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The IQ Consortium reports on the current state of process analytical technology (PAT) for active pharmaceutical ingredient (API) manufacturing in branded pharmaceutical companies. The article describes the application of PAT in manufacturing and provides representative examples in four common pharmaceutical unit operations: reaction and workup, crystallization, drying, and milling.

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.

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This manuscript represents the view of the Dissolution Working Group of the IQ Consortium on the challenges of and recommendations on solubility measurements and development of dissolution methods for immediate release (IR) solid oral dosage forms formulated with amorphous solid dispersions. Nowadays, numerous compounds populate the industrial pipeline as promising drug candidates yet suffer from low aqueous solubility. In the oral drug product development process, solubility along with permeability is a key determinant to assure sufficient drug absorption along the intestinal tract. Formulating the drug candidate as an amorphous solid dispersion (ASD) is one potential option to address this issue. These formulations demonstrate the rapid onset of drug dissolution and can achieve supersaturated concentrations, which poses significant challenges to appropriately characterize solubility and develop quality control dissolution methods. This review strives to categorize the different dissolution and solubility challenges for ASD associated with 3 different topics: (i) definition of solubility and sink conditions for ASD dissolution, (ii) applications and development of non-sink dissolution (according to conventional definition) for ASD formulation screening and QC method development, and (iii) the advantages and disadvantages of using dissolution in detecting crystallinity in ASD formulations. Related to these challenges, successful examples of dissolution experiments in the context of control strategies are shared and may lead as an example for scientific consensus concerning dissolution testing of ASD.