This Combined Synopsis/Solicitation opportunity from Department Of Health And Human Services was posted on May 11, 2026. The submission period has ended. Browse the details below for market research, or find similar active opportunities.
Artificial Intelligence and Computational Statistics Platform for Biosimilar Subvisible Characterization
Contract Overview
Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.
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AI Contract Overview
The FDA’s Office of Product Quality Research is seeking a machine learning and computational statistics platform aimed at detecting and classifying protein aggregates in biosimilar drug products. This platform will support feasibility studies evaluating AI and machine learning applications for biosimilar comparability, quality assessment, and surveillance. Key requirements include the ability to generate morphological fingerprints specific to protein aggregates based on product type and stress mechanisms, differentiate particles from various stress conditions and container systems, and apply computational statistics combined with neural network-based metric learning to characterize heterogeneous suspensions of subvisible particles under 100 microns. It must also work with Flow Imaging and Backgrounded Membrane Imaging data without needing prior image processing, offer quantitative data beyond size and count, employ advanced statistical tools, and provide visual examination capabilities of particle images within the generated fingerprints. The contract will be awarded as a fixed-price, lowest price technically acceptable (LPTA) agreement, with proposals evaluated on total price and technical compliance with specified requirements. The selected platform must be recognized and trusted within the biopharmaceutical industry, demonstrate experience in supervised and unsupervised machine learning for classifying biologic particle images, and compensate for optical phenomena at varying scales. Additionally, training on AI/ML applications for particle classification and interpretation will be provided to FDA staff. Submission deadline is May 26, 2026, and the place of performance is Silver Spring, Maryland. The solicitation does not have a set-aside designation and falls under NAICS code 513210.
General Info
Agency
NAICS
Place of Performance
Silver Spring, MD, 20993, USASet-Aside
Timeline
Submission Closed
Organization & Contact Information
Full Description
The Food and Drug Administration’s Office of Product Quality Research (OPQR) require a machine learning (ML/AI) and computational statistics platform with associated services to detect and classify protein aggregates in biosimilar drug products. This capability will support a feasibility study assessing the utility of artificial intelligence/machine learning and computational statistical analysis for biosimilar comparability assessment, quality assessment, and quality surveillance.
The platform:
• Shall combine machine learning to generate morphological fingerprints of protein aggregates
• Shall generate morphological fingerprints specific to product and underlying stress or mechanism of aggregation
• Shall be able to differentiate particles from different stress types, the product, and container closure system.
• Shall combine computational statistics and neural network-based metric learning to characterize heterogeneous suspensions of subvisible particles (those <100 microns) in biologic and biosimilar drug products
• Shall be compatible with Flow Imaging and Backgrounded Membrane Imaging data with no prior requirement for image processing
• Shall combine computational statistics and neural network-based metric learning to characterize and predict potential root cause of particle formation in biosimilar drug products
• Shall provide quantitative data on the aggregate and particle population inherent in biopharmaceuticals as opposed to simple size and count method used to characterize particles in drug solutions.
• Shall employ statistical analysis tools such as Euclidian distance, similarity score based on the Kolmogorov-Smirnov test or superior statistical tool
• Shall be a trusted, acceptable model used by the biopharmaceutical industry
• Shall have demonstrable experience and prior publications in applying supervised and unsupervised machine learning approaches to classify visible and subvisible particle images in biologics
• Shall compensate for optical phenomenon at different length scales
• Shall allow visual examination of at least the twenty nearest images to any point selected on the Fingerprint.
• Training provided to DPQR staff on application of AI/ML for particle classification and interpretation of results from AI particle classification approaches for product quality analysis
The Government will award a contract resulting from this solicitation to the responsible quoter as a fixed‐price contract on the lowest price technically acceptable (LPTA) evaluation method. Award will be made on the basis of the lowest evaluated price meeting or exceeding the non‐cost factor (technical conformance to the requirements of the solicitation). The Quoter’s initial quotation shall contain the Quoter’s best terms from a price standpoint. Failure to demonstrate meeting any of the requirements will result in a rating of technically unacceptable and will not be considered for award.
The following factors shall be used to evaluate quotes:
• Total price.
• Technical features meeting/exceeding requirements specified.
For further details, please review the attached RFQ_FDA-75F40126Q00142 document.