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site=aa6f9fcc-8c60-4e6d-90ca-8c73a12c9f03 "Untitled design (10)")
Aniek Sies \; Principal Scientist at Johnson &\; J ohnson
Aniek Si es is a Principal Scientist in Manufacturing Science and Technology (MSAT) in Johnson &\; Johnson\, working on the application of statistical met hods to manufacturing risk assessment and regulatory decision-making. She holds a PhD with a focus on statistical methodology for estimating optimal treatment regimes.
A statistical risk-based framew
ork for sunsetting one-sided release tests: from limited data to regulator
y approval
Manufacturing control strategy changes su ch as removing routine release tests (&ldquo\;sunsetting&rdquo\;) require a defensible quantitative assessment of residual quality risk. While this may appear straightforward\, it is statistically challenging due to the ir reversible nature of the decision and the frequent reliance on limited his torical batch data that are often heavily left censored.
I present a pragmatic statistical framework designed to quantify the probability of m issing future out-of-specification batches. It integrates well-established statistical tools\, including process capability analysis with confidence bounds to account for finite sample uncertainty\, predictive probability statements\, and Bayesian posterior predictive modelling.
The framew ork is illustrated using an established drug product for which sunsetting of release testing was approved by both FDA and EMA. The discussion subseq uently focuses on early lifecycle scenarios with limited and highly censor ed data. Throughout\, the emphasis is on statistical judgment: making assu mptions explicit\, acknowledging identifiability limits\, \;and supporting transparent a nd defensible decision-making when data are far from ideal.
84d7ccff3ad665b3a176ff00001f6b
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-4e6d-90ca-8c73a12c9f03 "Un
titled design (11)")
S wetlana Berger Associate Director Biostatistics of UCB
At U CB\, I support biomarker and immunogenicity (ADA) assay development and va lidation from a statistical perspective. Working side by side with Bioanal ytical Sciences and other cross-functional stakeholders\, I help define ob jectives and acceptance criteria\, develop robust analysis approaches\, an d communicate results in a clear\, decision-oriented way.
Applying DoE to Bioassays: Faster Optimization\, Relevant Perform ance\, Better Decisions
Bioassays are a key analytical tech nology from basic research to drug discovery in the biopharmaceutical indu stry. Because these assays are complex and often involve multiple handling steps\, optimization should be guided by the intended use (fit-for-purpos e assay) and focused on the performance characteristics that matter most f or that use&mdash\;for example precision\, accuracy\, sensitivity\, dynami c range\, selectivity\, robustness\, and throughput.
For process opt imization\, Design of Experiments (DoE) has long been established as a mor e powerful strategy than a One-Factor-at-a-Time approach. DoE enables syst ematic exploration of factors and their interactions\, helping teams conve rge efficiently on settings that improve the targeted performance characte ristics within the expected operating window. Despite these advantages\, D oE is still not used as broadly as it could be\, often due to perceived co sts\, effort\, and complexity.
We will share how implementing DoE as a regular technique for bioassay optimization can be accelerated by (1) a ligning experiments to the assay&rsquo\;s intended use and critical perfor mance characteristics\, (2) identifying and enabling a key user group\, an d (3) providing JMP-specific training \;using the JMP DoE platform.
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emp=true&\;sf_site=aa6f9fcc-8c60-4e6d-90ca-8c73a12c9f03 "Untitled design (9)")
Michele Firmo \; Data Scientist at F Hoffman-La Roche Ltd
Michele holds an MSc in Mathematics from the Uni versità\; Cattolica del Sacro Cuore (IT) and a PhD in Applied Statis tics from the University of Bath (UK).
Statistically De rived MAD for TOST: A Combined Accuracy and Precision Approach for PSD Co- validation Studies
Within the Synthetic Molecules (SM) drug development process\, robust methods to evaluate Particle Size Distributi on (PSD) for drug substances are essential. When manufacturing occurs acro ss multiple sites\, these analytical methods must be transferred effective ly.
The Two One-Sided t-Test (TOST) is the standard statistical fram ework for assessing site-to-site comparability. However\, its application requires a Maximum Allowable Distance (MAD) to be predefined and justified . While available guidance documents provide clear limits for repeatabilit y\, specific thresholds for site-to-site comparison are lacking. Consequen tly\, a MAD is typically established based on expert judgment\, which can be difficult to defend during regulatory review.
We propose a novel methodology for deriving statistically justifiable MAD values by leveragin g the Combined Accuracy and Precision (CAP) framework from ICH Q2(R2). The CAP approach evaluates the joint contributions of accuracy and precision to the total variability of a quantity of interest\, defined here as the 9 0% confidence interval of the difference between site means. Our approach reverses the CAP logic: starting from the regulatory bound on method repea tability\, we partition the accuracy and precision contributions to total variability. By subtracting the reference site&rsquo\;s method precision\, this process allows derives an acceptance limit for the site-to-site diff erence of means. This value is subsequently utilized as the MAD for the TO ST\, ensuring the MAD is directly linked to the established regulatory acc eptance criteria for repeatability.
We demonstrate an application of this technique using synthetic datasets to illustrate its performance and limitations across various scenarios. Finally\, we provide a comparative analysis of this approach against other methods commonly employed by analy tical scientists to assess \;site-to-site comparability.