PDA Biosimilars Workshop Report (September 27—28, 2018)—Getting It Right the First Time for Biosimilar Marketing Applications
Bibliographic record
Abstract
This workshop report summarizes the presentations, the breakout session outcomes, and the speaker panel discussions from the PDA Biosimilars Workshop held September 27–28, 2018, in Washington, DC. This format was deliberately selected for the workshop with the expectation of delivering a post-workshop paper on current best practices and existing challenges for sponsors. The event, co-chaired by Dr. Stephan Krause (AstraZeneca Biologics) and Dr. Emanuela Lacana (CDER/FDA), was attended by 140 agency and industry representatives. The workshop was separated into three major sessions P1: Regulatory Perspective, P2: Challenges in Biosimilar Development, and P3: Demonstrating Analytical Similarity. Each of the three sessions started with agency and industry presentations. Participants then split into two concurrent roundtable discussion groups to hear the answers to questions that had been provided to all participants one week prior to the event. The sessions were recorded. This paper provides consolidated answers to specific case studies for current challenges to sponsors and agencies. In addition, the panel discussion notes following each breakout roundtable session, as well as brief talk summaries of all speakers, are provided. The first session explored the challenges encountered with submission of biosimilar marketing applications from the perspectives of regulatory agencies. Expectations for a successful submission of the chemistry, manufacturing, and controls (CMC) information were described. The second session addressed high-level technical challenges and how to avoid pitfalls frequently encountered during biosimilar candidate development, including data quality expectations, creation of the final control strategy, and strategic choices necessary for candidate selection and development. Both regulatory perspectives and industry experience were shared. The last session explored the use of statistical tools to provide meaningful contributions to the demonstration of analytical similarity. The presentations highlighted common issues and practical challenges that arise during the application of statistical tools. LAY ABSTRACT: Significant challenges are still-remaining for sponsors and agencies to successfully develop and license Biosimilars. A Biosimilars Workshop was therefore held on 27-28 September 2018 in Washington, DC, to find practical solutions to the remaining challenges. The workshop planning committee with members from industry and agencies prepared specific case studies focused on some of most difficult situations. The workshop was separated into three major sessions (P1 - Regulatory Perspective; P2 - Challenges in Biosimilar Development; P3 - Demonstrating Analytical Similarity) and each session attempted to provide practical solutions to the relevant case studies. This first session explored the challenges encountered with submission of biosimilar marketing applications from the regulatory agencies9 perspectives. Expectations for a successful submission of the CMC information were described. The second session addressed high-level technical challenges frequently encountered during biosimilar candidate development, including data quality expectations, the creation of the final control strategy, and strategic choices necessary for candidate selection and development. The last session explored the use of statistical tools to provide meaningful contributions to the demonstration of analytical similarity and practical challenges that arise during the application of statistical tools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.092 | 0.064 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".