Automation and Efficiency Improvement Solutions (HPLC and LC-MS)

Automation and Intelligent Workflow Enhancements in HPLC/LC-MS Analysis

Significance of the topic

High-performance liquid chromatography (HPLC) and liquid chromatography–mass spectrometry (LC-MS) remain essential techniques across pharmaceutical, biopharmaceutical and industrial laboratories. As sample throughput grows and regulatory requirements tighten, automating routine tasks, improving method robustness and applying data-driven optimization become critical to ensure reliable results, reduce human error and maximize instrument uptime.

Objectives and Overview

This collection of application notes and technical reports outlines approaches to:

• Automate sample pretreatment for complex biomolecules
• Streamline system startup, monitoring and shutdown
• Implement real-time mobile-phase consumption tracking
• Minimize downtime through auto-diagnostic and auto-recovery functions
• Accelerate method development with AI-driven gradient optimization
• Embed Analytical Quality by Design (AQbD) principles via LabSolutions MD software

Methodology and Used Instrumentation

Key instruments and technologies include:

• MUP-3100 fully automated sample preparation module for glycan analysis coupled to Shimadzu Nexera XR HPLC
• FlowPilot function and intelligent startup/shutdown routines on Nexera LC systems
• Mobile Phase Monitor Module (MPM-40) for gravimetric tracking of solvents
• Solvent delivery units with built-in auto-diagnostics and auto-recovery pumps
• LabSolutions MD software for method scouting, AQbD workflows and AI-based gradient optimization
• Single-quadrupole LCMS-2050 and SPD-M40/20Axs detectors for UV and MS data acquisition

Automated Sample Pretreatment

The MUP-3100 module automates antibody purification, glycan release, labeling and cleanup in a single workflow. Video-based reagent placement checks and tip sensors eliminate variation. In a case study profiling N-linked glycans from trastuzumab-producing CHO cell supernatant, 24 samples were processed in under 6 hours with relative standard deviations below 8%. The automated workflow captured temporal glycan profile shifts during cell culture with high reproducibility.

System Startup and Management

Intelligent startup routines integrate FlowPilot, warm-up profiles and automatic system suitability tests (SST). FlowPilot ramps flow rates in sync with column oven stabilization to avoid pressure shocks and prolong column life. Custom SST parameters can be embedded in methods to halt or continue batches based on pass/fail criteria, enabling unattended operation with minimal intervention.

Mobile Phase Monitoring

The MPM-40 module provides real-time gravimetric measurement of up to 12 solvent bottles, updating LabSolutions and mobile apps via LAN. Predicted consumption for pending analyses is compared to actual solvent levels, issuing warnings or auto-stopping runs when volumes fall below thresholds. Integration with IoT platforms supports remote oversight and proactive lab maintenance.

Auto-Diagnostics and Auto-Recovery

Shimadzu solvent delivery units detect abnormal pressure drops associated with air bubbles and automatically initiate purging routines. A built-in algorithm compares pressure variability against reference profiles, suspends analyses, clears bubbles and resumes runs, reducing re-injection rates and sample loss during unattended sequences.

AI-Driven Method Development

LabSolutions MD leverages experimental design and a proprietary AI algorithm to automate gradient optimization and define robust design spaces. Case studies include:

• Separation of 15 catechins, theaflavins and gallic acid in tea extracts: automated gradient searches achieved minimum resolution criteria in four iterative correction analyses.
• Peptide impurity profiling: molecular weights deconvoluted from MS data enabled automated identification and optimization of isocratic/isocratic portions to separate β-Melanotropin and related deletion/oxidation products.
• Small-molecule impurity methods for montelukast and ketoprofen: screening multiple mobile phases/columns followed by design-space visualization delivered conditions satisfying resolution and run-time targets, with robust performance across column lots.

Main Results and Discussion

The integrated automation yielded:

• Over 70% reduction in sample preparation time for glycan analysis with high precision
• Automated system readiness in minutes and consistent SST outcomes to secure data quality
• Real-time solvent monitoring preventing run failures and enabling remote alerts
• Significant drop in downtime from pressure-related blockages
• Method development cycles compressed from days to hours using AI-driven gradient searches and design-space mapping

Benefits and Practical Applications

Laboratories implementing these solutions can expect:

• Higher throughput with less manual pipetting and system checks
• Improved method robustness and reproducibility across analysts and sites
• Automated QC/SST workflows meeting pharmaceutical compliance
• Optimized resource allocation via real-time solvent tracking
• Accelerated method deployment and reduced time-to-market

Future Trends and Applications

Looking ahead, innovations will likely include:

• Cloud-based orchestration of fully automated HPLC/LC-MS chains
• Self-optimizing chromatography systems using machine learning
• Digital twins for predictive maintenance and method translation
• Seamless integration with LIMS and electronic lab notebooks
• Sustainability-driven solvent reduction and green chromatography protocols

Conclusion

The convergence of advanced hardware automation with AI-powered software and AQbD principles transforms HPLC/LC-MS laboratories. From sample pretreatment to method validation, these technologies boost efficiency, ensure consistent data quality and enable scientists to focus on analytical interpretation rather than routine tasks.

References

• MUP-3100 Fully Automated Sample Preparation Module for Glycan Analysis (Shimadzu Application Note, C190-E304)
• Analytical Intelligence Series Part 1–3: FlowPilot Startup, Mobile Phase Monitor MPM-40, Intelligent SST (Shimadzu Technical Reports C190-E225, C190-E226, C190-E227)
• AI-Driven Gradient Optimization for LC Method Development (Shimadzu Technical Report C190-E309)
• Efficient Method Development via AQbD with LabSolutions MD (Shimadzu Technical Report C190-E284)