Tool Calling and Plugins for Pharmacovigilance in Regulatory

Data Characteristics in This Domain

Pharmacovigilance data in regulatory submissions originates primarily from clinical trial reports, non-clinical study reports, Good Manufacturing Practice (GMP) documents, and post-market surveillance data. This data typically exists in a mixed format of structured and unstructured information. Structured data includes patient demographics, adverse event codes (e.g., MedDRA codes), dosage, and start/end dates, commonly found in database exports or spreadsheets. Unstructured data encompasses detailed medical descriptions, expert assessment opinions, and causality judgments, mainly presented as PDF documents, Word documents, or plain text reports. Data updates align with the submission cycle, usually occurring during the submission of clinical trial phase reports, supplemental applications, or periodic safety updates. Document structures are highly standardized, adhering to regulatory agency guidelines (e.g., FDA, EMA, NMPA), and include fixed sections, tables, and appendices. Fields and units are strictly defined according to medical and pharmaceutical standards; for example, doses are measured in milligrams (mg) or micrograms (μg), time in days, weeks, or months, and adverse event severity is clearly graded.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The standardized document structure and strict field definitions of pharmacovigilance data in regulatory submissions require tool calling and plugins to possess high precision and strong constraints during data parsing. For example, recognizing and extracting specialized terms like MedDRA codes requires customized dictionaries or model support; generic entity recognition models may introduce biases. The concentrated and periodic nature of data updates means that tool calling needs to support batch processing capabilities and version management to handle incremental updates of large-scale documents. Furthermore, complex medical descriptions and expert opinions within unstructured data pose challenges for text understanding and information extraction, requiring plugins to have advanced semantic analysis capabilities to accurately determine the causality and severity of adverse events. Additionally, compliance requirements mandate that all data processing and tool calling operations must leave an audit trail, ensuring data traceability and reproducible results.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`4000-8000 characters`	Regulatory submission documents often contain detailed descriptions, requiring a large context window to capture complete information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDFs or documents with complex tables can take a long time.
`embeddingModel`	`text-embedding-ada-002` or newer	Ensures high semantic understanding accuracy for medical terminology and professional descriptions.
`similarityThreshold`	`0.78-0.85`	Pharmacovigilance information requires high relevance; setting the threshold too low may introduce noise.
`recallNum`	`top 10-15 entries`	Ensures coverage of all potentially relevant information in adverse event reports.
`toolCallStrategy`	`sequential`	Pharmacovigilance analysis often involves multiple steps, such as extracting events before assessing severity.

Common Pitfalls

Tool call fails with MedDRA_Code_NotFound in logs: This indicates that the custom MedDRA dictionary is not loaded or is outdated, preventing recognition of the latest adverse event codes.
After parsing a report, key fields like ADVERSE_EVENT_DOSE are empty: This occurs when the document parsing plugin's regular expressions or XPath paths are incorrectly configured, failing to extract dosage information from non-standard table formats.
During a single query, tool calls are out of order or interrupted: This happens when toolCallStrategy is not set to sequential, or when the output of one tool in the toolchain does not match the input of the next tool.

Verification Steps

Select a PDF document containing a typical adverse event report, upload it, and perform parsing. Check if key fields like ADVERSE_EVENT_TYPE, ONSET_DATE, and OUTCOME are accurately extracted.
Simulate a query containing specialized medical terminology. Observe whether the AI's response accurately cites relevant passages from the document and verify the MedDRA code recognition results.
Configure a multi-step toolchain, for example, first extracting adverse events and then calling a plugin for causality assessment. Check if the tool execution order is as expected and if the output of each step is correctly passed to the next tool.

The values provided are common starting points and should be measured against specific samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.