HTTP Interface and External Systems for Antibody-Drug

ADC Pharmacovigilance Data Characteristics

Antibody-Drug Conjugate (ADC) pharmacovigilance data originates from clinical trial reports, real-world studies, post-market surveillance, and patient self-reports. This data typically exists in structured and semi-structured formats. It includes drug batches, patient demographics, dosing regimens, adverse event (AE) descriptions, severity, outcomes, causality assessments, and actions taken. Data updates frequently, especially during post-market surveillance, requiring continuous collection and rapid response to emerging adverse reaction signals.

Common document structures include ICH E2B XML files. These files contain fields such as patient.reaction.reactionmeddrapt (adverse reaction MedDRA Preferred Term) and drug.drugcharacterization (drug characteristics). Unstructured text data, such as clinical notes and pathology reports, also exists. The data's unique nature stems from its highly specialized medical terminology and the complex way adverse reactions relate to specific antibodies, linkers, and cytotoxic drugs.

Constraints on HTTP Interfaces and External Systems

The complexity of ADC pharmacovigilance data imposes specific requirements on HTTP interfaces and external systems. First, high-frequency data updates and large volumes of structured/semi-structured data demand interfaces with high throughput and stable data transfer capabilities to support continuous data ingestion.

Second, parsing capabilities for specialized formats like ICH E2B XML are essential. This requires accurate identification and extraction of key fields such as messageheader.messagetype and safetyreport.safetyreportid. Unstructured text data requires the interface to receive and pass it to subsequent Natural Language Processing (NLP) modules for entity recognition and event extraction.

Furthermore, the specificity of ADC adverse reactions, such as immunogenicity and off-target toxicity, requires the system to handle fields with specific medical coding and ontological associations, for example, MedDRA coded reaction.reactionoutcome. Large data volumes involving sensitive patient information also impose strict requirements on interface security and data integrity validation. Examples include using HTTPS and API Keys for authentication, and checksum validation for received data.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxPayloadSize`	`50 MB`	ICH E2B files can contain numerous adverse event reports, requiring a large request body size to support large-scale data transfer.
`requestTimeoutSeconds`	`600 seconds`	Parsing and processing large XML files or bulk data uploads can take longer, preventing failures due to timeouts.
`contentType`	`application/xml; charset=utf-8`	ADC pharmacovigilance data is often transmitted in XML format; explicitly specifying the content type aids correct parsing.
`errorHandlingStrategy`	`Retry 3 times，Interval 30 seconds`	Addresses transient network fluctuations or temporary unavailability of external systems, improving data transfer robustness.
`authenticationMethod`	`API Key`	Ensures only authorized external systems can access the interface, protecting sensitive pharmacovigilance data.
`dataValidationSchema`	`ICH E2B R3 XML Schema`	Strictly validates the structure and fields of incoming data according to industry standards, ensuring data quality and compliance.

Common Pitfalls

An HTTP request returns a 400 Bad Request error. This commonly occurs when the uploaded XML file does not conform to ICH E2B specifications, for example, missing the safetyreport.safetyreportversion field or having structural errors.
The interface call succeeds, but some critical fields (e.g., reaction.reactionmeddrapt) are empty. This often happens because the external system did not correctly map or encode medical terminology when generating the data, preventing FastGPT from recognizing it.
System response is slow or a 504 Gateway Timeout occurs after submitting a large amount of data. A possible reason is that the requestTimeoutSeconds configuration is too short to accommodate the computational time required to process complex or large-scale XML data.

Verification of Configuration

Submit a complete ICH E2B XML file containing an adverse event report. Check if the interface successfully returns a 200 OK status code and confirm that all critical fields (e.g., drug.drugname and patient.patientinitial) are correctly parsed and stored.
Use a deliberately corrupted XML file (e.g., modify the format of a mandatory field). Test whether the interface returns a 400 Bad Request error with clear error information, indicating that the data validation mechanism is effective.
Continuously monitor interface request latency and success rate metrics. Ensure that request latency remains within an acceptable range during high-concurrency data transfer scenarios, and that the success rate approaches 100%. This verifies the effectiveness of performance-related configurations like requestTimeoutSeconds.

The values given 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.