PROCESS ANALYTICS especially spectroscopy
- 2. Process Analytical Technology • Process Analytical Technology (PAT) was defined by the Food and Drug Administration guidance “PAT- A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance” as a mechanism to design, analyze, and control pharmaceutical manufacturing process through the measurement of Critical Process Parameters (CPP) with Critical Quality Attributes (CQA) of the product.
- 3. Spectroscopy for process monitoring in chromatography. • UV/vis spectroscopy • This measures the absorption of proteins generally in the range between 240 and 340 nm. Particularly at 280 nm, is widely used to measure protein concentrations due to the significant absorption of aromatic amino acids. Its sensitivity, reproducibility, and robustness make it a primary choice for protein concentration detection.
- 4. • Fourier-transform infrared spectroscopy (FTIR) • This is a well-established spectroscopic method in the analysis of small molecules and protein secondary structure. • FTIR is a widely used method for assessing the structural integrity of proteins during protein purification and formulation.
- 5. • Near-infrared spectroscopy (NIR) • NIR spectroscopy is a non-invasive analytical method that operates based on the principle that the atoms of molecules are in constant motion and vibrate at specific frequencies. It is used to analyze the chemical composition of materials by measuring the absorption of near-infrared light.
- 6. • Nuclear magnetic resonance (NMR) • NMR is a spectroscopic technique that utilizes magnetic fields to study atomic nuclei of a sample. Since this method provides information for each nucleus in the entirety of a sample and is directly proportional to the intensity of the signal measured, NMR can be used to provide information about the characterization, and quantification of a variety of different materials.
- 7. • Raman spectroscopy • This is a form of vibrational spectroscopy that can provide information about chemical properties and molecular interactions of various compounds, which in turn can provide a molecular fingerprint of any given compound.
- 8. • Dynamic light scattering (DLS) • This is a technique that correlates the Brownian motion and resulting light scattering of particles in solution and is used to provide information about particle size and distribution.
- 9. • Particle count and imaging probes • Technological advancements in PAT that utilizes biochemical and biophysical analyses make it possible to monitor processes in-line and in real time often by probe insertion directly into the manufacturing line in reactors, connecters, and vessels.
- 10. Strategies for Bioprocess Control • Bioprocess control comprises of a set of operations that supervise the process in an unpredictable environment with the objective to maintain the process within the desired design space. • The control strategy is created during process development. • Creation of a robust control strategy depends on how deep our understanding of the process is and if we have accurate process models.
- 11. Control Strategy Description Control Structure Open loop control • Pre- computed and sequential control actions are stored in a controller and executed on demand. • Control action cannot be adjusted based on system response. • Can only give instruction to the equipment • No means of data acquisition Closed loop control • Outputs are continuously compared with the set point. • Incase of deviation, controller output is adjusted to minimize the error and forcing response towards the set point. • Applied in automation for continuous production of biotherapeutics. Cascade control • Input to the primary loop process transfer function is obtained from the output of secondary loop process transfer function.
- 12. Artificial neural network-based control • Comprises of input layer, hidden layer, and output layer • • Hidden layer has weights that transform input into a quantity that can be used by output layer. The technique adjusts hidden layer to match the desired output. • • NN are designed for pattern recognition, classification, clustering, and prediction. • • Limitations include the extensive amount of data required and computational time Model based control • Implemented in 4 steps: plant modelling, analyzing and developing controller , simulating plant, and controller. • Simple and intuitive design with better performance than PID Model predictive control • Multivariate control algorithm that uses real- time process model for making predictions by optimizing cost function at each step to reach the reference value. • Improves steady state response, predicts upcoming disturbances, and guarantees
- 13. • Why bioprocess control and automation • Off line, in line, up line, in real time processes • Labview, plant simulation
- 14. THANK YOU