QbD implementation in Generic Industry

33 Pages·2013·1.02 MB·English

 
QbD implementation in Generic Industry:
IFPAC
Overview and Case-Study JAN
2013
RR&&DD
IInnnnaa BBeenn-AAnnaatt, QQbbDD SSttrraatteeggyy LLeeaaddeerr, TTeevvaa PPhhaarrmmaacceeuuttiiccaallss
Three Core Components of QbD and Generic Industry:
 
How Do They Overlap
Quality by Design Generic Industry
11. CCllearlly ddeffiiniing thhe iintenddedd purpose off 11. RReprodduciibblly MMakkiing ““AA ddrug prodductt
r
the future developed product and design a that is comparable to brand/reference
e
l
c
this product to fit its purpose listed drug product in dosage form,
s
i

n ssttrreennggtthh, rroouuttee ooff aaddmmiinniissttrraattiioonn, qquuaalliittyy
oo
i
t
2. Understanding what attributes of this ec and performance characteristics, and
n
n
intended use"
product are critical so it (product) will keep o
c

e
servingg its intended ppurppose h
TT
2. Providing uninterrupted supply of high
3. Enhanced understanding ‘what’ impacting quality and affordable medication to our
the critical quality attributes and ‘how’ patients
(materials, process, packaging etc) ; define
control strategies so that the intended
3. Efficiency and Speed
purpose of the product will reproducibly
maiinttaiin iitts iinttegriitty
 
QbD for Generics: Finding the right balance between
Speed, Efficiency and Excellence
 Overview of QbD (GPhA, May 2012)
QbD Guide for Generics:
 
Step 1-Product Design
 RLD Characterization
 Quality Target Product Profile
 CCrriittiiccaall QQuuaalliittyy AAttttrriibbuutteess
GPhA/FDA CMC Workshop, May 2012
QbD Guide for Generics:
 
Step 2 - What are the potential Risks
Risk Assessment Defines the Development Strategy
What are the Risks?...
 AAPPII
 Excipients
 Formulation and Process
How do we stay efficient
 Equiipment
 Effective Prior Knowledge utilization
 Testing
and management
 Packaging
 GGeenneerriicc IInndduussttrryy hhaass aa lloott ooff
 …
information and in-house
knowledge available
 DDaattaa bbaasseess ooff pprree-ccrreeaatteedd
Ishikawa diagrams in order to
harmonize and streamline the Risk
Assessment pprocess
 Historical data-mining
 HHiissttoorriiccaall DDaattaa MMiinniinngg:: DDrruugg LLaayyeerriinngg ooff PPeelllleettss EExxaammppllee
Example: Previously developed product,
multiply batches are available for Data Mining:
In-Process Pellets Assay vs. Fines Correlation
BBaasseedd oonn tthhee ffoouunndd rreellaattiioonnsshhiipp, AAssssaayy
decreases ~0.6% with each % fines
How do we control low % fines
by process parameters
((DDrruugg LLaayyeerriinngg))…
‘All examples are for illustration purposes only’
 HHiissttoorriiccaall DDaattaa MMiinniinngg:: DDrruugg LLaayyeerriinngg ooff PPeelllleettss EExxaammppllee
AAccttuuaall PPrroocceessssiinngg PPaarraammeetteerrss ffrroomm aallll aavvaaiillaabbllee hhiissttoorriiccaall lloottss wweerree
ccoolllleecctteedd aanndd ‘‘ddaattaa--mmiinneedd’’
PPaarrttiittiioonn ppeerr mmoosstt ccrriittiiccaall ffaaccttoorr aaffffeeccttiinngg %% FFiinneess
All Rows
Count 31 LogWorth Difference 1. Most Significant parameters affecting
MMeeaann 33.666666112299 11.66223322555588 11.9988559966
Std Dev 2.4278793 %Fines are Slit Temp and Exhaust
Temp
Slit Temp Actual (°C) -max<74.3 Slit Temp Actual (°C) -max>=74.3
2. Lower Slit Temperature (<74˚C)and
Count 19 LogWorth Difference Count 12
Mean 2.8973684 0.6352248 1.21364 Mean 4.8833333 lloowweerr EExxhhaauusstt TTeemmppeerraattuurreess ((<<4444˚CC))
Std Dev 2.1367027 Std Dev 2.4430061
will generate less % Fines
Exhaust Temp-AVG<44.4 Exhaust Temp-AVG>=44.4
Count 11 Count 8
Mean 2.3863636 Mean 3.6
Std Dev 2.1165362 Std Dev 2.0894223
PPootteennttiiaall DDOOEE FFaaccttoorrss ffoorr ffuuttuurree ssiimmiillaarr
products/processes or for further process
fine-tuning
‘All examples are for illustration purposes only’
QbD Guide for Generics:
 
Step 3 - Plan the right/relevant Experiments
Efficient and Informative DOE: CQAs= f (CPPs, CMAs)
 How do we stay efficient
o Effective Prior Knowledge Utilization
 WWhhaatt ddoo wwee vvaarryy aanndd wwhhaatt ddoo wwee ffiixx??
 What target and range do we evaluate and why?
 What statistical model do we use and why? (Can we assess
what interactions are most likely to occur? Can we assess what factors
would have non linear relationship with the response?)
o Modern DOE techniques for efficient yet powerful designs (D-
Optimum, I-Optimum)
o Monte Carlo Simulations to assess the process robustness using
historical data to assess expected variabilityy
LLeett’’ss ttaakkee aa ttyyppiiccaall mmaannuuffaaccttuurriinngg pprroocceessss ffoorr ttaabblleettss
 
aass aann eexxaammppllee ttoo ssttaarrtt wwiitthh……
WWeett GGrraannuullaattiioonn FFlluuiidd BBeedd DDrryyiinngg MMiilllliinngg BBlleennddiinngg CCoommpprreessssiioonn
How many potentially Critical Process Parameters do
we need to assess?
5? 10? 25?