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Biopharmaceuticals

Biopharmaceuticals

Beyond-Use Date (BUD) Prediction

Explainable estimation of the beyond-use date for biopharmaceuticals — proteins, monoclonal antibodies, peptides, or viral particles — based on the API’s molecular properties, formulation characteristics (buffer, pH, cryoprotectants, surfactants), packaging (glass, polymer, closure, light exposure), and environmental parameters (temperature, agitation, oxidative stress), leveraging published stability data.

What is this module for?

This module helps scientifically justify the beyond-use date (BUD) for biopharmaceutical preparations, optimize formulations (buffer selection, additives, cryoprotectants) with respect to protein stability, and support pharmacy decision-making with an exportable report (PDF/CSV) and an explainable analysis of degradation drivers.

Inputs

Required data include the API’s physicochemical characteristics (molecular weight, structure, isoelectric point, hydrophobicity), formulation parameters (pH, buffer, sugars, surfactants, amino acids, cryoprotectants), as well as packaging and environment (container material, closure system, light, temperature, agitation).

Outputs

The module provides the predicted beyond-use date (BUD) with an confidence interval, a ranking of influential factors (aggregation, oxidation, denaturation, adsorption), and an exportable report for traceability and scientific justification.

Worked examples

Simple case
Monoclonal antibody in aqueous solution with a standard formulation → direct BUD prediction.
Advanced case
Protein unstable without surfactant or under oxidative stress → estimation complemented by analysis of degradation causes.
Alternative packaging
Stability comparison between a glass vial and a polymer syringe (adsorption, light sensitivity).

Limits & precautions

The model’s applicability domain is restricted to protein- or virus-based formulations covered by published data. Under extreme conditions (mechanical stress, intense light, extreme pH), increased uncertainty may require additional experimental validation.

FAQ

Can the model predict if a parameter is missing?
The model can estimate stability from available variables, but missing a critical parameter (pH, surfactant, exact temperature) widens the confidence interval. Supplying comprehensive excipient and condition details improves robustness.
How should I interpret a wide confidence interval?
It reflects high variability in experimental data or missing information on critical parameters. Adjusting the formulation (pH, buffer, surfactant, container) or performing confirmatory experiments can reduce this uncertainty.
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