Ansamycins are a very specific class of macrocyclic antibiotics of which the rifamycins are among the better known members. Rifamycins bind to and inhibit DNA polymerase. Rifamycin B (the most easily obtained ansamycin) is negatively charged and is shown to associate with and enantioselectively resolve several chiral amino alcohols including terbutaline, isoproterenol, bamethan, metaproterenol, synephrine, metanephrine, salbutamol, epinephrine, norphenylephrine, ephedrine, psi-ephedrine, octopamine, norepinephrine, normetanephrine, metoprolol, alprenolol, atenolol, and oxprenolol. A description of the structure and properties of rifamycins, in general, and rifamycin B, in particular, is given. The complexation and chiral recognition of the aforementioned racemic compounds by rifamycin B is afforded by multiple interactions of which charge-charge, hydrogen-bonding, and hydrophobic inclusion interactions most likely dominate in hydroorganic solvents. The effect of various experimental factors on enantiomeric resolution is discussed in terms of optimizing the CE separations. Since most chiral antibiotic macrocycles are ionizable, somewhat flexible, and contain hydrophobic and hydrophilic moieties, they tend to be significantly affected by variations in the solution environment.

The IQ Consortium reports on the current state of process analytical technology (PAT) for active pharmaceutical ingredient (API) development in branded pharmaceutical companies. The article uses an API process workflow (process steps from raw material identification through to finished API) to provide representative examples, including why and how the pharmaceutical industry uses PAT tools in API development. The use of PAT can improve R&D efficiency and minimize personnel hazards associated with sampling hazardous materials for in-process testing. Although not all steps or chemical processes are readily amenable to the use of the PAT toolbox, when appropriate, PAT enables reliable and rapid (real or near time) analyses of processes that may contain materials that are highly hazardous, transient, or heterogeneous. These measurements can provide significant data for developing process chemistry understanding, and they may include the detection of previously unknown reaction intermediates, mechanisms, or relationships between process variables. As the process becomes defined and understanding is gained through these measurements, the number of parameters suspected to be critical is reduced. As the process approaches the commercial manufacturing stage and the process design space is established, a simplification of the monitoring and control technology, as much as is practical, is desired. In many cases, this results in controls being either off-line, or if in situ control is required, the results from PAT are correlated with simple manufacturing measurements such as temperature and pressure.

Detector response is not always equivalent between detectors or instrument types. Factors that impact detector response include molecular structure and detection wavelength. In liquid chromatography (LC), ultraviolet (UV) is often the primary detector; however, without determination of UV response factors for each analyte, chromatographic results are reported on an area percent rather than a weight percent. In extreme cases, response factors can differ by several orders of magnitude for structurally dissimilar compounds, making the uncalibrated data useless for quantitative applications. While impurity reference standards are normally used to calculate UV relative response factors (RRFs), reference standards of reaction mixture components are typically not available during route scouting or in the early stages of process development. Here, we describe an approach to establish RRFs from a single experiment using both online nuclear magnetic resonance (NMR) and LC. NMR is used as a mass detector from which a UV response factor can be determined to correct the high performance liquid chromatography (HPLC) data. Online reaction monitoring using simultaneous NMR and HPLC provides a platform to expedite the development and understanding of pharmaceutical reaction processes. Ultimately, the knowledge provided by a structurally information rich technique such as NMR can be correlated with more prevalent and mobile instrumentation [e.g., LC, mid-infrared spectrometers (MIR)] for additional routine process understanding and optimization.

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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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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.

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