FDA-research Related to Formulation & Pharmaceutical Product Stability

Research Related to Formulation and Pharmaceutical Product Stability

Mansoor A. Khan, R.Ph., Ph.D.CDER/OPS/OTR/DPQR

Advisory Committee for Pharmaceutical Scienceand

Clinical PharmacologyApril 14, 2010

Mansoor Khan, FDA ACPS 20102

Stability• Drug substance and products could degrade by

oxidation, hydrolysis, racemization etc.• Factors such as temperature, humidity, light, pH,

ionic strength, buffer strength, residual metals could enhance the degradation.

• It is expected that a well designed formulation and packaging protects the product from degradation


Stability Testing• Once a product is designed and developed, the

stability of a finished product is required to be demonstrated for the “shelf-life” of a product

• Shelf life refers to the time for which the drug product retains the quality specifications

• Depending upon the clinical consequence, the Agency might tighten the potency/strength requirement- e.g. levothyroxine


Stability• If a product is stable for two years, a sponsor

doesn’t have to wait for that time to market a product with two years expiration dating. It can do stability studies in exaggerated/accelerated conditions of temperature and humidity (e.g.. 40°C/75%RH) and get an ESTIMATE of real time stability

• The ESTIMATED/TENTATIVE shelf-life is backed by real time stability studies at controlled room temperature


Accelerated Stability Studies - Theoretical

TIME

37 oC% Drug Remaining 45 oC

55 oC

60 oC

TIME

37 oC% Drug 45 oC

55 oC

60 oC

• Products degrade at various rates, e.g.. 0-Order, 1st Order, 2-nd Order.

We need Ea values

ShelfLifekCC o *ln 25=

⎥⎦

⎤⎢⎣

⎡ −Δ=

21

21

2

1lnTTTT

RE

kk a

RTELnAk a−=25ln

•We may able to use microcalorimetry or other methods for

•Know the limitations of aEΔ

aEΔ


Current Stability Requirements• 25°C/60%RH, 40°/75%RH and if necessary,

30°/65%RH

• For SLEP Program, the conditions are 25°/60%RH and 50°/75%RH

• We may able to use microcalorimetry or other methods for Activation Energy


Analytical Methods

QBD studies and

Shelf-life predictions

Productdegradation

studiesPackaging

variablestudies

Drug-Excipient interactions

Degradationmechanism

Environment &Formulation

Factors

API Reaction Rate Order

Stability

Stability indicatingHPLC, LC-MS, GC, GC-MS etc.Validation

Solution state kineticsSolid state kineticsOther physical attributes

TemperatureMoisturepHLightIonic strengthBuffer conc.Dielectric constant

Physical attributeschemical degradationActivation energy determination

OxidationHydrolysisReductionDecarboxylationRacemizationDeaminationDehalogination

DSC, TgA, MicrocalorimetryHPLC, MS studies etc to justify the use of excipients


Stability Test Attributes in Shelf-Life Extension Program

Solid Orals•

Potency Assay•

Impurities•

Dissolution•

Water Content•

Appearance

Injectables•

Potency Assay•

Impurities•

Preservatives•

pH•

Appearance•

Color•

Particulates

Powders•

Potency Assay•

pH•

Water Content•

Appearance

Creams/Ointments•

Potency Assay•

pH•

Appearance•

Separation

Lyon et. al., J. Pharm. Sci., 2006, 95, 1549-1560.


SUMMARY

Results from 3005 lots (122 drug products) were evaluated.

88% of the lots were extended for an average of 66 months past the original expiration date.

Of the 2650 lots extended, 18% were eventually terminated due to failure. The rest are still active or discontinued by the military.

40 Drug Products showed no signs of stability failure (at least 5 lots of each tested).

10 Drug Products were unstable with most lots failing initial extension.


Diazepam Injectors

0

24

48

72

96

120

A B C D E F G H I J K L M N O P Q R S T U V W X Y

Dormant Impurity Failure

pH RecrystallizationFailure

StillActive

Average extension 54 months

Results: Example of Variability

Lot Designation

Leng

th o

f Ext

ensi

on (m

onth

s)


Example: Real-Time StabilityDiazepam Assay Test Results

80

90

100

110

120

0 12 24 36 48 60 72 84

Time in Program (Months)

HPL

C A

ssay

(% o

f Lab

el)

Regression Line

Test Results

Limit Line


Original packaging tablets Time 0

Repackaged tablets Stored at 40°C 75%RH for 4 Weeks

Original packaging tablets Stored at 25°C 60%RH for 4 Weeks

Repackaged tablets Stored at 25°C 60%RH for 4 Weeks

Original packaging tablets Stored at 40°C 75%RH for 4 Weeks

Repackaged Product Stability

Yang et al., Int. J. Pharm., 385, 92-97, 2010.


