Data Characteristics
Stability study data primarily originates from laboratory test reports. This data details changes in physical, chemical, and biological properties of drugs or biological products over time, under various conditions (temperature, humidity, light). Data typically exists in structured tabular formats, such as Excel, CSV, or XML files exported from LIMS (Laboratory Information Management System). Each record includes test results for a specific batch, time point, and test item. Fields include batch number, sample ID, test time point, test item name, test method, test result (numerical or qualitative description), unit, deviation range, and conclusion. Data update frequency depends on the study protocol, usually at predefined intervals (e.g., 0, 1, 3, 6, 9, 12, 18, 24, 36, 48, 60 months). This data demands high precision and consistency; minor deviations can impact product approval for market release.
Constraints Imposed by "HTTP Interface and External Systems"
The highly structured and time-series nature of stability study data requires HTTP interfaces to maintain strict field mapping and data type consistency during transmission. Due to periodic data updates, external systems need to support scheduled or event-driven data retrieval mechanisms to ensure timely access to the latest test results. While the overall data volume is not massive, a single file can contain numerous test items across multiple batches and time points. Therefore, interfaces must support efficient bulk data processing. Metadata such as units and deviation ranges within test results must be fully transmitted via the interface for accurate logical judgments by downstream pre-screening systems. Data source diversity (LIMS, Excel) also necessitates interface compatibility and preprocessing capabilities to standardize data formats.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
requestTimeout | 30000 milliseconds | Accounts for bulk data transfer and external LIMS system response times, preventing timeouts due to network fluctuations or large data volumes. |
maxRetries | 3 | Addresses temporary network failures or transient unavailability of external systems, improving data acquisition stability. |
contentType | application/json or application/xml | Aligns with mainstream output formats from LIMS or data export systems, ensuring correct data parsing. |
headers | Includes Authorization or X-API-Key | External systems typically require authentication for secure data transmission and access control. |
pollingInterval | 24 hours or adjusted per study protocol | Stability study data updates are usually periodic, avoiding resource waste from frequent requests. |
dataValidationSchema | JSON Schema or XML Schema file path | Ensures received data structure, field types, and units conform to predefined specifications, identifying data anomalies early. |
Common Pitfalls
- HTTP requests return 401 or 403 errors, indicating interface call failure and inability to retrieve data. This occurs when the external system requires authentication, but the request header lacks a valid
AuthorizationorX-API-Keyfield. - Interface calls succeed, but critical fields in the returned data are empty or have type mismatches, leading to abnormal downstream pre-screening logic. This happens when the external system's data format or field names do not match interface expectations, or when necessary data transformation and validation are absent.
- Data retrieval tasks frequently time out, even with small data volumes. This is due to
requestTimeoutbeing set too short, with external system processing or network transmission times exceeding expectations.
Verification Steps
- Review FastGPT's HTTP request logs. Confirm that each interface call returns a 200 status code and that response times are within the expected range. An average response time threshold can be established based on historical data.
- Perform a spot check on retrieved stability study data. Verify that critical fields (e.g., batch number, test result, unit) are complete and correctly formatted. Compare with original report data to confirm data consistency.
- Execute the pre-screening process. Observe whether it correctly identifies batches that meet or do not meet stability criteria. Evaluate the concordance between pre-screening results and manual assessments to gauge data quality and logical accuracy.
Note: The values provided are common starting points. They 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.