A Δ‑based framework for internal release limits in plateau stability systems

Internal Release Limits (IRLs) in pharmaceutical manufacturing are commonly derived using statistical tools originally developed for shelf‑life assignment, most notably time‑series regression and kinetic extrapolation. While appropriate for expiry determination, such approaches implicitly assume that degradation slopes are statistically identifiable and that prediction beyond observed timepoints is required. In many practical IRL scenarios, neither assumption holds: release decisions must ensure end‑of‑life compliance without predicting new future timepoints, and stability datasets are frequently sparse, misaligned, or plateau‑dominated. In these settings, regression‑based inference is not only unnecessary but often statistically unsupported. The only defensible stability parameter is the observed end‑to‑end change between release and the practical equilibrium (plateau) region. This work introduces a Δ‑based framework for IRL determination that formalizes identifiability conditions, certifies plateau behavior, partitions analytical and manufacturing variance, and applies one‑sided tolerance intervals to provide explicit future‑batch population coverage. The proposed approach is model‑independent, data‑appropriate, and aligned with contemporary expectations for uncertainty transparency and analytical lifecycle management. A controlled simulation study under plateau‑forming kinetics shows that Δ‑based IRLs provide direct and accurate estimates of future‑batch‑protective release limits, while aligned model‑based IRLs introduce additional uncertainty through parametric structure even under correct specification. Using the publicly accessible monoclonal‑antibody dataset of Kuzman et al. (2021), the framework yields the first Δ‑based tolerance‑interval IRL for a plateau‑confirmed biologic, demonstrating how a sparse stability record can be transformed into a regulatorily actionable release‑control strategy without reliance on kinetic extrapolation. In addition, a worked industrial illustration based on a Bayesian probability‑of‑success IRL workflow (Sondag, 2017) shows that, once plateau behavior is verified, Δ‑based inference recovers the same IRL decision as time‑dependent modeling under substantially weaker assumptions. Together, these results establish Δ‑based IRL determination as the identifiable limiting case of established IRL practice in stability systems where late‑age behavior is time‑invariant and kinetic parameters are not reliably estimable.