Miquel Galán - Bio and Pharmaceutical Technology: What can we learn from Chemical Engineering?

12th MEDITERRANEAN CONGRESS OF CHEMICAL ENGINEERING

Bio and Pharma Technology:
What Can We Learn From
Chemical Engineering?

Miquel Galan
TELSTAR TECHNOLOGIES
Innovation + R&D Dept.
Terrassa,
Terrassa SPAIN

Barcelona, November 2011

Chemical Engineering

Chemical engineers apply the principles of chemistry, math,
and physics to the design and operation of large-scale
chemical manufacturing processes:
• Translate processes developed in the lab into practical
applications for the production of products (plastics,
medicines, detergents, fuels, etc.).
• Design plants to maximize productivity and minimize
costs, and evaluate plant operations for performance and
product quality
quality.
• Solve problems that occur during the daily plant
operation, analyzing samples from the system and
evaluating process parameters to determine the origin.

M. Galan – Telstar Barcelona, November 2011 2

Pharma Industry / Chemical Industry
• Very often, Pharmaceutical Industry is perceived as a
particular case of the Chemical Industry:

• Conventional pharmas rely on a chemical-based synthetic
process
p ocess to de elop small molec le drugs.
develop small-molecule d gs

• By contrast, biotechs use “biotechnology” to manufacture
drugs, which involves the manipulation of microorganisms
(such as bacteria) or biological substances (like enzymes)
to perform a specific process. Biotech drug makers
essentially use those microorganisms or highly complex
proteins from genetically-modified living cells as
components in medications to treat various diseases and
t i di ti t t t i di d
conditions, from cancer to rheumatoid arthritis to multiple
sclerosis...


Pharma Industry Peculiarities

• But Pharmaceutical and Biotech Industries have a very
unique peculiarity:

They are regulated i d t i
Th l t d industries


Regulation

• The origins of regulation in the United States dates back to
1906 when President Roosevelt persuaded Congress to p
p g pass the
first Food and Drug Act and the FDA (Food and Drug
Administration) was formed. First job: preventing the
adulteration of food products and medicinal drug products. Thus
the concept of having to prove product purity.

• Response to a book, “The Jungle”, describing brutal sanitary conditions in
stockyards and meat markets in Chicago.
• Public outcry plus drop in meat sales prompted the FDA creation
creation.


Regulation

• By 1938, Congress passed the Food, Drug and Cosmetic Act
(FD&C). The
(FD&C) Th FDA now h d th mandate to ensure that
had the d t t th t
companies who supplied any food, drug or cosmetic products to
the consumers also had to prove product safety.
• Manufacturers had to submit an application and get approval
from FDA prior marketing any new product.

• I 1937 a T
In Tennessee chemist d
h i t developed an elixir f th t i f ti
l d li i for throat infections. API
(sulfamide) was poorly soluble in water. Best media for dissolution was
diethyl glycol. Small dosages taken by the chemist to find sweet tasting
mixture.
mixture
• 1 batch of 240 gallons fabricated. 107 people (many children) died.
• The company refused to divulge any information: trade secret
• After legislation, recall recovered 234 gallons
legislation


Regulation

• In 1962, the FDA issued its first set of GMPs (Good
Manufacturing Practices) delineating g
g ) g guidelines on how to
produce, package, store, market and distribute Pharmaceuticals
and Medical Devices. Manufacturers must prove product or
device efficacy prior to market launch.
• Clinical testing of products prior to commercialization was
introduced and potential side effects had to be disclosed to
physicians and general public

• After-effect of the Thalidomide incident (thalidomide was a drug given to
pregnant women to prevent morning sickness). The drug had horrific side
effects on the embryo (many babies born with deformed or missing limbs)


Regulation

• In 1976 the FDA issued new cGMPs. These proposals were
declared substantive, which meant that non-compliance
with the new regulations had now become directly a
prosecutable criminal act. The ‘c’ indicates that the regulations
are in constant evolution: what is perfectly acceptable today,
may become passé tomorrow. The agency can decide to
review each situation on a case-by-case basis within the
context of the ‘current’ practices. Producers must prove
current
product purity, safety, efficacy, and consistency…

• Before 1976, each time the FDA tried to prosecute, had to prove that
, p , p
each point it was trying to prosecute was what Congress had in mind
when passed the 1938 FD&C Act.
• FDA enforcement Agency. Representatives are called “investigators”.
They have the authority to ask any record they feel may be pertinent to an
audit: In 1992 in a letter to a large US Pharma manufacturer:
“the FDA is entitled to any document it wants and we will bring in the
marshals with guns and we can take what we want”


Validation

How all of this is tied together?

