ArticleChemical science2024
Simultaneous reaction- and analytical model building using dynamic flow experiments to accelerate process development.
Article in Chemical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The trial behind it
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Who cites it
2 citing papers in PubMed.
- Automated Dynamic Flow Experimentation for Rapid Kinetic Fitting of Transition Metal Catalysis.Angewandte Chemie (International ed. in English) · 2026Article
- Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning.Beilstein journal of organic chemistry · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In modern pharmaceutical research, the demand for expeditious development of synthetic routes to active pharmaceutical ingredients (APIs) has led to a paradigm shift towards data-rich process development. Conventional methodologies encompass prolonged timelines for the development of both a reaction model and analytical models. The development of both methods are often separated into different departments and can require an iterative optimization process. Addressing this issue, we introduce an innovative dual modeling approach, combining the development of a Process Analytical Technology (PAT) strategy with reaction optimization. This integrated approach is exemplified in diverse amidation reactions and the synthesis of the API benznidazole. The platform, characterized by a high degree of automation and minimal operator involvement, achieves PAT calibration through a "standard addition" approach. Dynamic experiments are executed to screen a broad process space and gather data for fitting kinetic parameters. Employing an open-source software program facilitates rapid kinetic parameter fitting and additional
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.