0

25

50

75

100

0 25 50 75 100

RH (%)

Wei

ght (

%)

PCPSSGACCSCSCSDMCC

0

1000

2000

0 10 20Time (days)

Peak

are

a (m

Au)

T4AAAPIT3AA

-200

-100

0

100

200

300

400

Time (min)

Peak

are

a (m

AU

)

5 10 15 20 25

Tyr

The

MIT

T0 T2 T3T4 T3

AA

T4A

A

DITI

I

I

I

Levothyroxine structure

Shah RB et al. International Journal of Pharmaceutics, 360:77-82, 2008Collier J etal. AAPS PharmSciTech, 2010, in press

Levothyroxine Research

30

30


Presentations in this Session• Pharmaceutical Stability: CMC Review

Perspectives by Dr. Stephen Miller, ONDQA

• Predicting Product Stability During Process Development and Scale-Up by Dr. James Drennen – NIPTE

• Opportunities to Modernize Pharmaceutical Stability Evaluation by Dr. Lee Kirsch – NIPTE

1

Pharmaceutical Stability: CMC Review Perspectives

Stephen P. Miller, Ph.D.Branch Chief (acting)

Office of New Drug Quality AssessmentCDER / FDA

Advisory Committee for Pharmaceutical Science and Clinical PharmacologyApril 14, 2010

2

Overview•

Regulation & Guidance–

IND Phase

–

NDA Phase•

Purposes and Practices

•

Gaps•

Looking Forward

•

Conclusions

3

Stability Requirements for INDs Code of Federal Regulations

•

CFR 312.23 (a)(7)(iv)(a): ….and information sufficient to support stability of the drug substance during the toxicological studies and the planned clinical studies

•

CFR 312.23 (a)(7)(iv)(b): …and information sufficient to assure the product’s stability during the planned clinical studies

4

Stability Guidance –

IND•

FDA Guidance for industry: Content and format of investigational new drug applications (INDs) for phase 1 studies of drugs, including well characterized, therapeutic, biotechnology-

derived products http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInfor

mation/Guidances/ucm074980.pdf

•

FDA Guidance for industry: INDs for phase 2 and phase 3 studies. Chemistry, Manufacturing and Controls information http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInfor

mation/Guidances/ucm070567.pdf

5

Stability Regulations -

NDA•

CFR 314.50(d)(1)(i): Drug Substance -

A full description

of the drug substance including its physical and chemical characteristics and stability…

•

CFR 314.50(d)(1)(ii)(a): Drug Product -

…stability data with

proposed expiration dating

•

CFR 211.137(a): To assure that a drug product meets applicable standards of identity, strength, quality, and purity at the time of use, it shall bear an expiration date

determined by appropriate stability testing

described in 211.166

•

CFR 211.166: There shall be a written testing program designed to assess the stability characteristics of the drug

products

6

Stability Guidance -

NDA•

Guidance for Industry: ICH Q1A(R2)-Stability testing of new drug substances and products

•

Guidance for Industry: ICH Q1B-Photostability testing of new drug substances and products

•

Guidance for Industry: ICH Q1C-Stability testing for new dosage forms

•

Guidance for Industry: ICH Q1D-Bracketing and matrixing designs for stability testing of new drug substances and drug products

•

Guidance for Industry: ICHQ1E-Evaluation of stability data

http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/

Guidances/default.htm

7

Purposes for Stability Studies: Early Developmental Studies

•

Identify chemical or physical interactions with excipients

•

Understand need for protection from moisture, oxygen, etc.

•

Understand photostability•

Identification of degradation pathways–

Stress studies (thermal, pH extremes, oxidation)

–

Select analytical procedures that can detect potential degradants

Appropriate Packaging

Rational Formulation

(“Stability-Indicating Method”)

8

•

Assurance of efficacy (assay)•

Assurance of safety (degradant

levels)

–

Toxicological Qualification of degradants

at anticipated maximum level of exposure

•

Establish appropriate storage conditions•

Determine expiration dating period

•

Assurance of safety and efficacy during use by patients–

“In-Use”

studies; length and conditions according to expected use

•

Resuspendability

/ dosing accuracy for suspension

• Thermal cycling (e.g., emulsions, dispersions)

Commercial product equiv or better than clinical studies

Purposes for Stability Studies: Formal Stability Studies

9

Batch Selection per ICH Q1ADrug substance Drug product

# and size of batches

3 batches, at least pilot scale

3 batches, 2 at least pilot, one can be smaller with justification

Manufacturing and Container/closure (CC)

•Representative of proposed commercial material (same synthetic route)•Stored in CC same as, or simulates, that proposed for commercial storage

•Manufactured using different DS batches•Formulation, CC and manufacturing representative of commercial product•Represent each strength and CC unless bracketing/matrixing applied

Additional Any supportive batches. Any supportive batches.

