21st International Conference Organic Process Research & Development 2010 San Diego
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21st International Conference Organic Process Research & Development 2010 San Diego

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Value of Process Automation, Real-Time Measurements to Improve Operational Efficiency from Laboratory to Production

Value of Process Automation, Real-Time Measurements to Improve Operational Efficiency from Laboratory to Production

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21st International Conference Organic Process Research & Development 2010 San Diego 21st International Conference Organic Process Research & Development 2010 San Diego Presentation Transcript

  • Value of Process Automation, Real-Time Measurements to Improve Operational Efficiency from Laboratory to Production Dominique Hebrault Sr. Technology & Application Consultant San Diego, January 21, 2010
  • The Paradigm of Faster and Better…
  • Presentation Outline  Case Studies - Process Research using ATR-FTIR Spectroscopy with ReactIRTM - ReactIRTM, FBRM®, and PVM® for Process Development - RTCalTM Calorimetry : Enabling Real Time Process Characterization - Understanding Crystallization with ReactIRTM and EasyMaxTM  Conclusions
  • Combining Real Time Analytics & Process Control  Characterize Particles  Analyze Reaction Chemistry  Expand Productivity  Data Capture and Understanding
  • Mid-IR Real-time Reaction Analysis ReactIR
  • Mid-IR Real-time Reaction Analysis In-situ reaction results Absorbance Time  ConcIRT live  Peak height profiling  Quantitative model Component Spectra Component Profiles Relative concentration Absorbance or Time
  • Case Study: FTIR, PAT tool in Pharma Development Study of lactol activation by trifluoroacetic anhydride via in situ Fourier transform infrared spectroscopy  Introduction Accurate charge of TFAA critical to minimize by-product and reagent use Chromatography not appropriate: TFAA reactivity, activated lactol unstable Rapid, reliable, quantitative method needed to determine activation endpoint Source: Yadan Chen, George X. Zhou∗, Nicole Brown, Tao Wang, Zhihong Ge, Merck Research Laboratories, Rahway, NJ, USA, Analytica Chimica Acta 497, 2003,155–164; Other examples: Mettler Toledo 15th International Process Development Conference 2008, Annapolis, USA
  • Case Study: FTIR, PAT tool in Pharma Development  Project Challenges TFAA amount is key to reaction control: - TFAA hydrolysis with moisture - Unstable activated lactol → lactol - Excess TFAA reacts with chiral alcohol - Undercharge of TFAA → dimer HPLC Prep Source: Yadan Chen, George X. Zhou∗, Nicole Brown, Tao Wang, Zhihong Ge, Merck Research Laboratories, Rahway, NJ, USA, Analytica Chimica Acta 497, 2003,155–164; Other examples: Mettler Toledo 15th International Process Development Conference 2008, Annapolis, USA
  • Case Study: FTIR, PAT tool in Pharma Development Reference spectra
  • Case Study: FTIR, PAT tool in Pharma Development  Initial/qualitative investigation Stepwise changes - 1- Acetonitrile (solvent) at -5°C, 2- lactol in solvent, 3- intentional TFAA overcharge (1.04 eq), 4- more lactol added Observations - Lactol poorly soluble in solvent - Rapid reaction upon TFAA (5’) addition - Activated lactol profile qualitative only - 10°C rise: safety/quality issue Source: Yadan Chen, George X. Zhou∗, Nicole Brown, Tao Wang, Zhihong Ge, Merck Research Laboratories, Rahway, NJ, USA, Analytica Chimica Acta 497, 2003,155–164; Other examples: Mettler Toledo 15th International Process Development Conference 2008, Annapolis, USA
  • Case Study: FTIR, PAT tool in Pharma Development  Quantitative Experiments TFAA model in reaction mixture - TFAA spiked into solvent Known: 14.3 mg/ml - Peak area for band at 1875 cm-1 Known: 11 mg/ml Predicted: 10.2 mg/ml Predicted: 14 mg/ml - 2 models: [0-100mg/ml] and [60-350] - Model tested in reaction mixture: consecutive additions of TFAA Observations - Good prediction from calibration model Source: Yadan Chen, George X. Zhou∗, Nicole Brown, Tao Wang, Zhihong Ge, Merck Research Laboratories, Rahway, NJ, USA, Analytica Chimica Acta 497, 2003,155–164; Other examples: Mettler Toledo 15th International Process Development Conference 2008, Annapolis, USA
