Docstoc

hussain

Document Sample
hussain Powered By Docstoc
					   Quality by Design and Risk
   Based Regulatory Scrutiny:
      Connecting the Dots
AAPS 39th Annual Pharmaceutical Technologies
Conference at Arden House
Quality by Design in Pharmaceutical Development and
Manufacturing: Principles, Applications and Scientific
Risk-Based Regulatory Decisions

                   Ajaz S. Hussain, Ph.D.
                     Deputy Director
      Office of Pharmaceutical Science, CDER, FDA
  A Convoluted Journey Towards QbD
• Starting with Arden House 2001 going back to
  1997 - 1998 - 1999 - 2000 - 2001 - ….
• Art Vs. Science - SUPAC - BCS - PAT - QbD
• Your task is to deconvolute this convoluted
  journey (to QbD) using your knowledge and
  experience and from what you learn from the other
  speakers and articulate:
   – What is QbD?
   – How should QbD be achieved and communicated?
   – Why can it be used to justify a risk-based regulatory
     scrutiny?
 Ajaz S. Hussain, 26 January 2001, Arden House

                     Art and Science of Pharmaceutical
                           Product Development
               • Art                             • Science
                   – the power of                  – accumulated and
                     performing certain              accepted knowledge
                     actions esp. as                 that has been
                     acquired by                     systematized and
                     experience, study, or           formulated with
                     observation                     reference to the
                                                     discovery of general
                                                     thruths or the
                                                     operation of general
                                                     laws
        #1 SUPAC: Inspection/PAS
   are the only way to appreciate “Art”    #1 SUPAC: Inspection/PAS are not
      #2 Specifications: Tighter than     the only way to appreciate “Science”
    Clinical Product/Process capability     #2 Specifications: Satisfy clinical
Justification:Testing to Document Quality      need and process capability
              26 January Arden house 2004          Justification: QbD
 Art, Science, and Regulation of
Pharmaceutical Unit Operations*                                         Regulatory Applications of RSM?
                                                                      • IND/NDA                             • Validation
                                                                         – Causal links between               – improve confidence
                   Ajaz S. Hussain, Ph.D                                   quality and clinical                 and minimize
          Director, Office of Testing and Research                             • critical formulation and       likelihood of product
                        CDER, FDA                                                process variables              failure
                                                                               • optimal products
                  26 January 2001 Arden House                                                               • SUPAC
                                                                         – Support/justification
                                                                                                              – supporting changes
                                                                           for quality
 * University of Bradford, Prestige Lecture and Pharm. Engineering                                               • reduce the need for
                                                                           specifications and
                   Seminar, University of Michigan                                                                 additional testing and
                                                                           controls                                reporting
                                                                                                              – “Make-Your-Own
                                                                                                                SUPAC”


                                                             Connect the Dots
                                          • Thanks for tolerating my three presentations
                                          • If you are interested in developing a focus
                                            group on DOE & Optimization Techniques
                                            please contact me or David Oakley
                                              – hussaina@cder.fda.gov
                                              – dmoakl@magellanlabs.com
                                          • I hope to see you at Arden House 2003
                                              – (Modern In-Process Controls)
Pharmaceutical Product Development: Past,
          Present, and Future

                   Ajaz S. Hussain, Ph.D.
           Division of Product Quality Research
         Center for Drug Evaluation and Research
              Food and Drug Administration

         The Expert System Focus Group Meeting,
                Baltimore, 13 August 1997




                                                   EVOLUTION OF PRODUCT
                                                   DEVELOPMENT PROCESS
                                           Art
                                     Trial-and-Error
                                  Optimum formulation ?
                                    Human experts and
                                      Technical staff
                                  Number of experiments ?
                                                                 Science & Engineering
                                                                 Designed Experiments
                                                                 Optimized formulations
                                                                  Human experts and
                                                                     Technical Staff
                                                                 Number of experiments
                                                                        (defined)
Product Development: Evolution
• Over the last 100 (30) years
  – “Art” Science & Engineering based
  – Trial-and-Error  DOE  CAD
  – Dosage forms  Drug Delivery Systems 
    Intelligent Drug Delivery
  – Few creative options tested  Many creative
    options tested
  – Batch processing  Continuous (automated)
    processing
   Product Development Knowledge
Level of Sophistication                Details Resolved
                          Rules
HIGH                                             HIGH
                      MECHANISTIC
                        MODELS


                     EMPIRICAL
                      MODELS                 MEDIUM
MEDIUM
                 HEURISTIC RULES
                 “Rules of Thumb”

LOW        HISTORICAL DATA DERIVED FROM          LOW
           TRIAL-N-ERROR EXPERIMENTATION
        CAD Tools: Artificial Intelligence
      (AI) and Information Technology (IT)
Expert Systems (AI)      Rules


