Rapid identification of protein formulations with Bayesian Optimisation

Protein formulation is a critical aspect of the pharmaceutical industry which aims to improve the efficacy and the safety of the active drug ingredients during the storage, transportation and administration of the drug. Buffer screening is the first stage of this formulation process that selects the promising combinations of buffer and excipients that can help maintain both the stability and efficacy of the drug. In this paper, we propose an interactive Bayesian Optimisation approach that streamlines the buffer screening process and reduces the number of experiments needed to identify an optimal combination of buffer and excipients. Our approach employs two novel formulations of the (multi-buffer) optimisation problem: (i) one that unifies all buffers into a single Bayesian Optimisation framework, and (ii) the other that performs meta-learning to aggregate important excipient information over multiple buffers, in order to predict the most promising buffer and excipients combination to sample next. Our experimental results show that the proposed approach can identify an optimal combination of buffer and excipients while minimising the number of experiments required, and demonstrate the potential of using Bayesian Optimisation to enhance the protein formulation process.