Practical challenges in the CMC development of biosimilars. Simon Hotchin Executive Director Regulatory Affairs Amgen Inc.

Transcription

1 Practical challenges in the CMC development of biosimilars Simon Hotchin Executive Director Regulatory Affairs Amgen Inc.

2 Overview Introduction Statistical methodologies in the assessment of analytical similarity Selection of analytical methods for similarity assessment Reference product bridging assessments Discussion points 12 Nov

3 Introduction

4 Analytical similarity remains the cornerstone of biosimilar development Clinical Clinical Pharmacology Nonclinical Analytical Analytical data form the cornerstone of the similarity assessment Products must be highly similar to be eligible for approval as biosimilars Increasing the extent and discriminatory power of the analytical assessment may permit a more targeted and selective approach to nonclinical and clinical studies. 12 Nov

5 Approval requires a careful assessment of the totality of evidence Biosimilar or Biosimilarity means: that the biological product is highly similar to the reference product notwithstanding minor differences in clinically inactive components; and there are no clinically meaningful differences between the biological product and the reference product in terms of the safety, purity, and potency of the product. Analytical and Functional Data Clinical Data 12 Nov

6 The definition of biosimilarity poses some practical questions How similar is similar and how can a level playing field be assured? How does analytical method capability impact the assessment of similarity, and what methods should be used? How to conduct efficient global biosimilar development when the approval pathway has an inherently national aspect? 12 Nov

7 Analytical similarity standards: statistical methodologies

8 FDA has recommended a tiered attribute assessment for demonstrating similarity Tier 1 is based on a test of equivalence Analytically similar if 90% confidence interval of the true mean diff. is within equivalence margins (δ1, δ2) The equivalence margin is 1.5sR where sr is the true standard deviation of the RP 90% CI Fail Yi Tsong, FDA, DIA/FDA statistics Forum Nov (δ 1 δ 2 ) Fail Tier 2 is based on majority of results falling within a quality range of +/- X SD

9 There are potentially some practical challenges with the proposed approach Existing knowledge of attribute criticality may not be sufficient to clearly delineate the appropriate testing tier Reliable estimates of the true mean and variance of the reference product can be difficult to establish during development, and both can change over the development time frame A statistical approach based on data from to-becommercial lots may not be the most effective means to assure a robust process to deliver similar product 12 Nov

10 The appropriate tier assignment for an attribute may be less than obvious Reproduced from: Jiang et al. Advances in the assessment and control of the effector functions of therapeutic antibodies. Nature Reviews Drug Discovery. February Product labels and literature may include information suggesting a role for e.g. effector function in the MoA of a product However, understanding regarding the quantitative relationship between attributes and safety/efficacy in patients is typically incomplete Scientific judgment on tiering assignment must be made based on the available evidence and application of risk management principles Differing conclusions may be reached FDA vs Sponsor Sponsor vs Sponsor 12 Nov

11 Estimating the true mean of the reference product can be challenging Cell Line and Process Development Nonclinical Clinical Submission and Approval Post Approval Reference product lifecycle Simulated data for illustrative purposes Cell Line and Process Development Nonclin and Clinical Dev. Submission and Approval Biosimilar product lifecycle 12 Nov

12 Identifying Tier 1 attributes early is critical to demonstration of similarity Early cell line and process development focused on matching mean for attribute Similarity testing protocol captured sufficient results for attribute Probability to pass Tier 1 Adequate power Means match Some results slightly outside RP range 12 Nov Early cell line and process development targeted matching RP range RP variability greater than anticipated Similarity testing protocol assumed reduced testing sufficient Probability to pass Tier 1 Inadequate power Means do not match All results inside RP range Simulated data for illustrative purposes

13 Actual shifts in the reference product may also be apparent Pre-Approval Post-Approval Reference product lifecycle Simulated data for illustrative purposes Cell Line and Process Development Nonclin and Clinical Dev. Submission and Approval Biosimilar product lifecycle 12 Nov

14 Data from at-scale lots may not be the sole means to establish similarity The Tier 1 statistical approach rewards increasing sample size Minimum of 6 lots recommended for equivalence testing 10 or more required to achieve power levels that sponsors may deem appropriate when multiple attributes are included in Tier 1 May drive sponsors to manufacture additional lots for analytical similarity purposes Is this form of extended process validation the most appropriate means to ensure similarity of future lots? Could data from a validated small scale model and/or generated by mathematical process modelling be included in the statistical analysis? Should data from at-scale production be considered as confirmatory evidence? 12 Nov

