DOE to optimize process parameters
Machine learning-guided design of experiment (DOE) identifies sequence-specific optimal process parameters for Solid Phase Oligonucleotide Synthesis (SPOS). Enable construction of a complete, precise molecular structure during pre-production, recognising complex patterns missed by existing methods.
Generate new insights
Streamline data capture and analysis in mass spectroscopy workflows with easy upload mechanisms for different chromatography systems. The machine learning can then offer unique insights including breakdowns of impurity contributions, giving you confidence in impurity assignment.
Reduce critical impurities
Machine learning can use the oligonucleotide sequence and known impurity and process information to identify and propose routes to minimize impurities. This results in higher product yields and improved productivity, with fewer experiments required for process development.
Built on collaboration, tailored for oligos workflows
Alchemite™ for Oligonucleotide Manufacturing was developed and validated with oligonucleotide experts at the Centre for Process Innovation (CPI), with direct input from six leading pharma companies and biotechs. It applies the powerful Alchemite™ machine learning (ML) method via a user interface tuned tothe specific data upload, analysis, and prediction requirements of oligonucleotide manufacturingworkflows. The solution enables routine process optimization with less input from senior experts,provides researchers with valuable new insights, and captures vital knowledge in ML models. Driveefficiency and innovation across your organisation while retaining corporate memory.
