A Simulation Approach to Evaluate the Impact of Patterns of Bioanalytical Bias Difference on the Outcome of Pharmacokinetic Similarity Studies

Pharmacokinetic (PK) similarity studies are vital to assess the biosimilarity of a biosimilar to a reference product. Systematic bias in a bioanalytical method that quantify products could be a potential source of error affecting the variability of the data and influencing the outcome of a PK similarity study. We investigated the impact of six varying patterns of bioanalytical bias difference (biasdiff ) between the similar products on the probability passing the PK similarity test. A population PK model was used to simulate concentration-time profiles for a biosimilar and a reference product and added biasdiff ranging from 030%. The probability of achieving the PK similarity criteria (90% confidence interval between 0.8 and 1.25) for the maximum serum concentration (Cmax ) and area under the curve (AUC) was assessed. The data indicate that an increase in absolute biasdiff between products of ≥ 10% would decrease the power to assess the similarity criteria for Cmax and AUC.