Dissolution Testing and Isothermal 
     Microcalorimetry in QbD
             Outline
             O li
             • Qua...
Quality by Design
                  Product Quality Implementation Lifecycle Initiative (PQLI)




• Quality by Design is ...
Design Space: ICH Q8
         Design Space: ICH Q8

• Multi‐dimensional space that 
  encompasses combinations of 
  produ...
Key Elements of QbD
            Key Elements of QbD
• D fi d i
  Define design space
   – Properties
• Risk assessment
  R...
Design Space (PQLI)
           Design Space (PQLI)
• Integration of prior knowledge

• Creation of a design space is to 
 ...
Design Space (PQLI)
          Design Space (PQLI)
• The functional relationships between Process 
  Parameters and CQAs ar...
A Process is well understood when…


• All critical sources of variability are 
  identified and explained;
  identified a...
Evaluating Variability
          Evaluating Variability
• Utilize Process 
     l
  capability analysis –
  reduce/control...
Risk Assessment in QbD
        Risk Assessment in QbD
• Probability – the likelihood of a consequence.
• Severity – the ma...
Start of the project
•Limited Knowledge
•Defined Designed space
•Broad Control space
                          QbD & Lif
 ...
The Six Things to Remember
      The Six Things to Remember

•   We know….
•   We want to know…
•   We learned …..
    We ...
Product Lifecycle Challenge
       P d     Lif    l Ch ll
Time                5‐12 years   1‐2 years    20‐ 50 years


Pro...
Regulatory Lifecycle of a Product
           Regulatory Lifecycle of a Product
            What do I have to do when a cha...
Biowaiver Today
                                 y
 BCS Classification
 BCS Classification
                               ...
Biowaiver Tomorrow
                            BCS class 1 and Extensions to
       BCS Classification   class 3 and certa...
Example for an predictive model
                In vitro/in vivo Correlation
   USP:  the establishment of a rational rela...
Deconvolution based IVIVC methods
     Deconvolution‐based IVIVC methods
• These methods are
  two‐stage modeling 
  two s...
Convolution‐based IVIVC methods
     Convolution based IVIVC methods
• Convolution‐based IVIVC methods are one‐
  Convolut...
GastroPlus Software
            GastroPlus Software


• Ph i h i l
  Physicochemical 
  Data as Input
• Dissolution Data 
...
Dynamic Dissolution of a Lipophilic 
              Drug
•   pKa = 2.8 and 5.7 Basic and Acetic
    pKa = 2 8 and 5 7 Basic...
Solubility in Blank Buffers
                           Solubility in Blank Buffers

                   0.4


             ...
Solubility in Biorelevant media.
                   Solubility in Biorelevant media

                   5.9

             ...
Dose/Solubility Ratio
                                                         Dose (mg)
                                 ...
Dissolution Profiles
        100


                 80


                 60
   % Dissolved
             d




           ...
Dynamic Dissolution
                              y
                      pH=2    pH=6.5                   pH=7.5         ...
Simulations Results
                      0.8



                      0.6
                mL)




                       ...
Prediction error statistics
    Observed values       AUC=3.552 μg.h/mL          Cmax =0.4796 μg/mL
                      ...
Example Conclusions
              p
• Simulations showed that the drug is
  Simulations showed that the drug is 
  complet...
Oral Dosage Forms
                                                                                         Formulation
   ...
Dissolution Requirements
             Dissolution Requirements



                           Minimum
% released
         d...
Conclusions
• The BCS has changed the way we look at drugs and the drug 
  development process
  development process
• The...
Acknowledgements

     •     Faculty of Pharmacy University of Alberta
     •     SimulationsPlus
     •     NSERC
     • ...
Upcoming SlideShare
Loading in...5
×

Qbd

3,677

Published on

Published in: Business, Technology
1 Comment
3 Likes
Statistics
Notes
No Downloads
Views
Total Views
3,677
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
1
Likes
3
Embeds 0
No embeds

