All emerging biotechs face a similar challenge: they’re expected to build pharmacovigilance (PV) infrastructure that meets the same regulatory standards as large pharmaceutical companies, but with a fraction of the budget, headcount, and institutional experience. 

That disconnect becomes particularly noticeable in core functions, such as safety case management, literature monitoring, and signal detection, where even small inconsistencies can affect regulatory compliance or put patients at risk. 

This white paper takes a closer look at those three key areas, focusing on what it takes to build compliant and scalable systems without overburdening lean teams. By the time you’re finished reading, you’ll have a structure you can use to scale your PV program as your company grows.

Safety case management: Building for volume and velocity

Pharmacovigilance is essential to a trial’s success. Once a company receives its first Investigational New Drug (IND) or Clinical Trial Authorization (CTA), it takes on a legally binding obligation to monitor, assess, and report safety information, but those requirements don’t change regardless of your company’s size.

In early-stage biotechs, safety responsibilities typically aren’t centralized. A Chief Medical Officer, a contract research organization (CRO), or a small cross-functional team may be required to manage adverse event reporting alongside other priorities. This approach can work at low volume, but it becomes increasingly difficult as clinical activity expands and reporting timelines tighten.

Safety case management is an ongoing and necessary process. Every adverse event, serious adverse event, and adverse drug reaction has to be received, triaged, entered into a safety system, coded using MedDRA, medically reviewed, and, when required, reported to health authorities within defined timelines. Cases are then incorporated into aggregate reports such as Development Safety Update Reports (DSURs) before being closed with appropriate quality checks.

A safety case follows a predictable lifecycle, and each stage builds on the previous one, so errors in earlier steps can affect compliance later. Each stage also carries specific compliance expectations:

• Receipt and triage involve acknowledging the case, confirming completeness, and determining whether it qualifies as serious, which triggers reporting timelines.
• Data entry and coding require accurate narrative capture and MedDRA coding using the current version, since errors here carry through the entire process.
• Medical review focuses on causality and expectedness, typically requiring physician oversight and, in some regions, formal Qualified Person Responsible for Pharmacovigilance (QPPV) involvement.
• Regulatory reporting must meet strict timelines, including expedited submissions for serious and unexpected events.
• Aggregate inclusion and closure ensure cases are reflected in Development Safety Update Reports (DSURs) or other documentation and undergo quality control before finalization.

One of the more common challenges is deciding how much infrastructure to put in place, and when. Some organizations wait too long to invest in a safety database or formal processes, which leads to disorganized workflows that are hard to scale, while others go too far in the opposite direction, putting systems in place too early and adding unnecessary complexity to their operations.

A more proportionate approach aligns infrastructure with case volume and development stage:

• Early-stage programs can operate effectively with a validated SaaS safety database and minimal internal IT burden, as long as processes are clearly defined.
• Mid-stage development often benefits from partnering with a CRO or PV service provider who can assist with case processing, while internal teams retain medical oversight and accountability.
• Late-stage programs typically require more formal internal PV structure, including leadership oversight and, in some cases, partial insourcing of operations.
• Pre-commercial and commercial readiness demands continuous intake coverage, scalable systems, and alignment with global reporting requirements.

Technology can assist in these efforts, but it won’t necessarily guarantee compliance. Standard operating procedures (SOPs) remain the foundation of a functional PV program and should be updated regularly to reflect changes in regulations, systems, or processes, which helps maintain ongoing compliance.

Even at an early stage, companies should have coverage across:

• Adverse event processing and expedited reporting, including clear timelines and escalation pathways.
• MedDRA coding and version control, ensuring consistency across all cases.
• Literature monitoring and signal detection, which often intersect with case management workflows.
• Aggregate reporting and training, so outputs like DSURs are supported by documented processes.
• Audit and inspection readiness, including documentation practices and internal review procedures.

Data quality and audit readiness also require early attention. The ALCOA+ principles apply to all pharmacovigilance records, meaning data must be attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, and available. In practice, that equates to a few non-negotiables:

• All case activities are documented with timestamps and user identification, creating a clear audit trail.
• Any corrections are made through documented amendments, rather than overwriting original data.
• Reconciliation between clinical and safety databases happens on a defined schedule, not ad hoc.
• Training records remain current for anyone involved in PV activities.

When these elements are in place, safety case management becomes easier. The structure carries most of the burden, allowing lean teams to stay compliant while adapting to increasing volume and complexity.

Literature monitoring: Systematic, efficient, and defensible

Literature monitoring is a required component of pharmacovigilance, but it’s also one of the areas where emerging biotechs are most likely to face challenges. On the surface, the process seems straightforward: search the published literature, identify relevant safety information, and determine whether any findings require further action. But in practice, maintaining a consistent and defensible process over time takes more discipline than many teams expect.

Regulatory expectations are clear. Companies are responsible for identifying safety information reported in the literature, even when it doesn’t originate from their own clinical data. This includes not only direct references to their product, but also potential class effects associated with similar compounds or mechanisms of action.