• Simply put, validation or proving that the system or
equipment will do what it is designed to do, time after time,
every ti
time,… ensuring product purity, safety, efficacy, and
i d t it f t ffi d
consistency.

• In 1987, the FDA issued its Guideline on General Principles of
Process Validation.

Validation was defined as:
“establishing documented evidence which provides a
high degree of assurance that a specific process
will consistently produce a product meeting its
pre-determined specifications and quality
attributes”


Validation
• (FDA) Establishing documented evidence which provides
a high degree of assurance that a specific process will
consistently produce a product meeting its predetermined
l d d d d
specifications and quality attributes.

• (EC GMP Guide) The action of proving, in accordance with
the principles of Good Manufacturing Practice, that any
p p g , y
procedure, process, equipment, material, activity or
system actually leads to the expected results.

Prove that a specific process does
what is intended to do!


Compliance Aspects of Validation

• Well-Defined, Planned and Documented Studies
• Adherence to Protocols
• Performance of all Tests and Procedures.
• Proper Reporting of Failures

If it isn’t documented - it is not done!
i ’t d t d i td !

A risk analysis, a decision, training for employees, an inspection,
an operation (i.e. cleaning step) is considered “done” only if
documents prove it.


Validation vs. Qualification

Process Validation

Equipment Qualification


GEP (Good Engineering Practices) vs. Validation

GEP VALIDATION

Time From Planning (order) to From Planning to Scratch
Commissioning

Scope Everything All critical systems

Objective Fulfill specifications Documented evidence that
process is under control

Approach Should be planned Written detailed plan with all
(heuristic ?) testing criteria

Changes Record, add notes Systematic recording
Documented impact analysis

Structure Can be very Particular DQ
IQ No media involved
OQ Water substitutes media
PQ Process media involved
Responsibility Vendor Buyer


Validation: Life Cycle

Specification
S ifi ti

Qualification (DQ IQ OQ PQ)
(DQ,IQ,OQ,PQ)

Process Validation

Change Control

Periodic Revalidation


Key Regulatory Bodies

• US
• FDA (Food and Drug Administration)

• EUROPE
• EMA (European Medicines Agency)
• I di id l C
Individual Countries
t i
• UK – MHRA (Medicines and Healthcare products Regulatory
Agency)
• S i – AEMPS (A
Spain (Agencia E
i Española d l M di
ñ l del Medicamento y
Productos Sanitarios)
• …

• JAPAN
• PMDA (Pharmaceuticals and Medical Devices Agency)


Regulatory? Advisors?
• Regulators (=regulations)
• US, European, Japanese, others

• Influential Bodies (=advice, opinion, guidance)
• PIC/S ISPE PDA, PHSS (formerly PS), ...
PIC/S, ISPE, PDA PS)

• Industry associations (=information)
y ( )
• EFPIA (European Federation of Pharmaceutical Industries
Associations)
• A
Associations from individual countries: (AEFI etc)
i ti f i di id l t i (AEFI, t )

PIC/S: Pharmaceutical Inspection Convention and Pharmaceutical Inspection Co-operation
Scheme
ISPE: International Society of Pharmaceutical Engineers
PDA: Parenteral Drug Association
PHSS: Pharmaceutical and Healthcare Sciences Society


FDA
• 21CFR 210 & 211 (cGMPs)
• 21CFR 11
• FDA Guidelines (Guidance for Industry)
G id li (G id f I d t )
http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm
• Process Validation: General Principles and Practices (2011)
• Sterile Drug Products Produced by Aseptic Processing — Current
Good Manufacturing Practice (2004)
• Q7A Good Manufacturing Practice Guidance for Active
Q g
Pharmaceutical Ingredients (2001)
• Guide To Inspections of Lyophilization of Parenterals (1993)
• FDA Powers (21CFR210 1(b))
(21CFR210.1(b))
“The failure to comply with any regulation...in the manufacture,
processing, packaging or holding of a drug shall render such drug
to be adulterated...and such drug as well as the person who is
adulterated and
responsible for the failure to comply shall be subject to regulatory
action.”
Role extends to drugs that are to be used in USA, wherever they
USA
are manufactured

