Analytical Issues in Process Development – QdB

Analytical Issues in Process Development – QdB

PhD

IN INDUSTRIAL CHEMISTRY AND

CHEMICAL ENGINEERING (CII)

Sampling

• Sampling methodology (analytical sample must be representative of the whole batch)

• Can be a problem with large batches

• Variations can occurs due to

- Position in filter, centrifuge or drier, leading to different amount of solvent

- inadequate agitation in vessels causing non-uniform reactions

- Differences in heating (e.g. baking on sides of reactor) may cause variations in level of impurities

- Physical contamination - Non-uniform particle size

• Before sampling final products should be sieved to ensure

uniformity

In-Process Checks

 Sampling is the greatest source of error

 Require semi-quantitative (or better quantitative) methods for following reactions

- Analyse starting materials and products quantitatively - Do they total 100%?

- Are there transient intermediates?

 Analyse during work-up

 Analyse upper and lower layers in separations

 Avoid derivatising methods where possible

n-BuLi

AlCl ₃ Cl

Cl

O O

O

O O

O Cl

Cl

O

CO ₂ H

BF ₃ MeOH

Cl

O

CO ₂ H Cl

Cl

Cl

O

CO ₂ Me Cl

O

CO ₂ Me Cl

+

(95%) (5%)

Derivatizing Problem

Analytical Methodology

• Develops alongside synthetic work

• Assay and impurity profile

• Reference samples

• Isolation and characterization of impurities

• Quality control as chemistry changes

• Need to review methods in the light of new information

Reference Standards

• Required early in the development process to determine response factors

• For each step standards are needed for main product and impurities

• Primary reference standard - highly purified

Specifications

• Bulk drug product must meet high specifications

- No opportunity for later upgrading

• Other chemical products should also have high specifications where this is practicable

• Raw materials and intermediates may have looser specifications

- Chemical processing tends to remove impurities BUT

- This should be investigated and demonstrated on a case-by-case basis

• The ultimate purpose of all specifications is a high quality

final product

Problems Arising from Impurities

 May be carried through the synthesis

- e.g. positional isomers, homologues

 May catalyse side-reactions

- e.g. acids in aldehydes

 May poison metal catalysts in later steps

- e.g. sulphur compounds

 Will demand considerable effort

- Isolation, analysis, investigation of fate

formaldehyde

+

HN N HN N

OH

HN N

OH

HN N OH

+

OH

HN N S

H N NHMe NCN

+ isomers & bis adducts

Isomers are Critical

HN N S

H

N NHMe NCN

Impurities in cysteamine

Impurities in methylamine, e.g. dimethylamine impurities in cyanamide, e.g. dicyanamide

For a final drug, it would be important to check for the absence

Isomers are Critical

CHO HO

MeO

CHO HO

MeO

+ HO CHO

MeO

Cl Br

Chlorine

Analysts need assistance from organic chemists to decide what to look for and in synthesis of potential impurities.

GC-MS or HPLC-MS are useful in identifying small peaks in chromatograms.

Isomers are Critical

Bromine in chlorine - bromine is only a small amount but reacts fast:

Key raw material

Possible impurities

Likely to be carried through synthesis tight spec required

Unlikely to react further large amounts (e.g. 5%)

HO MeO

MeO

OH

MeO

OH

S S

Evaluation of Impurities

Na ₂ Cr ₂ O ₇

Main product

N N

O +

N CO ₂ H O

N O

impurity

Unexpected Impurities

Evolution of RM Specifications

Early laboratory syntheses

 Accept supplier’s spec

 Note supplier and lot number with all experiments

 Perform simple identity tests (IR, melting point) and record results

 Retain a small sample of each lot for possible testing

later

Evolution of Specifications

First clinical batches or bio-batches

 Formal system of sampling and analysis

 Must set tentative specifications

 Test against supplier’s specifications

 Consider if tighter specifications are required

 Develop test methods which are specific for the compound

 Use tests

Certificates of Analysis

 Obtain supplier’s C. of A. for all raw materials

 Most suppliers need constant reminding to send C. of A.

 Do not rely on C. of A. alone

- Supplier’s specs may be inappropriate for the intended use - Manufacturer may change process without notification

- Assay figures may come from non-specific methods, e.g. titrations

- Material may have deteriorated in storage or in transit

Water

 Specifications for water-quality required, especially for later processing steps

- c hemical and biological quality to be assured

 Distillation and deionizing units should be avoided

- Provide ideal conditions for microbial growth - Require complicated sterilisation & validation

 Potable mains water suitable for most chemical

processing

Hazardous Raw Materials

 Some materials are too dangerous to be sampled or analysed under normal laboratory conditions

 e.g. Bromine, sodium hydride, fuming nitric acid

 For these, a certificate of analysis from the supplier will be sufficient

 There should still be evidence that the identity of the

substance has been assured as far as possible, if only

from its appearance

Final Product and Key Intermediates

 Appearance - Colour check, visible spectrum?

 Identity - usually by IR spectrum

 Assay - By HPLC, HPTLC, GC etc

 Impurity profile - By HPLC, HPTLC, GC

 Solvents, incI. H ₂ O - Loss on drying

 Specific tests (GC, NMR, KF)

 Other purity checks - Microanalysis, NMR, MS

Final Product and Key Intermediates

 Inorganics - Sulphated ash or ROI - IR for ammonium salts

- Specific tests for metals (AA) - Anion analysis

 Crystal form - Melting point - DSC

- Particle size analysis

 Optical purity - By methods other than rotation - NMR, GC, HPLC

- Avoid derivatization if possible

Final Drug Specifications

 Assay ^{98 -102%}

(possibly 99-101%)

 Impurities

- Specific, named <0.5%

- Unknown <0.1 % - Total <2.0%

 Ash < 0.2%

 Heavy metals <20 ppm

 Solvents ^<0.2%

but lower for specific solvents

 Crystal form as required

 Particle size as required

Impurities in Drug Substance

 Alt impurities > 0.1% w/w to be identified and characterised

 All impurities > 0.01% w/w to be identified if possible

- If not possible - designate by e.g. T _R

 Toxicity data required for impurities

- from studies on isolated impurity OR

- from studies on drug substance lots containing typical levels of the impurity

 Impurity content may be estimated from area normalisation

- Response factors must be known and taken into account

Impurities in Drug Substance

 Levels of toxic or carcinogenic impurities may have to be set lower than 0.5%

- < 0.1% of minimum toxic dose in daily dose of drug product.

 Example

Daily dose of drug substance - 100 mg Minimum toxic dose of impurity - 20 mg Maximum permitted impurity level -

20 mg / 100 mg x 0.1% = 0.02%

 For carcinogenic impurities, level to be reduced by at

least one further power of ten

Impurity Identification Programme

 Identify impurities >0.1 %

- Isolation by prep. HPLC or prep. TLC

- Chromatographic comparison with samples of known compounds

 Prepare reference samples (ca. 59) and obtain response factors

- Chromatographic isolation - Independent synthesis

 Repeat for remaining impurities >0.01%

- GC/MS may help in identification

 Synthesise potential impurities and check - against

chromatographic system

Class 1 Solvents

in Pharmaceutical Products

(solvents that should be avoided)

Solvent Concentration Limit Concern (ppm)

Benzene 2 Carcinogen

Carbon tetrachloride 4 Toxic and

environmental hazard

1,2-Dichloroethane 5 Toxic

1,1-Dichloroethene 8 Toxic

1,1,1-Trichloroethane 1500 Environmental hazard

(solvents to be strongly limited)

Solvent PDE Concentration Limit (mg/day) (ppm)

Acetonitrile 4.1 410

Chlorobenzene 3.6 360

Chloroform 0.6 60

Cyclohexane 38.8 3880

1.2-Dichloroethene 18.7 1870

Dichloromethane 6.0 600

1,2-Dimethoxvethane 1.0 100

N,N-Dimethylacetamide 10.9 1090

Class 2 Solvents

in Pharmaceutical Products

Solvent PDE Concentration Limit (mg/day) (ppm)

Ethylene glycol 3.1 310

Formamide 2.2 220

Hexane 2.9 290

Methanol 30.0 3000

2-Methoxyethanol 0.5 50

Methylbutylketone 0.5 50

Methylcyclohexane 11.8 1180

N-Methylpyrrolidone 8.4 4840

Nitromethane 0.5 50

Pyridine 0.2 200

Sulfolane 1.6 160

Tetraiin 1.0 100

Toluene 8.9 890

1.1.2-Trichloroethene 0.8 80

Class 2 Solvents (2)

