Developing an Analytical Impurity Control Strategy …

1 Developing an Analytical Impurity Control Strategy Using QbD. Mark D. Argentine Quality by Design in Development Relies upon Developing knowledge around processes and products . for effective process and product design and Control . Ref: M. Nasr, 2006. IFPAC 2014 2. M. Argentine An understanding of What goes in and What goes on in development . solvent 1 solvent 2, reagent A+B C API. time, temp., (B') pH . (C') (API'). (BP1, BP2 ) (D1, D2 ). A, B = starting materials C = intermediate API = active pharmaceutical ingredient B' = starting material Impurity with potential to form C' and API'. BP = reaction by-product D = degradation product leads to knowledge and development of suitable, robust manufacturing and Analytical controls for your process IFPAC 2014 3. M. Argentine Building a Process Knowledge Base Consider multiple synthetic variations, suppliers for starting materials, etc. Utilize chemistry-guided and knowledge-guided approaches to potential impurities Knowledge-guided: What has been observed in selected samples Chemistry-guided: What is possible/likely from and understanding of the synthetic processes used or considered Any potential genotoxic impurities likely/possible?

2 Use fit-for purpose development methods to explore for potential impurities. Leverage appropriate tools to develop process and Impurity formation/fate understanding - , MS, NMR, IR/NIR/Raman Control Strategy development is a dynamic partnership between process and Analytical and an iterative Strategy To search for and identify impurities To understand Impurity formation and fate To design appropriate process and Analytical controls to minimize or eliminate impurities. IFPAC 2014 4. M. Argentine Example: An HPLC-IR-NMR system to generate development knowledge Obtain valuable Optical Spectrometer information on kinetics, (mid-IR, NIR, Raman, UV-vis) reactive intermediates, relative response factors, etc. From NMR to From NMR to HPLC HPLC. To NMR. From HPLC to Reactor Pump HPLC NMR. (configured for on-line). Reactor 5. Impurity Controls Based upon Process Understanding NMT NMT. Imp B''. Imp D''. Step 1. 30% 70% 90%. 10% Step 2. NMT. Imp A' Imp B' Imp C' NMT Imp D'.

3 80% Step 3. >98% 90% 10% 10% 90%. 20%. AU. NMT NMT NMT. Imp A Imp B Imp C NMT Imp D. C. B. D. A. Minutes IFPAC 2014 6. M. Argentine Process knowledge informs Analytical controls Knowledge space studies Understanding of measurement requirements for the quality attributes of a product that must be controlled What analyte(s). What level(s) An Analytical Target Profile (ATP). What precision Analytical method design space studies IFPAC 2014 7. M. Argentine Building an Analytical Knowledge Base - Control Strategy Development Development Methods HPLC broad polarity screens, multiple detectors orthogonal techniques, on-line analysis targeted methods Process knowledge - what needs to be monitored/controlled ( Analytical Target Profile definition). Quality Control Methods Integral to specifications and/or process controls Optimized for ruggedness IFPAC 2014 8. M. Argentine QbD Tools in Impurities Method Development Strategy Identify key impurities from: Typical drug substance/product samples Design Space Authentic Impurity samples Process development samples with likely impurities ( , reaction samples, mother liquors).

4 Samples from designed, forced degradation studies Potential drug product impurities --- review degradation and excipient interaction design study data Use systematic tools and designed studies to aid in appropriate method development ( , column screening program for design space impurities). IFPAC 2014 9. M. Argentine An example: Analytical Design Space for Impurity Method Development Zorbax SB C8. Platinum C18. Hy persil Gold aQ. Use column Sy nergy Hy dro-RP. Platinum EPS C18 classification system*. to identify column Acquity UPLC BEH Shield RP18. Xterra MS C18. Sy nergy Max-RP. Gemini C18. Hy persil Gold C18. similarities and Acquity UPLC BEH C18. Xbridge C18 differences Atlantis dC18. Acquity UPLC BEH HSS T3. Alltima C18. Luna C18(2)-HST. And Zorbax Eclipse Plus C18. Halo C18. Microtech C18 Exploit column differences in HPLC. Polaris C8-Ether Acquity UPLC BEH C8. Gemini C6-Pheny l SunFire C8. Xbridge C8. method development ACE Pheny l Acquity UPLC BEH Pheny l * , Gilroy, Jonathan J.

5 ; Dolan, John W.;. Discov ery HS F 5. Snyder, Lloyd R. Column selectivity in Hy persil Gold PFP. ACE CN reversed-phase liquid chromatography IV. Bonus RP Type-B alkyl-silica columns. Journal of Chromatography, A (2003),1000(1-2), 757-778. IFPAC 2014 10. M. Argentine Column 1 - C8, low pH. 0. 24. 0. 22. 0. 20 1. 0. 18 5. 0. 16 1 5. 0. 14. Perform screen with distinctly 6 6. U. 0. 12. 4 4. A. 0. 10. 7. 0. 08. Methanol 2 2 7. different conditions/phases 0. 06. 3. 0. 04. 0. 02. 0. 00. 3 Acetonitrile -0. 02. 1. 00 2. 00 4. 00 5. 00 8. 00 9. 00 10. 00 12. 00 13. 00. Minutes Column 2 Polar Embedded, low pH. Optimize separation with modeling 1 1. 4 5 6,7. U. A. 4. 2 5. 6,7. 2 3 Methanol 3 Acetonitrile -0. 02. 2. 00 3. 00 5. 00 7. 00 9. 00 10. 00 11. 00 12. 00 13. 00. Minut es 0. 24. Column 3 Phenyl, low pH. 1. 0. 22. 1. 0. 20. 0. 18 5 5. 0. 16. 0. 14. 4 6. 6. U. 0. 12. A. 0. 10 4. 0. 08. 7. 2 7. 2 Methanol 0. 06. 3. 0. 04. Acetonitrile Predictions for directing additional studies 0.