10

Drug Product Stability Studies per ICH Q1AProposed Storage

Study Type Storage Condition

Time

Controlled room temp.

Long term 25°C ± 2°C / 60% ±5% RH 30°C ± 2°C / 65% ±5% RH

12 months

Intermediate 30°C ± 2°C / 65 ±5% RH 6 mo (of 12)

Accelerated 40°C ± 2°C / 75% ±5% RH 6 months

Refrigerator Long term 5°C ± 3°C 12 months

Accelerated 25°C ± 2°C / 60 ±5% RH 6 months

Freezer Long term -20°C ± 5°C 12 months

11

Stability Commitments•

Provide appropriate post-approval stability commitment so that a total of three commercial batches

have acceptable long-term and

accelerated stability data to confirm the assigned expiration period

•

Annual stability commitment–

Provide commitment to put one annual batch

of each

marketed strength in each packaging configuration (unless bracketing/matrixing is proposed) on long-

term stability•

Report stability failures for the marketed batches as per 21CFR314.81(b)(1)(ii)

12

Gap Analysis•

Without sufficient product knowledge–

Unanticipated stability issues may occur

–

Usually encountered and resolved during IND–

Can delay approval or impact availability

•

Drug development incorporating QbD –

Considers intrinsic stability of drug substance

–

Selects formulations to improve or maintain stability in the dosage form

13

Risk-Based CQAs Example : The Levothyroxine Story

•

Narrow therapeutic index; low dose; multiple tablet strengths (25-150 µg); intermediate strengths separated by ≤10% of dose

•

Marketed without approved NDAs prior to 2000, many with overages due to stability issues

•

Many products approved since 2000 showed–

History of sub-optimal stability profile

–

Significant loss (5-10%) of potency over shelf life–

Inconsistent stability profiles within an individual manufacturer’s drug product line

14

Risk-Based Regulatory Approaches•

Current NDA/ANDA specification and USP monograph permit assay of 90 to 110% labeled claim

•

Theoretically, a tablet can degrade to contain less levothyroxine than a lower strength tablet

•

Levothyroxine is labile to heat, moisture, oxidative conditions, chemical reactions

Understand factors affecting stabilityDesign formulation and process to minimize degradationTighten assay limit to 95-105% to ensure efficacy

Apply

science &

risk-

based

approach

15

Conventional

versus QbD Approaches•

Reduce expiration

•

Change packaging•

Reduce storage temperature

•

Reformulate and begin stability studies again

•

Drug recalls and compliance challenges

•

Possible negative impact on availability

•

Understand DS stability –

solid-state form(s)•

Formulate to meet quality target product profile [QTPP, ICH Q8(R2)]

•

Maximize expiry –

Earlier start on primary stability studies

•

Incorporate knowledge from commercial experience

•

Assure quality and availability for patients

16

Developments Underway•

HL-7 [Health Level 7] (http://www.hl7.org)

–

Developing standards for health care information–

Goal: Standardized model for stability data

•

XML-based approach based on model created by FDA’s Jon Clark & Naiqi

Ya•

Industry-FDA working group established to test standard and associated Implementation Guide

•

Zeneth

(https://www.lhasalimited.org/index.php/vitic/FDA_Vitic/)

–

Goal: Predicting degradation pathways•

CDADA (Cooperative Research and Development Agreement) with CDER and CFSAN

17

Conclusions•

Many appropriate regulatory and scientific tools are available for understanding stability of drug substances and dosage forms

•

Drug development based on QbD principles should:–

increase assurance of quality including stability

–

avoid surprises during IND development

18

James K. Drennen, III, Ph.D.Associate Dean, Research and Graduate Programs

Duquesne UniversityGraduate School of Pharmaceutical Sciences

Predicting Product Stability During Process Development and

Scale-Up

What is NIPTE?

Duquesne University

Illinois Institute of Technology

Purdue University

Rutgers University

University of Puerto Rico

University of Connecticut

University of Iowa

University of Kansas

University of Kentucky

University of Maryland

University of Minnesota

•

NIPTE is a multi-university non- profit organization federally funded to address fundamental research and education on the science of pharmaceutical development & manufacturing.

•

NIPTE will develop new science & engineering to create a paradigm shift in how pharmaceutical products are designed, developed and manufactured after the new molecules are discovered.