No notes for slide

Qbd

  1. 1. Dissolution Testing and Isothermal  Microcalorimetry in QbD Outline O li • Quality by Design Q y y g • Product lifecycle • Biowaivers • Process knowledge Process knowledge • Predictive Model • Conclusions
  2. 2. Quality by Design Product Quality Implementation Lifecycle Initiative (PQLI) • Quality by Design is a systematic approach to  development that begins with predefined objectives  development that begins with predefined objectives and emphasizes product and process understanding  based on sound science and quality risk  based on sound science and quality risk management. – ICH Q8 Pharmaceutical Development ICH Q8 Ph ti l D l t – ICH Q9 Quality Risk Management – ICH Q10 Quality Systems ICH Q10 Q li S – FDA Pharmaceutical cGMPs for the 21st Century ‐ A Risk‐ Based Approach. Final Report Based Approach Final Report 3/2/2010 2
  3. 3. Design Space: ICH Q8 Design Space: ICH Q8 • Multi‐dimensional space that  encompasses combinations of  product design, manufacturing  product design, manufacturing process design, manufacturing  process parameters and raw  process parameters and raw material quality that provide  assurance of suitable quality and  performance
  4. 4. Key Elements of QbD Key Elements of QbD • D fi d i Define design space – Properties • Risk assessment Risk assessment – Critical vs. Non‐Critical Parmeters • Control strategy Control strategy – Key raw material and excipient properties – Key processing parameters • Set points • Processing times – Process Analytical Technology (PAT) Process Analytical Technology (PAT) – Product testing requirements. 
  5. 5. Design Space (PQLI) Design Space (PQLI) • Integration of prior knowledge • Creation of a design space is to  develop sufficient process  d l ffi i understanding to describe the  functional relationships  between Process Parameters  between Process Parameters and Critical Quality Attributes. 
  6. 6. Design Space (PQLI) Design Space (PQLI) • The functional relationships between Process  Parameters and CQAs are best described with  a predictive model based upon sound  scientific principles, simulations, or  scientific principles simulations or experimentation. 
  7. 7. A Process is well understood when… • All critical sources of variability are  identified and explained; identified and explained; • Variability is managed by the process; and,  • Product quality attributes can be Product quality attributes can be  accurately and reliably predicted over the  design space • An ability of continuous improvements  • “Real Time" assurance of quality CONTROL SPACE
  8. 8. Evaluating Variability Evaluating Variability • Utilize Process  l capability analysis – reduce/control  / “common cause”  variability p • Develop effective  Corrective Action and  Preventive Action will  Preventive Action will eliminate “special  cause variability cause” variability
  9. 9. Risk Assessment in QbD Risk Assessment in QbD • Probability – the likelihood of a consequence. • Severity – the magnitude of the impact of a the magnitude of the impact of a  consequence. • Detectability – the level or ability at which a  bili h l l bili hi h consequence can be measured. • Sensitivity – the attenuation of interactions  between multivariate dimensions.  between multivariate dimensions
  10. 10. Start of the project •Limited Knowledge •Defined Designed space •Broad Control space QbD & Lif & Lifecycle l End of the Lifecycle •Large Knowledge L K l d •Same Design Space •Narrow Control space 3/2/2010 10
  11. 11. The Six Things to Remember The Six Things to Remember • We know…. • We want to know… • We learned ….. We learned • We control ……. • We understand, agree & approve & revise • We monitor and release!!! W it d l !!!
  12. 12. Product Lifecycle Challenge P d Lif l Ch ll Time 5‐12 years 1‐2 years    20‐ 50 years Product SUPAC /  QbD NDA Development Career  Career Cycle Basic  Basic Medical Regulatory Science Science Science
