Tool Calling and Plugins for CMC Research Pharmacovigilance

Data Characteristics

Chemistry, Manufacturing, and Controls (CMC) research data in pharmacovigilance focuses on drug manufacturing processes, quality control, and batch release information. Data sources include drug registration dossiers (e.g., CTD Module 3), manufacturing batch records, quality inspection reports, stability study reports, supplier qualification documents, and change control documents. This data combines structured formats (e.g., database records, LIMS system data) and unstructured formats (e.g., PDF reports, Word documents, scanned images).

Data updates align with the drug's lifecycle. Batch data generates per batch, stability data updates at preset time points, and change data updates when changes occur. Document structures are rigorous, with fields often following industry standards. Examples include batch number, manufacturing date, expiry date, inspection item, inspection method, result, unit (e.g., mg/mL, ppm, pH value, IU), impurity limits, and degradation product content. Change management records and deviation investigation reports are important unstructured data sources.

Constraints on Tool Calling and Plugins

The mixed nature of CMC research data imposes requirements on data processing capabilities for tool calling and plugins. Unstructured documents, such as change reports and deviation investigations, require precise text extraction and semantic understanding to identify key quality events, root causes, and corrective and preventive actions.

The diversity of units in structured data and the presence of specific fields (e.g., batch numbers, inspection item codes) require tools to accurately identify and maintain data integrity during parsing, avoiding misjudgments due to unit confusion. The asynchronous nature of data updates, where batch data generates in real-time but stability data updates quarterly or annually, means plugin calls must consider data source timeliness and may require multi-source data fusion. Additionally, drug quality standards vary by country or region, necessitating flexible rule engines for tool calling to match alert trigger conditions under different standards.

Configuration Settings

Common Pitfalls

• External API calls return empty data fields because null values or non-standard field names from external systems are not handled correctly.
• A ModuleNotFoundError occurs during plugin execution because required third-party libraries are not installed in the plugin's runtime environment.
• Tool-generated reports contain incorrect or missing units for certain values because data parsing did not adequately consider the diversity of units in CMC data (e.g., distinguishing between μg/mL and mg/mL).

Verification Steps

• Select a typical document containing batch release data and quality inspection reports. Use FastGPT to call relevant plugins and verify that the output batch numbers and key inspection results match the original document.
• Simulate a drug manufacturing deviation scenario. Verify that the tool correctly identifies the deviation type and root cause, triggers preset alert processes, and confirms that relevant notifications or records are generated.
• For data from multiple sources (e.g., LIMS system, PDF reports), verify that the plugin can successfully integrate and perform correlation analysis. For example, compare batch inspection data with stability data to confirm abnormal batches are correctly identified.

The values provided are common starting points and should be measured against the reader's own 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.