Gene Therapy AAV Pharmacovigilance: HTTP Interface and

Data Characteristics

Gene therapy AAV (adeno-associated virus) pharmacovigilance data is highly specific and complex. Data sources include clinical trial reports, real-world evidence (RWE) data, patient reports, and post-market surveillance databases. Data update frequencies vary. Clinical trial data typically releases in batches at specific times. Patient reports and post-market surveillance data may stream in real-time. Document structures are diverse, often consisting of unstructured or semi-structured text, such as adverse event descriptions, patient histories, and medication histories. Fields include general pharmacovigilance fields (e.g., event type, severity, occurrence date). Gene therapy AAV-specific fields include AAV serotype, vector dose, gene copy number, target cell type, and pre-existing immunity status. Units commonly involve dose (e.g., vg/kg viral genomes per kilogram of body weight), time (e.g., days, weeks, months), and biological indicators (e.g., IU/mL antibody titer).

Constraints on HTTP Interfaces and External Systems

The complexity of gene therapy AAV pharmacovigilance data imposes multiple constraints on HTTP interface design and external system integration. First, diverse data sources require interfaces with robust data parsing and standardization capabilities. This handles varying data input formats and origins. Second, highly specific fields (e.g., AAV serotype, pre-existing immunity status) require dedicated field mapping and validation rules. This ensures data accuracy and consistency. Third, batch update patterns for some data (e.g., clinical trial reports) require interfaces to support bulk data transfer and transactional processing. This prevents incomplete data. Real-time patient reports and post-market surveillance data require low-latency, high-concurrency interface designs. Furthermore, the diversity of biological indicator units requires the system to correctly identify and convert them. This avoids data misinterpretation due to unit errors. Parsing unstructured text requires integrating natural language processing (NLP) capabilities.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`maxContext`	`2000–3000 characters`	Accommodates lengthy text descriptions in AAV adverse event reports, ensuring complete context.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles parsing complex PDFs or unstructured reports, preventing timeouts due to extended parsing times.
`similarityThreshold`	`0.75–0.85`	Balances similarity recall for AAV variants and adverse event descriptions, avoiding over-generalization or omissions.
`recallCount`	`10–15 items`	Considers potential rare adverse events in AAV pharmacovigilance data, increasing recall to improve detection rates.
`requestTimeoutMs`	`30000 milliseconds`	Addresses potentially long response times from external systems processing AAV-specific data (e.g., gene sequence alignment, immunogenicity assessment).
`customHeaders`	`X-AAV-Type: AAV2`	Used to pass key metadata like AAV serotype during external system integration. This enables fine-grained routing and processing.

Common Pitfalls

HTTP status code returns 200 OK, but critical fields in the response body (e.g., adverseEventSeverity) are empty. This usually indicates mapping failures or incomplete data cleansing by the external system when processing AAV-specific fields.
Frequent 504 Gateway Timeout errors during API calls. This occurs when external systems take too long to process complex calculations involving AAV vector dose or gene copy number, exceeding FastGPT's default waiting time.
Data imported via the HTTP interface shows low relevance for AAV serotype-related results when retrieved in FastGPT. This may be due to incorrect identification and indexing of AAV serotype information during data import, or tokenization strategies unsuitable for biological terminology.

Verification Steps

Using FastGPT's data management interface, randomly sample imported AAV pharmacovigilance reports. Verify that key fields, such as AAV serotype and vector dose, match the original data.
Perform searches in FastGPT using query statements that include AAV-specific keywords (e.g., AAV5, hepatotoxicity, immunogenicity). Observe if the recalled results include relevant and accurate documents.
Simulate an external system sending a batch of adverse event reports to FastGPT. These reports should contain varying severity levels and AAV vector information. Check the update status of the corresponding knowledge base in FastGPT and the accuracy of field parsing.
Monitor HTTP interface logs. Confirm no significant number of 4xx or 5xx error codes appear. Also, verify that data transfer rates meet expectations.

The values provided are common starting points. Measure them 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.