Discovery of New Molecules

Product Development Manufacturing

Safety & Clinical Studies

NIPTE Scope

NIPTE Scope

FDA Project: Development of QbD Guidance Elements on Design Space Specifications across

Scales with Stability Consideration

Project ObjectivesImprove pharmaceutical product quality, maximize process

innovation & continuous quality improvements, and reduce manufacturing costs by developing:

•

QbD guidance elements on process design space, scale-up and process validation

•

Framework for optimizing design space specifications across scales with considerations to stability

Unit Operations Team•

High Shear Granulation–

Purdue•

Initial Development and Regime Map Characterization

–

Duquesne•

Granulation Transfer•

Process Optimization (DOE)

•

Fluid Bed Drying–

Duquesne•

Hybrid Control (Thermodynamic and PAT)

•

Process Optimization (DOE)•

Scale-up Experimentation–

Illinois Institute of Technology (IIT)

•

Computation Fluid Dynamics (Optimizing Scale-up DOE)

•

Blending– Duquesne

•

PAT: Online NIR Method Development

•

Process Optimization (DOE)– Rutgers

•

Final Blend Characterizations

•

Tablet Compression– Rutgers

•

Small Scale Compression Optimization

– FDA/University of Maryland•

Full Scale Tablet Feasibility/Optimzation

•

Note: Abbott is scale-up partner– Lab Scale to Pilot Scale– Pilot Scale to Full Scale

Case Product Formulation

Gabapentin: crystalline, anhydrous, form II;

OOH

NH2

Material Intra/Extragranular

Function Amount

Gabapentin Intragranular API 600 mgHydroxypropyl cellulose Intragranular Synthetic binder, added to

promote cohesiveness,

dry mixing before adding

water

40 mg

Crospovidone, NF Extragranular Disintegrant 22 mgPregelatinized corn

starch, NF

Extragranular Disintegrant 60 mg

Magnesium stearate, NF Extragranular Lubricant 7 mg

Microcrystalline cellulose,

NF (Avicel PH102)

Extragranular Diluent 100 mg

Talc, USP Extra‐fine Extragranular Glidant 9 mgHydroxypropyl cellulose Extragranular Synthetic binder 40 mg

Basic Processing Sequence

WetGranulation

Fluid BedDrying

Blending Tabletting

Excipients

API

Binder

Lubricants

Key Technical Issues•

Stability–

Analytical methods to detect degradation products–

Understanding of degradation factors & conditions–

Processing induced stresses favorable to degradation–

Long term stability factors•

Design Space Development–

Risk assessment of product & manufacturing process–

Determination of critical quality attributes for product & intermediates

–

Development of on-line sensing protocols–

Model assisted development of unit & process design spaces –

Prediction of effects of scale-up on design space–

Confirmation via laboratory & larger batch experiments

Scope of Activity Characterization of gabapentin:•

Chemical & physical characterization of gabapentin•

Development of stability-indicating analytical methods•

Studies of degradation products & conditions

Risk Assessment:•

Determination of CQA’s of intermediates and final product via multistage FMEA

•

Review & identification of relevant models & scale-up relations

Unit-operations:•

Granulation trials to confirm base formulation & nominal processing conditions•

Scoping experiments to confirm processing scheme•

Definition & implementation of on-line instrumentation for tracking CQA’s and controlling end points

•

Design of lab scale experimental program for processing line•

Completion of lab scale manufacturing

Scope of Activity Modeling:•

Use lab scale data to evaluate /improve model fidelity•

Use model-guided experimental program to define design space for unit ops and process

Scale-up:•

Execute larger batch experiments to confirm capability to translate design space under scale-up

•

Analysis of larger batch experiments to confirm design space provides assurance of stability under processing

Design Space Refinement:•

Execute selected experiments demonstrating approach to design space refinement with additional data

•

Complete shelf-life stability studies

Provide Guidance Elements:•

Report results & formulate guidance elements

Stability Investigation

•

Development of physical & chemical property base for API–

Identification of forms–

Full range of analytical tools: SSNMR, Raman, DSC, TGA, XRD, BET, Water vapor sorption, HPLC

•

Development of stability indicating methods & protocol for processed material (HPLC)

•

Studies of degradation products & conditions–

API degradation produces lactam (limit ≤

0.5%)–

Temperature, processing stress, humidity

•

Preliminary studies of longer term stability issues–

Observation of some anomolous behavior

See paper 166d

0

0.5

1

1.5

2

0 20 40 60 80 100

% la

ctam

(m

ola

r)

Hours

sealed

31% RH

47% RH

74% RH81% RH

0

0.5

1

1.5

2

0 20 40 60 80 100

% l

acta

m (

mo

lar)

Hours

sealed

31% RH47% RH74% RH81% RH

2009 AAPS mtg

Lactam formation for milled API (45 min) stored at 50C at various humidity environments

Lactam formation for milled API (15 min) stored at 50C at various humidity environments

Lactam formation decreases with increasing relative humidity Effect greater for longer stress times ( milling)

Anomalous Behavior Details reported at

Determination of CQA

•

Establish quality Target Product Profile•

Identify Critical Quality Attributes (product, substance & excipients)