15 Analytical similarity methodology: selection of analytical procedures

16 Comprehensive analytical similarity assessment reduces the degree of uncertainty Integrity of the secondary, tertiary, and quaternary structure Degradation profiles denoting stability Attributes related to the amino acid sequence and all post-translational modifications, including glycans Properties of the finished drug product, including strength and formulation Higher order structure General properties and excipients Primary structure Product-related substances and impurities Stability Processrelated impurities Biological function Particles and aggregates Biological and functional activities, including receptor binding and immunochemical properties Impurities from host cells and downstream process Quantitative levels of product variants and their identities Subvisible and submicron particles and aggregates of various sizes 12 Nov

17 The choice and performance of analytical tools matters Reliability - Relevance - Resolution - Methods are qualified and fit-for-use for intended purposes Methods provide meaningful information and may predict clinical performance Methods are sensitive and capable of resolving differences which are critical attributes Slide from Kozlowski, S (CDER) presentation at 2014 Biomanufacturing Technology Summit, Rockville, MD, June 13, Nov

18 Relevance A1G0F A2G0 M5 A1G1F A2G1F(a) A2G1F(b) M6 A2G2F M7 A2G0F Different methods provide differing levels of information relative to criticality and similarity Confidence in similarity Glycan map Critical glycan attribute min Glycan Map profile Monosaccharide analysis Monosaccharide analysis Reliability 12 Nov

19 A1G0F A2G0 M5 A1G1F A2G1F(a) A2G1F(b) M6 A2G2F M7 Relevance A2G0F Knowing the correlation of orthogonal methods helps develop a robust control strategy Orthogonal assays with increasing biological relevance to discern clinically meaningful differences and confirm functional similarity ADCC vs. Afucosylated glycan Confidence in manufacturing PBMC ADCC NK92 ADCC Chung, mabs, , May 2012 FcgRIIIa binding High resolution and reliable methods suited for process control can guide the process and product development. Glycan map 30.0 Response (LU)) min Nov Reliability

20 Analytical similarity requirements: reference product considerations

21 Background Biosimilar regulations continue to roll-out & evolve around the world The majority of the regulations necessarily require demonstration of similarity to a local reference product approved on that market Use of analytical, nonclinical and clinical data generated with a foreign reference product can be negotiated, by providing evidence to demonstrate the equivalence of the local and foreign reference products. The level of evidence required for bridging varies. 12 Nov

22 Why bridging? Is a foreign comparator truly representative of the local reference product with respect to quality, safety and efficacy? Some regulators may require analytical (± PK) bridging to a locally procured reference product Possible factors causing geographic divergence in originator product quality attributes Supply chain partitioning between geographies (eg, local manufacturing) Licensing arrangements without shared manufacturing Asynchronous implementation of major process variations Hypothetical concerns? Consider epoetin alfa, supplied by 3 distinct companies using independent DS and DP sources. Only one originator product was associated with a specific quality related safety issue (eg, PRCA cluster in Europe, ). Casadevall et al. NEJM 346: (2002). 12 Nov

23 Problem Statement: How to go from this Map of comparisons made between product lots (originator to biosimilar or originator sourced in EU to originator sourced in US) Pivotal Trials Analytical Sim Bridging Analytical CORE EU US Bridging PK Nonclinical Analytical Sim X Originator product lots sourced from country X 12 Nov

24 To this CAN AUS JP????? KR Pivotal Trials Analytical Sim Bridging Analytical CORE CN EU US RUS? Bridging PK Nonclinical Analytical Sim? RSA??? MEX BR TUR X Originator product lots sourced from country X 12 Nov

25 Various bridging options are possible. Some may represent a higher challenge. No additional justification Paper based justification Minimal similarity risks and costs Lacking other evidence of a harmonized originator product supply would these approaches Minimal protect similarity patients from risks geographic and costs divergence? Limited analytical bridge Similarity risks and costs Comprehensive analytical bridge Analytical plus BE Stand-alone development Similarity risks and costs Significant similarity risks and costs High similarity risks and costs 12 Nov

26 Conclusions High standards will help to build confidence and drive biosimilar adoption. Statistics can play a role in assessment of similarity, but approaches based on comparisons of means may be practically challenging to implement. Ensuring early transparency to the identity of the highest risk attributes for a given molecule would increase predictability in biosimilar development. The application of methods that have been demonstrated to be capable of discriminating differences in critical attributes will increase confidence in similarity. Flexibility in the application of regional bridging requirements will ensure global access to high quality biosimilar medicines 12 Nov

27 Acknowledgements Jennifer Liu Margaret Karow Rick Burdick Gino Grampp Richard Markus 12 Nov