  • Case Study: FTIR, PAT tool in Pharma Development  Order of reactant addition Slow addition of TFAA to lactol - Exotherm is feed-controlled → safer, better quality - 0.2-0.5mol% dimer still present Reverse addition: Lactol to TFAA - No free lactol in the reaction mixture - Less dimer (<0.15mol% HPLC) Source: Yadan Chen, George X. Zhou∗, Nicole Brown, Tao Wang, Zhihong Ge, Merck Research Laboratories, Rahway, NJ, USA, Analytica Chimica Acta 497, 2003,155–164; Other examples: Mettler Toledo 15th International Process Development Conference 2008, Annapolis, USA
  • Case Study: FTIR, PAT tool in Pharma Development  Conclusions Key parameters to prevent dimer - Temp.: -5 → 0⁰C; dimer < 0.3mol% - Undercharge TFAA (bp 39°C) favors dimer > 0⁰C - Overcharge TFAA suppresses dimer even above 30⁰C Benefits of ReactIR™ for this project - Used to determine conditions leading to high level of dimer impurity - Amount of dimer determined by HPLC - Helped identify critical process para- meters, and obtain kinetic information in real time Source: Yadan Chen, George X. Zhou∗, Nicole Brown, Tao Wang, Zhihong Ge, Merck Research Laboratories, Rahway, NJ, USA, Analytica Chimica Acta 497, 2003,155–164; Other examples: Mettler Toledo 15th International Process Development Conference 2008, Annapolis, USA
  • Presentation Outline  Case Studies - Process Research using ATR-FTIR Spectroscopy with ReactIRTM - ReactIRTM, FBRM®, and PVM® for Process Development - RTCalTM Calorimetry : Enabling Real Time Process Characterization - Understanding Crystallization with ReactIRTM and EasyMaxTM  Conclusions
  • Case Study: Dev. of Manuf. Process for LY518674 The Role of New Technologies in Defining a Manufacturing Process for PPAR#  Challenge Agonist LY518674 Development of a robust impurity control  Introduction strategy LY518674 highly potent and selective History shows 5 impurities > 0.1% agonist of peroxisome proliferator- despite final crystallization activated receptor alpha (PPARR) One single HPLC method challenging Recently evaluated in phase II clinical because of polarity differences studies in patients with dyslipidemia and hypercholesterolemia Source: Mark D. Argentine, Timothy M. Braden, Jeffrey Czarnik, Edward W. Conder, Steven E. Dunlap, Jared W. Fennell, Mark A. LaPack, Roger R. Rothhaar, R. Brian Scherer, Christopher R. Schmid, Jeffrey T. Vicenzi, Jeffrey G. Wei, John A. Werner, and Robert T. Roginski, Lilly Research Laboratories, IN, USA; Org. Process Res. Dev., 2009, 13 (2), 131-143
  • Case Study: Dev. of Manuf. Process for LY518674  Towards a “One-Pot Process” using innovative PAT approach - ReactIRTM to develop kinetic model for KOCN concentration → control KOCN and minimize 20 - FBRM® and PVM®: Design crystallization to reach 17<0.5% Source: Mark D. Argentine, Timothy M. Braden, Jeffrey Czarnik, Edward W. Conder, Steven E. Dunlap, Jared W. Fennell, Mark A. LaPack, Roger R. Rothhaar, R. Brian Scherer, Christopher R. Schmid, Jeffrey T. Vicenzi, Jeffrey G. Wei, John A. Werner, and Robert T. Roginski, Lilly Research Laboratories, IN, USA; Org. Process Res. Dev., 2009, 13 (2), 131-143
  • Case Study: Dev. of Manuf. Process for LY518674  ATR-FTIR spectroscopy to minimize by-product 20 - Develop kinetic model - Calibration model developed for [OCN-] - Integration over the 2088-2254cm-1 - 1st order in 15 and KOCN - Rate constant determined Source: Mark D. Argentine, Timothy M. Braden, Jeffrey Czarnik, Edward W. Conder, Steven E. Dunlap, Jared W. Fennell, Mark A. LaPack, Roger R. Rothhaar, R. Brian Scherer, Christopher R. Schmid, Jeffrey T. Vicenzi, Jeffrey G. Wei, John A. Werner, and Robert T. Roginski, Lilly Research Laboratories, IN, USA; Org. Process Res. Dev., 2009, 13 (2), 131-143