•

Develop Ishikawa (cause & effect) diagrams for product CAQ’s

•

Identify process variables key to stability•

Develop FMEA analysis

•

Establish critical process parameters & CQA’s for intermediate materials

Ishikawa Cause & effect diagram for chemical stability CQA

Unit Operations Models

•

Intensive assessment of literature completed•

Identified approach appropriate for each unit operation–

Response surface model–

Dimensionless variable modes–

Semi-empirical /reduced order model–

Rigorous simulation model

•

Preliminary assessment of appropriate scale-up methodology–

Dimensionless quantities–

Similarity “rules”

& tests–

Rigorous simulation

Preliminary experimentsGabapentin + HPC

0.00

5.00

10.00

15.00

20.00

25.00

10 100 1000

Mass %

Particle size (μm)

28.5 g

57.1 g

85.7 g

Figure 3. The effect of binder liquid content on the size distribution of the Gab+HPC granules.

The label shows the total amount of water added to the system

Microscope image of granulated gabapentin + HPC

1.9 % 5.7 %3.8 %

APPROACH

Nucleation regime mapHapgood, Litster & Smith,AIChE J, 49, 350-361, 2003

10

Ψad

VAd

=3

2

&

&

θγμ

ε cos.35.1.. 2

3/2

LVeffeff

drop

R

VTimePenDrop =τpDrop Penetration Time

Spray Flux

1.0E-4

1.0E-3

1.0E-2

1.0E-1

1.0E+0

1.0E+1

0.600 0.800 1.000 1.200 1.400

Pore Saturation (-)

Stde

f =

U2 /2

Y (-

)

InductionNucleationSteady Grow thRapid Grow th

NucleationRegion

RapidGrowth

Steady Growth

Induction

Tardos et al . (1998) Crumb

Chalcopyrite in Drum

Ballot ini & Water in Drum

Iron Ore in Drum

Ballot ini & Glycerol in Drum

Ballot ini & PEG in M ixer

Iron Ore in M ixer

Na2SO4 in M ixer

( )min

min2 1;

2 ερερρ

l

sgdef

wSYU

St −==

Stokes Deformation

Max PoreSaturation

Growth Regime Map

High Shear Wet Granulation StudiesPurdue Granulation; 2.0% H2

O

2.0% H2O; 2 Min Wet Mass

0

5

10

15

20

25

30

< 63 63-88 88-125

125-177

177-250

250-354

354-500

500-707

707-1000

>1000

Size Fraction (um)

Mas

s %

DCPT Granulation; 2.0% H2

O

The transfer of the high shear wet granulation process from Purdue’s Diosna (bottom driven) system to Duquesne’s GEA (top driven) system was successful based on similar particle size distributions.

The regime map characterization by Purdue’s team allowed this process to be completed in only 4 experiments.

Comparison of PSDs Produced at Purdue and Duquesne

Scale-up Suggestions •

Scale impeller speed with constant tip speed and constant shear rate rules provided that the impeller speed is above the critical Froude number (Fr). The change from bumping to roping flow occurs at 250 rpm at the smallest scale, then the impeller speeds at the critical Fr number for the other scales will be ca

tion:

impeller speed and D is the impelle

for constant tip speed rule will be cation:

for constant shear stress rule will bquation:

2

1

1

2

DD

NN

=

2

1

1

2

DD

NN

=

8.0

2

1

1

2⎟⎟⎠

⎞⎜⎜⎝

⎛=

DD

NN

lculated with the following equa

where N is the r diameter.

•

Impeller speed lculated with the following equa

•

Impeller speed e calculated

with the following e

Scale-up Suggestions•

The spraying time and the dimensionless spray flux will be kept constant

4 l 10 l 75 l

Batch Size (kg) 0.6 1.8 17.7Water amount (g) 30 90.9 883.6Water flow rate (g/min) 15 45.5 441.8

Scale-up Suggestions

•

Ratio of fill height to granulator diameter at all three scales should be same.

4 l 10 l 75 l

Batch Size (kg) 0.6 1.8 17.7Diameter of the granulator bowl (cm) 17 24.6 52.5Fill height / Diameter 0.27 0.27 0.27

Fluid Bed Instrumentation & Control

Fluid Bed PAT scheme• Pressure drop across

bed to control air flow•NIR for granule moisture control end point•Model based EEF factor

to control inlet air T•Control software: Emerson Delta V

Duquesne Installation

Hybrid Controls (First-Principles and Empirical)

•

Establish a thermodynamic environment inside the dryer that ensures a constant drying mechanism and product properties.–

Manages environmental fluctuation by adjusting process parameters that are easily controlled to maintain a constant drying environment

•

Uses empirical measurements to define meaningful phase endpoints and observe process changes quickly.