EMA

• European Medicines Agency (formerly EMEA)
• Established 22 J l 1993 Located in London
July 1993.
• In charge of coordinating scientific resources in Member
States, to evaluate and supervise medicinal products for
human & veterinary use
• According to EMA opinions, EC authorizes products and
arbitrates between member states
• Each EU member has it’s own Authority
• E hM
Each Member E
b Estate i implements the directive as national
l h di i i l
law


Validation without understanding

A whole industry has grown up around process validation:
with a proliferation of validation p
p protocols,
,
validation reports,
and validation documentation;

but there are still processes that work poorly.

We have lost the goal, which is that
before trying to demonstrate the process reliably does what it's
y g p y
supposed to do, we must “know” the process in depth.


Validation without understanding
• The traditional approach presupposes that if nothing is
changed from the validation batches, everything will
remain the same.
h
• But this assumption is false, because neither ingredients
nor processing conditions can remain fixed
fixed…
There will be small changes from batch-to-batch, there may
be further changes over time, that operators can introduce,
or the equipment will be moved from one site to another.
There will be a new supplier for a certain material, and this
new material may be within specifications…
y p

It was never real that everything could be kept the same !


Managing variability

Inputs + Process Outputs

Variable + Inflexible (fixed) Variable
Inputs
I Process
P Outputs
O

Variable + Adjustable Constant
Inputs Process Outputs

Does it look new to Chemical Engineers?


Quality in pharmaceutical products

• Automated manufacturing facilities dominate the biomedical
industries.
industries Inert and active ingredients are mixed… They are
mixed
compressed into tablets, filled into capsules or dissolved in
liquids that may be subsequently lyophilized… And they are
tracked throughout the packaging, and delivery processes.

• Problems in these automated steps can result in large quantities
of pills, capsules, vials, bottles bags … that must be
pills capsules vials bottles, bags,
quarantined, retested, rejected, reprocessed, or destroyed, all
at significant expense.

• Of course, the worst case scenario would be that defective
manufactured products were not detected, but were
inappropriately shipped f use b patients who, at b t would
i i t l hi d for by ti t h t best, ld
receive ineffective medications, but potentially might receive
toxic or harmful products.


• Conventional pharmaceutical manufacturing is generally
accomplished using batch processing with laboratory testing
conducted on collected samples to evaluate quality This
quality.
conventional approach has been successful in providing quality
pharmaceuticals to the public.
• However, significant opportunities exist for improving
pharmaceutical development, manufacturing, and quality
assurance through innovation in product and p
g p process
development, process analysis, and process control.

Source: Gold Sheet 2009



• Pharmaceutical industry has been hesitant to introduce
innovations i t th manufacturing sector:
i ti into the f t i t
• Regulatory uncertainty, resulting from the perception that
existing regulatory system is rigid and unfavorable to the
introduction of innovative systems For example, many
systems. example
manufacturing procedures are treated as being frozen and
many process changes are managed through regulatory
submissions.

• Efficient pharmaceutical manufacturing is a critical part of an
effective health care system. The health of persons and animals
system
depends on the availability of safe, effective, and affordable
medicines.


PAT (Process Analytical Technology)

Guidance for Industry PAT — A Framework for
Innovative Pharmaceutical Development, Manufacturing,
and Quality Assurance (FDA, September 2004)

“The Agency considers PAT to be a system for designing
The designing,
analyzing, and controlling manufacturing through timely
measurements (i.e., during processing) of critical quality and
performance attributes of raw and in-process materials and
in process
processes, with the goal of ensuring final product quality.
…….. The goal of PAT is to enhance understanding and
control the manufacturing process which is consistent with
process,
our current drug quality system: quality cannot be tested into
products; it should be built-in or should be by design.”


What is PAT?

