HTTP Interface and External Systems for siRNA Nucleic Acid

Data Characteristics

siRNA nucleic acid drug pharmacovigilance data originates from clinical trials, real-world studies, post-market surveillance reports, and relevant literature databases. This data typically exists in structured or semi-structured formats. It includes patient demographics, medication history, adverse event descriptions (primarily MedDRA coded), event timing, severity, outcome, and related laboratory test results. Data updates frequently, especially during initial market release and critical clinical research phases, with new data potentially generated daily or weekly. The document structure usually adheres to standards such as ICH E2B R3 or FDA Adverse Event Reporting System (FAERS), featuring clear fields and extensive medical terminology. Dosage units often involve mg/kg or μg/kg, time units are precise to days and hours, and adverse reaction descriptions rely on standardized medical dictionary coding.

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

The high update frequency of siRNA nucleic acid drug data requires HTTP interfaces to have efficient data retrieval mechanisms. This prevents delayed alerts due to outdated data. The coexistence of structured and semi-structured data means the interface must support multiple data formats, such as JSON or XML, and effectively handle nested structures. The specialized nature of medical terminology and coding standards (e.g., MedDRA 26.0 version) demands that interfaces preprocess data before transmission or perform strict validation upon reception to ensure data consistency and accuracy. Specific dosage units and time precision impose strict requirements on field data types and lengths. For example, the dosageValue field must support floating-point numbers, and the eventTimestamp field must include timestamps precise to the second. Furthermore, due to data sensitivity, secure authentication and authorization mechanisms for interfaces are critical. These often employ OAuth 2.0 or API Key authentication and require encrypted transmission.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale for this Value
`requestTimeoutSeconds`	`60 seconds`	Prevents request failures due to network latency or long backend processing times when handling large datasets or complex queries.
`maxConnections`	`10`	Balances data retrieval efficiency with the external system's concurrent processing capabilities, preventing overload.
`retryAttempts`	`3 times`	Increases the success rate of data acquisition during transient network fluctuations or brief external system unavailability.
`pollingIntervalMinutes`	`15 minutes`	Balances real-time data needs with external system load, addressing higher data update frequencies.
`responseBodyMaxSizeMB`	`100 MB`	Allocates sufficient space to handle response bodies containing detailed medical reports or multiple adverse event records.
`authHeaderName`	`Authorization`	Follows industry-standard HTTP authentication header conventions, facilitating integration and maintenance.

Common Pitfalls

The pageNum consistently returns the same content during data retrieval. This occurs due to the external interface's caching mechanism or incorrect parameter passing, causing pagination logic to fail.
A 429 Too Many Requests error appears after frequent interface calls. This happens when reasonable request intervals are not configured or rate limiting responses are not handled.
The received adverse event description field is empty or garbled. This occurs when the character encoding returned by the external interface is not handled correctly or medical coding versions are mismatched.

Verification Steps

Use FastGPT's interface testing feature to simulate a complete HTTP request with test data. Check if the returned status code is 200 OK and verify that key fields (e.g., adverseEventId, meddraCode) contain valid data.
Observe log output in the actual operating environment. Check for error messages triggered by requestTimeoutSeconds or retryAttempts and confirm that the retry mechanism functions as expected.
Periodically compare the siRNA nucleic acid drug adverse event data imported into FastGPT with the external system's source data. Check data completeness, field mapping accuracy, and whether the update frequency meets expectations. For example, verify the eventTimestamp of the latest events.
Review the external system's API documentation. Confirm that the authHeaderName and authentication credentials configured in FastGPT match the documentation requirements, ensuring correct interface permissions.

Note: The values provided are common starting points. Measure 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.