•

The first-principles modeling enables the reduction of input variables for efficient experimental designs.–

The thermodynamic environment variable (environmental equivalency factor (EEF)) accounts for 4 of the input variables (airflow, input temperature, environmental humidity, starting material moisture content) to reduce the necessary number of experiments.

•

The thermodynamic environment is directly scaleable. (Theoretically)

Environmental Equivalency Factor•

A single calculated value that represents the environmental condition in the bowl at which the process take place

•

2 mechanisms control drying

–

Heat Transfer – driven by temperature

–

Mass Transfer – driven by differences in vapor pressure

•

EEF is equal to the ratio of the heat-transfer surface area to the mass-transfer surface area

–

Regressed against quality attributes.

–

Potentially useful scale factor

)(

][

Bp

tgw

w

M

H

TTC

hRTMp

RTMp

AA

−

−=

∞

∞

∞

ρ

1Ebey GC. 1987. A thermodynamic model for aqueous film-coating. Pharmaceutical Technology 11(4): 40-50.

Hybrid Controls

1000 1200 1400 1600 1800 2000 2200 24000

0.2

0.4

0.6

0.8

1

1.2

1.4

Wavelength (nm)

Abs

orba

nce

NIR Reflectance Spectra from an FBP Drying Run

0 200 400 600 800 1000 1200 1400 1600 1800 20000

1

2

3

4

5

6

Time (sec.)

Pre

dict

ed M

oist

ure

Con

tent

(%

)

Drying Run Moisture Profile

PLS

0 200 400 600 800 1000 1200 1400 1600 1800 2000-10

0

10

20

30

40

50

60

70

80

90

Time (sec.)

Pro

cess

Uni

ts

EEF Input Factors over a Drying Run

Inlet TemperatureProduct Temperature

Outlet Temperature

Inlet Humidity

Outlet HumidityAirflow Velocity

Spray Rate

Environ. Temp. (Inlet)Environ. Temp. (Outlet)

0 200 400 600 800 1000 1200 1400 1600 1800 2000-25

-20

-15

-10

-5

0

5

10

15

20

25

Time (sec.)

EE

F

The EEF Calculation over a Drying/Cooling Run

X: 410Y: 0.1916

Cooling Begins

0 200 400 600 800 1000 1200 1400 1600 1800 200020

30

40

50

60

70

80

90

100

Time (sec.)

Tem

pera

ture

(o C)

Inlet Air Temperature to Control EEF over a Drying Run

EEF Calc.

Temp. Calc

.

1.

The thermodynamic (EEF) calculations use 9 input variables to predict the necessary inlet air temperature to maintain a constant drying environment.

2.

The NIR spectra are used to identify a moisture endpoint for drying.

1 .

2 .

•Hybrid control modeling is used to control the drying process

•PAT defines phase endpoints and enhances process understanding

CFD ModelingSimulated three different particle size

150 micron, 225micron, 350 micron (Inlet velocity: 0.876m/s, Auxiliary: 0.03m/s)

Umf =0 .067m/sUt = 1.12m/s

Umf =0 .143m/sUt = 2.03m/s

Umf =0 .312m/sUt = 3.24m/s

1s 2s 3s 4s 5s 6s

Summary

•

NIPTE project to enable framework for optimizing design space specifications across scales with considerations for stability

•

Risk-based development and manufacturing is possible–

improve pharmaceutical product quality –

maximize process innovation–

continuous quality improvements–

reduce manufacturing costs

Thank you!

Opportunities to Modernize Pharmaceutical Stability Evaluation and Prediction

[email protected] (4/14/10)

Lee E. KirschUniversity of Iowa

National Institute of Pharmaceutical Technology and Education

The Pharmaceutical Stability Predicament


PerformanceDrug release kinetics

PotencySafetyUtility

Acceptability

Manufacturing stress

Storage stress

Shipping stress

Product use stress

Prob

abili

ty o

f fai

lure

(mul

timod

al

Accumulative stress and time

gradual

catastrophic

stable

critical failure

Current and Future Paradigm• Deterministic

– stable or not

• Measurability-based– “significant change” based

on detection

• Impact arbitrary– historical rather than

situational-based

• Prediction based on post-assembly stress

– storage environment and time

• Stochastic– based on probability

• Performance-based– “significant change” based

on performance

• Therapeutic impact– evaluation of the effects

dose regimen, patient population, in vivo performance on stability limits

• Prediction includes design, assembly and post-assembly stress


Research Opportunities


Current state

Future state

Fundamental physical and biophysical studies of exemplary drug instability processes in complex systems

