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Light induced fluorescence for predicting API content in tablets: sampling and error.

Reuben Domike¹, Samuel Ngai, Charles L Cooney

¹Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. rdomike@upei.ca

International Journal of Pharmaceutics

|February 17, 2010

Summary

This summary is machine-generated.

A novel light-induced fluorescence (LIF) instrument accurately estimates active pharmaceutical ingredient content in tablets via surface sampling. This method shows high accuracy, with potential to streamline calibration and validation for content uniformity analysis.

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Area of Science:

Pharmaceutical analysis
Analytical chemistry
Spectroscopy

Background:

Accurate determination of active pharmaceutical ingredient (API) content is crucial for tablet quality control.
Traditional methods for content uniformity testing can be time-consuming and destructive.
Non-destructive techniques offer potential advantages in efficiency and sample preservation.

Purpose of the Study:

To demonstrate the efficacy of a light-induced fluorescence (LIF) instrument for estimating total API content in tablets using surface sampling.
To compare different LIF sampling strategies against a standard total tablet ultraviolet (UV) absorbance test.
To develop and validate a theoretical error model for LIF surface sampling.

Main Methods:

Utilized a light-induced fluorescence (LIF) instrument for surface sampling of tablets.
Compared LIF results with total tablet UV absorbance measurements for tablets containing triamterene and caffeine.
Developed a theoretical error model based on empirical surface deviation parameters.

Main Results:

The LIF instrument accurately estimated API content within 10% of the total tablet test over 95% of the time.
The maximum error observed was 13%, with Root Mean Square Error of Prediction (RMSEP) ranging from 4.4% to 7.9%.
The developed error theory accurately predicted experimental errors.

Conclusions:

Light-induced fluorescence (LIF) surface sampling is a viable and accurate method for estimating total active pharmaceutical ingredient content in tablets.
The developed error prediction theory can expedite calibration and validation of non-destructive surface measurement techniques.
This approach may accelerate the selection of appropriate analytical techniques for tablet content uniformity assessment.