(603g) Advances and New Developments on Eiot for PAT Applications in Pharmaceutical Development and Manufacturing

The EIOT method[1] was proposed as an extension to the IOT[2] method as an alternative approach to extracting information from spectroscopic data into interpretable mass fractions of the resolved chemical species in the powder mixture.

The method in essence seeks to use Beer-Lambert’s law as the deterministic functional relationship between spectra and mass fractions. The main difference with IOT is that EIOT does not assume the spectrum of the pure components is a measurable quantity; but a quantity that can only be estimated from measurements of known composition. The EIOT method proposes to decompose the measured spectra as the summation of the chemical signal plus the effect of the non-chemical interferences. The chemical signal is given by the summation of the apparent pure spectrum weighted by the mass fraction of each specie; the non-chemical interferences as the summation of the non-chemical interference signals weighed by their respective strength. The method relies on optimization techniques to estimate the apparent pure spectra and the signal of the non-chemical interferences; and when the method is used in real-time, to estimate the mass fractions and the strengths of the non-chemical interferences in each new sample.

Since the method was published, a number of enhancements have been done to the method, this talk presents such enhancements that i) the consideration of supervised signatures and ii) the consideration of exclusivity in a set of signatures (i.e. only one in a set is allowed to contribute). Details around the maintenance and long-term ownership of the EIOT method are discussed for its potential inclusion as a central component for a control strategy in drug product manufacture.

EIOT (as well as its predecessor IOT) bring a disruptive change in the practice of chemometrics; retiring the use of fixed regression models, to the use of real-time optimization and computer decision-making instead. As such, the requirements and expectations for use and long-term maintenance might have to be revised and adapted.

[1] Z. Shi, J. Hermiller, S.G.J.A.J. Muñoz, Estimation of mass‐based composition in powder mixtures using Extended Iterative Optimization Technology (EIOT), (2018).

[2] K. Muteki, D.O. Blackwood, B. Maranzano, Y. Zhou, Y.A. Liu, K.R. Leeman, G.L. Reid, Mixture component prediction using iterative optimization technology (calibration-free/minimum approach), Industrial & Engineering Chemistry Research, 52 (2013) 12258-12268.