Title:

Model-based solutions to advance pharmaceutical small molecule solid dose development and manufacturing.

Speaker:

Dr. Rohit Ramachandran

Affiliation:

Rutgers University

When:

Thursday, November 14, 2024 at 11:00:00 AM   Add to Calendar

Where:

MRDC Building, Room 4211

Host:

Dr. Peter Loutzenhiser
peter.loutzenhiser@me.gatech.edu

Abstract

The development and manufacturing of small molecule solid doses is a complex process that can benefit significantly from the development and deployment of model-based approaches. This work explores the development/application of mathematical process models to enhance product and process understanding, enabling more efficient development and optimization of solid dose pharmaceuticals. The use of mechanistic models such as Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) provide detailed, physics-based simulations of particulate and fluid dynamics, offering insights into material behavior and equipment performance. Semi-mechanistic models, often described by systems of differential equations, combine first-principles knowledge with empirical data, providing a balance between accuracy and computational efficiency. In addition to these, statistical models play a critical role in identifying key process parameters and their interactions, facilitating design space exploration and process robustness. Machine learning (ML)-based models, both data-driven and physics-informed, offer novel opportunities to capture complex, nonlinear relationships within high-dimensional datasets, enhancing predictive capabilities in the absence of comprehensive mechanistic understanding. Hybrid models, which integrate mechanistic and ML-based approaches, further extend the capabilities of process models by leveraging the strengths of both physics-based and data-driven frameworks. The adoption of these diverse model types can significantly improve process control/optimization, quality assurance, and risk management, resulting in enhanced performance and reduced time-to-market. By supporting data-driven decision-making and optimization in advanced manufacturing, model-based solutions offer a pathway to more agile, efficient, and cost-effective pharmaceutical production.

Biography

Dr. Rohit Ramachandran is full Professor at the Dept. of Chemical & Biochemical Engineering at Rutgers University, NJ, USA. He obtained his PhD in Chemical Engineering at Imperial College London, UK which was followed by a postdoctoral associate position at MIT. Prior to that he completed his B.S. and M.S in Chemical Engineering at the National University of Singapore. His research interests are at the interface of Process Systems Engineering and Particle Technology with applications in Pharmaceutical and Chemical Processes. He has published over 135 journal papers and 8 book chapters in these areas and has presented his work at numerous conferences and invited seminars. His research work has been cited approximately 5900 times with a H-index of 46. He has received several awards such as the National Science Foundation (NSF) CAREER award, the National Institute of Technology and Education (NIPTE) Young Investigator award, the American Institute of Chemical Engineering (AIChE) Quality-by-Design in Drug product manufacturing award, and the Rutgers Chancellor’s Scholar and Board of Trustees awards. Dr. Ramachandran is currently on assignment at the National Science Foundation (NSF) as Program Director for the Process Systems, Reaction Engineering and Molecular Thermodynamics (PRM) program which is part of the Engineering/CBET directorate/division.

Notes

Refreshments will be served.