Data Characteristics
Bioequivalence (BE) clinical trial pre-screening primarily uses pharmacokinetic (PK) data from marketed drugs, in vitro dissolution data, pharmaceutical research reports, clinical trial reports, and regulatory guidelines. This data is typically in structured tables, PDF documents, or Word documents. Update frequency is relatively low, changing with new drug approvals, guideline revisions, or major study publications. Core fields in these documents include drug name, active ingredient, dosage form, administration route, PK parameters (e.g., AUC, Cmax, Tmax), dissolution curves, BE trial results (R/T ratio, confidence intervals), subject characteristics, and bioanalytical methods. Units involve concentration (ng/mL), time (h), dose (mg), and volume (L).
Constraints on Tool Calling and Plugins
The diversity and specialized nature of bioequivalence data impose high demands on tool calling. Structured data, such as PK parameters and dissolution curves, requires precise parsing. Non-structured text in PDF reports needs conversion to a processable format via OCR and information extraction. Data updates are infrequent, reducing real-time pressure on the knowledge base, but accuracy is critical. The R/T ratio and confidence intervals from BE trial results are key judgment criteria and require precise calculation or comparison during tool calls. Understanding textual descriptions of subject characteristics and analytical methods requires the AI Agent's semantic understanding capabilities, combined with external specialized tools. Examples include calling drug interaction query tools or pharmacokinetic model simulation tools. Identifying and converting these specialized fields and units is essential for accurate tool call results.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | PDF reports often have long paragraphs; this ensures semantic completeness while respecting model input limits. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures high relevance for recalled pharmaceutical and PK parameter information, reducing misjudgments. |
Rerank result count (Reranked Return Count) | Top 5 | Precisely filters the most relevant key information, reducing irrelevant interference. |
maxContext | 16000 tokens | Provides sufficient context for comprehensive analysis of complex BE trial reports. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large PDFs or documents with complex tables, preventing parsing timeouts. |
API_KEY | Provided by the actual external pharmaceutical database or model service used | External tool call credentials ensure data access and proper service usage. |
Common Pitfalls
- Tool calls return null values or error codes because external pharmaceutical database API request parameters are incorrect or lack necessary authentication information.
- Pre-screening results show significant deviations due to incorrect unit identification for PK parameters in documents, leading to errors in numerical calculations or comparison logic.
- Parsing large clinical reports times out because
PARSE_FILE_TIMEOUT_SECONDSis not adjusted, and the default value is insufficient for complex documents.
Verification Steps
- Execute the pre-screening process for known drugs with different dosage forms and administration routes. Compare the AI Agent's BE trial judgments with actual regulatory approval results for consistency.
- Randomly select multiple PDF documents containing PK parameters and dissolution curve data. Check if the key field values extracted by the knowledge base perfectly match the original text.
- Simulate calls to external pharmaceutical databases or pharmacokinetic model tools. Check if request parameters and return result formats meet expectations, and confirm the correct parsing of key fields.
The values provided are common starting points and 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.