Impurity Profiling for a Scalable Continuous Synthesis and Crystallization of Carbamazepine Drug Substance

Matthew Glace,Wei Wu,David Acevedo,Dongxia Liu,Thomas D. Roper,Adil Mohammad

Organic process research & development（2024）SCI 3区

Division of Product Quality Research

Cited 1|Views22

Abstract

A scalable continuous manufacturing process for the synthesis and crystallization of form III carbamazepine (CBZ) from iminostilbene (ISB) has been established. A high-yielding synthesis was first obtained using a plug flow reactor (PFR) and then scaled up using a continuous oscillatory baffled reactor (COBR). A real-time in-line Raman spectroscopy method was implemented to ensure that the conversion of the starting material ISB to the product CBZ was maintained above 99.0%. The monitored product stream was telescoped into a mixed-suspension mixed-product crystallizer (MSMPR-1) and a filtration unit to isolate the preliminary CBZ form I polymorph. A cooling recrystallization process was designed by using a crystal growth model derived from microscopy measurements. The impurity purging capacities and polymorph attainments were compared for the batch and flow processes. This study outlines the role of process modeling and process analytical technology (PAT) for impurity purging in a telescoped continuous manufacturing process.

Translated text

Key words

Continuous Manufacturing,Flow Chemistry,ContinuousCrystallization,Process Analytical Technologies (PAT),Continuous Oscillatory Baffled Reactor,Carbamazepine,Polymorph

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper