Fully automated sample preparation for liquid chromatographic content determinations
Posters | | MetrohmInstrumentation
The analysis of ionic constituents in solid dosage forms is essential for quality control and regulatory compliance. Fully automated inline sample preparation bridges the gap between solid samples and liquid chromatography by enabling direct injection of homogenized and filtered extracts. This approach enhances reproducibility, throughput, and safety while reducing manual labor and costs.
This work demonstrates the integration of an 815 Robotic Soliprep sample processor with ion chromatography for the quantification of anions and cations in pharmaceutical tablets. The main goal is to validate a fully automated workflow for two model drugs, Singulair and Bezafibrat, highlighting performance metrics such as accuracy, precision, and linearity over a broad concentration range.
Sample preparation is carried out by the Robotic Soliprep platform equipped with a Polytron PT 1300 D disperser and an 800 Dosino for solvent addition. Tablets are weighed, solvent is added, and the mixture is homogenized, filtered, and either injected directly into the chromatograph or transferred for dilution. Chromatographic separation employs:
Data acquisition and ion quantification are managed by MagIC Net software, which calculates contents based on calibration and sample metadata.
Six‐point calibration curves from 0.2 to 50 mg/L showed excellent linearity (correlation coefficients >0.99990). Relative standard deviations at sub‐ppm levels for sulfate, nitrate, calcium, and magnesium were below 3.0%, while chloride exhibited RSD below 0.83%. Tablet assays yielded results consistent across triplicate preparations:
The inline comminution performed during chromatography run time does not extend total analysis duration.
Advances in inline sample processing are expected to enable real‐time monitoring in industrial workflows and environmental analysis. Integration with robotics and AI for method optimization could further reduce analysis time and resource consumption. Customizable modules will facilitate routine screening in complex matrices.
The integration of automated sample preparation with ion chromatography delivers robust, precise, and efficient analysis of ionic components in solid dosage forms. The validated methodology meets stringent performance criteria and offers versatile adaptation for a range of analytical challenges.
No external references cited.
Ion chromatography
IndustriesManufacturerMetrohm
Summary
Significance of the Topic
The analysis of ionic constituents in solid dosage forms is essential for quality control and regulatory compliance. Fully automated inline sample preparation bridges the gap between solid samples and liquid chromatography by enabling direct injection of homogenized and filtered extracts. This approach enhances reproducibility, throughput, and safety while reducing manual labor and costs.
Objectives and Study Overview
This work demonstrates the integration of an 815 Robotic Soliprep sample processor with ion chromatography for the quantification of anions and cations in pharmaceutical tablets. The main goal is to validate a fully automated workflow for two model drugs, Singulair and Bezafibrat, highlighting performance metrics such as accuracy, precision, and linearity over a broad concentration range.
Methodology and Used Instrumentation
Sample preparation is carried out by the Robotic Soliprep platform equipped with a Polytron PT 1300 D disperser and an 800 Dosino for solvent addition. Tablets are weighed, solvent is added, and the mixture is homogenized, filtered, and either injected directly into the chromatograph or transferred for dilution. Chromatographic separation employs:
- Metrosep A Supp 5 – 100 column at 30 C with 3.2 mmol/L sodium carbonate and 1.0 mmol/L sodium hydrogen carbonate eluent for anions
- Metrosep C 4 – 100 column at 30 C with 1.7 mmol/L nitric acid and 0.7 mmol/L dipicolinic acid eluent for cations
Data acquisition and ion quantification are managed by MagIC Net software, which calculates contents based on calibration and sample metadata.
Main Results and Discussion
Six‐point calibration curves from 0.2 to 50 mg/L showed excellent linearity (correlation coefficients >0.99990). Relative standard deviations at sub‐ppm levels for sulfate, nitrate, calcium, and magnesium were below 3.0%, while chloride exhibited RSD below 0.83%. Tablet assays yielded results consistent across triplicate preparations:
- Singulair: chloride 60.1 mg/kg, nitrate 10.9 mg/kg, sulfate 27.8 mg/kg; sodium 1125 mg/kg, calcium 9.1 mg/kg, magnesium 5.5 mg/kg
- Bezafibrat: chloride 571 mg/kg, nitrate 22.7 mg/kg, sulfate 92.9 mg/kg; sodium 3254 mg/kg, calcium 2674 mg/kg, magnesium 282 mg/kg
The inline comminution performed during chromatography run time does not extend total analysis duration.
Benefits and Practical Applications
- Complete automation enhances sample traceability and reduces human error
- Consistent high precision and accuracy support regulatory requirements
- Modular inline steps, including pulverization, extraction, and dilution, allow adaptation to diverse matrices such as food, feed, and sediments
- Increased lab throughput and improved safety compared to manual methods
Future Trends and Applications
Advances in inline sample processing are expected to enable real‐time monitoring in industrial workflows and environmental analysis. Integration with robotics and AI for method optimization could further reduce analysis time and resource consumption. Customizable modules will facilitate routine screening in complex matrices.
Conclusion
The integration of automated sample preparation with ion chromatography delivers robust, precise, and efficient analysis of ionic components in solid dosage forms. The validated methodology meets stringent performance criteria and offers versatile adaptation for a range of analytical challenges.
Used Instrumentation
- 850 Professional IC AnCat – MCS
- 815 Robotic Soliprep for Liquid Chromatography
- Polytron PT 1300 D dispersing device
Reference
No external references cited.
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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