Strategic use of calibration transfer to minimize experimental runs for multivariate calibrations within the QbD design space

In Quality by Design (QbD) frameworks, the analytical design space defines parameter combinations that ensure reliable product quality. However, changes in process conditions often necessitate new multivariate calibrations, creating a substantial experimental burden. We hypothesize that full factorial calibrations redundantly cover response levels across conditions, inflating time, cost, and material use. This study proposes a strategic calibration transfer approach to minimize experimental runs within the factorial design space while preserving predictive accuracy. Using two complementary pharmaceutical case studies-inline blending and spectrometer temperature variation-we systematically compared partial least squares (PLS) and Ridge regression models under standard normal variate (SNV) and orthogonal signal correction (OSC) preprocessing. Iterative subsetting of calibration sets and optimal design criteria (D-, A-, and I-optimality) were evaluated for their ability to maintain robust prediction across the remaining unmodeled design space regions. Results demonstrate that modest, optimally selected calibration sets combined with ridge regression and OSC preprocessing deliver prediction errors equivalent to full factorial designs, reducing calibration runs by 30-50 %. Ridge regression consistently outperformed PLS, eliminating bias and halving error, while I-optimality most effectively minimized average prediction variance. Context-specific considerations remained critical: blending applications required strict edge-level representation, whereas temperature-driven variability showed more forgiving transfer dynamics. This protocol provides a scalable, resource-efficient pathway for integrating calibration transfer into QbD-driven process analytical technology, offering substantial gains in efficiency without compromising regulatory robustness.