  13. 13. Regulatory Lifecycle of a Product Regulatory Lifecycle of a Product What do I have to do when a change is necessary? • SUPAC 1995 SUPAC 1995  – ingredients, equipment – dissolution test as surrogate – Scale‐Up and Postapproval Changes: Chemistry, Manufacturing, and Controls, In Vitro  Dissolution Testing, and In Vivo Bioequivalence Documentation • IVIVC 1997 ER – Modeling and dissolution as surrogate using a level A correlation – Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In  Vitro/In Vivo Correlations • Biowaiver 2000 – Drug classification as surrogate Drug classification as surrogate – Waiver of in vivo bio‐equivalence studies for immediate release solid oral dosage forms  containing certain active moieties/active ingredients based on a Biopharmaceutics  Classification System”  y 3/2/2010 13
  14. 14. Biowaiver Today y BCS Classification BCS Classification BCS class 1 Oral Dosage Forms Level A correlations based on: 1) Deconvolution methods 2) Convolution methods Bio‐ SUPAC waiver 3/2/2010 14
  15. 15. Biowaiver Tomorrow BCS class 1 and Extensions to BCS Classification class 3 and certain class 2 Acids CQA: Particle size QbD Surface area Design Space Biowaiver Dissolution profile p Predictive Model Predictive Model Clinical Observations Therapeutic outcome 3/2/2010 15
  16. 16. Example for an predictive model In vitro/in vivo Correlation USP:  the establishment of a rational relationship between a  USP: “the establishment of a rational relationship between a biological property, or a parameter derived from a biological  property produced by a dosage form, and a physicochemical  property of characteristic of the same dosage form” (USP 29)  ( ) FDA defines IVIVC as “A predictive mathematical model describing  the relationship between an in vitro property of a dosage form  the relationship between an in vitro property of a dosage form (usually the rate and extent of drug dissolution or release) and a  relevant in vivo response, e.g., plasma drug concentration or  amount of drug absorbed” (FDA September 1997)
  17. 17. Deconvolution based IVIVC methods Deconvolution‐based IVIVC methods • These methods are two‐stage modeling  two stage modeling procedures • In the first stage, a deconvolution I h fi d l i method is used to estimate the time  course of in‐vivo absorption (fraction  absorbed fabs vs. time t).  • In the second stage, the in‐vivo  absorption (or release) time profile  obtained in this first stage is plotted  vs. the time course of the in vitro  vs. the time course of the in‐vitro dissolution profile.  • Usually a point‐to‐point relationship is  established between the in‐vivo and  in‐vitro parameters of the same time  in vitro parameters of the same time point (e.g., in‐vivo fraction absorbed  Journal of Controlled Release 72 (2001) 127–132 fabs vs in‐vitro fraction dissolved fdis); Regulatory perspectives on in vitro (dissolution) /  • Linear or sigmoidal curves in vivo (bioavailability) correlations Venkata Ramana S. Uppoor
  18. 18. Convolution‐based IVIVC methods Convolution based IVIVC methods • Convolution‐based IVIVC methods are one‐ Convolution based IVIVC methods are one stage modeling approaches, and they directly  relate the time course of the in‐vivo measured  relate the time course of the in vivo measured plasma concentration to the time profile of  the in‐vitro dissolution. h i i di l i • integral or differential equations g q Gillespie 1997; Modi et al 2000; Veng‐ Pedersen et al 2000; Balan et al 2001;  Pedersen et al 2000; Balan et al 2001; O’Hara et al 2001; Pitsiu et al 2001;  Gomeni et al 2002
  19. 19. GastroPlus Software GastroPlus Software • Ph i h i l Physicochemical  Data as Input • Dissolution Data  as Input • Permeability
  20. 20. Dynamic Dissolution of a Lipophilic  Drug • pKa = 2.8 and 5.7 Basic and Acetic pKa = 2 8 and 5 7 Basic and Acetic • logP = 7.01, highly lipophilic • >99% bound to plasma proteins • Oral bioavailability variable 58‐70%  (Cheng, et al 1996) 
  21. 21. Solubility in Blank Buffers Solubility in Blank Buffers 0.4 0.3 Solubility mg/mL 0.240 m 0.2 0.1 0.002 0.010 0.007 0.011 0.00018 0 U -SG SP F SGF-SLS pH AC T Buff. E Buff Blank- Blank- Blank- pH-1.2 2.0 pH 4.0 FeSSIF pH FaSSIF pH FaSSIF pH 5.0 6.5 7.5 Media and pH
  22. 22. Solubility in Biorelevant media. Solubility in Biorelevant media 5.9 4.690 4.9 olubility mg/mL 3.9 Blanks 3.370 m Low Quality 2.9 High Quality 1.9 So 0.9 0.205 0.01 0.030 0.01 0 5 0.007 0.020 0.011 -0.1 01 5.0 6.5 7.5 Media and pH
