Aqueous phosphoric acid (85 wt %) is an effective, environmentally benign reagent for the deprotection of tert-butyl carbamates, tert-butyl esters, and tert-butyl ethers. The reaction conditions are mild and offer good selectivity in the presence of other acid-sensitive groups, including CBZ carbamates, azetidine, benzyl and methyl esters, TBDMS, and methyl phenyl ethers. The mildness of the reaction is further demonstrated in the synthesis of clarithromycin derivative, in which a tert-butyl ester is removed in the presence of cyclic carbamate, lactone, ketal, acetate ester, and epimerizable methyl ketone functionalities. The reaction preserves the stereochemical integrity of the substrates. The reactions are high yielding, and the workup is convenient.

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).

Malonic acid derivatives undergo unusually mild decarboxylation when treated with N,N'-carbonyldiimidazole (CDI) at room temperature to generate the carbonyl imidazole moiety in high yield, which can be reacted further with a variety of nucleophiles in an efficient one-pot process.

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This is the first in a series of three papers describing commercial manufacturing process development for palbociclib (1). This manuscript focuses on the SNAr coupling between aminopyridine 3 and chloropyrimidine 7. The regioselectivity of the SNAr coupling was studied from a synthetic and mechanistic perspective. Grignard bases were identified as the preferred class of bases for this reaction, allowing for a simplified process and reduced usage factor for aminopyridine 3. The development of this SNAr reaction into a scalable commercial manufacturing process is also described.