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A ToolBox of techniques for analytical Process Control in pharmaceutical industry

A ToolBox of techniques for analytical Process Control in pharmaceutical industry. Stephan Küppers Schering AG, VT, Müllerstrasse 170-78 13342 Berlin, Germany. Motivation. “up-to-the-minute” information on process status fast response to production problems

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A ToolBox of techniques for analytical Process Control in pharmaceutical industry

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  1. A ToolBox of techniques for analytical Process Control in pharmaceutical industry Stephan Küppers Schering AG, VT, Müllerstrasse 170-78 13342 Berlin, Germany

  2. Motivation • “up-to-the-minute” information on process status • fast response to production problems • ability to demonstrate of the quality of product and process • possibility to optimize the process • more security in measuring harmful substances • prevention and reduction of both errors(systematic and unsystematic) • less „re-work“, „re-processing“ • overall: more consistent production

  3. Major issues • automation of sampling to receive homogeneous and representative samples • automation of data evaluation • simplification of the use of analytical techniques • establishment of a meaningful collaboration between analytical department and production plant • equipment that is robust, small and ex-proof

  4. Sensors MS UV-VIS TOOLS IR LC FIA Raman Chemometrics

  5. T. Wessa, S. Küppers, U. Stahl, M. Rapp; Fres. J. Anal. Chem., (1999) 363:744-748 Prinzip eines SAW Sensors

  6. Data evaluation

  7. SAW-Sensor in plant

  8. T. Wessa, S. Küppers, M. Rapp; J. Reibel, Sensors and Acutators B, 70 (2000) 203-213 Specifications for P-HPLC Validation:performed according to ICH-guidelines

  9. O Mg Br R O Monitoring of Grignard-reaction(MIR) O 2 R-Br + Mg +

  10. K. Lovis, N. Dantan, U. Tilstam, W. Frenzel S. Küppers; Analytica Conference, München 11.-14.4.2000 Reaction monitoring using MIR

  11. Raman • Promising technique with still some problems:- expensive- validation not yet a routine technique • Three examples tested:- one works fine- acylation shows fluorescens problems- sensitivity

  12. Cis/Trans Calcinol

  13. Peak identification: 1=air 2=methanol 5=methylenchloride 7=hexane 3=ethanol 8=THF 4=acetone 6=methoxymethylpropan • S. Küppers and A. Walte; PREACH Kolloquium, Leipzig, 4.-7.10.2000 mass spectrum

  14. time axis in sec.! using a GC/MS

  15. N. Dantan, W. Frenzel, S. Küppers, Talanta, 52 (2000) 101-109 FIA for water determination Methanol Methanol KF-Lösung Inj.-Ventil 200 cm/ 0.5 mm Detektor 660 cm/ 0.5 mm

  16. N. Dantan, S. Kröning, W. Frenzel, S. Küppers; Analytica Chimica Acta, 420 (2000) 133-142 LOD´s and LOQ´s of water using KF-FIA

  17. FIA with photoisomerisation

  18. Anthracen TRANS Diethylphtalat CIS FIA with photoisomerisation Inj.-VentilDiethylphat. Inj.-VentilProbe Hexan LC-Pumpe Mischung 300 cm/ 0.5 mm Hexan/EE 96/4 Inj.-VentilHPLC, 1µl SäuleSpherisorb SI Detektor

  19. ? spectra Cis/Trans Gemisch 2.5 2.0 1.5 AU 1.0 0.5 0.0 220 240 260 280 300 320 340 360 nm

  20. Cis/Trans lab-data Software:SIMCA, Method:PLS, 6 Components

  21. Consequences • as the number of analytical techniques grows, analytical departments becomes „know-how-managers“ • customers perform more analyses on their own • sample prep will became automated • costs become more transparent, because more instrumentation will be dedicated

  22. What are the implications for industry? • change of the way of collaboration between departments is difficult • ex-security is expensive! • analytics is a service department • process analytics needs to be simple enough to be operated by plant personal

  23. E. Biller N. Dantan T. Davies W. Frenzel K. Lovis P. Malz M. Rapp Acknowledgement • T. Wessa • W. Hill • U. Tilstam • A. Walte

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