(solvents with low toxic potential)

Acetic acid Ethyl acetate Methylethyl ketone

Acetone Ethyl ether Methylisobutyl ketone

Anisole Ethyl formate 2-Methyl-1-propanol

1-Butanol Formic acid Pentane

2-Butanol Heptane 1-Pentanol

Butyl acetate Isobutyl acetate 1-Propanol tert-Butylmethyl ether Isopropyl acetate 2-Propanol

Cumene Methyl acetate Propyl acetate

Class 3 Solvents Limited by GMP or other

Quality based requirements

1,1-Diethoxypropane Methylisopropyl ketone 1,1-dimethoxymethane Methyltetrahydrofuran 2,2-dimethoxypropane Petroleum ether

Isooctane Trichloroacetic acid

Isopropyl ether Trifluoroacetic acid Ethyl lactate

Manufacturers should supply justification for residual levels of these solvents in pharmaceutical products.

Solvents without Adequate Toxicological

Data

Good Manufacturing Practice

“That part of Quality Assurance aimed at ensuring products are consistently manufactured to the quality appropriate to their intended use.”

Code of Practice to BS 5750 Pt 2 (1987), P3.8

• Should get right result every time

• No “Acceptable Quality Limits”

• No undue reliance on Final Testing

• Quality cannot be tested into the product

Gimme More Paper!

 SOPs

 Training Records

 Equipment Logs

 Inventory Control

 Qualifications

 Validations

 Batch Records

Process Validation

“Establishing documented evidence which provides a high degree of assurance that a specific process will consistently produce a product meeting its predetermined

specifications and quality attributes.”

MATERIALS +

EQUIPMENT +

PROCEDURES +

= PROCESSES

Process Validation

 Comes late in the development process

 All reagents, solvents, stoichiometries have been fixed

- i.e. process has been optimised

 Understanding of each process step is vital

- What can go wrong?

- How robust is the process?

- What happens if reaction conditions changed slightly?

 Statistical designs may help convince authorities that

quantitative evaluation of parameters has been carried out

 Concentrate on later stages initially and work back

Process Validation

For each process stage

 Define raw materials and conditions

 Determine CRITICAL parameters and set limits

 Determine worst case within limits

 Determine edge-of-failure limits

 View each step deeply

 Define monitoring strategy

 Set standard yields and a variance

Process Validation

 Scale-Up - need to prove that quality and yield do not change

 Documentation, recording info in lab and plant

 In-process analysis

 QC on intermediates

 Specifications on intermediates

 Development reports

 Justification for changes to parameters during development

 Combining steps more difficult

DEFINE PRODUCT ATTRIBUTES

DEFINE PROCESS

STEPS

DEVELOP PROCESS

VERIFY PROCESS

DESIGN EQUIPMENT

FACILITY

INSTALL EQUPMENT

QUALIFY EQUPMENT

ONGOING

TEST INTEGRATED

SYSTEM

INTEGRATE SYSTEM

TEST PROCESS

STEPS

Process Validation Cycle

Validation Procedure

• Prepare validation protocol in advance

- Detailed instructions for steps to be validated - Acceptance criteria at appropriate points

• Perform process at least 3 times consecutively

• All predetermined specifications and criteria must be met each time

• Inexplicable failures render the process invalid

- Applies also to subsequent batches

• Material produced in the course of a successful validation

may be used further

Retrospective Validation

 May be applied to processes which have been operated successfully over a long period

 Prepare detailed description of process as described before for prospective validation

 Justify process by reference to existing historical data from previous batches

 Consider at least thirty consecutive batches

 Demonstrate that the process has not changed

Change Control

 Process changes should be anticipated

- New equipment

- New suppliers for raw materials - More efficient chemistry

 Formal system for handling changes

- SOP

- All changes documented

- Review to assess potential impact on quality

 Minor changes require little further action

- Evaluation of batches produced by new method

 Major changes require revalidation

Validation and Development

• R&D processes cannot themselves be validated

• Development chemist must be aware that process must eventually be validated for manufacturing

• Development chemists provide much of the data for validation reports

- Choice of synthetic route - Detailed processing steps - Critical parameters

- Identification and control of impurities

• Validation begins with earliest clinical batches

Edge of failure limits

Proven acceptable range Normal

operating range

critical parameter

Operating Zone Diagram

Setting Ranges for Process Parameters

 Vital part of validation procedure

 generated by experiment, not during the validation runs

 Development work must define for each “critical parameter”

- Normal operating range - Validated range

- Edge of failure limits

 Validation runs confirm these results

Setting Ranges for Process Parameters

• What can the process tolerate?

