DoE training course for Chemists and Engineers

I will be running a 3 day training course with Scientific Update on the use of Design of Experiments (DoE) @ the Sea Princess hotel in Mumbai - 13th to 15th April 2015. DoE is a statistical tool which enables the efficient exploration of potential reaction parameters and their effect on a process. DoE is a fundamental aspect of Quality by Design. The course will introduce the DoE Process and how to use it to develop and understand chemical reactions and processes.

Case Study 6

A Client observed a more than 10% of a high molecular weight impurity during the large-scale manufacture of an Active Pharmaceutical Ingredient (API). A Redox-Neutral Alcohol-Amine Coupling reaction was a potential alternative process replacing the SN2 displacement of a tosylate. The Redox Neutral coupling removes the Potential Genotoxic Impurity (PGI).

Scientific Update OPRD Conference

Dr Paul Murray, Paul Murray Catalysis Consulting, is speaking at the Scientific Update Organic Process Research and Development conference in Cologne on ‘The application of advanced DoE for the efficient development of chemical processes’.

Intelligent Selection of Greener Solvents

Dr Janet Scott, University of Bath, present this week on the Intelligent Selection of Greener Solvents at the Gordon Green Chemistry Conference in Hong Kong. The talk was based on a collaborative project involving the use of Principal Component Analysis and Design of Experiments to efficiently identify suitable alternative solvents for chemical processes. The identification of alternative solvents for nucleophilic aromatic substitution was exemplified. The project was funded by the ACS GCI Pharmaceutical Roundtable.

Case Study 5

A Client observed a new impurity doing a large-scale manufacture. Through a detailed understanding of catalytic mechanism, the root cause of the impurity formation was identified immediately. The theory was rapidly proven by small scale experimentation and a modified production procedure adopted to prevent any reoccurrence of the problem.

Case Study 4

A leading pharmaceutical company needed to manufacture 60kg of an active pharmaceutical intermediate using a Suzuki reaction. The catalyst for this particular process would have cost £240,000 and the previous manufacture unexpectedly afforded < 50% yield. We identified alternative robust reaction conditions which enabled the significant reduction in the catalyst loading reducing the catalyst cost of this process by more than £140,000 while reliably achieving more than 95% isolated yield. This case study demonstrates how the new reaction conditions were identified.

Case Study 3

A pharmaceutical company needed to manufacture 5kg of an active pharmaceutical intermediate using a Suzuki reaction. The reaction was low yielding (< 45%) with a very high catalyst loading. We identified a new, high yielding and robust process for the rapid delivery of 5 kg of the required API intermediate in 1 week by kinetic evaluation for this Suzuki reaction leading to understanding of reaction mechanism. This case study demonstrates how.

Case Study 2

A leading pharmaceutical company needed to manufacture 80kg of an active pharmaceutical intermediate using a Buchwald Hartwig amination reaction. The catalyst for this particular process would have cost $300,000. We identified alternative catalyst systems which reduced the catalyst cost of this process by more than $280,000. This case study demonstrates how the new catalyst system was identified by combining Design of Experiments (DoE) and Principal Component Analysis (PCA) in the development of catalytic reactions.

Case Study 1

A leading pharmaceutical company needed to manufacture 100kg of an active pharmaceutical intermediate using a Suzuki reaction. The catalyst for this particular process would have cost $500,000. We identified alternative catalyst systems which reduced the catalyst cost of this process by more than $400,000.

Information Sheets

Paul Murray Catalysis Consulting is delighted to provide the following information data sheets: