Evaluation of in vivo drug release by numerical deconvolution using oral solution data as weighting function

Area of Science:

• Pharmacokinetics
• Drug Delivery Systems
• Computational Modeling

Background:

• Compartmental model analysis for in vivo drug release determination faces challenges like flip-flop phenomena.
• Accurate estimation of in vivo drug release is crucial for optimizing dosage forms.

Purpose of the Study:

• To evaluate the utility of numerical deconvolution for estimating in vivo drug release.
• To compare the performance of numerical deconvolution with simulated data from solid dosage forms versus solutions.

Main Methods:

• Simulated concentration-time data were generated using a linear two-compartment body model.
• First-order release and absorption rate constants were varied, with 5% and 10% random errors added.
• Numerical deconvolution was applied to error-free and error-containing data sets.

Main Results:

• Numerical deconvolution showed excellent agreement with theoretical values for error-free data.
• Added random errors led to significant fluctuations in the in vivo release profile, precluding single rate constant assignment.
• The method accurately reflects inherent data error rather than introducing additional computational error.

Conclusions:

• Numerical deconvolution is a viable method for in vivo drug release estimation when high-quality data is available.
• The accuracy of numerical deconvolution is highly dependent on the quality of the input concentration-time data.
• Further investigation into smoothing or fitting techniques may be necessary for noisy data to obtain meaningful drug release profiles.