HTTP Interface and External Systems for Regulatory

Data Characteristics

Pharmacovigilance data in regulatory submissions primarily originates from clinical trial reports, post-market surveillance reports, and regulatory feedback. This data typically exists as structured documents (e.g., ICH E2B XML files, PDF reports) and semi-structured text (e.g., investigator brochures, drug labels). Data update frequency varies: clinical trial data is submitted periodically as research progresses, while post-market data depends on the real-time nature of adverse event reporting. Fields include patient demographics, medication history, adverse event descriptions (MedDRA coding), event time, and outcome. Units involve dosage (mg, g, IU), frequency (times/day, week), and duration (days, hours). Accuracy requirements are very high for identifying fields such as drug names, batch numbers, and manufacturers.

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The complex structure and high accuracy requirements of regulatory submission data challenge the data parsing capabilities of HTTP interfaces. Document formats like XML and PDF require specialized parsers to ensure accurate field mapping. Data updates may involve importing large volumes of historical data and incremental updates, so interfaces must support efficient bulk data transfer and include a resume mechanism for network fluctuations. Identifying and standardizing professional terminology like MedDRA codes is critical, requiring external systems to provide terminology services or pre-processing for conversion. Additionally, handling sensitive patient information requires interfaces with strict data encryption and access control to ensure data transmission compliance. Precise matching of critical fields like drug batch numbers means external systems cannot tolerate fuzzy matching or algorithms with high error tolerance during data comparison.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`MAX_PAYLOAD_SIZE_MB`	`50 MB`	Regulatory submission files may contain large amounts of text and images, requiring support for larger payloads to avoid the complexity of fragmented transfers.
`HTTP_TIMEOUT_SECONDS`	`120 seconds`	Parsing and processing large XML files or high-concurrency requests can take a long time, preventing task interruption due to timeouts.
`MAX_RETRIES`	`3 times`	Automatic retries improve data transfer success rates when external systems experience occasional network glitches or temporary service unavailability.
`REQUEST_HEADER_AUTH_TYPE`	`Bearer Token`	This aligns with common industry API security authentication standards, facilitating integration with external systems and permission management.
`DATA_ENCODING`	`UTF-8`	Ensures that Chinese, English, and special characters do not appear garbled during cross-system transmission, especially when processing adverse event descriptions.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	PDF document parsing, especially with complex tables and multi-language content, can be time-consuming.

Common Pitfalls

HTTP requests return a 500 error or data parsing fails: This often occurs when the XML or JSON structure returned by the external system does not match expectations, or critical fields like MedDRA codes are not provided in the agreed-upon format.
File upload processing stalls: This may happen if the HTTP interface lacks https_proxy support, preventing normal access to external storage or processing services in specific network environments.
Some fields in imported data are empty or inaccurate: This is due to insufficient coverage of all variations by the data source document parsing rules, such as inconsistent table structures in PDF reports leading to misaligned field extraction.

Verification Steps

Simulate requests to check if the HTTP interface can successfully receive and process ICH E2B compliant XML data, and verify that the returned status code is 200.
Upload a large PDF document containing complex tables and multi-language content. Observe if the external system correctly parses it and check if critical fields (e.g., drug name, adverse event description) are fully extracted.
In an environment configured with a proxy server, test file upload and data synchronization functions to confirm that the https_proxy configuration is effective and data transfer is normal.
Randomly select a batch of imported data and compare it with the original files. Verify the accuracy and completeness of core fields such as patient information, medication dosage, and adverse event codes. Determine data quality thresholds.

The values given are common starting points and should be measured against your 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.