Interchanging Biosimilars Using Proteomics

Abstract

The development and implementation of bioequivalence (BE) approaches can be considered one of the major innovations in clinical pharmacology.¹ Before the 1984 Waxman-Hatch Act (Drug Price Competition and Patent Term Restoration Act) created the Abbreviated New Drug Application as a regulatory approval path using BE, only 19% of drugs prescribed in the United States (US) were generic. This number has now increased to ~90%, saving society and the healthcare system trillions of dollars and making treatments available to patients across the world who could previously not afford them.¹

Because exact molecular copies cannot be made of a biological therapeutic, there is no such thing as a “generic biological.” Even if the amino acid sequence of a therapeutic protein is completely identical, there may be changes in the glycosylation pattern, differences in protein folding or oxidation or deamination, changes at the start or end of the protein (e.g., loss of terminal amino acids through enzymatic processes) or changes in the grade of aggregation. Therefore, demonstrating BE alone is not sufficient to claim biosimilarity. As a result, randomized, controlled non-inferiority or equivalence studies must generally be conducted to demonstrate the efficacy and safety of the biosimilar compared to the original, making the path to approval complex and costly.² The best we can aim to achieve is a “biosimilar”: a biological product that is highly similar to and has no clinically meaningful differences from an approved reference product.³

As a result, despite the significant shift in recent decades from small molecules drugs to biotherapeutics in drug development and clinical practice, most biologicals do not have an approved biosimilar. There is therefore a huge need to make biosimilar development and approval more efficient, streamlined, and cheaper. Achieving this through clinical pharmacology innovations will be a future milestone of major impacts of our discipline and elevate pharmaco-equity in global healthcare.

The importance of this topic was illustrated by the January 2023 issue of Clinical Pharmacology & Therapeutics (CPT), which was entirely dedicated to the theme of “Advancing Innovations in Biosimilars.”³ The paper by Chekka and colleagues from the US Food and Drug Administration (FDA) in the current issue (Figure 1) reports the latest innovation in regulatory science and how proteomic pharmacodynamic (PD) biomarkers can be used in biosimilarity assessment to reduce the need for comparative clinical efficacy studies.⁴ The authors analyzed candidate proteins from longitudinal plasma samples from healthy subjects who received interferon (IFN)β-1a or pegylated (peg)IFNβ-1a. Several hundred candidates were differentially expressed compared to the placebo and nine were prioritized as potential PD biomarkers common to both biologics. Subsequently, the identified biomarkers were further evaluated using FDA's criteria for desirable characteristics for a PD biomarker for biosimilarity assessment, such as a true dose–response relationship, relevance to mechanism of action, return to baseline post-treatment, sensitivity, and low variability. This finally resulted in the selection of three candidates (Lymphocyte activation gene 3 protein [LAG3], granulins [GRN], and CXC motif chemokine 11 [I-TAC]) that may have the greatest potential as PD biomarker for INFβ-1a biosimilars. It should be emphasized that not only is the reproducibility of the results of great importance, but upstream pathway analyses should also be performed to demonstrate the causal relationship between the respective biological drug and the identified biomarkers. This was the case for all three candidates mentioned above.

Uncertainty in regulatory acceptance has been a main barrier in the utilization of PD biomarkers for biosimilar development.⁵ The study by Chekka et al. is the first study that provides proof-of-concept that proteomics can be used to identify and develop PD biomarkers to demonstrate biosimilarity and provides a framework for how this can be applied across biosimilar development and approval.

No funding was received for this work.

The author declared no competing interests for this work.