                      MECHANISTIC
                        MODELS


                      EMPIRICAL
                       MODELS

                  HEURISTIC RULES
Fuzzy ES
                  “Rules of Thumb”
                                            ANN
            HISTORICAL DATA DERIVED FROM    (IT)
            TRIAL-N-ERROR EXPERIMENTATION
 Manufacturing Changes:                 Powerx Symposium, Japan, 1998
Impact on Product Quality
    and Performance

        Ajaz S. Hussain, Ph.D.
            Deputy Director
 Division of Product Quality Research
   Office of Pharmaceutical Science
    Center for Drug Evaluation and
          Research, U. S. FDA
Powerx Symposium, Japan, 1998
        Link to Safety & Efficacy
   Attributes
    –   Identity
    –   Strength - assay, content uniformity
    –   Quality - physical, chemical, biological
    –   Purity - impurities, degradation products
    –   Potency - pharmacological activity (bioavailability
        /dissolution)
   Test Methods
    – Physical, Chemical, Biological, Clinical
   Acceptance Criteria
    – Based on Safety & Efficacy considerations


                                                 Powerx Symposium, Japan, 1998
Powerx Symposium, Japan, 1998
        Powerx Symposium, Japan, 1998




RISK
Based
   SUPAC: Adherence to Established In-
Process Controls and Product Specifications
Sufficient to Document Unchanged Product
       Quality and Performance?

            Ajaz S. Hussain, Ph.D.
  Director, Division of Product Quality Research,
                     CDER, FDA



                         Annual Meeting - New Orleans
                         November 14 - 18, 1999
                                                        Regulatory Hypothesis Approach
                                                         Drug    Product Technical Committee
                                                           Ho:  Adherence to CGMP’s, which include
                                                            validation, and appropriately established
                                                            product specifications are sufficient to assure
                                                            consistent quality and performance (or
                                                            equivalence) of drug products that are
                                                            manufactured at different locations using
                                                            alternate pharmaceutical unit operations,
                                                            excipients, and container/closure systems
                                                           Initial Projects: IR Dosage Forms
                                                           Outcome: ????
                                                    5                                                     aaps
               Desired Outcome
 Reduce time and cost for implementing
  manufacturing changes (industry)
 Reduce the number of CMC/Biopharm
  supplements (currently ~ 5000)
       Reduce review load - one time review by
        CDER (FDA)
    Facilitate introduction of new
    technology and maintain the
    competitive edge of US industry
   Ensure that quality is „built-in”         aaps
6
             Different Perspectives
     SUPAC-IR
      CGMP’s,   which include validation, and product
       specifications are NOT sufficient to assure
       consistent quality and performance (or
       equivalence) of MOST IR drug products that
       are manufactured at different locations using
       alternate pharmaceutical unit operations, and
       excipients (container/closure systems not covered under SUPAC-IR)
                                                               SUPAC-

      Why? Ajaz Hussain
      Why not? Sid Goldstein, Arni Repta, and Stve Byrn

7                                                                   aaps
          FDA Perspective: CMC
     Releasetesting at the time of
     manufacture does not provide
     information that assures “shelf-life”
     Stability  commitment may identify
       stability problems at a later time when
       the product is already in use by the
       patients, recall takes time and may be
       incomplete

8                                          aaps
         FDA Perspective: CMC
 A    combination of long term and
     accelerated stability testing (and
     PAS) are currently the only means
     for assuring correct expiry date
     principlesof accelerated stability may
      not be appropriate for predicting
      “physical” stability
  Potency   related recalls may
     suggest blend uniformity problems
10                                       aaps
      FDA Perspective: Biopharm
  In  Vitro dissolution specification
     may not assure bioequivalence
     dissolution  test is for QC only
        one point acceptance criterion
        media and hydrodynamic conditions
         may not reflect in vivo conditions
        IVIVC needed - tends to be
         “formulation specific”
        excipients may alter absorption
11                                       aaps
Root Cause Analysis of OOS
 Observations for Product X
         Ajaz Hussain, Ph.D.
       Deputy Director (Acting)
   Office of Pharmaceutical Science
             CDER, FDA
                X/X/01
             Summary: Solutions
• Reformulation appears to be the preferred solution
   – XX and the manual XXX process are likely to be the primary root
     cause for OOS with the dissolution specification
       • Possible solution: Automation plus optimization of XX amount
       • Immediate solutions - reduce XXX and increase the number of XXX
         XXXX applications (dilute), preferably use an XXX
   – XXX is a natural material ….it is not an ideal candidate for
     slow/controlled release application
       • Possible solution: New synthetic polymer