  • Case Study: Dev. of Manuf. Process for LY518674  Results from the model - Model time for cyanate conversion to reach completion - For three different cyanate addition times - 99.9% cyanate consumed within 5-6 h - Little impact from addition time (0.25h versus 1h) - 20 minimized Source: Mark D. Argentine, Timothy M. Braden, Jeffrey Czarnik, Edward W. Conder, Steven E. Dunlap, Jared W. Fennell, Mark A. LaPack, Roger R. Rothhaar, R. Brian Scherer, Christopher R. Schmid, Jeffrey T. Vicenzi, Jeffrey G. Wei, John A. Werner, and Robert T. Roginski, Lilly Research Laboratories, IN, USA; Org. Process Res. Dev., 2009, 13 (2), 131-143
  • Case Study: Dev. of Manuf. Process for LY518674  FBRM® and PVM® to improve purification of 16: - History: impurity 17 ≈ 0.1-1.2% depending upon washing protocol (goal<0.5%) - 17 more soluble in 5N aq. HCl - FBRM ®: 5N aq. HCl → Count # large particles drops, fine particles count increases - PVM®: Needle shaped small particles not visible to the eye identified as 22 - Crystallization of 22 prevented by decreasing concentration: From 11mL/g to 16mL/g 15 Source: Mark D. Argentine, Timothy M. Braden, Jeffrey Czarnik, Edward W. Conder, Steven E. Dunlap, Jared W. Fennell, Mark A. LaPack, Roger R. Rothhaar, R. Brian Scherer, Christopher R. Schmid, Jeffrey T. Vicenzi, Jeffrey G. Wei, John A. Werner, and Robert T. Roginski, Lilly Research Laboratories, IN, USA; Org. Process Res. Dev., 2009, 13 (2), 131-143
  • Case Study: Dev. of Manuf. Process for LY518674  Conclusions Extensive use of various Process Analytical Technologies at lab and pilot plant scale - ReactIRTM used to develop a kinetic model for a one-pot preparation of a semicarbazide intermediate - FBRM® and PVM® to help in the - Shortened development cycle times development of several challenging crystallization processes - Process knowledge → control strategy - Comparison of performance at laboratory and pilot-plant scale - Obviated the requirement of PAT for process control at larger scale Source: Mark D. Argentine, Timothy M. Braden, Jeffrey Czarnik, Edward W. Conder, Steven E. Dunlap, Jared W. Fennell, Mark A. LaPack, Roger R. Rothhaar, R. Brian Scherer, Christopher R. Schmid, Jeffrey T. Vicenzi, Jeffrey G. Wei, John A. Werner, and Robert T. Roginski, Lilly Research Laboratories, IN, USA; Org. Process Res. Dev., 2009, 13 (2), 131-143
  • Presentation Outline  Case Studies - Process Research using ATR-FTIR Spectroscopy with ReactIRTM - ReactIRTM, FBRM®, and PVM® for Process Development - RTCalTM Calorimetry : Enabling Real Time Process Characterization - Understanding Crystallization with ReactIRTM and EasyMaxTM  Conclusions
  • Real Time Calorimetry: RTCal™ on RC1e  Heat flow  Real Time Calorimetry - Well established, accurate - Real time, no calibration, no evaluation measurement - Automated heat exchange area (A) - Calibration required determination - Allows non-isothermal calorimetry, - Insensitive to reaction mass properties some level of expertise required (viscosity) - Sensitive to reaction mass properties - Feedback control based on energy output
  • Case Study: RTCalTM, PAT Tool for Polymerization Effect of Monomer Grade on Inverse Initial Charge Exp. Conditions Acryl amide Inverse emulsion Emulsion Polymerization of Acrylamide emulsion (562 ml) Using RTCal™ Calorimetry Technology polymerization: water in oil, batch, shots of initiator  Introduction Polymerization Process info 57 – 65 C, 6h Kinetics: initial rateLow Strongly exothermic acrylamide copper > initial rateHigh copper AIBN (5x0.1ml) polymerization reactions Cu = monomer stabilizer Change of acrylamide copper grade in Investigation manufacturing: standard → low Polymerization rate determination: Comparison of low and high copper grade based on heat flow/flux Safety assessment/validation required Process safety evaluation: H , T Ad MTSR (Real time) heat measurement invaluable monitoring technology AIBN n n O NH2 O NH2 acrylamide poly(acrylamide) Source: Jeffrey H. Peltier, Kate M. Lusvardi, Michael Mitchell Ashland, Hercules Water Technologies Wilmington, DE, USA, Internal Publication, 2009