Tools to assemble scientifically‐rational stability design space models

Methodologies for incorporatingdesign space models into stability

prediction models

Design of models to link design space‐stability to clinical performance in relevant patient populations

based on intended therapeutic use regimens

Overarching objective: integrating stability in QbD

2. Design Space Model

L0

&F0

3.Post-Manufacturing Degradation Model

Lt

1.Physical and Chemical Markers

4. Therapeutic Utility/Safety Model


NIPTE Project Team for Gabapentin Case Study


Research

•

H. Arastapour , ChE, IITFluidization & multiphase systems

•

R.Bogner, PhSci, UCONNDrug release, solid dosage forms

•

A.Cuitino, ME, RutgersMaterial mechanics, Multiscale modeling

•

J. Drennen, PhSci, DuquesnePAT and Risk Management

•

S. Hoag, PhSci, Umarylandcompression modeling

•

M. Khan, PhSci, FDAPharmaceutical Technology

•

L. Kirsch, PhSci, IowaDrug stability & quality

•

J. Litster, ChE & IPPH, PurdueGranulation & Powder Technology

•

E. Munson, PhSci, KansasCharacterization of solid pharmaceuticals

•

F. Muzzio, ChE, RutgersPowder mixing & flow behavior

•

G.Reklaitis, ChE, PurdueProcess systems engineering

•

R. Suryanarayanan, PhSci, UMinnMaterial science of pharmaceuticals

NIPTE Administration

•

P. Basu, Exec Director, NIPTEQbD & Pharmaceutical economics

•

V. Gurvich, Assoc Director, NIPTEMedicinal chemistry & organic technology

1. Exploring the molecular basis for instability case study: gabapentin


Form II: API

Form III

Form I:hydrate Chemically stableDesolvates Chemically &

physically stable

Less chemicalstability and readily converts to Form II

gabapentin gabapentin-lactam

Physical and Chemical Probes

Method NIPTE/FDA project

assignment1 Solid State NMR Kansas2 Powder x‐ray diffraction (XRD) Minnesota3 Calorimetry Minnesota4 HPLC Iowa


0

0.5

1

1.5

2

2.5

3

3.5

0 10 20 30 40 50 60

Lact

am (m

ole

%)

days

cturing-stress lactam

Initial lactam formation fromprocess-damaged crystals

Long-term lactam formation consistent with autocatalytic model

Multiphasic lactam kinetics of stressed-gabapentin


Manufainduced

Determination of lactam and “crystal-disordered” gabapentin by chemical/kinetic analysis

• In process lactam (L0 )– Change in lactam levels during specific treatment or unit

operation in % lactam/gabapentin on molar basis• Initial Rate of Lactam Formation (V0 )

– Daily rate of lactam formation upon thermal stress at 50°C under 0% RH

• Fraction of damage crystal (F0 )

dayCk

kVF

FkV

oAD

AD

AD

%/37.0)50(

00

200

=

=

=


Essential research questions for addressing instability mechanisms

• What are the relevant structural probes for identifying and quantifying reactive forms?

• What is the relationship between physical and chemical transitions?

• Are there underlying rules that can be used to predict instability based on inherent chemical and physical properties of drug substances and excipients in complex milieu (e.g. solid state formulations) or for complex drugs (e.g. biopolymers)?


2. Integrating stability probes into design space models: Traditional approach using response surface (e.g. milling)


Batch size

2 4 6 8 10

Mill

ing

Spee

d

4

5

6

7

8

5 10 15 20 25 30

Batch Size

2 4 6 8 10M

illin

g Sp

eed

4

5

6

7

8

0.6 0.8 1.0 1.2 1.4 1.6 1.8

Predicted Degradation (% mole)

Surface Area Stability

220

2111211222110, PPPPPPStabilitySA ββββββ +++++=

Design Space: acceptable surface area and stability


Batch Size

2 4 6 8 10

Mill

ing

spee

d

4

5

6

7

8

Essential research questions for advancing design space

• What are sophisticated modeling approaches that move away from the flashlight in the cave syndrome?– Methods that incorporate prior knowledge (e.g. Bayesian

approaches)

– Methods that make realistic parameter distribution estimations

– Modeling methods that incorporate our understanding of unit operations physics and material properties

• Dr. Drennen’s review of recent approaches


3. Linking shelf-life and manufacturing models

STORAGESTRESS CONDITIONS

IntactAPI

DegradedAPI

AlteredAPI

DegradedAPI

FormulationShelf-life

API stressed-process offraction totalingmanufactur of endat API altered-process undegraded of (%)fraction

ingmanufactur of endat API degraded chemically of (%)fraction

00

0

0

=+===

FLFFL

total


Post-Manufacturing Stability

IntactAPI

DegradedAPI

AlteredAPI

)conditions storage ,(API) altered-process d(undegrade)conditions storage,,( API) (degraded 0

tfFtFfLL

t

tt

=+=

Degradation Model


Autocatalytic Solid-state Degradation Model

product n Degradatio :Defects][][_ DefectsxAPIRate Deg ∝

0

20

40

60

80

100%

deg

rada

tion

TIME

0

2

4

6

8

10

12

14

% d

egra

datio

n pr

oduc

t

TIME

Realistic Perspective


What if manufacturing causes un-degraded defects?