• Revalidation of the current
  formulation/process is not likely to
  provide a reliable solution
                          Summary
• OOS problems directly linked to the xxxx and xxx
  xxxx process
      • Current controls (raw materials and in-process not sufficient)
      • Changes or deviations (since XXXX), if any, may not be apparent
   – Compnay Y’s commitment for bio studies and
     process/formulation improvements is an excellent
     opportunity to address long standing regulatory issues -
     based on scientific data
• FDA working as team will be able to define
  meaningful milestones and timelines and assure
  compliance
CMC Review and CGMP Inspection
          Interface
• A simplified and hypothetical case to explore
  some aspects of the CMC review (e.g.
  specification setting) and CGMP inspection (e.g.,
  issue of a Warning Letter) interface.
   – During an inspection of a manufacturing facility, an FDA
     investigator observed a very high batch rejection frequency (at
     release and upon stability testing) of an inhalation product with
     respect to its DCU specification (batch rejection and recall of
     batches with Out of Specification (OOS) results had occurred).
     The product had been on the market for more than 5 years and the
     observation led to a Warning Letter; the reason cited was "failure
     to adequately validate the manufacturing process." The company's
     response to the Warning Letter was a proposal to revalidate the
     process.
              DCU Specification
• The DCU test is designed to demonstrate the uniformity of
  delivered dose consistent with the product label and
  provides an overall performance evaluation of a batch;
  assessing the formulation, the manufacturing process, the
  valve, and the actuator or other related inhaler components.
  The recommended DCU is predominately a non-
  parametric limit test that counts the number of
  determinations in a sample within and outside certain pre-
  fixed limits and includes a criterion referred to as a "zero
  tolerance criterion" for the test sample (i.e., no test sample
  is outside 75-125% of the label claim).
           Questions

• From a science and risk
  management (to
  patient/quality as well as
  regulatory risk) perspective
  was this an optimal solution?
What constitutes an OOS result? How does
  one distinguish such a result from a
          statistical "outlier"?
• The US District Court for the District of New Jersey (Civil
  Action No. 92-1744) expressed an Opinion that outlier
  analyses should not be used for chemical assays, because if
  they were appropriate, the USP would have recommended
  the procedure. This opinion suggested the fact that the
  outlier test in the USP is only directed toward biological
  assays; because no mention is made of chemical assays,
  the test (for outliers) was not applicable to chemical
  assays.
   – In practice, the FDA has recognized the need to address "outliers"
     for any assay in its draft Guidance, Investigating Out of
     Specification (OOS) Test Results for Pharmaceutical Production
     (September 1998)
  What are the criteria for high batch rejection
frequency? What is the relationship between this
       frequency and the state of process
              control/validation?

• The Opinion in the Court case cited above
  suggests that a rejection of 10% or more of
  manufactured batches may be considered as high
  batch rejection frequency. However, scientific
  answers to these questions are long overdue, as is
  a discussion on the issue of outliers; FDA
  guidance is needed to clarify this issue. Clearly,
  the batch rejection frequency is one of the
  dimensions (probability) of risk to quality.
           Root Cause Investigation
• What factors (root causes) contributed to the high
  batch rejection frequency?
• How does one ensure adequacy of an root cause
  investigation?
• What data/information was available for the root-cause
  investigation?
   – Does the current system encourage collection of information (other than
     that available in the batch records), so as to be able to identify root cause?
   – If, for example, one increases the sample size for testing, in order to get a
     robust estimate of product variance, this may simply increase the
     probability of rejecting a batch. Moreover, data in batch records pertaining
     to currently used non-parametric (compendial) specifications may not
     provide a means to get a robust estimate of variability. If this is the case,
     how should we identify relevant sources of variability in a CGMP setting?
Assuming that the root-cause investigation is
          judged to be adequate
• If an assignable cause for high batch rejection frequency
  cannot be identified (a frequently observed scenario), what
  options are available for resolving this dilemma?
• Should it be deduced that the product/process design is
  inherently variable (random variability)?
   – Should it be interpreted that this product/process design is not
     capable of consistently meeting the set DCU specification
   – Should the process be revalidated? If so, what will this activity
     entail?
   – Should the process be improved (reduced variability)? If so, how?
   – Could attempts to address inherent (random) variability, without
     addressing the basic product and process design, increase the risk
     of the process truly going out of control?
 Consequence (or severity of harm)?
• What are the consequences of high batch rejection
  frequency or what is the estimated risk to quality?
• Since the risk-benefit decision for the DCU specification
  and approval were based predominantly on assessment of
  clinical and CMC data, derived from the clinical batches
  on the same product/process design, how can this
  knowledge be used to evaluate risk to quality?
• What data/information is necessary for this evaluation?
  (e.g., consumer complaints, AERs, dose-response
  relationships, development information, etc.)
Risk Based Regulatory Decision?
• If clinical data, consumer complaint analysis,
  AERs, and other relevant data do not identify an
  increased risk (relative to the original approval
  risk-benefit decision) should the DCU
  specification be modified? Why?
   – To be consistent with the inherent variability of the
     approved and validated process (e.g., to minimize
     unnecessary batch rejections and the need for frequent
     OOS investigations, so that company and Agency
     resources can be focused on other more important high
     risk situations)
Risk Based Regulatory Decision?
• Alternatively, should the original DCU specification be
  retained because clinical data, AERs, and consumer
  complaints are often considered to be insufficiently
  discriminating to detect the impact of variability in DCU on
  an individual patient basis?
   – If the original DCU specification is to be retained,
     could this not be considered to be an "arbitrary"
     public standard? Alternatively, does the high
     manufacturing cost (low production cycle time due to
     frequent OOS investigation, batch rejection, recall of
     released batches, land-fill or incineration costs for disposal
     of rejected/recalled batches, etc.) provide the necessary
     incentive to improve the product/process design? Why is
     this so and ultimately, who pays for this inefficiency?
  What lessons can be learned from
        these types of cases?
• Is the current approach ("one-size-fits-all") to
  setting specifications an optimal risk-mitigation
  approach?
• How should specifications be set to satisfy both
  clinical objectives and process capability?
• Would an interim specification approach at the
  time of approval plus a Phase IV commitment to
  finalize the specification based on process
  capability data and other relevant supporting data
  provide an improved manufacturing science
  approach for preventing these types of problems?
   What lessons can be learned from
         these types of cases?
• What type of pharmaceutical development
  (particularly, the Manufacturing Controls
  (the MC portion of CMC) information
  would be most useful to assess quality by
  design and establish risk-based
  specifications?
  What lessons can be learned from
        these types of cases?
• Was a Warning Letter approach the optimal action
  in this case? The risk-benefit decision during
  NDA review and approval were based on clinical
  data derived for the approved product/process
  design, and as part of this process if the DCU
  acceptance criteria set conservatively and without
  considering process capability to minimize
  concern by ensuring a high rejection frequency
  and to exclude perceived risks from the
  marketplace - the high failure rate was by design?
  What lessons can be learned from
        these types of cases?
• What should the Agency do to ensure
  continuous improvement?
• Are new procedures necessary to ensure
  coordinated and synergistic interactions
  between CMC review and the CGMP
  inspection process?
• How do we move from a "testing to
  document quality" to "quality by design"?
  What lessons can be learned from
        these types of cases?
• Is the current approach ("one-size-fits-all") to setting
  specifications a significant hurdle for introducing
  new non-CFC based inhalation products and novel
  products? If yes, what information is available to
  enable FDA to evaluate that the current
  manufacturing technology is truly not capable of
  meeting a "one-size-fits-all" standard? Furthermore,
  how should FDA ensure continuous improvement in
  technology to minimize risk, improve risk-benefit
  decisions, minimize multiple CMC review cycles
  and ensure a timely drug approval process?
              Fundamental Premise

