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Parenteral & ocular dosage forms

Parenteral and Ocular Dosage Forms

Prediction of Beyond-Use Date (BUD)

Explainable estimation of the beyond-use date for solutions and suspensions intended for parenteral or ocular administration, based on the molecular descriptors of the API, the formulation characteristics (pH, osmolarity, solvent, preservative), the packaging (glass, plastic, filter, syringe), and the environmental parameters (temperature, light, sterilization), using published stability data as the scientific basis.

What is this module for?

This module helps to scientifically justify the Beyond-Use Date (BUD) of parenteral and ocular preparations, to optimize formulation by considering sterilization, material compatibility, and chemical stability, and to document pharmaceutical decisions through an exportable report (PDF/CSV) including the ranking of influential factors.

Input Data

The parameters to be entered include the pharmaceutical characteristics and API concentration, the formulation parameters (pH, osmolarity, viscosity, preservative, solvent), as well as the packaging (syringe, vial, filter, material) and the environmental parameters (temperature, exposure, sterilization).

Outputs

The module provides a predicted beyond-use date (BUD) with a confidence interval, an analysis of influential factors (effect of pH, temperature, packaging, sterilization), as well as an exportable report for traceability and pharmaceutical justification.

Example Cases

Simple Case
Sterile aqueous solution with controlled pH and standard packaging → direct BUD prediction.
Advanced Case
Concentrated solution with extreme pH or excipients sensitive to sterilization → detailed analysis of destabilizing factors.
Alternative Packaging
Comparative study: vial vs prefilled syringe, with or without filter.

Limitations & Precautions

The applicability domain of the model is limited to sterile dosage forms covered by published data. Under extreme conditions (pH or osmolarity outside typical ranges, new materials), increased uncertainty may occur, requiring additional experimental validation.

FAQ

Can a prediction be made with a missing parameter?
In some cases, the model can estimate stability based on available data, but the absence of critical parameters (pH, packaging, sterilization) increases uncertainty. Providing as much information as possible is recommended for a robust prediction.
How should a wide confidence interval be interpreted?
It reflects significant variability in the reference data or heterogeneity in formulations (e.g., multicomponent solutions, viscous suspensions). In such cases, additional experimental validation may be warranted.
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