently or exhaustively understood.
Commercial experience may provide additional evidence about the robustness of operating conditions considered acceptable during development and validation. This does not necessarily mean the original validation was inadequate; lifecycle experience has added knowledge to be evaluated through the quality system.
Validation should be regarded as a strong foundation for process understanding, not the end of learning.
7. As real-world data, Track & Trace, analytics and AI become more connected, how can manufacturers build a stronger feedback loop between the plant and the patient? What capabilities will matter most if manufacturing is to learn from market experience earlier?
The next major advance will come from integrating data and expertise across the product lifecycle.
Manufacturing systems contain batch-production information; laboratory systems contain analytical and stability results; Quality systems contain complaints, deviations, investigations, CAPA and changes; serialization identifies packs; supply-chain systems record movements; pharmacovigilance captures patient and healthcare professional experiences; and real-world data provides clinical context.
Data interoperability. Systems need reliable identifiers for products, batches, presentations, components, markets and serialized packs. Without sound master data and terminology, analytics may generate misleading associations.
Data quality and provenance. Information must remain traceable to its source, distinguishing reported information, coded data, analytical outputs and expert conclusions.
Natural Language Processing. Relevant pharmacovigilance and complaint information is often contained in unstructured narratives. Patients may say, “the tablet broke,” “the injection was painful,” “the product looked different,” or “the device did not deliver the dose.” NLP may identify similar descriptions and bring patterns to expert attention while preserving the original narrative and clinical context.
Advanced analytics and AI-assisted signal convergence. AI may identify clusters across safety cases, complaints, manufacturing parameters, component lots and distribution records. An association would not establish causality; it would provide a testable hypothesis.
Human scientific oversight. AI can identify correlations and unusual patterns, but cannot establish whether an association is clinically meaningful, technically plausible or causal. Pharmacovigilance, medical, quality, engineering, manufacturing and data-science professionals remain essential, with governance, model validation, access controls, auditability and documented human review.
Manufacturing and material data + Laboratory and quality data + Serialization and distribution data + Patient experience
↓
Integrated signal assessment → Investigation → CAPA or change control → Effectiveness monitoring → Updated product and process knowledge
The objective is not to make manufacturing responsible for every adverse event. It is to ensure potentially relevant patient experience is not excluded from product and process learning because it originated outside the factory.
About the Guest
Dr. Rahul Somani – Head of Global Pharmacovigilance at Alkem, brings a global pharmacovigilance perspective to the pharmaceutical product lifecycle. His work and insights span post-market safety signals, product quality, patient experience and the connection between pharmacovigilance and manufacturing.
In this conversation with Hello Pharma, he explores how real-world product experience can contribute to manufacturing and quality decisions, and how stronger integration across pharmacovigilance, quality, manufacturing, supply chain and process development can support better lifecycle knowledge.
Disclaimer
The views and opinions expressed in this editorial are those of the interviewee and are based on his professional experience in pharmacovigilance and pharmaceutical product lifecycle management. They do not necessarily reflect the official views, policies, or positions of Hello Pharma, its management, or its affiliates.
Hello Pharma does not endorse or take responsibility for any specific technical, clinical, regulatory, or quality interpretations presented in this article. Readers are encouraged to independently evaluate the information shared, review applicable regulatory guidance, and rely on their own experience, expertise, and professional judgement before making decisions related to pharmaceutical manufacturing, product quality, pharmacovigilance, or regulatory compliance.