• The fundamental premise of our pharmaceutical quality
  system - that quality must be designed into products – is a
  recognition that end product testing alone, with or without
  zero tolerance, can not eliminate or minimize risk of
  nonconformance.
• In the absence of 100% nondestructive testing (e.g., in
  cases where only a sample can be tested), risk of
  nonconformance can only be minimized through proper
  design, development, process understanding and control,
  and by ensuring an adequate quality assurance system.
                           100
                                                                True Mean (μ)
                           90
                                                                 at 100% LC
Probability to Accept, %




                           80
                           70
                           60                                                        FDA DCU&TCL
                           50                                                        PTI 12/36
                           40                                                        USP 10/30
                           30
                           20
                           10
                            0
                                 6   8      10     12     14     16        18   20
                                         Standard Deviation,  (% of LC)
       CMC-GMP Disconnect
• In most cases, pharmaceutical development
  information is not included in the CMC sections of
  applications received by CDER/FDA (even
  though this has been held on site for audit during
  CGMP inspections).
• Absence of this information, in some ways, may
  have focused the attention of CMC reviewers and
  forced a conservative approach to setting
  specifications, as the only available tool to
  minimize their concerns on behalf of the US
  patient.
         CMC-GMP Disconnect
• A further dimension which has added problems to the
  CMC review process has been a less than optimal
  appreciation of what is accomplished during process
  validation efforts; this for two prominent reasons: - A less
  than optimal interaction has existed between CMC experts
  (particularly in CDER) and their colleagues conducting
  CGMP inspections. Also criticisms have appeared (in
  scientific literature and elsewhere) which imply that the
  practice of process validation may be losing its focus on
  science and engineering.
       CMC-GMP Disconnect
• From one of the best and respected CMC
  Team Leaders in CDER (published in the
  October 2003 issue of the Gold Sheet):
     • "Closer cooperation between ORA and the Center
       review chemists: I can tell you that I have been here
       for 15 years and it is still not completely clear to me,
       even after having taken some GMP training
       recently, what exactly it is that the ORA folks look
       for………we don’t really understand what the field
       folks do and I think the field folks are not completely
       clear on what we do. .. these current initiatives …
       are going to bring us closer together...”
            Dimensions of Quality
• Performance                             • Features
    – Will the product do the                  – Does the product have
      intended job?                              multiple features or use
• Reliability                                    characteristics?
    – How often does the product          • Perceived quality
      fail?                                    – What is the reputation of
• Durability                                     the company or its
                                                 products?
    – How long does the product
      last?                               • Conformance to Standards
• Aesthetics                                   – Is the product made exactly
                                                 as the designer intended?
    – What doe the product look
      like?                               • (Serviceability)
Douglas C. Montgomery. Introduction to Statistical Quality Control, 4th Ed., Wiley
                            Definition
• Quality means fitness for use
    – Quality of design
         • Formulation design
    – Quality of conformance
         • Process and QA design
• Quality is inversely proportional to variability
    – If variability in the important characteristics of a
      product decreases, the quality of the product increases
• Quality Improvement is the reduction of
  variability in processes and products


