A Simulation Approach to Evaluate the Impact of Patterns of Bioanalytical Bias Difference on the Outcome of Pharmacokinetic Similarity Studies
| Autor: | Obinna N. Obianom, Theingi M. Thway, Olanrewaju O. Okusanya, Justin C. Earp |
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| Rok vydání: | 2020 |
| Předmět: |
Pharmacology
education.field_of_study Bioanalysis Population Cmax Area under the curve Models Biological 030226 pharmacology & pharmacy Outcome (probability) Confidence interval 03 medical and health sciences 0302 clinical medicine Bias Therapeutic Equivalency Pharmacokinetics Similarity (network science) Area Under Curve 030220 oncology & carcinogenesis Statistics Humans Computer Simulation Pharmacology (medical) education Biosimilar Pharmaceuticals Mathematics |
| Zdroj: | Clinical Pharmacology & Therapeutics. 108:107-115 |
| ISSN: | 1532-6535 0009-9236 |
| DOI: | 10.1002/cpt.1767 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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