Surreal illustration of pills forming a sample size bell curve with DNA strands, symbolizing bioequivalence in pharmaceutical research.

Decoding Bioequivalence: How Sample Size Impacts Generic Drug Studies

Avery Sinclair in Health & Wellness November 2025 • 5 min read.

"Navigating the complexities of generic drug approval: Understanding intra-subject variation and sample size optimization for reliable bioequivalence studies."

When a pharmaceutical company wants to release a generic version of a brand-name drug, it needs to prove to regulatory agencies that its version is bioequivalent, meaning it performs in the same way in the human body. Bioequivalence studies are performed using a crossover design, information on the intra-subject coefficient of variation (intra-CV) for pharmacokinetic measures is needed when determining the sample size. One of the most important aspects of these studies is determining the appropriate sample size. This ensures that the study has enough statistical power to detect any real differences between the generic and brand-name drugs.

The intra-subject coefficient of variation (intra-CV) plays a vital role in the determination of sample size. The intra-CV reflects the variability within a single subject's response to a drug. This variability can arise from various factors, including differences in metabolism, absorption, and elimination. Therefore, accurately estimating intra-CV is crucial for calculating the appropriate sample size.

According to a study published in Translational Clinical Pharmacology, calculated intra-CVs based on bioequivalence results of identical generic drugs produce different estimates. To address this challenge, researchers analyzed data from numerous bioequivalence studies to better understand the variability of intra-CVs and their impact on sample size calculations. The data was collected from public resources from the Ministry of Food and Drug Safety (MFDS) and calculated the intra-CVs of various generics.

Why Sample Size Matters in Bioequivalence Studies

Surreal illustration of pills forming a sample size bell curve with DNA strands, symbolizing bioequivalence in pharmaceutical research.

Statistically, power represents the probability the null hypothesis will be rejected when the alternative hypothesis is true. In bioequivalence studies, the null hypothesis posits that the substances are bioinequivalent. Therefore, the power of a bioequivalence study is the probability of proving bioequivalence when the products are in fact bioequivalent. Because finding the optimal sample size ensures adequate power, the sample size calculation is one of the most important steps in designing a bioequivalence study.

Choosing the right sample size is a balancing act. A sample size that is too large can unnecessarily increase the cost of the study and expose an excessive number of participants to the drug. On the other hand, a sample size that is too small increases the risk of a type II error, which means failing to detect a real difference between the drugs. This can result in the rejection of a bioequivalent generic drug.

Regulatory Guidelines: According to statistical guidelines from the U.S. FDA and EMA, a power of 80% or 90% is generally recommended for bioequivalence studies.
Impact of Intra-CV: Determining the sample size requires considering the intra-subject coefficient of variation (intra-CV) of pharmacokinetic measures. However, intra-CVs can vary considerably among studies of identical generics.
Variability in Practice: For example, the reported intra-CVs of metformin's maximum concentration (Cmax) were 12.1% and 24.8% in two different bioequivalence studies. This highlights that relying on a single bioequivalence result may not provide sufficient power for planning a trial.

The Ministry of Food and Drug Safety (MFDS) of Korea has been releasing bioequivalence study results to the public since January 2014. These data include information for power and sample size calculations in bioequivalence studies, such as 90% confidence intervals for the area under the concentration-time curve (AUC) and Cmax, as well as sample sizes. However, these data also demonstrate considerable variability in sample sizes for bioequivalence studies on the same generic drugs.

Implications for Future Research

The insights from this study emphasize the importance of carefully considering intra-subject variability when designing bioequivalence studies. By using pooled intra-CVs from multiple studies, researchers can obtain more reliable estimates for sample size calculations, ultimately leading to more robust and conclusive results. These findings can help ensure that generic drugs are properly evaluated and that patients have access to safe and effective medications.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.12793/tcp.2018.26.1.6, Alternate LINK

Title: A Post Hoc Analysis Of Intra-Subject Coefficients Of Variation In Pharmacokinetic Measures To Calculate Optimal Sample Sizes For Bioequivalence Studies

Subject: Pharmacology (medical)

Journal: Translational and Clinical Pharmacology

Publisher: Korean Society for Clinical Pharmacology and Therapeutics

Authors: Inbum Chung, Jaeseong Oh, Seunghwan Lee, In-Jin Jang, Youngjo Lee, Jae-Yong Chung

Published: 2018-01-01

Everything You Need To Know

Why is determining the right sample size so critical in bioequivalence studies?

In bioequivalence studies, sample size is crucial because it directly impacts the study's statistical power. Adequate power ensures that if the generic and brand-name drugs are indeed bioequivalent, the study is likely to demonstrate this. An insufficient sample size increases the risk of failing to detect the bioequivalence, potentially leading to the rejection of a safe and effective generic drug. Conversely, an excessively large sample size can unnecessarily increase costs and expose more participants to the drug. Therefore, optimizing sample size is a balancing act to achieve reliable and ethical study outcomes.

What is the 'intra-subject coefficient of variation (intra-CV),' and why is it so important when designing bioequivalence studies?

The intra-subject coefficient of variation (intra-CV) reflects the variability in how a single person responds to a drug over time. This variability arises from differences in metabolism, absorption, and elimination processes within an individual. Accurately estimating the intra-CV is crucial because it directly influences the sample size calculation in bioequivalence studies. Higher intra-CV values indicate greater variability, necessitating larger sample sizes to achieve adequate statistical power. Failing to account for intra-subject variability can lead to underpowered studies and unreliable conclusions about bioequivalence.

What power do regulatory guidelines from the U.S. FDA and EMA generally recommend for bioequivalence studies, and what does this mean?

Regulatory bodies like the U.S. FDA and EMA provide statistical guidelines recommending a power of 80% or 90% for bioequivalence studies. This means that there should be an 80% or 90% chance of correctly concluding bioequivalence if the generic and brand-name drugs are truly equivalent. These guidelines ensure that bioequivalence studies have sufficient statistical rigor to protect public health by ensuring that generic drugs are as safe and effective as their brand-name counterparts. Adhering to these guidelines is essential for obtaining regulatory approval for generic drugs.

What kind of bioequivalence study data does the Ministry of Food and Drug Safety (MFDS) of Korea release, and how can this information be useful?

The Ministry of Food and Drug Safety (MFDS) in Korea releases data from bioequivalence studies, including information on 90% confidence intervals for AUC (area under the concentration-time curve) and Cmax (maximum concentration), as well as sample sizes. These data are valuable because they provide researchers with real-world examples of variability in bioequivalence studies. Analyzing this data helps researchers better understand the range of intra-CVs and their impact on sample size calculations. However, the data also reveal considerable variability in sample sizes for studies on the same generic drugs, emphasizing the need for careful consideration of intra-subject variability.

How can using pooled intra-CVs from multiple studies improve the reliability of bioequivalence studies, and what are the implications of this approach?

Using pooled intra-CVs from multiple bioequivalence studies leads to more reliable estimates for sample size calculations. Single-study intra-CVs can be highly variable, potentially resulting in underpowered or overpowered studies. By pooling data from multiple studies, researchers can obtain a more stable and representative estimate of intra-subject variability. This approach enhances the robustness and conclusiveness of bioequivalence studies, ultimately contributing to the assurance that generic drugs are properly evaluated and that patients receive safe and effective medications. This approach acknowledges the inherent uncertainties and variations in drug responses, providing a more evidence-based foundation for regulatory decisions and clinical practice.