Douglas C. Montgomery. Introduction to Statistical Quality Control, 4th Ed., Wiley
    Operational effectiveness of this
              definition

                                                   Japan


                                                   US




                   LSL             Target               USL



Douglas C. Montgomery. Introduction to Statistical Quality Control, 4th Ed., Wiley
      Phase Diagram of the Use of Quality
          Engineering Methods (non-
               pharmaceutical)
100
               Acceptance
                Sampling

                                           Process
                                           Control
                                                                               *Shangraw, R. F., and
                                                                              Demarest, D. Survey of
                                                         Design             current industrial practice in
                                                                                the formulation and
                                                           of                manufacture of tablets and
                                                       Experiments            capsules. Pharm. Tech.
                                                                                17(1): 32-44 (1993).

                                                                            Pharmaceutical*
 0
                          (TIME) Level of Maturity

      Douglas C. Montgomery. Introduction to Statistical Quality Control, 4th Ed., Wiley
                Six Sigma?
• 3 Sigma quality performance
  – Probability of producing a product within these
    specification is 0.9973, i.e., 2700 ppm defective
  – Not bad?
  – If all specs (e.g., incoming raw materials, in-
    process, final product) are 3 sigma
  – 0.9973X0.9973…X0.9973 = 0.7631 (if 100
    components) , i.e. 23.7% of the products
    produced under 3-sigma can be defective.
              Quality Improvement

                                           Acceptance Sampling



                                           Statistical Process Control



                                           Design of Experiments

      LSL         Mean         USL
Douglas C. Montgomery. Introduction to Statistical Quality Control, 4th Ed., Wiley
              Quality by Design:
               A Way Forward

• Prospectively designate critical quality parameter
  during development (product & process)

• Evaluate and refine

• Create robust link between process parameter,
  specifications and clinical performance


                                 Janet Woodcock, M.D.
                                  September 17, 2003
                             “Creating Quality Places: Successful Communities By Design”

                                                                   STRUCTURE-BASED
                          Luxury by Design, Quality by Chance        DRUG DESIGN




 CEO draws quality lessons from design failures
http://www.eetimes.com/story/OEG20000322S0023                                 nelfinavir
                                                                      Arabians By Design
        Gardens By Design




http://www.gardensbydesign-michigan.com/
                                                                http://www.arabiansbydesign.freeservers.com/
              What is QbD?

• Design decisions based on through
  formulation and process understanding as
  these relate to the intended use

• What is the relationship between QbD and
  Risk?
  – Within a given quality system and for a product:
    inverse relationship between level of QbD and Risk
Example Attribute: Bioavailability
• Objective: Maximize & reproducible
  – Absorption mechanism (passive, active, site
    specific)
  – Physico-chemical attributes (solubility, dissolution
    rate, salt selection, particle size, morphic form,
    stability of drug substance ….)
  – Formulation design (disintegrating agent, wetting
    agent, solubilizer, pH modifiers, absorption
    enhancers,..)
  – Process design (wet/dry granulation, lubrication,
    compaction,….)
  – Specifications and controls on all critical variables
  Formulation & Process Design
• Starting at small scale – pilot – clinical/prod.
• Need tools to screen/evaluate various design
  prototypes
   – In Vitro Dissolution Test
      • bio-studies to ensure relevance of in vitro dissolution test
      • Relevance based on physico-chemical aspects of the drug
        and formulation
• Observations (personal)
   – Often a dissolution test is used to screen/evaluate
     experimental formulations without sufficient
     considerations or verification of its in vivo
     predictability (relevance)
      Dissolution Test &
Bioequivalence: Risk Assessment
                        Dissolution
                YES      generally
                           “over-
Bioequivalent



                      discriminating”


                                        Dissolution fails   Why?
                                           to signal
                NO




                                          bio-in-equi
                                          ~ 30% (?)


