Process
analytical technology (PAT) plays an important role in
the pharmaceutical industry. PAT is used extensively in process development,
process understanding, and process control. Often, quantitative measurements
are desired/required and a calibrated model will have to be developed
and implemented. The development, implementation, and maintenance
of these quantitative models are both resource and time intensive.
This paper describes a calibration-free/minimum approach, iterative
optimization technology (IOT), which is used to predict (without calibration
standards) the composition of a mixture while maintaining a similar
predictability to calibration standard models. It typically involves
using only pure standard spectra (collected prior to the analysis)
and sample spectra collected during the analysis. This technology
is applicable for predicting compositions during development of pharmaceutical
products (where the synthetic route, formulation, or process is not
set) and is not intended for use in good manufacturing practice (GMP)
manufacture where quantitative
measurements are made using validated models. For ideal mixture cases,
the mixture composition is iteratively computed at every sample time
point to minimize an excess absorption subject to constraints (e.g.,
mixture constraints, upper/lower limits). Linear IOT is used to describe
these ideal mixture cases. For nonideal mixture cases, the excess
absorption, including the nonlinear characteristic, is first represented
by a Box-Cox transformation. A limited number of training/calibration
samples is required for these nonlinear examples. The mixture composition
is then iteratively obtained in a similar optimization framework as
linear IOT. Nonlinear IOT is used to describe these nonideal mixture
cases. Linear and nonlinear IOT have provided comparable prediction
accuracy on binary and ternary mixtures as compared to a calibrated
partial least squares (PLS) model. IOT enhanced the understanding
of dosage form blending processes by determining the composition/ratio
of all (spectrally discriminated) components in the blend in real
time. As composition is predicted each revolution, determination of
the blending end point (does each component trend meet the known target
mixture ratio) can be easily determined. Linear and nonlinear IOT
can also be used to aid process understanding via detecting/representing
molecular interaction effects utilizing the excess absorption calculation.
The effectiveness of the linear and nonlinear IOT is demonstrated
through four online and offline pharmaceutical process examples (bin-blending
process, rotary tablet press feed frame process, and two different
solvent mixtures).
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