Workflow Orchestration for Bispecific Antibody Clinical

Data Characteristics

Bispecific antibody (BsAb) clinical trial pre-screening data comes from global clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), internal pharmaceutical R&D databases, academic journal articles, patent literature, and third-party bioinformatics platforms. Data update frequencies vary. Registries may update weekly or monthly, while papers and patents update according to publication cycles. Document structures are diverse. They include structured trial protocols, unstructured Investigator Brochures (IBs), Clinical Study Report (CSR) PDF files, and semi-structured genomic and proteomic data reports. Key fields include target information, molecular structure features, indications, dosing regimens, primary/secondary endpoints, subject inclusion/exclusion criteria, adverse event reports, and biomarker data (e.g., PD-L1 expression level, tumor mutational burden TMB). Units involve concentration (nM, µg/mL), dosage (mg/kg), time (days, weeks, months), and biomarker expression levels (%).

Constraints Imposed by Data Characteristics on Workflow Orchestration

The heterogeneity of bispecific antibody data challenges workflow orchestration. Multi-source data requires robust data ingestion and standardization capabilities. For example, configure multiple data source connectors and extract/structure inclusion/exclusion criteria text from various formats. Asynchronous data updates mean pre-screening results may become outdated. The workflow must integrate timed triggers to regularly pull and incrementally update the latest data. The unstructured nature of documents requires advanced Natural Language Processing (NLP) modules during data preprocessing. These modules accurately identify key entities and relationships from large volumes of text descriptions, such as extracting adverse event types and incidence rates from clinical study reports. The accuracy and unit consistency of numerical data, like biomarkers, are prerequisites for correct pre-screening logic. This demands strict unit conversion and missing value imputation strategies during data cleaning.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`3000 Tokens`	Ensures the AI conversational model can handle complex inclusion/exclusion criteria and subject descriptions.
`Chunk size` (Segment Length)	`800 characters`	Balances semantic completeness and retrieval efficiency, avoiding critical information splitting.
`Recall count` (Recall Count)	`Top 10 entries`	Increases coverage of relevant clinical trial document segments, boosting the probability of hitting key information.
`Similarity threshold` (Similarity Threshold)	`0.75`	Filters clinical trials highly relevant to patient characteristics, reducing interference from irrelevant information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates parsing time for large `PDF` research reports, preventing timeout interruptions.
`httpRequest timeout` (HTTP Request Timeout)	`120 seconds`	Handles occasional response delays from external databases or `API` interfaces.

Common Pitfalls

The user-input file link variable in an AI conversation node is not correctly passed to downstream file processing or HTTP request nodes. This causes file parsing or access failures, manifesting as file processing errors or HTTP request 404 errors.
The Content-Type of the body parameter in an HTTP request node is not set to application/json or another type consistent with the backend API's expectation. This prevents the backend from correctly parsing the request body, manifesting as an HTTP request 400 Bad Request error.
An AI conversation node after a judgement node fails to receive the user's original query from before the judgement. This prevents the AI Chat from responding based on the complete context, manifesting as the AI's answer deviating from the user's initial intent.

Validation Steps

Simulate various subject profiles. Run the workflow. Verify the final pre-screening results from the AI conversation node, checking if it accurately lists bispecific antibody clinical trials meeting the inclusion/exclusion criteria.
Check workflow logs. Confirm all HTTP request nodes return a 200 status code and data parsing nodes have no error messages. This indicates normal external data source connection and data processing.
For clinical trials involving complex PDF documents, examine the output of the file parsing node. Verify that key information (e.g., primary endpoint, molecular target, adverse events) is accurately extracted and structured.
Adjust the Similarity threshold (similarity threshold). Observe changes in the number of pre-screening results. Ensure effective filtering of irrelevant trials while maintaining recall rate.

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.