                         NO            YES
                      Dissolution Specification
                   False Positives and False
                         Negatives!!!
                                                                          Test/Ref. Mean
     15 min 30 min 45 min AUC Cmax
 Ref   95     96     98   100  100
  B    96     97     97   104  95
  C    62     84     92    84  55
  D    82     94     95    88  87
  E   103    103    103   112  120
  F    13     35     53   100  102
I. J. MacGilvery. Bioequivalence: A Canadian Regulatory
 Perspective. In, Pharmaceutical Bioequivalence
. Eds. Welling, Tse, and Dighe. Marcel Dekker, Inc., New York, (1992)).
    Failure to Discriminate Between Bio-in-
equivalent Products: Inappropriate Test Method?
                                                        (weak acid, rapid dissolution in SIF)

                                                                  Capsule (Ref.)
      Drug Concentration in Plasma (ng/ml)




                                             1800
                                             1600
                                                                               Tablet 1
                                             1400                       (wet-granulation - starch)
                                             1200                            Tablet 2
                                             1000                           (direct compression -
                                                                            calcium phosphate)
                                             800
                                             600
                                             400
                                             200
                                                        USP Paddle 50rpm, Q 70% in 30 min
                                               0
                                                    0       1       2        3          4    5       6
                                                                        Time in Hours
            NDA #X: Bioequivalent?
• Drug X (100 mg dose, volume        • The company wants to
  required to dissolve the dose at     manufacture the product using
  pH 8, lowest solubility, is 230      direct compression.
  ml, extent of absorption from a    • To-Be-Marketed formulation:
  solution is 95%)                     Direct compression, drug
• Weak base exhibits a sharp           particle size (D50%) 300
  decline in solubility with           microns, dicalcium phosphate,
  increasing pH above 3                MCC, Mg-stearate, silicon
• Clinical-trial formulation: Wet      dioxide. Tablet weight 500 mg.
  granulation, drug particle size      Dissolution in 0.1 N HCl - 85%
  (D50%) 80 microns, lactose           in 15 min., and 95% in 20 min.
  MCC, starch, Mg-stearte,             Disintegration 1 min.
  silicon dioxide. Tablet weight     • Clincal product exhibits poor
  250 mg. Dissolution in 0.1 N         dissolution in pH 7.4 media
  HCl 65% in 15 min and 100 %          (about 30% in 60 minutes). Data
  in 20 minutes. Disintegration        for T-b-M not available.
  time 10 minutes.
       Failure of Dissolution Tests to
         Signal Bio-in-equivalence
• Inappropriate “acceptance criteria”
   – One point specification
   – Set “too late”
• Inappropriate test method
   – media composition (pH,..)
   – media volume
   – hydrodynamics
• Excipients affect drug absorption
• Other reasons
ICH Q6A DECISION TREES #7: SETTING ACCEPTANCE CRITERIA
FOR DRUG PRODUCT DISSOLUTION

 What specific test conditions and acceptance criteria are appropriate? [IR]



             dissolution significantly        YES         Develop test conditions and acceptance
                    affect BA?                            distinguish batches with unacceptable BA

                          NO


               Do changes in
               formulation or                 YES              Are these changes controlled
           manufacturing variables                                by another procedure
             affect dissolution?                                      and acceptance
                                                                        criterion?

                                                     YES
                          NO                                                 NO

       Adopt appropriate test conditions             Adopt test conditions and acceptance
        and acceptance criteria without                  criteria which can distinguish
       regard to discriminating power, to            these changes. Generally, single point
       pass clinically acceptable batches.             acceptance criteria are acceptable.

                                         aaps Annual Meeting                                  59
      Dissolution - Attributes: Casual Link

Dissolution is a function of processing variables:


Dissolution = f (Ex1, Ex2, P1, P2, PS…)
                     Ex1, Ex2 = Excipients (USP/NF)
                     P1, P2 = Process parameters (time, hardness ...)
                     PS = Drug particle size (specification)


   y = 0 + 1 x1 + 2 x2 + 3 x3 +
       12 x1 x2 + 13 x1 x3 + 23 x2 x3 + ...
Christopher Sinko, Ph.D.
Pfizer Global Research & Development



           Achieving Quality by Design?
        Integrity
                                             In vitro                Chemical
       Uniformity
                                           Dissolution                Purity
      Weight Control



                     API, Excipients, Manufacturing Process

    Pharmaceutics
                                                Chemical
        Profile                                                  Process Simulation
                                               Compatibility
                       API Particle Size
                                              Degradation
 API Salt Selection                                            Material Property
                                                Pathway
                                               Prediction      Characterization
                      Design
Christopher Sinko, Ph.D.          BA Data from pre-CAN and
Pfizer Global Research &                                                    In-process
                                   Exp Toxicology Studies
Development                                                                   Sample

                                 Simulations Using Dissolution
                                     Absorption Model**
                                                                              Drug
                           BA is expected to be                             Substance
                            PS Independent**      BA is expected to
                                                  be significantly
                                                  PS Dependent**              Delump
                                                                            (e.g. pass
                             Is desired PS readily achievable?                through
                           Yes                    No                         20 mesh)
                                    In vivo studies in animals
                                                                           PS Analysis
                                                                   No
                                     Consistent with Model ?
                                                  Yes
                                                                    Improve
                     Tablet Content Uniformity Model                 Model