- Quality considerations - Economic considerations

- Environmental considerations - Safety considerations

• What can the plant equipment achieve?

- Anything is technically possible, but at at price

• Set normal operating range narrowly around optimum conditions

• Set validated range as wide as possible without

compromising quality

Justification of Operating Ranges

 The wider the range the more difficult it is to justify experimentally

 The more parameters involved the more complicated it becomes

 Cannot test every possible combination of values

 Cannot assume that worst case occurs at the limit of the domain

 Can use Response Surface Analysis to find worst case

Process Analytical Technology (PAT)

Process control trough new technologies (innovations), focus on manufacturing science

A system for designing (process development), analyzing and

controlling manufacturing processes, based on timely measurements of critical Q & performance attributes of raw-materials, in-process materials and processes with the goal of ensuring final product Q.

Processes to assure acceptable end-product Q at the completion of the process (quality by design)

Focus of PAT is understanding

PAT tools:

 process analyzers

 multivariate tools for design, data acquisition, anal.

 process control tools

 continuous improvement/knowledge management tools

PAT & closing the loop

ho ld rel ea se

LIMS Lab Process

Close loop control

(physical / chemical parameters only)

Temp., pH, pO

, pressure, …

Temperature, pH, pO2 pressure

M

Bio-

Advanced Process Control

PAT

Process

Qualitative Fingerprint

Monitoring

 Quality build in by design

 Right first time

Real-time

release

The Regulatory changes impacting R&D and Manufacturing

Today Vision

New initiatives to:

improve manufacturing quality

accelerate development

Lower the regulatory burden

FDA new principles:

Quality by design &

design space Quality systems approach

Reflecting product &

process

understanding and knowledge

FDA’s focus:

Keynote address at IFPAC February 2007, by FDA's Chief Medical Officer, Dr. Janet Woodcock, on

Development &

manufacturing should be integrated

Development of quality surrogates for clinical performance

(link critical product attributes to clinical outcomes)

rigorous, mechanistically

based and statistically

controlled processes

The PAT Implementation Roadmap

Select Appropriate Process Analyser

Laser diffraction Spectroscopy Raman

SpectroscopyNMR Spectroscopy

IR

Spectroscopy Weighing

Technology

Level Flow

Liquid Analytics Laser Diode

Spectrom.

Temperature Positioners

Pressure

Gas Analytics

Gas Chroma- tography NIR

Spectrosco py

Mass Spectroscopy

Process Analytics Chemometrics

/ MVDA DoE

Information management

tools Data

Modelling/

Mining Product &

process

design regulatory (advanced)

Controls

PAT Toolbox

In situ NIR Analysis

Concentration monitoring with NIR

time

Amount in %

TBP addition Azide

Intermediate

Amine

Amine TBP or

50 100 150 200 250

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3

Chemical Imaging

A picture says more than 1’000 words

Dissolution Problem: too much Mg stearate at the surface

Pix els

10 20 30 40 50

Pix els

10 20 30 40 50

GOOD SAMPLES BAD SAMPLES

PC3 : Active

GOOD SAMPLES BAD SAMPLES

PC2 : Magnesium stearate

Measurements Across the Process

• Reaction monitoring

Process Monitoring

UV and NIR optical fibers

NeSSI as Enabling technology for...

• Miniature physical sensors

• Miniature chemical

composition sensors Panametrics & Swagelok

Porter Instruments and the Swagelok Co.