A system for:
• designing, analyzing, and controlling manufacturing
• timely measurements (i.e., during processing)
• critical quality and performance attributes
• raw and in-process materials
• processes

“Analytical” includes:
• integrated chemical, physical, microbiological,
g ,p y , g ,
mathematical, and risk analysis

Focus of PAT is Understanding and Controlling the
manufacturing Process


PAT = Process understanding

A process is well understood when:
• all critical sources of variability are identified and explained
• variability is managed by the process
• product quality attributes can be accurately and reliably
predicted

Accurate and Reliable predictions reflect process
A t d R li bl di ti fl t
understanding

Process Understanding inversely proportional to risk


PAT Tools: Process Control Tools

• Monitor the state of a process and actively manipulate it
to maintain a desired state
state.

• Strategies should accommodate:
• attributes of input materials
• the ability and reliability of process analyzers to measure
critical attributes
• achievement of process end points to ensure consistent
quality

• End points = achievement of the desired material
attribute (not process “time”)


Terminology

There are 4 categories of sampling and analyzing:

• “off – line”: Sample is extracted, tagged and sent to the
laboratory to analyze.
y y

• “at – line”: Sample is extracted from the process and analyzed
close to th fabrication flow.
l t the f b i ti fl

• “on – line: Sample is extracted from the process, but it can be
on
reinserted without affecting quality.

• “in – line”: Sample is not extracted from the process.


PAT: “Right First Time”

• Historically, the emphasis of the PAT applications
have been on the following:
a e bee o e o o g
• Enable process understanding
• Identify and remove the sources of variability
• Monitor processes on-line to provide real time data for information
p p
purposes
• Determine process endpoints in chemical reactions, drying, etc. to
allow better timing of the off-line release samples

• As the reliability and performance of the Process
Analytical systems improve, the potential for use of
PAT as an Integral part of pharmaceutical processes
increases. Within this context, PAT is increasingly
used to:
• R l
Replace off-line fi l product tests with at-line or online PAT b
ff li final d tt t ith t li li based
d
release tests
• Provide the basis for Process Control Strategy
• Enable Continuous Quality Verification and Real Time Release


Process Control Strategy: The Current State

• Traditionally, Process Control achieved through tight control of
Critical and Key Process Parameters at pre-determined
setpoints or ranges
t i t

• The premise for this approach is the assumed or established
relationship between the Process Parameters (Process Inputs)
and Critical and Key Product Attributes ( Process Outputs)

• This control strategy doesn’t allow startup or mid-course
doesn t
correction to account for variation in starting materials or
process upsets

• No flexibility within or between production runs to utilize the
concept of the “Design Space”

• Process Output specifications are most often met, but can be
subject to considerable variations


Indirect Control

Limited Control Variable
Y = f(X)
Input 1
Raw Materials
Input 2 Outputt 1

Process Parameters: Product Attributes:
Temperature, pH,
Input 3
Process Outputt 2 Potency, Particle
size, etc.
,
Reaction Time, etc
Time
Input 4

Typically,
Typically no direct
Measured & tightly controlled at measurement or control
predetermined setpoints or ranges during the process. Usually
variable


“Advanced” Process Control

• Mathematically advanced control algorithms that use
predictive, adaptive, and optimization techniques to control
multi-inputs, multi-output processes.

• Control strategies that utilize PAT, process models or other
techniques, to manipulate Process Parameters (Xs) within any
required constraints, in order to actively control one or more
q , y
Drug Product Attributes (Ys) at a especified setpoint or within a
tight range.

• Although a new concept in the Pharma Industry, this is a
“mature technology” commonly used in all other industrial
sectors (chemical, petrochemical, etc.) to improve quality,
consistency, and process efficiency.


“Advanced” Process Control

• Advanced Process Control provides a new and promising
paradigm for controlling Pharmaceutical processes
processes.

• These applications aim to address some of the most technically
pp y
challenging control problems in our industry that can provide
tangible quality and business benefits.

• Important technical challenges to implement this methodology
in the classical environment of “Validated Process”.


