Presentation by Dr. Christina Schenk of Carnegie Mellon University

Dr. Christina Schenk, an ALOP Alumni and current post doctoral fellow at Carnegie Mellon University, will join us for research collaboration and speak during our research seminar on August 1, 2018 on the following topic:

Parameter Estimation for Pharmaceutical Processes – Advancements and Case Studies

Abstract:

The development of drug manufacturing processes involves dealing with spectroscopic data. When dealing with
spectroscopic data, the identification of parameters and variances still remains a challenging task. In many cases
kinetic parameter identification from spectroscopic data has to be performed without knowing the absorbing
species in advance, such that they have to be estimated as well. However, kinetic parameter estimation is
important in order to lower production costs, i.e. save measurements and equipment. Furthermore, scaling up
from laboratory to industrial level relies on accurate kinetic parameter values.
That is why, we take a closer look at the development of optimization-based procedures in order to estimate the
variances of the noise in the system variables and spectral measurements. Then, with the estimated variances
we determine the concentration profiles and kinetic parameters simultaneously using adequate strategies. The
work is based on the approach proposed by Chen et al. (2016) using maximum likelihood principles for simultaneous
estimation of reaction kinetics and curve resolution from process and spectral data. For this a new
software environment was developed which is continuously enhanced. These investigations and advancements
are presented within this talk and illustrated by several case studies of pharmaceutical processes.

Joint work with Lorenz T. Biegler (Carnegie Mellon University), Lu Han (Pfizer Inc.) and Jason Mustakis (Pfizer Inc.)

References
W. Chen, L. Biegler, and S. Muñoz. An approach for simultaneous estimation of reaction kinetics and curve resolution
from process and spectral data. Journal of Chemometrics, 30:506–522, 2016. doi: 10.1002/cem.2808.
URL http://dx.doi.org/10.1002/cem.2808.