                       Recommend Appropriate PS


                                 Done
                                                                 ** At expected dosing range in humans
                                          PS Reduction            integrating data from pre-clinical studies.
                Is Dissolution Rate Limiting?
                3.5                            Capsule
                3.0                            Solution
Concentration




                2.5
                2.0
                1.5
                1.0
                0.5
                0.0
                      0   4   8    12    16   20          24
                                  Time
                                Metoprolol IR Tablets:
                            In Vitro - In Vivo Relationship
                  110                                                            1.2
                  100                       Rapid




                                                    AUC, AND Cmax RATIOS (T/R)
                   90                                                            1.1
                                                                                                                                      AUC
                   80                       Slow                                 1.0
                  70
% DRUG RELEASED




                                                                                                                                      Cmax
                  60                                                             0.9
                  50
                  40                                                             0.8




                                                                                                                          SOLUTION
                  30                                                             0.7
                  20            FDA-UMAB
                                 (931011)                                                   FDA-UMAB

                  10                                                             0.6         (931011)


                   0                                                             0.5
                    0    5 10 15 20 25 30 35                                        0.2     0.4         0.6   0.8   1.0              1.2
                                                                                          RATIO (T/R) OF % DISSOLVED AT
                        TIME IN MINUTES                                                             10 MINUTES
               u t
               mesu n se t D
                u
                li
               C a D t a i tr n a
                  vioo di g oa
                     s i
                      l n Dnai t:
                  ra r a
                   i
                   t Fu n i e
                    i
                    c
                 C l o li V l
                       m oa
                         t  a
                            b
                           r s
           2
           0
           1
                             MC
                              C-
                               ()
                             S)
                              G
                              (
                             S+
                             MS(
                               -
                               )
                             [g ]
           0
           0
           1

                       C
                       M(
                        C)
                         -
                       S)
                       G(
                       S+
               8
               0       g
                       M-
                        (
                        S)




               0
               6
%Drugisolved

               0
               4



               0
               2



               0
                o on
                 rr n
                  e d
                  s
               C pi   g
               i tr n
               se t T
                i
                na  i
               D g oiem
                  Dt
                   aa


                   0   5   0
                           1   5
                               1    2
                                    0   5
                                        2   0
                                            3   5
                                                3
                             ieM
                             mi t
                              i n
                             Tn us
                                 e
                   Metoprolol IR Tablets: Experimental &
                              Simulation Data
                                                                       Mean Intestinal Transit Time = 1.67 h
                                                          85%                                                           2.0
                                                                              0.70
                                                                                    0.75
                                                                                                                0.90
                                                          D
         AUC
1.2      Cmax
                                                                                                                        1.5
         Plot 1 Regr
                                                          I                  0.80 0.85                          0.95
                                                          S                                                             1.0
1.1
                                                          S
                                                          O
1.0                                                       L                                                             0.5
                                                          U
0.9                                                       T                                                             0.0
                                                                        Mean Intestinal Transit Time = 3.33 h
                                         SOLUTION
                                                          I
                                                                2.0      0.75
0.8                                                       O
                                                                                0.80
                 ~ 30 min




                                                          N
                  in vitro
                  T 85%




                                                                1.5                                             0.95
0.7                                                                                0.85
                                                          T
0.6                                                       I     1.0               0.90
                                                          M
0.5                                                       E     0.5
  0.2          0.4     0.6   0.8   1.0              1.2
                                                          (h)
        RATIO (T/R) OF % DISSOLVED AT 10 MINUTES
                                                                 0.0
                                                                            0.1           0.2   0.3      0.4           0.5
                                                                             Gastric Emptying Half-Time (h)
      An hypothetical case study: Critical Formulation variables?


          Dissolution predominantly effected by disintegrant level
         and by interaction terms involving disintegant and dilutent
                        and dilutent and mg stearate.

              AL
              R T
              ET
               O
              PPO
                                                      0
                                                      0
                                                      1


                                                           0
                                                           8


                                                           0
                                                           6


                                                           0
                                                           4
                                      PREDICTEDISOLUTION
                                                           0
                                                           2
               D*N T
               IEIE
               LD R
                U T
                N G
                TI
                 SAN
                   IT T
                   SA
                    IE
                    N
                   DRGN
                                                           0
                                                           0   0
                                                               2   0
                                                                   4   6
                                                                       0   0
                                                                           8   0
                                                                               0
                                                                               1
                                                               BDL
                                                               S D T
                                                                E I U
                                                                RS I
                                                                V SO
                                                               OE O  N


Unpublished Data from DPQR/CDER/FDA
                                    NIR - Dissolution Correlation:
                                Direct Compression (% Diss at 15 min)
                          100
                                    Direct Compression (Lots 121-132)
                                    PLS1 Model
                                    2nd Derivative Spectra
                           80
Predicted % Dissolution




                           60         Training Set (n=72)
                                      y = 0.9771x + 1.2735
                                      R2 = 0.9771