This is not PAT !
• 2005 – 2006: PAT was the hot issue, but the message was not
“well sold”.
• Th concept was being presented with too much focus on
The b i d ih hf
technological advances:
• Management perception was, mainly, costs for expensive
a ag p p o a , a y, o o p
analytical instruments (at-line):
NIR
Chemometrics
Ch t i
Multivariate analysis
…


QbD: Quality-by-Design

• Quality by Design (QbD) is an initiative of the United States
Food and Drug Administration, and the biomedical industries it
g ,
regulates, intended to integrate the quality process through
research, development, manufacturing and distribution.

• When properly implemented, Quality by Design should improve
speed to market; reduce product variation; improve operating
efficiency and reduce costs at all stages of the process.

QbD (
b (Quality-by-Design)
l b ) ⇔ QbT ( l b
b (Quality-by-Testing)
)


QbD: Quality-by-Design
• QbD consists of three key elements:
• the use of Design Space to establish elastic quality
standards;
• the use of Risk Assessment to define the boundaries of
those standards;
• and the implementation of Process Analytical Technology
(PAT) to monitor and adjust to those standards.
• The resulting cost controls and regulatory streamlining
should significantly increase the efficiency of the industry.


How a process can be measured?

• By using sensors able to measure the desired property.

• With models
ih d l


Sensors

• PAT “stamped” analyzers have proliferated (NIR, Raman, etc)

• First presented “success case studies” (2005) were based in
processes where the regulatory aperture (“not ask if analyzers
to get knowledge were added into the process”) allowed direct
process )
applications:
blending, coating, etc

• Typically all them were stirred processes. A single sensor could
acquire batch representative data

• Unfortunately, more complex processes (lyophilization,
biological processes, etc) continued stuck to the traditional way
due to a lack of available sensors


Why using a model?

• The engineers that built this
bridge did not use trial and
error.

• The models told them how to
do it right the first time.

• The Treasury (taxpayers)
cannot afford “too expensive”
bridges.

• Politicians cannot accept
collapses.


Which type of model?

Mechanistic models vs. Empirical /Statistical

• A mechanistic model is derived from the knowledge about the
underlying science (physics, etc) of the unit operation.
(physics operation
• If there is no knowledge about the mechanism, there is only
the option of traditional statistical DoE

• Statistical models: maximize knowledge getting a robust
design space
• Mechanistic models: “in-line” monitoring of the required
variables controlling the process even in the case of
variabilities


Freeze Drying Case
• Freeze drying, also called lyophilization, is a drying process
where the wet product is first frozen to a solid phase and
subsequently dried to vapour phase through sublimation, that
sublimation
is, without passing through the liquid phase, by exposing it to
a low partial pressure (vacuum) of water vapor.


Lyophilization Challenges

• Collapse:

• Speed of the process:

5ºC Speed x 2


Lyophilization Process Definition Parameters

• It is usually specified the recipe (Shelf temperatures and
chamber pressures vs. time) but this doesn t guarantee
vs doesn’t
that the sublimation parameters are constant

Temperature, pressure and time are intensive variables (not scalable)

• For Primary Drying, what it would be desirable is knowing (and
controlling !!)
• The sublimation front temperature (to avoid collapse)
• The sublimation speed (to optimize productivity).


Classical Temperature Monitoring
• Insertion of a thin thermocouple (or a more bulky Pt-100)
in few vials is a widely used method to “measure” the
product temperature during the process
process.

Disadvantages:
• Intrusive for the product
• Influence ice nucleation =>
morphology => sublimation
>
• Problems concerning the sterility of
the product
• Impossible with automatic loading

• Using a thermocouple we can measure the temperature only
in one point.


Primary Drying: temperature measurement
• What is product temperature? Discrete temperature probes
don’t measure real temperature: sublimation front moves
during primary drying.
• The most critical parameter is ice temperature at sublimation
front (Tice). Collapse and/or melting, and sublimation speed
depend di
d d directly on Tice.
tl
Primary Drying
Heated shelf at -10ºC

-25
-24

-20

-15

-10
Dry product
Frozen
interface
moving Frozen product
downwards

-25
-24

-20

-15

-10
Heated shelf at -10ºC
Temperature ºC


Soft-sensors

In many engineering applications it is desirable to have estimates of hard-to-
measure or non-measurable quantities.

