ACD/Labs announced a new release of Katalyst D2D, the industry-leading software for workflow digitalization and data management in chemical and process development.
Originally designed to streamline high throughput experimentation (HTE) workflows, the newest release of Katalyst D2D software (version 25.4) supports a wider variety of study DMTA cycles for process development—both synthetic route optimization and process scale-up—and material characterization studies. Pharmaceutical development project teams will now be able to use Katalyst to rapidly develop and validate processes for manufacturing, accelerating the journey from discovery to clinical trials.
Katalyst D2D is differentiated in the scientific informatics space by its comprehensive integration with third-party informatics systems, laboratory automation and execution hardware, and analytical instrument data. By managing experimental data across workflows, Katalyst reduces transcription errors and provides rich context for experimental results.
The software supports automated experiment execution by generating machine-ready instruction lists for robotics platforms (and instruction lists for manual steps); automated processing of analytical data (e.g., LC/MS, LC/UV, NMR, XRPD, thermal, microscopy); and import and management of data from a wide range of laboratory instruments. The new release imports in-situ sampling data from lab reactors and a variety of laboratory instruments including IR/Raman, TGA, DSC, and Karl Fischer Titrators. The ability to assemble the large variety of data collected in process development studies with experimental context ensures data integrity and traceability, and empowers scientists to make decisions with all the relevant data, in one place and in a standardized format.
Katalyst users can design CMC studies manually or leverage integrable third-party AI tools to guide their design of experiments (DoE). Additionally, the structured, contextualized data assembled and managed by Katalyst is used by scientists for cross-experiment analysis and advanced data science (AI/ML) applications.