MLAPI: A framework for developing machine learning-guided drug particle syntheses in automated continuous flow platforms

Recently, machine learning (ML) models are increasingly being used in process analytical technology (PAT) frameworks for pharmaceutical manufacturing. Yet, the applications of ML-integrated PAT frameworks are limited by big data requirements. This work introduces a computational framework to develop data-efficient ML models to guide drug particle synthesis in an automated continuous flow precipitation platform. The framework incorporates classification algorithms to identify feasible (fouling-free) operating regions of the precipitation platform, a multiple-output Gaussian process (GP) regression model to relate key process parameters to the drug particle size, and active learning to optimally generate new data for training and validation of the GP model. The usefulness of the proposed framework is demonstrated on the synthesis of ibuprofen microparticles in an automated flow precipitation platform. We envision that properly trained GP models developed using the proposed framework can be employed to fine tune the drug particle size, targeting desired particle bioavailability and processability. • A computational framework is proposed to develop machine learning models to assist antisolvent precipitation of ibuprofen microparticles in flow. • The framework incorporates multi-class classifier models to identify the feasible (fouling-free) operating space for the precipitation process. • To model the precipitation process, a multiple-output Gaussian process regression model was developed to relate key process parameters to moments of the particle size distribution. • Active learning was employed to design the most informative experiments to train the regression model.