Defect Crystal andproduct n Degradatio :Defects][][_ DefectsxAPIRate Deg ∝

0

2

4

6

8

10

12

14


Multiphase lactam kinetics of process-damaged gabapentin


0

0.5

1

1.5

2

2.5

3

3.5

0 10 20 30 40 50 60

Lact

am (m

ole

%)

days

Lactam Formation: Storage effects


0

0.5

1

1.5

2

0 20 40 60 80 100 120

gaba

-L (%

mol

e)

hours

0

0.5

1

1.5

2

2.5

3

3.5

0 20 40 60 80 100

gaba

-L (%

mol

e)

hours

Effect of temperature

60 °C

50 °C

40°C

0% RH

31% RH47% RH

74% RH81% RH

Effect of humidity

Recovery kinetics based on XRD dataXRD changes for process‐damaged gabapentin

at 31% RH and 40 C

XRD peak at 16.1 (Form III) decreases and peak at 16.8 (Form II)

increases

[email protected]

(4/14/10)

Modified Degradation Model for Post‐manufacturing Stability

sitesdefect crystalfor account to including APIintact is and

API altered-process undegraded is where API) (degraded

0

0

FFF

FkFkLL

ID

t

IDIDtADt ++=

DegradedAPI

AlteredAPI

kAD

kID

kRIntactAPI

Recovered

API

[email protected]

(4/14/10)

Key research questions: linking DS to stability prediction models

• What are effective methods for incorporating the output of design space models (stability-relevant material characteristics) into shelf-life prediction models ?– Application of Bayesian approaches to estimate parameter

distributions rather than single-point estimation

– Development of biomolecule and small molecule stability models based on isoconversional concepts

– Determination of key manufacturing –induced physical changes that form the basis for subsequent physical and chemical instability under environmental stress

– Assessment of excipient roles in shelf-life prediction models : Do they catalyze/stabilize chemical or physical transformations


Incorporating Stability in Design Space

Design Space Model

L0

&F0

Post-Manufacturing Degradation Model

Lt

Physical and Chemical Markers

Therapeutic Utility/Safety Model


What is a meaningful stability specification?


• Is 90 or 95 % potency relevant for the therapeutic use of all drugs irrespective of therapeutic use and index, population variability, pharmacokinetics or pharmacodynamics?

• Is 1% or 2% level of a specific related substance meaningful irrespective of the drug-like properties, pharmacokinetics, dosage regimen, or toxicokinetics of that related substance?

• Does it make sense from a QbD-standpoint to fix the impurity profile of a drug product based on toxicology studies on pre- clinical drug product batches?

• How can we meaningfully address the potential safety and efficacy issues that relate to drug product stability as determined by product design, manufacturing and storage?

Integrating design-space, shelf-life and population-based therapeutic models


Stability DSModel

Manufacturing Design

Formulationdesign

API Physical properties Dosage ParametersMolecular-basis for instability

Drug Substance

Post-manufacturingstability model

Virtual PatientPopulation (PBPK, PDPK)

DegradantToxicokinetics

Drug-like propertiesof critical

DEGRADANT

MeaningfulDEGRADANTSpecification

Dosage Regimen,Patient population parameters

Simplified model for illustration


Degradationproduct profile

Dosage RegimenRanges

ClearanceVariation

AverageSteady-state

Concentration

ResponseModel

Variation

Probabilityof Mild

AdverseEffects

Monte-Carlo simulation and logistical regression


0.00

0.25

0.50

0.75

1.00

0 .01 .02

Prob

abili

ty o

f MA

E

fraction of degradation product

Maximum acceptable risk

Meaningful Degradation Product Specification

Essential research issues in development of population-based clinical product stability models • Options for model design and structure• in silico, in vitro and in vivo evaluation of drug-like

properties of degradation products and API and their incorporation into meaningful therapeutic models

• Improved approaches to estimate parameter distributions and account for new genomic information


Summary of Suggested Stability Research Investments1. Molecular basis of instability pathways for complex

molecules or for simple molecules in complex formulation milieus

2. Development of quantitative frameworks for relating the effects of product design variation and manufacturing stress on stability-relevant material characteristics

3. Methodologies for incorporating the output of design space models shelf-life prediction models

4. Design and development of population-based clinical product performance models to link design space- stability models to clinical performance in relevant patient populations based on intended therapeutic use regimens


Documents

FDA-research Related to Formulation & Pharmaceutical Product Stability