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Aniek Sies \; Principal Scientist at Johnson & \; Johnson
Anie k Sies is a Principal Scientist in Manufacturing Science and Technology (M SAT) in Johnson &\; Johnson\, working on the application of statistical methods to manufacturing risk assessment and regulatory decision-making. She holds a PhD with a focus on statistical methodology for estimating opt imal treatment regimes.
A statistical risk-based fr
amework for sunsetting one-sided release tests: from limited data to regul
atory approval
Manufacturing control strategy change s such as removing routine release tests (&ldquo\;sunsetting&rdquo\;) requ ire a defensible quantitative assessment of residual quality risk. While t his may appear straightforward\, it is statistically challenging due to th e irreversible nature of the decision and the frequent reliance on limited historical batch data that are often heavily left censored.
I prese nt a pragmatic statistical framework designed to quantify the probability of missing future out-of-specification batches. It integrates well-establi shed statistical tools\, including process capability analysis with confid ence bounds to account for finite sample uncertainty\, predictive probabil ity statements\, and Bayesian posterior predictive modelling.
The fr amework is illustrated using an established drug product for which sunsett ing of release testing was approved by both FDA and EMA. The discussion su bsequently focuses on early lifecycle scenarios with limited and highly ce nsored data. Throughout\, the emphasis is on statistical judgment: making assumptions explicit\, acknowledging identifiability limits\, \;and supporting transpare nt and defensible decision-making when data are far from ideal.
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Swetlana Berger Associate Director Biostatistics of UCB
Applying DoE to Bioassays: Faster Optimization\, Relevant Performance\, Better Decisions
Bioassays are a key analyti cal technology from basic research to drug discovery in the biopharmaceuti cal industry. Because these assays are complex and often involve multiple handling steps\, optimization should be guided by the intended use (fit-fo r-purpose assay) and focused on the performance characteristics that matte r most for that use&mdash\;for example precision\, accuracy\, sensitivity\ , dynamic range\, selectivity\, robustness\, and throughput.
For pro cess optimization\, Design of Experiments (DoE) has long been established as a more powerful strategy than a One-Factor-at-a-Time approach. DoE enab les systematic exploration of factors and their interactions\, helping tea ms converge efficiently on settings that improve the targeted performance characteristics within the expected operating window. Despite these advant ages\, DoE is still not used as broadly as it could be\, often due to perc eived costs\, effort\, and complexity.
We will share how implementin g DoE as a regular technique for bioassay optimization can be accelerated by (1) aligning experiments to the assay&rsquo\;s intended use and critica l performance characteristics\, (2) identifying and enabling a key user gr oup\, and (3) providing JMP-specific training \;using the JMP DoE platform.
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p\;sf_site_temp=true&\;sf_site=aa6f9fcc-8c60-4e6d-90ca-8c73a12c9f03 "Untitled design (9)")
Michele Firmo \; Data Sc ientist at F Hoffman-La Roche Ltd
Michele holds an MSc in Mathematics from the Università\; Cattolica del Sacro Cuore (IT) and a PhD in Ap plied Statistics from the University of Bath (UK).
Stat istically Derived MAD for TOST: A Combined Accuracy and Precision Approach for PSD Co-validation Studies
Within the Synthetic Molecul es (SM) drug development process\, robust methods to evaluate Particle Siz e Distribution (PSD) for drug substances are essential. When manufacturing occurs across multiple sites\, these analytical methods must be transferr ed effectively.
The Two One-Sided t-Test (TOST) is the standard stat istical framework for assessing site-to-site comparability. However\, its application requires a Maximum Allowable Distance (MAD) to be predefined a nd justified. While available guidance documents provide clear limits for repeatability\, specific thresholds for site-to-site comparison are lackin g. Consequently\, a MAD is typically established based on expert judgment\ , which can be difficult to defend during regulatory review.
We prop ose a novel methodology for deriving statistically justifiable MAD values by leveraging the Combined Accuracy and Precision (CAP) framework from ICH Q2(R2). The CAP approach evaluates the joint contributions of accuracy an d precision to the total variability of a quantity of interest\, defined h ere as the 90% confidence interval of the difference between site means. O ur approach reverses the CAP logic: starting from the regulatory bound on method repeatability\, we partition the accuracy and precision contributio ns to total variability. By subtracting the reference site&rsquo\;s method precision\, this process allows derives an acceptance limit for the site- to-site difference of means. This value is subsequently utilized as the MA D for the TOST\, ensuring the MAD is directly linked to the established re gulatory acceptance criteria for repeatability.
We demonstrate an ap plication of this technique using synthetic datasets to illustrate its per formance and limitations across various scenarios. Finally\, we provide a comparative analysis of this approach against other methods commonly emplo yed by analytical scientists to assess \;site-to-site comparability.