  23. 23. Dose/Solubility Ratio Dose (mg) 4 10 Solubility pH (mg/mL) Dose/solubility ratio SGF (without enzymes) 1.2 0.00018 27777.8 55555.6 SGF-0.25% SLS 2.0 0.240 16.7 41.7 Acetate Buffer 4.1 0.002 2500.0 5000.0 LQ-FeSSIF 5.0 0.030 133.3 333.3 HQ-FeSSIF HQ F SSIF 5.0 50 0.015 0 015 266.7 266 7 666.7 666 7 LQ-FaSSIF 6.5 0.020 200.0 500.0 HQ FaSSIF HQ-FaSSIF 6.5 0.205 19.5 48.8 LQ-FaSSIF 7.5 3.370 1.2 3.0 HQ-FaSSIF 7.5 4.690 0.9 2.1 According to BCS, this drug is a poorly soluble drug
  24. 24. Dissolution Profiles 100 80 60 % Dissolved d 0.25% SLS-H2O FaSSIF-500 mL-100 RPM(n=3) 40 FaSSIF-900 mL-75 RPM(n=3) FaSSIF-500 mL-75 RPM(n=6) USP-SIF pH 6.8 (n=3) USP-Phos. Buff pH 6.8 (n==3) 20 Blank FaSSIF (n=3) Flow Through Cell 0 0 40 80 120 160 200 240 Tim (m e in) 1. Fast and complete dissolution in 10 min H2O-0.25% SLS. O 0.25% 2. Incomplete dissolution in biorelevant media (89, 77 and 69%) in FaSSIF 500-100 RPM, FaSSIF-900 & FaSSIF-500-75 RPM 3. SIF and Phosphate buffer <10%, and insignificant in blank FaSSIF p , g 4. Insignificant difference between 500 and 900 mL at 75 RPM 3/2/2010 24
  25. 25. Dynamic Dissolution y pH=2 pH=6.5 pH=7.5 pH=5.0 100 80 ved % Dissolv 60 40 20 0 0 30 60 90 Time (min) 120 150 180 210 240 Dissolution rate relatively fast in SGF‐SLS, slows down at pH 6.5, then  increases when pH is Changed to 7.5 increases when pH is Changed to 7 5
  26. 26. Simulations Results 0.8 0.6 mL) Observed ma Conc(ug/m 0.4 0.2 Plasm 0 0 4 8 12 16 20 24 Time(h) 1. 1 AUC ffrom Flow through, FaSSIF-500 mL-100 RPM, FaSSIF- Fl th h F SSIF 500 L 100 RPM F SSIF 900 ml, 75 RPM and H2O-0.25% SLS are not significantly different from observed mean value (p>0.05) 2. 2 Cmax from H2O-0 25% SLS is significantly different from H2O-0.25% observed mean value (p<0.05
  27. 27. Prediction error statistics Observed values AUC=3.552 μg.h/mL Cmax =0.4796 μg/mL Predicted Values AUC Cmax AUC Cmax Media (ug.h/mL) (ug h/mL) (ug/mL) %PE %PE Flow through cells 3.52 0.567 1.0+ 18.2* FaSSIF-500 mL-100 RPM 3.49 3 49 0.535 0 535 1 7+ 1.7 11.6* 11 6* FaSSIF-900 mL-75 RPM 3.31 0.462 6.9+ 3.6+ FaSSIF-500 mL-75 RPM 2.68 0.426 24.5+ 11.2+ USP-SIF 0.64 0.061 81.9+ 87.3+ H2O-0.25% SLS 3.54 0.803 0.4* 67.4+ Flow thru-No FPE 5.67 0.913 59.6* 90.3* *means %Higher and + means %Lower than mean observed value means %Higher, and + means %Lower than mean observed
  28. 28. Example Conclusions p • Simulations showed that the drug is Simulations showed that the drug is  completely absorbed and throughout the  GIT. GIT • Its bioavailability appears to be dissolution  rate controlled. • Level A IVIVC can best be established using Level A IVIVC can best be established using  the flow through cell dissolution • The drug appears to be a BCS class 2 drug.
  29. 29. Oral Dosage Forms Formulation Does the  change formulation  impact  NO dissolution rate YES Why did you do it? Is  YES Dissolution slower  than slower than absorption Behavior like a IVIVR NO Controlled Release Does the  YES Dosage form Scale Factor Computer formulation impact Dissolution permeability Simulations Controlled IVIVC Established  NO Unlikely to Establish a  R l i hi  Relationship  Relationship  R l i hi  Relationship 
  30. 30. Dissolution Requirements Dissolution Requirements Minimum % released d Dissolution  eleased for BE  Minimum/Maximum Reference  Dissolution for BE  % re % Product Reference  Permeability  P bilit Product Controlled Dissolution Controlled time time
  31. 31. Conclusions • The BCS has changed the way we look at drugs and the drug  development process development process • The BCS is the mechanistic foundation for Biowaivers • BCS is a risk management tool g • BCS allows us to ask the right questions to find the solutions in drug development • S ft Software can assist to estimate critical formulations variables i tt ti t iti l f l ti i bl • Fewer in vivo studies • QbD is a product specific extension of SUPAC is a product specific extension of SUPAC • Need to educate students in QbD 3/2/2010 31
  32. 32. Acknowledgements • Faculty of Pharmacy University of Alberta • SimulationsPlus • NSERC • Merck Frosst • USP • Dr M Di Maso Dr. M. Di Maso • Arthur Okumo 3/2/2010 32

×