6 02. 0. 00. 3. -0. 02. 1. 00 2. 00 4. 00 5. 00 8. 00 9. 00 10. 00 12. 00 13. 00. Minutes Column 4 C18, high pH. 6. 0. 24. 5. 0. 22. 0. 20. 0. 18 0. 16 5 7. 4. AU. 0. 14. 5 1. 6. U. 0. 12. A. 0. 10 6 1 0. 08. 4 2. 7. 7 1 Methanol 3. 0. 06. 2 0. 04. 4 3. 2 Acetonitrile 0. 02. 0. 00 3 -0. 02 1. 00 2. 00 4. 00 5. 00 8. 00 9. 00 10. 00 12. 00 13. 00. Minutes Minutes Assessing method robustness w/o doing experiments power of modeling tools evaluate parameters such as temperature, organic strength, and gradient sensitivity for impact on expected minimum resolution IFPAC 2014 12. M. Argentine Gradient Assay Identified in Development for Impurity Control Method selectivity of gradient: A= unspiked matrix 8. 7 B=matrix with 4 5 B spike for 6. 1 2 3 impurities mV. A. 1,2,6,7,8 and Impurity 5. spiked at Minutes Historically, a gradient assay has been used in development to evaluate material quality and confirm the lack of late-eluting impurities IFPAC 2014 13.

7 M. Argentine Initial Gradient Method no significant later-eluting impurities for drug LOT G. LOT F. LOT E. LOT D. LOT C. LOT B. LOT A. BLANK. 0 295 590 885 1180 1475 1770 2065 2360 2655 2950. TIME (SECONDS). IFPAC 2014 14. M. Argentine Can be simplified to an isocratic assay for routine Control Impurity 2 (DI). Impurity 3 (DI). Impurity 5 (PI). Impurity 4 (PI). Impurity 1 (DI). Impurity 6 (DI). SYS SUIT. LOT G. LOT E. LOT D. LOT C. LOT B. LOT A. BLANK. 0 120 240 360 480 600 720 840 960 1080 1200. TIME (SECONDS). IFPAC 2014 15. M. Argentine System Suitability Definition Impurity 4. Impurity 5. API. Impurity 6. mV. System Suitability Blank CP 1 CP 2 Late eluting marker Minutes Identify meaningful system suitability controls to ensure reliable method performance IFPAC 2014 16. M. Argentine Example Chromatography Using an API. Sample Impurity 6. Impurity 5. Impurity 4. API. SYSTEM SUITABILITY. API. 0 120 240 360 480 600 720 840 960 1080 1200. TIME (SECONDS).

8 Use of system suitability samples and a method performance sample (if it contains measurable Impurity levels and is stable) can be useful for method robustness and method transfers IFPAC 2014 17. M. Argentine Using Design Studies to Evaluate Method Robustness Column Temp %ACN-Start %ACN-End %TFA Flow Rate Iso. Hold Patten # Design ( 3 C) ( 2%) ( 2%) ( ) ( ) ( ). pattern 0 0 40 18 80 5. pattern 1 ++++++ 43. Consider 20 82. design studies pattern 2 ++ 43. during 20. method 78. development pattern 3 + + + 37. for 20more method 78. knowledge pattern 4 ++ 37 16 82 pattern 5 + + + 43. Leverage 16. modeling 82 studies pattern 6 + ++ 43. ( , 16. DryLab). 78. pattern 7 ++ 37. operating 16 78. ranges, when possible, in final method pattern 8 ++ + 37 20 82 pattern 0 0 40 18 80 5. IFPAC 2014 18. M. Argentine Robustness Statistical Design Results Total Impurities Total Impurities (vs Rs (resolution Rs (resolution Patten # Design Standard Area (Direct area-%) ext std)) pair 1) pair 2).

9 Pattern 0 000000 10002 pattern 1 ++++++ 9908 pattern 2. Collect important performance ++ 10614 pattern 3. information to justify + + + 10759 pattern 4 ++ . acceptable method 9985 pattern 5. performance aids in setting + + + 10384 pattern 6. meaningful system suitability + ++ 8978 pattern 7 ++. criteria 9145 pattern 8 ++ + 9525 pattern 0 000000 9983 IFPAC 2014 19. M. Argentine Wavelength Robustness Evaluate response for each Impurity to assess robustness as structural differences may cause spectral response differences Ref: J. Chrom A, 762 (1997), 227-233. IFPAC 2014 20. M. Argentine Wavelength Robustness Indeed, different spectral profiles exist for the various compounds (with need for ~280 nm detection) . and a simple wavelength system suitability sample can be developed with commercially- available materials Ref: J. Chrom A, 762 (1997), 227-233. IFPAC 2014 21. M. Argentine Method Transfers and Maintenance Method transfers should include: An assessment for capability to perform the analysis AND.

10 The process and product knowledge and overall Control Strategy for intended use of the method Important for proper method use and for reference in any future improvements Useful tools to assess performance/variability: Data from robustness studies to interpret system suitability results Data from method performance ( Control ) samples in development aid in confirming appropriate performance during transfer and beyond. Transfer of knowledge regarding investigational methods ( , broader-based gradient methods, spectroscopic studies) is also useful for studying potential impact of future process design space changes relative to the Analytical design space Example: Evaluation of new sources of starting materials or excipients IFPAC 2014 22. M. Argentine Performance data allows trends to be identified (and understood). peak tailing Then . 0 20 40 60 80 100. data point New instrumentation peak tailing and more recently 100 120 140 160 180. (Tailing limit NMT ) data point Change in instrumentation affected performance IFPAC 2014 23.