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A data-based approach to the development of industrial polymer blends with specified final properties is presented. There are basically three major degrees of freedom to control the final product properties: the selection of raw materials, the selection of the ratios in which to blend them, and the selection of process conditions used to manufacture them. In this paper, we present a new optimization approach that simultaneously addresses all of these degrees of freedom, but the primary focus will be on the selection of the materials and their ratios. The approach involves building partial least-squares (PLS) models that combine databases on previously made blends and databases on the properties of the component materials used in these blends. The resulting models are then used in an optimization framework to select raw materials from much larger databases (including materials never previously used) and to select the ratios in which to blend them in order to yield a blend product with specified end properties at a minimum cost. The methodology is applied to two industrial polymer blending problems that involve the replacement of raw materials while keeping the same product properties and minimizing total raw material cost.

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).

Quantitative structure retention relationships (QSRRs) can play an important role in enhancing the speed and quality of chromatographic method development. This paper presents a novel (compound-classification-based) QSRR modeling strategy that simultaneously accounts for the analyte properties, mobile-phase conditions, and stationary-phase properties. It involves the adoption of two models: (A) partial-least-squares discriminate analysis (PLS-DA) to classify compounds into subclasses having similar interactive relationships between the mobile-phase conditions and stationary phase; (B) L partial least squares (L-PLS) to predict the compound's retention time based on the mobile-phase conditions, stationary phase, and compound properties. For the retention time of a compound to be modeled, the most favorable compound class is identified in an optimization framework that simultaneously minimizes both the compound misclassification rate (based on PLS-DA) and the retention time prediction error (based on L-PLS) through a mixed-integer optimization. The proposed QSRR model (L-PLS with compound classification) significantly improves the retention time predictability compared with traditional QSRR or L-PLS models without compound classification. When combined with the linear solvation energy relationship parameters (using Abraham coefficients) as the column properties, the approach allows the following: (1) prediction of (new, never analyzed) compound retention times under chromatographic conditions (columns and mobile-phase conditions) used to train the model;(2) prediction of (previously analyzed under training conditions) compound retention times under chromatographic conditions that have not been previously evaluated; (3) optimization of the chromatographic conditions (mobile-phase and column selection) to maximize critical pair resolution, including new compounds; (4) enhanced mechanistic understanding of the interactive retention relationship between compounds, the mobile phase, and the column (e.g., compound retention mechanism). The effectiveness of the proposed modeling strategy will be demonstrated through two practical pharmaceutical applications in supercritical fluid chromatography and reversed-phase liquid chromatography.