A soft sensor combines a priori knowledge about the physical system
(mathematical model) with experimental data (in-line measurements) to
provide an in-line estimation of the sought quantities
in line quantities.

input output
Process

Soft Sensor
State
estimate

Patent pending


Soft-sensors

input output
Process

Soft Sensor
State
estimate

1) Introducing a small perturbation Specific parameters of the
model equations not known
2) Acquiring system response

3) Solving the equations to “reproduce” this response

4) Variables of interest can be calculated


Regression Analysis Results


Advantages & Limitations
Advantages:
• Consistent results up to the end of primary drying
• Both for R&D and production
• Robust monitoring tool. Capable to help in assessing production
process variations

Limitations:
• Indirect (?) measuring method
• Inaccuracy slightly increases at the end of primary drying (if
there are large heterogeneities between vials)
• Model (as it is) only valid for vials and bulk, but not applicable
for lyophilization of granules


Closing the loop: From Monitoring to Control

DPE output

• Front temperature (and T profile
vs. time) Lyo-Driver
• Mass Flux of water vapor (control system)

• Effective diffusivity
• Heat transfer coefficient


Closed Loop Control: the innovation

Goal:
Goal determination of an optimal heating shelf control strategy
for primary drying in order to minimize the drying time
avoiding to jeopardize the integrity of the material.
f

PROCESS

PRESSURE RISE
Tfluid, Batch Parameters,
etc.
DPE
Tproduct, Thickness,
? Tmax,etc.

CONTROLLER
CONTROLLED
Tfluid PROCESS
PROCES
PROCESccS MODEL
Tproduct Gain
ISE

Patent pending LyoDriver


Some experimental results
50

40

30 Tset_point

20
Tfliud
10
T,°C

0
Tthermocouple
-10
10

-20

-30 Tmax

-40 TDPE

-50
0 5 10 15 20 25 30 35 40
time,h
Tfluid,sp Tprod,max T_fluid TB, °C T_thermocouple


Case Study (1/5)

The recipe development and transfer of a formulation proposed to
lyophilize a protein has been studied. Its main excipients being
y p p p g
mannitol, sucrose and a buffer. By means of DSC and Freeze
Drying Microscope collapse temperature was determined: -26ºC


Case Study (2/5)

A cycle driven by LyoDriver was launched in an industrial lyophilizer,
establishing the maximum product temperature at -32ºC (safety reasons).
Primary d y g t e was de ed longer o purpose. Opt u primary
a y drying time as defined o ge on pu pose Optimum p ay
drying temperature profile can be observed in the figure
2
40

20 1.5

PP/PB
T,°C

0
1
-20
0.5
05
-40

-60 0
0 10 20 30 40
Time,h
Tfluid,sp Tprod,max T_fluid TB, °C TC1, °C End time PP/PB


Case Study (3/5)

Delivered cycle by LyoDriver:

Sublimation flow


Case Study (4/5)
With the obtained results a second cycle (NO CONTROL, JUST MONITORING!)
with the shown recipe was launched, with a more “conservative” approach
j
just at the beginning of the primary drying (as it would be done in the
g g p y y g(
production units), but with the optimum recipe parameters found by LyoDriver
in the rest of the primary drying.

Sublimation flow


Case Study (5/5)

The maximum shelf temperature at the end of primary drying was
deliberately not respected in this recipe (-25ºC instead of -30ºC delivered
by L D i
b LyoDriver).)
2
40
rature,°C
C

20 1.5
15

PP/PB
0
1
Temper

-20
0.5
-40
40

-60 0
0 10 20 30 40
Product overheat Time,h
Tfluid,sp
Tfluid sp Tprod,max
Tprod max T_fluid
T fluid TB, C
TB °C TC1, C
TC1 °C End time PP/PB


Advantages & Limitations
Advantages:
• Physically based predictive control algorithm.
• Control action is determined taking into account the real
dynamic response of the heating/cooling system
• Predicts potentially damaging temperature overshoots
anticipating the control. Fastest possible response

Limitations:
• Indirect (?) method
• N d some parameters from the plant (cooling&heating
Needs t f th l t( li &h ti
speed)
• Only valid for primary drying
y p y y g


