# Assessing Biosimilarity using Functional Metrics

## TL;DR

This paper introduces a functional distance-based method for assessing biosimilarity of biologics, considering the entire time course and offering increased sensitivity over traditional point-based tests.

## Contribution

The paper proposes a novel semi-parametric, functional distance approach for biosimilarity testing that improves sensitivity and incorporates bootstrap inference.

## Key findings

- More sensitive than classical equivalence tests
- Utilizes entire time course for assessment
- Includes bootstrap methods for inference

## Abstract

In recent years there have been a lot of interest to test for similarity between biological drug products, commonly known as biologics. Biologics are large and complex molecule drugs that are produced by living cells and hence these are sensitive to the environmental changes. In addition, biologics usually induce antibodies which raises the safety and efficacy issues. The manufacturing process is also much more complicated and often costlier than the small-molecule generic drugs. Because of these complexities and inherent variability of the biologics, the testing paradigm of the traditional generic drugs cannot be directly used to test for biosimilarity. Taking into account some of these concerns we propose a functional distance based methodology that takes into consideration the entire time course of the study and is based on a class of flexible semi-parametric models. The empirical results show that the proposed approach is more sensitive than the classical equivalence tests approach which are usually based on arbitrarily chosen time point. Bootstrap based methodologies are also presented for statistical inference.