  • Case Study: RTCalTM, PAT Tool for Polymerization  Standard vs low Cu grades Low copper What makes the difference (RTCalTM)? - Shape of heat generation curve Standard copper - Shorter induction period and more heat generated with low copper grade Low copper 256kJ Initiator Standard copper 241kJ Low Copper grade monomer - Higher initial heat rate using low copper - Higher heat removal rate needed Source: Jeffrey H. Peltier, Kate M. Lusvardi, Michael Mitchell Ashland, Hercules Water Technologies Wilmington, DE, USA, Internal Publication, 2009
  • Case Study: RTCalTM, PAT Tool for Polymerization  Process safety evaluation - iC SafetyTM minimizes risk of error - Maximum thermal accumulation (danger!) at starting point (batch) - Loss of cooling → Tcf > 200°C! Thermal accumulation Thermal conversion - Assessment of plant’s cooling capacity versus change in monomer copper Temp. cooling failure grade (low/standard): 58W versus 54W Heat max. heat output→ no change required Source: Jeffrey H. Peltier, Kate M. Lusvardi, Michael Mitchell Ashland, Hercules Water Technologies Wilmington, DE, USA, Internal Publication, 2009
  • Case Study: RTCalTM, PAT Tool for Polymerization  Conclusions - Validation of low copper grade acrylic acid for manufacturing scale polymerization → no major change - Validation of RTCalTM as an alternative to heat flow calorimetry AIBN n • Easier for non expert as not n O NH2 O NH2 sensitive to viscosity acrylamide poly(acrylamide) • Faster as no calibration needed Source: Jeffrey H. Peltier, Kate M. Lusvardi, Michael Mitchell Ashland, Hercules Water Technologies Wilmington, DE, USA, Internal Publication, 2009
  • The Road to API Kingdom… Nowhere Reaction Isolation Crystallization
  • Presentation Outline  Case Studies - Process Research using ATR-FTIR Spectroscopy with ReactIRTM - ReactIRTM, FBRM®, and PVM® for Process Development - RTCalTM Calorimetry : Enabling Real Time Process Characterization - Understanding Crystallization with ReactIRTM and EasyMaxTM  Conclusions
  • Case Study: Real Time Supersaturation Monitoring Paracetamol/water Supersaturation Monitoring Using In Situ Mid Infrared Spectroscopy  Why is supersaturation important? - Supersaturation is the driving force for crystal nucleation and crystal growth - Poor control of supersaturation may lead to: - By controlling supersaturation, • long filtration time nucleation and growth can be controlled, allowing the crystal size to • undesired polymorph be controlled • low purity Source: Anthony DiJulio, Novartis Pharmaceuticals Co., NJ, USA; A. Burke, D. O’Grady, D. Hebrault, METTLER TOLEDO, MD, USA, Internal Publication, 2009
  • Case Study: Real Time Supersaturation Monitoring  Equipment used - Hardware and software combination • ReactIR 45m probe based in situ real time mid-IR spectroscopy • EasyMaxTM automated reactor system - Benefits • Real time overlay of temperature, dosing, concentration data, and from source heat flow ATR crystal Liquid-Solid Slurry • Accurate control of temperature, liquid addition, and mixing to detector • Concentration feedback to control temperature 1~2 m Source: Anthony DiJulio, Novartis Pharmaceuticals Co., NJ, USA; A. Burke, D. O’Grady, D. Hebrault, METTLER TOLEDO, MD, USA, Internal Publication, 2009
  • Case Study: Real Time Supersaturation Monitoring  Experimental procedure: Model - Charge 100ml water - Add incremental amount of paracetamol (1.3, 0.5, 0.8, 1.2g) - For each concentration, collect spectra at 2 temperatures, 10⁰C apart, from 25 ⁰C to 60 ⁰C Mid-Infrared absorbance - Datapoints collected to build multivariate quantitative model Paracetamol 3.8 w/w% water Wavenumber (cm-1) Source: Anthony DiJulio, Novartis Pharmaceuticals Co., NJ, USA; A. Burke, D. O’Grady, D. Hebrault, METTLER TOLEDO, MD, USA, Internal Publication, 2009
  • Case Study: Real Time Supersaturation Monitoring 4 One-click calibration 5 3 Evaluate model consistency Visualize 1 model inputs Select trends (C, T) 2 Select samples 31
