BAGIM is an active community of Boston area scientists bringing together people from diverse fields of modeling and informatics to impact life and health sciences. BAGIM strives to create a forum for great scientific discussions covering a wide range of topics including data management, visualization, computational chemistry, drug discovery, protein structure, molecular modeling, structure-based drug design, data mining, software tools, and the sharing of goals and experiences. Our community is made up of participants from academia, government, and industry whose goal is to engage in the discussion of science involving a synthesis of theory and technology. Discussions sponsored by BAGIM are targeted to the needs and interests of informatics scientists, computational chemists, medicinal chemists, and statisticians. BAGIM also provides opportunities for networking within these disciplines as well as an arena for the dissemination of information of specific interest to the membership.

Wednesday, May 24, 2023

Michael Bellucci - Reducing Risk in Crystallization Process Development

 Date: September 13, 2023

Location: TBD (Cambridge, MA)

Attendance: 70

Mixer and Presentation: 5:30 PM - 7:00 PM
Welcome, Introductions, Presentation, Q&A, Conversation

Host: Dr. Sivakumar Sekharan, Senior Director Business Development

Speaker: Dr. Michael Bellucci, Senior Director, Solid Form Design

Abstract Title: Intelligent Cloud-Based Algorithms for Reducing Risk in Crystallization Process Development
Michael A. Bellucci*,1, Anke Marx*,2, Bing Wang1, Liwen Fang1, Yunfei Zhou1, Chandler Greenwell1, Zhuhong Li1, Dirk Wandschneider2, Jan Gerit Brandenburg2, Guangxu Sun1, Sivakumar Sekharan1, Axel Becker2

1 XtalPi, Inc., 245 Main Street, Cambridge, MA 02142
2 Merck KGaA, Frankfurter Str. 250, A022/001, 64293 Darmstadt, Germany

Crystallization is the most widely used separation and purification process in the pharmaceutical industry. The resulting crystal structure and corresponding crystal morphology isolated from this process can have a profound influence on the physical properties and manufacturability of drug product APIs. Consequently, the ability to characterize the crystal polymorph landscape and control the crystal morphology are two fundamental aspects of pharmaceutical manufacturing. At XtalPi, we have developed a cloud-based computational platform that combines advanced physics-based algorithms with A.I/machine learning algorithms in order to mitigate polymorph risk and support rational design of crystallization experiments for improved morphological control. We highlight various applications from our Crystal Structure Prediction and Morphology platforms and discuss our recent investigation of the effect of polymer additives on the crystal growth of metformin HCl. This study was performed both with experiments and computational methods with the aim of developing a combined screening approach for crystal shape engineering. Additionally, we have developed analysis methods to characterize the morphology “landscape” and quantify the overall effect of solvent and additives on the predicted crystal habits. Further analysis of our molecular dynamics simulations was used to rationalize the effect of additives on the growth rate of specific crystal faces.

No comments:

Post a Comment