                           40


                                                              Test Set (n=72)
                           20                                 y = 0.9463x + 0.6217
                                                              R2 = 0.9465



                            0
                                0                20               40                 60   80   100
                                                             Measured % Dissolution
                            NIR - Attributes: Casual Link
            3.0000                                                                                                                                   3.0000



            2.5000
                         Furosemide                                                                                                                  2.5000
                                                                                                                                                                  Lactose Monohydrate
                         Avicel                                                                                                                                   Ac-Di-Sol
            2.0000                                                                                                                                   2.0000
                         Magnesium Stearate                                                                                                                       Starch
            1.5000                                                                                                                                   1.5000
                                                                                                                                                                  Povidone
Intensity




                                                                                                                                         Intensity
            1.0000                                                                                                                                   1.0000



            0.5000                                                                                                                                   0.5000



            0.0000                                                                                                                                   0.0000



            -0.5000                                                                                                                                  -0.5000



            -1.0000                                                                                                                                  -1.0000



            -1.5000                                                                                                                                  -1.5000
                  1100     1300   1500   1700        1900   2100                             2300           2500                                           1100    1300   1500     1700        1900   2100   2300   2500
                                            Wavelength                                                                                                                                Wavelength




                                                                                             NIR - Avicel Correlation
                                                                                       250
                                                                                                 Direct Compression
                                                                                                 PLS1 Model
                                                                                                 2nd Derivative Spectra
                                                                                       200
                                                               Predicted Avicel (mg)




                                                                                       150
                                                                                                      Training Set (n=72)
                                                                                                      y = 0.9741x + 2.9759
                                                                                                      R2 = 0.9741
                                                                                       100


                                                                                                                             Test Set (n=72)
                                                                                        50                                   y = 0.9738x + 3.4292
                                                                                                                             R2 = 0.9742


                                                                                         0
                                                                                             0               50              100                        150        200       250
                                                                                                                         Measured Avicel (mg)
                                   cGMP
                                 regulatory
                                  oversight
                 Company’s
                Quality system



                                              Risk
  Process           Post
Understanding     approval
                   change



                    CMC
                  regulatory
                   oversight
   Process
 Understanding
                   Process          Process
                 Understanding    Understanding
   CMC
 regulatory          CMC
  oversight        regulatory
                    oversight           CMC
                                      regulatory
   cGMP                                oversight

 regulatory          cGMP
                                       cGMP
  oversight        regulatory        regulatory
                    oversight         oversight


 Company’s        Company’s        Company’s
Quality system   Quality system   Quality system

     Post
   approval                            Post
    change
                     Post            approval
                   approval           change
                    change              Risk
  Risk
   (P/R)             Risk
           Quality Risk Classification
                    (based on SUPAC and GAMP-4)
                                                      Quality by design +
                               Risk Likelihood        Systems approach




                                      Medium

                                               High
                                Low
                                                         Level 3
Impact on Quality




                     High
                                                          Level 2
                    Medium

                     Low                                  Level 1
                       Quality Risk Priority
Quality by design +
Systems approach              Probability of Detection




                                        Medium

                                                 High
                                  Low
                                                         High
 Risk Classification




                        3
                                                         Medium
                        2

                        1                                 Low
               Targeting for Maximum
                      Protection
       “amoral”
                                                    “incompetent”



                                                                                “Good
                                                                               Citizens”



  FDA Focus on                                                Science is
    High Risk                                                 the only
                            “political citizen”         fair and transparent
                                                         means to recognize
Kagan and Scholz. Perspectives on Regulation:
Law, Discretion, and Bureaucratic behavior, May 1980.
                                                                               Low Risk
        Product and Process Quality Knowledge:
             Science-Risk Based cGMP’s
Quality by Design                        GMP/CMC FOCUS
                              1st
Process Design             Principles    Design qualification

                        MECHANISTIC
                       UNDERSTANDING
Yes, Limited to the                            Focused; Critical
Experimental                                   Process Control
                       CAUSAL LINKS
Design Space       PREDICT PERFORMANCE         Points (PAT)

                    DECISIONS BASED ON            Extensive;
Maybe,             UNIVARIATE APPROACH            Every
Difficult to                                      Step
Assesses             DATA DERIVED FROM            (CURRENT)
               TRIAL-N-ERROR EXPERIMENTATION
                      Connecting-the-Dots
   Development - Manufacturing:: Review - Inspection
Discovery    Development Review      Marketing
      Pre-clinical Clinical
                   I, II, III  Approval   IV AER’s
      Pre-formulation
              Formulation (Clinical)      (Optimization)
                    Optimization      Scale-Up Manufac.
                                     (For Market) Changes


                                 ?   Appropriate labeling and risk management
                           Safety
                             &
Building Quality In   ?   Efficacy    ?                        ?
                                          Appropriate Controls & Specifications

				
DOCUMENT INFO
Shared By:
Categories:
Stats:
views:29
posted:5/14/2011
language:English
pages:76