Advantages
Production monitoring
• Detailed tracking of primary drying kinetics allow process
improvement maximizing productivity without impairing product
quality.
• Additional information on primary drying ending
• C l d i
Cycle design space definition (t ki
d fi iti (taking i t account product, container
into t d t t i
and lyo capabilities) extremely simplified
• Monitoring gives extra information on machine characterization, so
scale up or just process transfer simplified, helping to generate robust
support documentation

Closed loop control
• Optimum cycle determined in a single run (development tool)
• Constant quality no matter of intrinsic “process input” variations
• Much more robust process understanding has an inverse relationship
with the risk of producing a poor quality product. Significantly less
restrictive regulatory approaches and scrutiny should be expected


Models as control tools

• It is possible to design a process with a consistent output,
despite a very variable input
p y p

• With a mechanistic model, a powerful analysis of the
correlation bet een p ocess pa amete s
co elation between process parameters and process output
p ocess o tp t
can be done

• The simulations allow identifying key parameters and spend the
limited resources where most gain is expected


QbD Advantages

The pharmaceutical industry will benefit:
• Q alit by Design ens es better design of products with an
Quality b ensures bette p od cts ith
expectation of fewer problems in manufacturing.
• It reduces the number of manufacturing supplements for post-
market changes – relying on process and risk understanding with
k t h l i d i k d t di ith
commensurate risk mitigation.
• It allows implementation of new technology to improve
manufacturing without extraordinary regulatory scrutiny.
f h d l
• A possible reduction in overall costs of manufacturing – and less
waste – is probable.
• QbD promises less hassle during review –translated as reduced
deficiencies and quicker approvals.
• It may improve interaction with Regulatory Authorities allowing
y p g y g
industry to deal with them on a science level instead of on a
process level.
• Continuous improvements in products and manufacturing
processes are viable and significant outcomes of QbD.


QbD Advantages

• The FDA reported the benefits of implementing Quality by
Design for the Food and Drug Administration as consisting of
these enhancements to pharmaceutical manufacturing:
th h t t h ti l f t i

• It enhances scientific foundation for review.
• QbD will provide for better coordination across review, compliance
and inspection.
• It will also improve information in regulatory submissions.
• Better consistency will result along with improvements in quality of
review.
• More flexibility in decision making will be a result that is beneficial
to the industry and FDA.
• QbD ensures decisions will be made on science and not on merely
empirical information.
empi ical info mation
• It involves various disciplines in decision making.
• Resources will be used to address higher risks.


Traditional vs. QbD (FDA’s View)


Regulatory Expectations for Production

• In January 2011 FDA published:
FDA Guidance for Industry - Process Validation:
y
General Principles and Practices:
“More advanced strategies, which
may involve the use of process
analytical technology (PAT), can
include timely analysis and control
loops t adjust th processing
l to dj t the i
conditions so that the output
remains constant. Manufacturing
systems of this type can provide a
higher degree of process control
than non-PAT systems. In the case
of a strategy using PAT the
PAT,
approach to process qualification
will differ from that used in other
process designs.”
designs


Same focus?

• FDA: Process knowledge
“Go home and do the homework”

• EMA BfArM: PAT submission – yes!
EMA,
“…but we continue as we are used to”

• Industry: Design Space - QbD
“Less controls – more flexibility”

• Patient: Quality
“I rely on safe drugs”
l f d ”

(BfArM: Bundesinstitut für Arzneimittel und Medizinprodukte)


EQUIPMENT AND PROCESS MODELLING

• The engineers that built
this bridge did not use trial
and error.

• The models told them how
to do it right the first time.

• The Treasury (taxpayers)
cannot afford “too
e pe s e b dges
expensive” bridges.

• Politicians cannot accept
collapses.
ll


EQUIPMENT AND PROCESS MODELLING

• The engineers that
developed this process
did not use trial and error.

• The models told them how
to do it right the first time.

• The Patients cannot
afford “too expensive”
medicines.

• Reg. Authorities cannot
accept collapses.
ll


Thank you for your attention
Any question?

Miquel Galan
mgalan@telstar.eu


Miquel Galán - Bio and Pharmaceutical Technology: What can we learn from Chemical Engineering?

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