Chair: Fairouz Makhlouf, PhD (FDA)
Abstract: With the passage of the Hatch -Waxman Act in 1984, generic drugs have helped to reduce the time and cost of development of therapeutic products without compromising safety and effectiveness. In 2021, generic drug products accounted for 91% of the prescriptions, but only accounted for 18% of prescription drug expenses, saving the U.S. government USD 373 million on drugs, according to Association for Accessible Medicines (AAM) Generic Drug & Biosimilars Access & Savings Report 2021. Likewise, the Biologics Price Competition and Innovation Act (BPCI Act) of 2009, has created an abbreviated licensure pathway for biosimilar products that are demonstrated to be not clinically different from an FDA-approved biological product. The market share of biosimilar products has spiked in recent years, following the same trajectory of generic drugs.
However, in contrast to the fast rising of the market share of the generic and biosimilar drugs, statistical research in bioequivalence still lags that of the new drugs. How to advance statistical methodology in biosimilar and bioequivalence studies, is a pressing task for regulators and sponsors.
In this session, we will discuss the recent advances in statistical methodology in the development and evaluation of generic and biosimilar drugs.
The first presentation is from Dr Ramen Arani and Dr. Ethan Cao of Sandoz. They proposed a variation of matching method to utilize real world data (RWD) in the comparative clinical endpoint biosimilar study. Utilization of RWD/RWE can provide an important opportunity to increase access to biologic therapies, reducing cost by repurposing existing data.
In the second talk, Dr. Shein-Chung Chow and his student Weijia Mai will discuss a hybrid trial design in a biosimilar study utilizing an innovative two-stage adaptive trial design by combining a PK study and a clinical study. This novel design can avoid possible duplicated effort in demonstration of biosimilarity, reduce the development cost and hopefully increase the probability of success in biosimilar drug development.
In the third talk, Dr. Wanjie Sun from FDA will discuss the application of Bayesian Dynamic borrowing approaches in leveraging the fasting PK BE study data for the bioequivalence assessment in a fed PK BE study, which can potentially reduce the sample size if the treatment effect of the fasting and fed studies are congruent.
Lastly, Dr Stella Grosser from FDA will comment on the three presentations from the industry, academia, and regulatory agency, from both the regulatory and statistical perspectives. Speakers and the discussant will discuss the application of cutting-edge statistical methodology in biosimilar and bioequivalence studies to help promoting low-cost and highly effective generic and biosimilar drugs to the public.
Speakers:
Ramen Arani, PhD (Sandoz)
Ethan Cao, PhD (Sandoz)
Title: Utility of Real-World Evidence in Biosimilar Development
Abstract: Biosimilar development refers to the process of creating a biologic drug that is similar to an existing approved biologic drug, also known as a reference drug. Due to the complex nature of biologics drugs and the inherent variability in their manufacturing process biosimilars are not identical but highly similar to the reference drug in terms of quality, safety, and efficacy. Efficacy and safety trials for biosimilars involve large numbers of patients to confirm comparable clinical performance of the biosimilar and the reference product in appropriately sensitive clinical indications and for appropriate sensitive endpoints. The objective of a biosimilar clinical data is to address slight differences observed at previous steps and to confirm comparable clinical performance of the biosimilar and the reference product. In recent years with advances in big data computing, there has been increasing interest to incorporate the totality of information from different data sources (e.g. Real World data and published literature) in design and conduct of clinical trial to support regulatory objectives. The biosimilar development is an ideal framework for utilization of Real-World Evidence in design of trials as potentially large amount of data are available for the reference dug. Hence there may be an opportunity to use RWD in establishing, improving or validating equivalence margins (EQM) for biosimilar designs, specifically in the case there is no historical published data in the intended sensitive population. We proposed a variation of matching method that seems promising to identify the matched set from a real-world data for which the effect size of targeted endpoint would be comparable to historical data. We believe this is a reasonable approach because in design stage, we can view covariates and secondary endpoints as data feature that can be used in a matching method. This approach was illustrated through a case study which indicated the estimate of the primary endpoint is within 1% of published results and thus RWD may be used to justify or estimate the equivalence margin. To ensure consistent results we recommend using this approach in different indications and endpoint scenarios. Thus utilization of RWD/RWE can provide an important opportunity to increase access to biologic therapies, reducing cost by repurposing existing data.
Speakers:
Shein-Chung Chow, PhD (Duke)
Weijia Mai, PhD (Duke)
Title: Hybrid Trial Design in Biosimilar Product Development
Abstract: For approval of biosimilar drug products, the United States (US) Food and Drug Administration (FDA) recommends a stepwise approach for obtaining the totality-of-the evidence for demonstration of the biosimilarity between a proposed biosimilar product and its reference (innovative) product. The stepwise approach consists of the assessment of analytical similarity, pharmacokinetics (PK) similarity or pharmacodynamics (PD) similarity, and clinical similarity, which may be conducted either sequentially or at the same time. This stepwise approach, however, has been criticised of being not efficient in the regulatory review and approval process. In this article, under the Fundamental Biosimilarity Assumption that PK similarity is predictive of clinical similarity, a biosimilar study utilizing an innovative two-stage adaptive trial design by combining a PK study and a clinical study is proposed. The proposed two-stage PK-clinical adaptive trial is efficient in the sense that (i) it allows critical decision-making at end of the first stage, (ii) it can avoid possible duplicated effort in demonstration of biosimilarity, (iii) it will shorten (or expedite) the development process, and most importantly (iv) it may reduce the development cost and may hopefully increase the probability of success in biosimilar drug development. In addition, under the proposed innovative two-stage PK-clinical adaptive trial design, a biosimilarity index based on the concept of reproducibility probability was derived for demonstration of biosimilarity. The biosimilarity index can also be applied for assessment of drug interchangeability under FDA’s recommended switching designs.
Speaker: Wanjie Sun, PhD (FDA)
Title: Leveraging the Fast Study in the Bioequivalence Assessment of the Fed Pharmacokinetic Study using Bayesian Dynamic Borrowing Approaches
Abstract: Traditionally, the FDA has recommended conducting pivotal pharmacokinetic (PK) bioequivalence (BE) studies under both fasting and fed conditions. However, the International Council for Harmonization (ICH) M13 (2024) recently recommended that for low-risk products, BE may be demonstrated in a single study conducted under fasting conditions. For products with higher risk of bio-inequivalence due to food effects, both fasting and fed PK BE studies remain recommended. Nevertheless, if a smaller scale fed PK BE study can be conducted with sufficient scientific justification, it will alleviate the burden on generic drug applicants. Bayesian borrowing approaches offer a promising solution to reduce the sample size and overall sponsor burden by leveraging data from the fasting study in the subsequent fed study. In this paper, we evaluate several Bayesian borrowing methods, including the power prior (Ibrahim et al., 2015), calibrated power prior (Pan et al., 2017), mixture prior (reference), and self-adapting mixture prior (Yang et al., 2023). These methods are applied to the fasting/fed study framework, and their operating characteristics are compared to the standard frequentist two one-sided test (TOST) approach, Bayesian approach without borrowing, and group sequential design. The paper concludes with recommendations based on the comparative analysis of these methods, providing insights into their potential for improving the efficiency of a fed PK BE study.
Discussant: Stella Grosser, PhD (FDA)
