On average, the titrations were completed in just three iterations as indicated in Table , where the samples are labelled as SU-# (with SU representing scale-up and # representing the sample number). This constitutes an initialisation of two data points + one suggestion from the GP surrogate model, which highlights the potential of the titration algorithm to accelerate traditional pH adjustment workflows. We see that the algorithm overshoots the set-point for SU-1 and SU-6 (one third of the total samples tested), which is roughly consistent with the results presented in Figure -but is able to successfully reach the end point despite the formation of a complex citrate buffer system. The formulations start out predominantly basic, but the algorithm is able to adjust formulations from either side of the set-point, for e.g., SU-6 starts out acidic. We performed the scaled-up experiments in a 500 mL beaker with a high torque stirrereffectively a stirrer tank -and the pH of the formulation was continuously logged upon addition of acid or base, until it stabilised. See the materials and methods for details. We additionally note, as shown from Supplementary Table , that the optimal mixing time, i.e., the shortest time for the pH to stabilise, changed upon each iteration of the titration for some formulations.