In ‘ChemRxiv’
A new paper in ChemRxiv, authored by Intellegens scientists with collaborators from CPI discusses the potential of machine learning to assist the development of oligonucleotides, an important emerging class of therapeutics. The paper reports work from a two-year collaboration between Intellegens, CPI, and industrial partners.
Citation Padroni G., Phillips C., Whitehead T.M., Conduit G.J., MacLeod C., Green E. The Potential of Machine Learning in Oligonucleotide Therapeutics Manufacturing. ChemRxiv.
DOI 10.26434/chemrxiv-2025-qb9rz
Abstract
Approvals of synthetic oligonucleotide therapeutics are sharply rising and with it the challenges to achieve sustainable manufacturing strategies. These chain-like molecules are commonly assembled via solid phase oligonucleotide synthesis (SPOS), a method that allows for quick and efficient sequential additions of monomers up until the desired molecule length. A primary challenge in SPOS is the meticulous control of the multitude of variables at each additional step to avoid formation of complex and unwanted impurities. The analysis, identification, and quantification of these impurities can also be troublesome and time consuming. Optimisation of SPOS and related analytical methods is essential to guarantee reproducibility and robustness during manufacture. Furthermore, SPOS is heavily reliant on solvent washes that drastically increase the process mass intensity (PMI) and environmental impact of the synthesis which should be addressed during optimisation. In this perspective, we discuss the potential of machine learning (ML) models in oligonucleotide development and manufacturing to identify sequence-specific optimal process parameters, downstream processes, and impurity analysis; and recognise complex patterns that are often missed by existing development methods. The expected benefit of adopting ML is improved product yield, improved productivity and reduced time and cost through reduction in the number of experiments required for process development.
