Introduction
A talk from the PerkinElmer Chemicals Summit – Pharmaceutical Chemicals Symposium
Oncogen is a global scientifically-driven organization with an exclusive focus to develop, hard to source, niche Oncology products.
In this presentation, Dr. Prakash Muthudoss, Senior Manager, Formulation Analytical at Oncogen Pharma will discuss examples of NIR applications using statistical machine learning in the pharmaceutical industry, including API/excipient characterization and content uniformity, and moisture content analysis of tablets. The pharmaceutical industry must ensure correct dosing and manufacturing of stable products, which entails strictly controlling the raw materials and each of the steps involved in their processing. Determining parameters such as potency, moisture, density, viscosity, and particle size enable the identification and correction methods for any deviations in the manufacturing process in a timely fashion. Such controls can be achieved by quantifying the different parameters of API and excipients required using appropriate analytical methods such as NIR. Ensuring homogeneous mixing of the components (APIs and excipients) of a pharmaceutical preparation is a crucial prerequisite for obtaining proper solid dosages for tablets and capsules.
Such requirements are satisfied using HPLC. However, the HPLC technique is tedious, time-consuming, its destructive method, and it involves the use of caustic and/or hazardous solvents. NIR spectroscopy enables the analysis of complex matrices without the need to manipulate samples, resulting in substantially decreased analysis time relative to wet chemical methods, and it provides accurate means of characterizing tablets according to the FDA’s process analytical technology (PAT) initiative.
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