Online UPLC Method for the Support of Cleaning Validation and the Routine Monitoring of Cleaning Procedures

Importance of the Topic

The removal of active pharmaceutical ingredients (APIs) and related contaminants from production equipment is critical to prevent cross-contamination, ensure product quality, and comply with regulatory requirements. Traditional cleaning validation relies on offline sampling and analysis, leading to extended equipment downtime, increased solvent consumption, and potential exposure risks for personnel. Implementing an online UPLC method enables real-time monitoring of wash solvents, optimizing cleaning procedures and enhancing overall manufacturing efficiency.

Objectives and Article Overview

This study aims to develop and validate a fast, isocratic UltraPerformance LC (UPLC) method for direct, online monitoring of wash solvents during equipment cleaning. Key goals include:

• Establishing chromatographic conditions that resolve starting material, final product, and critical impurities within a 60-second run time.
• Demonstrating sensitivity down to low ng/mL levels suitable for cleaning validation limits (10 ppb to 1 ppm).
• Comparing online results from the PATROL UPLC Process Analysis System with conventional offline UPLC methods and swab analyses.
• Evaluating the impact on equipment downtime, solvent usage, and worker safety.

Methodology and Used Instrumentation

Chromatographic Method:

• System: PATROL UPLC Process Analysis System (online) and ACQUITY UPLC (offline).
• Column: ACQUITY UPLC HSS T3, 1.8 µm, 2.1×50 mm, operated at 50 °C.
• Mobile Phase: 75:25 water/acetonitrile with 0.1% formic acid, flow rate 1.0 mL/min.
• Injection: 1 µL, isocratic, 60-second run, 160-second cycle, detection at 230 nm.

Sampling and Cleaning Protocol:

• Reaction vessel used for acetylsalicylic acid (ASA) to salicylic acid conversion.
• Three 100 mL washes of 50:50 water/methanol inside reactor; two 200 mL washes at outlet.
• Wash solvent samples collected after each step; swabs taken periodically.

Quantitative Calibration:

• Standards ranging 10 ng/mL to 50 µg/mL (1/x weighting).
• Linearity (R²>0.999) across three orders of magnitude.
• Limit of detection (LOD) as low as 24 ng/mL; limit of quantification (LOQ) defined at signal-to-noise ratios of 3 and 10 respectively.

Main Results and Discussion

Chromatographic Performance:

• Baseline separation achieved for starting material, product, and two impurities.
• Run time of 60 seconds enabled near real-time analysis.

Sensitivity and Linearity:

• Calibration curves exhibited excellent linearity from 10 ng/mL to 50 µg/mL.
• LOD values for starting material and product were 31 ng/mL and 24 ng/mL respectively; LOQs at 102 ng/mL and 80 ng/mL.

Online vs. Offline Comparison:

• Online and offline measurements of wash solutions correlated closely across cleaning steps.
• No false negatives: when offline detected residue, online monitoring did as well; when online indicated cleanliness, offline swabs confirmed the result.
• Repeatability demonstrated over four cleaning cycles with consistent detection of residues.

Benefits and Practical Applications

Implementing online UPLC monitoring delivers multiple advantages:

• Equipment Downtime Reduction: “clean-until-clean” approach minimizes over-washing and idle time compared to worst-case offline protocols.
• Solvent and Waste Savings: real-time endpoint determination reduces excess solvent use and disposal costs.
• Enhanced Worker Safety: eliminates manual swabbing and handling of potentially hazardous wash fluids.
• Faster Decision Making: near-real-time results (<4 minutes) versus hours or days for offline QC testing.

Future Trends and Possibilities

Advancements and emerging opportunities may include:

• Integration with process control systems and PAT frameworks for fully automated cleaning validation workflows.
• Coupling with mass spectrometry or additional detectors to broaden the range of detectable residues.
• Miniaturized or portable UPLC platforms for on-site, decentralized monitoring across multiple assets.
• Data analytics and machine learning to predict optimal cleaning parameters and minimize resource usage.

Conclusion

The described online UPLC method provides a robust, sensitive, and rapid solution for monitoring cleaning processes in pharmaceutical manufacturing. By delivering equivalent results to offline testing, it enables significant reductions in equipment downtime, solvent consumption, and operator exposure. Adoption of this approach supports efficient cleaning validation and routine monitoring, aligning with quality, safety, and sustainability objectives.

References

• Guidance for Industry: Manufacturing, Processing, or Holding Active Pharmaceutical Ingredients, FDA Draft. March 1998.
• Cleaning Validation in Active Pharmaceutical Ingredient Manufacturing Plants, APIC. September 1999.
• Guidance on Aspects of Cleaning Validation in Active Pharmaceutical Ingredient Plants, APIC. December 2000.
• Fountain KJ, van Wingerden M, Diehl DM. A High Throughput UPLC/MS Method in Support of Cleaning Validation Studies. Waters. June 2007; 720002171en.
• Jenkins T. Online Reaction Monitoring of Inprocess Manufacturing Samples by UPLC. Waters. May 2008; 720002605en.