Rosemount Analytical

The Qualitative Fingerprint

Process data

NIR spectral data

End-product Quality data

Temp., pH, pO2, pressure, …

LIMS

Qualitative Fingerprint

MVDA (PCA)

MVDA (PCA)

MVDA

(PLS)

Quality by Design (QbD)

• Systematic approach to development

• Begins with predefined objectives

• Emphasizes product and process understanding and process control

• Based on sound science and quality risk management from ICH Q8(R1)

FDA Initiatives: “Pharmaceutical Quality for the 21st Century” - Final report 2004 – Objective:

“A maximally efficient, agile, flexible pharmaceutical

manufacturing sector that reliably produces high-quality

Elements of QbD

Define desired product performance

upfront;

identify product CQAs

Design formulation and process to meet

product CQAs

Understand impact of material attributes

and process parameters on

product CQAs Identify and control

sources of variability in material and

process Continually monitor

and update process to assure consistent quality

Product & process design and development

Quality by Design

Recent Quality Guidance and Initiatives (FDA)

INITIATIVES

2004 2005 2006 2007 2008 2009

Example QbD Approach (Q8R1)

• Target the product profile

• Determine critical quality attributes (CQAs)

• Link raw material attributes and process parameters to CQAs and perform risk assessment

• Develop a design space

• Design and implement a control strategy

Product profile CQAs

Risk assessment

Design

space

Control

strategy

Design Space

Definition

 The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality

Regulatory flexibility

 Working within the design space is not considered a change

Important to note

 Design space is proposed by the applicant and is subject to

regulatory assessment and approval

Design Space Determination

 First-principles approach

 combination of experimental data and mechanistic knowledge of chemistry, physics, and engineering to model and predict

performance

 Non-mechanistic/empirical approach

 statistically designed experiments (DOEs)

 linear and multiple-linear regression

 Scale-up correlations

 translate operating conditions between different scales or pieces of equipment

 Risk Analysis

 determine significance of effects

Design Space Example

• Design space proposed by the applicant

• Design space can be described as a mathematical function or simple parameter range

• Operation within design space will result in a product meeting the defined quality attributes

40

50

600

1

2 50.0

55.0 60.0 65.0 70.0 75.0 80.0 85.0 90.0 95.0 100.0

D is s ol ut ion ( % )

Par am eter 1

Par

am et er 2

40 42 44 46 48 50 52 54 56 58 60 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Dissolution (%)

Parameter 1

Parameter 2

90.0-95.0 85.0-90.0 80.0-85.0 75.0-80.0 70.0-75.0 65.0-70.0 60.0-65.0

Design Space and

Quality Control Strategy

Process

(or Process Step)

Design Space

Monitoring of Parameters or Attributes Input

Process Parameters Input

Materials

Product

(or Intermediate) Product Variability Reduced Product Variability

Process

Variability

Quality Risk Management Process (Q9)

Process

Development

Control Strategy Development

Continual

Role of Quality Risk Management in Development & Manufacturing

Manufacturing Process Scale-up

& Tech Transfer Process

Development Product

Development

Product quality control strategy

Risk Control Risk

Assessment

Process design Process Understanding

Excipient &

drug

Product/prior Knowledge

Risk

Assessment

Continual improvement

Process History

Risk

Review

Example Control Strategy for Real Time Release Testing

Tablet Compression

Pan Coating

Sifting Roller

compaction Blending

Raw materials &

API dispensing

• Specifications based on product

NIR Monitoring

Blend Uniformity Laser Diffraction Particle Size

Dispensing

NIR Spectroscopy (At-Line)

• Identity

• Assay

• API to Excipient

ratio

Business Drivers

Company Image

 Reduced risk via Validation Optimization

 Validation needs understanding

 Integral part of project

 Built validation into process

Improve Existing Process

 Gain new process understanding

 Process optimization

 Reduced cost of quality

 Raw material specifications

 Know product availability + yield

 Real Time Release

New Product Development



End of life-cycle

 Transferability of process

 Scale down Site to Site transfer

 Accelerate transfer

 Reduce validation effort

 Reduce project time

 Mitigate transfer risk

 Move manufacturing to most effective site

PAT/QbD

Miniaturized Analytical Equipments

Fast GC is a chip-based instrument with an integrated Thermal Conductivity Detector. A tiny and easy exchangeable GC cartridge (60*100*12.5 mm) contains injector, detector, column and heating capability up to 180°C.

C2V focuses on natural gas, oil and process applications, e.g., a BTU analysis is done in less than 20 seconds.

Thermo Fisher Scientific

Micro NMR

spectrum of a 3 micro liter

water sample

using a RF

Conclusion

• Development chemists must make a large contribution to the validation and design effort

• To ensure smooth validation at end of line, project must be well organised from the beginning by QbD techniques

based on risk analysis

• Development reports are vital

- Summarise efforts over a time period - Summarise work on a particular area - Collate raw data and put it in context

- Provide justification for the process to be validated