  • Case Study: Real Time Supersaturation Monitoring  Experimental: Crystallization - Cool down 3.8 w/w% paracetamol solution: 1⁰C/min, 55 → 20⁰C - Load multivariate calibration model, visualize concentration evolution in real time - Crystallization onset: • Concentration drop ≤ 38°C • Exotherm detected (heat flow) Source: Anthony DiJulio, Novartis Pharmaceuticals Co., NJ, USA; A. Burke, D. O’Grady, D. Hebrault, METTLER TOLEDO, MD, USA, Internal Publication, 2009
  • Case Study: Real Time Supersaturation Monitoring 2 Concentration stays constant as we cool down 1 Starting point 3 Nucleation 4 De-supersaturation 5 Final drop to solubility curve Solubility curve: Mitsuko Fujiwara, Pui Shan Chow, David L. Ma, and Richard D. Braatz, Crystal Growth & Design, 2002, 2 (5), 363-370
  • Case Study: Real Time Supersaturation Monitoring  Conclusions ATR-FTIR spectroscopy + controlled reaction vessel facilitates crystallization characterization: - Nucleation and growth kinetics of crystallization - Identification and control of critical - Qualitative and quantitative parameters supersaturation method facilitates development of process map - Combination of supersaturation assessment with FBRM® and PVM® for quantitative understanding of tech transfers and scale-ups - Constant supersaturation control possible On-Demand Webinar : “Calibration Free Supersaturation Assessment and Control for the Development and Optimization of Crystallization Processes”, Mark Barrett*, Mairtin McNamara and Brian Glennon, Crystallization Research Group, University College Dublin, Nov ember 2009
  • Presentation Outline  Case Studies - Process Research using ATR-FTIR Spectroscopy with ReactIRTM - ReactIRTM, FBRM®, and PVM® for Process Development - RTCalTM Calorimetry : Enabling Real Time Process Characterization - Understanding Crystallization with ReactIRTM and EasyMaxTM  Conclusions: Software for Design, Data Acquisition and Analysis
  • Software for Design, Data Acquisition and Analysis  Reaction Progress Kinetic Analysis: A Powerful Methodology for Mechanistic Studies of Complex Catalytic Reactions* Summary Data Reaction Progress Kinetic Fit Simulate Models Reaction Conditions Edit Model Parameter Axis Lo Hi Temperature Model Comment k: 1.00 A(0) X axis 10.0 20.0 a: 1.50 B(0) Constant 5.00 8.00 Only two data points. Rerun b: 0.01 T Y axis 40.0 60.0 E act: 24.3e-4 Apply Button/menu drop down – Simulation Output Options: 1) New Isothermal model Time to 95 % conversion of A 2) New temp. depend. model 3) New from selected model Conversion of A at 60 minutes Q Peak during 60 minute reaction New Isothermal model Delete This point the user clicked on represents A(0)=15 and T=48 C. The entire reaction is shown at right using these reaction conditions. • Reaction progress display Time to 95% conversion of A 16.000 T=48 C 14.000 • Temp. dependence model 12.000 10.000 [A],[B] 8.000 6.000 4.000 60.0 2.000 10.0 0.000 • Simulation 0.000 10.000 20.000 30.000 40.000 A(0) T 40.0 time 20.0 *Donna G. Blackmond, Angew. Chem. Int. Ed. 2005, 44, 4302 – 4320; Live webinar from Donna G. Blackmond on April 28, 2010 “Reaction Progress Kinetic Analysis: A Powerful Methodology for Streamlining the Study of Complex Organic Reactions” see www.mt.com/webinar
  • Software for Design, Data Acquisition and Analysis  iC SafetyTM for Evaluation of Thermal Risks of a Chemical Reaction at Industrial Scale* • MTSRsemi-batch trend • Integration of DSC data • Criticality index analysis Source: “Thermal Safety of Chemical Processes: Risk Assessment and Process Design”, Francis Stoessel, 2008, ISBN 978-3527317127, on-demand webinar from Francis Stoessel available at www.mt.com/webinar
  • Software for Design, Data Acquisition and Analysis  ConcIRT Pro: Advanced post-process analysis of single or multiple experiments from the same spectroscopy technique or from two different spectroscopy techniques (e.g., Raman and FTIR, UV/Vis and FTIR, UV/Vis and Raman)
  • On Adopting New Technologies…
  • Acknowledgements  Lilly Research Laboratories, IN, USA - Mark A. LaPack  Merck Research Laboratories, NJ, USA - George X. Zhou  Ashland-Hercules Water Technologies Wilmington, DE, USA - Michael Mitchell  Novartis Pharmaceuticals Co., NJ, USA - Anthony DiJulio 40