Bioequivalence evaluation of sparse sampling pharmacokinetics data using bootstrap resampling method.

Area of Science:

• Pharmacokinetics
• Drug Development
• Biostatistics

Background:

• Bioequivalence studies are crucial for generic drug approval, typically using crossover designs.
• Ophthalmic drug development presents challenges due to sparse, single-time point measurements.
• Traditional methods struggle with limited data points for calculating concentration-time profiles.

Purpose of the Study:

• To develop a novel nonparametric method for bioequivalence evaluation of generic ophthalmic products.
• To address the challenge of sparse, single-time point concentration data in ophthalmic studies.
• To enable the calculation of confidence intervals for AUC and Cmax ratios using bootstrap methods.

Main Methods:

• Developed a nonparametric bootstrap method for calculating 90% confidence intervals for AUC and Cmax ratios.
• Applied bootstrapping to subjects at each time point or all sampling time points with replacement.
• Extended the method to estimate standard errors for AUC and Cmax in parallel studies.

Main Results:

• The novel nonparametric bootstrap method successfully generates 90% confidence intervals for AUC and Cmax ratios.
• The method provides reliable estimates for standard errors of AUC and Cmax in sparse sampling scenarios.
• This approach is suitable for bioequivalence evaluation with limited, one-time point concentration data.

Conclusions:

• The proposed nonparametric bootstrap method is effective for bioequivalence assessment of generic ophthalmic drugs.
• It overcomes limitations of traditional methods when dealing with sparse pharmacokinetic data.
• This facilitates more accurate and reliable evaluation of generic ophthalmic product bioequivalence.