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When it comes to pharmacovigilance, many people may think it’s the responsibility of finished drug manufacturers or regulatory agencies, and has little to do with API production.
However, the opposite is true—fluctuations in API quality, changes in impurity profiles, and even adjustments to the synthesis route can directly impact the safety of the final drug. Today, I’ll discuss what pharmacovigilance is and why it’s so crucial for API companies, from a frontline perspective.
The official definition of pharmacovigilance (according to the WHO) is: “The science and practice of monitoring, evaluating, understanding, and preventing adverse drug reactions and any other potential drug-related problems.”
Sounds abstract? To put it simply, it’s a 24/7 drug safety monitoring system designed not only to detect adverse reactions after they occur, but also to prevent them before they occur.
Pharmacovigilance is particularly important for API companies:
Genotoxic impurities (such as nitrosamines), unknown impurities, and changes in crystalline form in APIs can all potentially trigger safety signals.
Changes in synthesis processes and adjustments to starting material sources require a risk reassessment based on pharmacovigilance data.
Drug regulatory authorities (such as the FDA and EMA) have significantly strengthened pharmacovigilance requirements for API companies in recent years, and audits often examine the robustness of relevant systems.
Many people think pharmacovigilance is simply about collecting adverse reaction reports, but it is actually a closed-loop management system that primarily includes:
Sources: This includes not only adverse drug reaction (ADR) reports from medical institutions, but also literature, social media, and even discussions on patient forums.
Our role as API companies: If a batch of intermediates or API is found to pose a potential risk (such as the detection of a new trace impurity), we must proactively notify downstream pharmaceutical manufacturers.
Methods: Statistical and epidemiological methods are used to determine whether the association between an adverse event and the drug is accidental or causal.
Typical scenario: Nitrosamine impurities are detected in an antihypertensive drug → The pharmacovigilance team needs to assess: What is the carcinogenic risk at different concentrations? Which batches are affected? Should a recall be required?
Measures: Modify drug inserts, issue medication warnings, restrict the intended use, or even recall or withdraw the product.
Drug API Level Interaction: If the risk is determined to be due to a specific impurity in the API, immediate optimization of the synthesis process and strengthening of purification controls are required.
Requirement: Pharmaceutical companies must submit PSURs to drug regulatory authorities on a regular basis (e.g., semi-annually), summarizing all safety data collected during the period and providing conclusions.
Drug API companies must also provide: process change records, impurity profile data, stability test results, etc.
Even if you don’t directly face patient contact, API companies need to establish basic pharmacovigilance awareness and systems:
Require sales and technical teams to report safety feedback from downstream customers to the quality department within 24 hours.
2.Develop a deep understanding of the impurity profile-toxicity relationship
Focus not only on impurities identified by ICH Q3, but also on the potential toxicity of unknown impurities (which can be predicted through QSAR predictions and in vitro testing).
3. Perform a safety assessment before any process changes.
Changing solvents, adjusting reaction temperatures, or using new starting materials? Beyond comparing quality, assess whether new genotoxic structural units will be introduced.
4. Maintain transparent communication with downstream pharmaceutical companies.
Proactively provide API impurity control strategies and structural confirmation data to help customers fulfill their pharmacovigilance responsibilities more efficiently.
AI Signal Mining: Natural language processing (NLP) technology can automatically scan massive amounts of patient feedback and medical literature, identifying potential risk signals over 10 times faster than manual analysis. Predictive Toxicology: Using machine learning models, we predict the toxicity of impurities based on molecular structure, enabling proactive risk mitigation.
Real-World Evidence (RWE): By analyzing electronic medical records and medical insurance data, we assess the safety of drugs in real-world clinical settings.
Pharmacovigilance has never been a “burden” imposed by regulators; rather, it is a core competency for protecting patients, products, and the company itself. Especially in the API industry, a seemingly minor process oversight can escalate across the supply chain into a public health crisis.
Therefore, deeply integrating pharmacovigilance concepts throughout the entire process, from R&D to production, is no longer an option but a necessity for survival and competition.
We provide high-purity APIs and intermediates to customers worldwide, strictly adhering to the pharmacovigilance requirements of the International Health and Family Planning Commission (ICH), the FDA, and the European Medicines Agency (EMA). Behind every batch of product is a comprehensive safety data tracking system.
For free samples and technical documentation, please contact sunqian0123@gmail.com
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