A defensible literature monitoring process starts with a well-defined search strategy. At a minimum, that strategy should include:

• Core database coverage, such as MEDLINE and Embase, with additional sources added for specific indications or geographies.
• Defined search terms, including the product name, known synonyms, and mechanism-of-action terminology, with rationale documented.
• Regular search frequency, typically monthly in early development and increasing as programs advance.
• Clear scope of review, covering adverse events, interactions, medication errors, special populations, and pregnancy outcomes.
• A deduplication approach, to prevent the same case from being reported multiple times across different sources.
• Complete documentation, including search logs, protocols, and article disposition decisions.

Manual workflows can support literature monitoring at very low volume, but they become difficult to sustain as the number of articles increases. Automation can help reduce that burden by handling repetitive tasks such as running searches, filtering results, and maintaining audit-ready documentation. However, human review remains essential, particularly when determining whether an article contains reportable safety information.

A structured triage process helps keep the workload manageable while maintaining consistency:

• Initial screening removes clearly irrelevant articles, such as those that don’t involve the product or indication.
• Abstract review determines whether a full-text evaluation is necessary based on potential safety relevance.
• Full-text review is performed by a medically trained reviewer to assess whether the article contains reportable information.
• Disposition and action ensure that relevant findings are entered into the safety system and considered within ongoing signal detection.

With a defined process, consistent documentation, and the right level of automation, literature monitoring can become more efficient and defensible, even if your team is operating with limited resources. 

Signal detection and management: Prioritizing safety risks as you grow

The third most important aspect of a PV safety net is signal detection and management. This is often considered the most complex area because it involves advanced statistics and large datasets, but with the right system, it doesn’t have to be overwhelming.

As soon as a trial begins, companies should continuously review safety data to identify any new or changing risks. A safety signal is any information suggesting a possible link between a product and an adverse event that warrants further evaluation. These signals can come from clinical trials, published studies, spontaneous reports, or other sources.

The signal management process typically has four stages: detection, validation, analysis and prioritization, and recommendation or implementation. For emerging biotechs, the important thing is to apply this process in a way that fits the amount and type of data available, rather than copying the approach of larger organizations.

In early development, signal detection is mostly guided by clinical and medical review:

• Investigator-reported events provide the first indication of potential safety concerns, particularly serious adverse events.
• Periodic review of adverse event listings helps identify patterns that may not be obvious at the individual case level.
• Incorporation of literature findings ensures that external signals are considered alongside internal data.
• Regular safety meetings create a structured forum for reviewing and discussing emerging concerns.

As programs expand, the process becomes more structured and data-driven:

• Comparison of event frequencies against expected background rates helps contextualize observed findings.
• Grouping related events using MedDRA classifications enables the identification of broader patterns.
• Aggregate data review supports a more formal assessment of trends across studies or populations.

Regardless of the stage, documentation remains central to a defensible signal-detection process. The signal tracking log serves as the primary record of all identified safety concerns and should capture:

• Signal identifier and detection date, establishing a clear starting point for evaluation.
• Data sources, including clinical, literature, or external databases, that contributed to the identification.
• Description and coding, ensuring consistent characterization of the event.
• Assessment of clinical significance, including rationale for prioritization.
• Actions taken or justification for no action, demonstrating decision-making transparency.
• Current status, indicating whether the signal is closed or under ongoing monitoring.

For early-stage companies, signal detection doesn’t require dedicated statistical expertise. Most validated safety databases include tools that support basic case series analysis, and physician-led review of safety data often works with small case volumes. External partners, such as CRO medical monitors, can also provide additional support if needed.

In later-stage trials, Data Safety Monitoring Boards (DSMBs) add an independent layer of oversight that strengthens the signal detection process. These groups review aggregate safety data at defined intervals and provide objective input on emerging risks, making them a valuable extension of internal PV capabilities.

Ultimately, signal detection is less about complexity and more about consistency. A structured, well-documented approach allows emerging biotechs to identify and respond to potential safety concerns in a way that remains proportionate to their size and stage, without placing unnecessary strain on limited resources.

Scaling ahead: Building for what comes next

Building a scalable pharmacovigilance program may seem daunting, but it can be done efficiently with the right approach. Teams should establish strong processes early on to support growth and meet regulatory requirements.

Safety case management, literature monitoring, and signal detection work together as parts of the same system, yet weaknesses in one area often affect the others. When these processes are clearly defined, consistently followed, and supported by appropriate technology and external expertise, even small teams can operate effectively.

The companies that successfully navigate these challenges tend to approach pharmacovigilance the same way they do clinical development. They understand the requirements, invest proportionately, and build ahead of where they are instead of reacting once problems appear.

If you’re interested in scaling your PV program, we can help. Our primary technology platform, Flex Database, assists with all aspects of PV, including adverse event reporting, signal detection, risk management planning, and more. To learn more about how we can support your work, fill out a digital contact form here, email [email protected].