Biochemistry & Analytical Biochemistry

Process Analytical Technologies for In Line Control and Real Time Release in Bio-manufacturing

Esther Kim^* Department of Process Analytical Science, Han River University, Seoul, South Africa
^*Correspondence: Esther Kim, Department of Process Analytical Science, Han River University, Seoul, South Africa, Email:

Received: 27-Aug-2025, Manuscript No. BABCR-25-30079; Editor assigned: 29-Aug-2025, Pre QC No. BABCR-25-30079 (PQ); Reviewed: 12-Sep-2025, QC No. BABCR-25-30079; Revised: 19-Sep-2025, Manuscript No. BABCR-25-30079 (R); Published: 26-Sep-2025, DOI: 10.35248/2161-1009.25.14.591

Description

In biochemical production, final quality control by end point assays no longer suffices in many high value or time sensitive settings. Instead, methods that assess quality attributes during the manufacturing run are being deployed to assure product consistency, reduce waste, and speed delivery. Real Time Release Testing (RTRT) purposefully uses analytical data acquired during process steps to decide if a batch meets specification, eliminating or reducing the need for destructive tests after production completes. Integrated with that is in line Process Analytical Technology (PAT), which samples or senses directly within flow streams to monitor critical parameters continuously. Together, these techniques shift manufacturing toward a responsive, data driven mode, where quality is observed rather than just declared.

Application of vibrational spectroscopies is among the more mature approaches in RTRT. Raman spectroscopy applied in bio-manufacturing can detect subtle chemical changes, identify and quantify biomolecular species, determine concentrations of excipients, and monitor variations in buffer conditions. Because Raman is nonintrusive and can probe aqueous samples, it is well suited to in line setups. It has been used, for instance, to monitor concentration of protein, sugars, buffer components, or product impurities in biologics production. Raman readings, once calibrated, correlate with concentration or purity metrics and thus feed into decision rules during the run.

Near Infrared (NIR) spectroscopy is another tool frequently paired with RTRT strategies. In pharmaceutical extract powder manufacturing, NIR has been used to quantify active ingredient concentrations in real time with multivariate calibration models. Through the estimation of confidence intervals around specification limits, batches can be accepted or rejected without destructive sampling. Integrating these spectroscopic sensors with process flows requires careful design. Optical probes may be inserted into vessels or pipelines, in flow cells with controlled path length, or via fiber optics. The data stream must be fast, synchronized with process stages (mixing, reaction, purification), and subjected to signal processing.

Multivariate calibration methods (e.g., partial least squares regression, principal component regression) convert spectral data into meaningful analytical values. Models require robust training over representative variability (temperature, pH, interfering substances) so that predictions remain valid under operational fluctuations. Process control becomes more dynamic when feedback loops use PAT data. Suppose a sensor detects the concentration of an intermediate deviating from setpoint; the controller might adjust flow rates, reagent feed, temperature, or mixing to steer the reaction back on course. To support decision making, statistical process control charts, multivariate control, and anomaly detection models are essential. Algorithms may flag drift or outliers early, triggering intervention or pausing the run.

Because real industrial biochemical processes may face fouling, baseline drift, optical alignment changes, or temperature variation, the analytical models must be robust to those influences, or calibration must be updated periodically. Redundancy (multiple sensor modalities) can help cross validate and reduce false alarms.

Adoption of RTRT and in line PAT in regulated industries demands validation and regulatory alignment. Calibration models, sensor placements, sample representativeness, and data integrity must meet audit standards. Correlation of sensor predictions with reference assays is necessary during method qualification. In many cases, risk assessment and design of experiments support selection of critical process parameters and analytical attributes that must be monitored. The regulatory agencies increasingly encourage adoption of such approaches under quality by design initiatives, emphasizing understanding of process behaviour and control strategy.

Examples in biologics manufacturing include using Raman or NIR to monitor cell culture metabolite concentrations, product glycosylation patterns, or buffer exchange performance. In formulations production, in line monitoring of excipient concentration, viscosity, or pH ensures consistency before packaging. In one application, Raman spectroscopy was used to monitor a monoclonal antibody manufacturing line, correlating spectral signatures with product quality attributes, thereby enabling real time release decisions.