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Liquid oral dosage forms

Oral Liquid Dosage Forms

Prediction of Beyond-Use Date (BUD)

Explainable estimation of the beyond-use date for oral solutions and suspensions based on the molecular descriptors of the API, the formulation characteristics (e.g. solvents, preservatives, viscosity), the environmental parameters (temperature, light/oxidation), and the packaging, using published stability data as a scientific foundation.

What is this module for?

This module helps to scientifically justify the Beyond-Use Date (BUD) of oral liquid preparations, to optimize formulation (choice of solvent, preservative, pH or viscosity adjustment) by quantifying their effects, and to document the decision-making process through an exportable report (PDF/CSV) including the ranking of influential factors.

Input Data

The required input variables include the pharmaceutical characteristics and API content (% API), the key excipients and formulation parameters (pH, solvent, preservative, viscosity), as well as the packaging (material, color) and environmental parameters (temperature, exposure to light).

Outputs

The module provides a predicted beyond-use date (BUD) with a confidence interval, a ranking of influential factors (pH, solvent/preservative, packaging, temperature, etc.), and an exportable report to support traceability and pharmaceutical justification.

Example Cases

Simple Case
Aqueous solution with controlled pH and appropriate packaging → direct BUD prediction.
Advanced Case
Viscous suspension with critical pH or specific solvent → detailed analysis of influential factors.
Alternative Packaging
Evaluation of the impact of a change in material/color/closure on the beyond-use date.

Limitations & Precautions

The applicability domain of the model depends on the available stability data for oral liquids. Under extreme conditions (pH, oxidation, or viscosity outside typical ranges), higher uncertainty may occur, requiring experimental confirmation.

FAQ

Can a prediction be made with a missing parameter?
In some cases, yes. The model can estimate the beyond-use date from the available variables; however, the uncertainty (confidence interval) increases when key data such as pH, preservative, viscosity, packaging, or temperature are missing.
How should a wide confidence interval be interpreted?
A wide interval reflects greater variability in the data or incomplete information (e.g., excipients, viscosity, pH, packaging). In such cases, refining the input data or confirming with targeted stability studies is recommended.
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