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ICHQ3D implementation: Use of published
data driven risk assessments
Dr. H. Rockstroh, F. Hoffmann-La Roche Ltd,
Basel, Switzerland
Disclaimer
The views and opinions expressed in this presentation are those of the
author and do not necessarily reflect the official policy or position of Roche
or its peer companies, affiliates, NGOs, Authorities, or any of their
personnel, volunteers, members, sub-bodies.
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Introduction to ICHQ3D
Risk Based Approach: Opportunities to use existing data
Data Sharing Consortium
A look at the (shared) data
Application: Using the database in your Risk based Approach
Overview
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Intro: ICH Q3D = Paradigm Change
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Water
Drug
Substance
Excipients
Manufacturing
Equipment Containers
Closures
EI in DP
Risk Based Approach: Where to startTHINK first to keep it simple, THEN TEST
Discouraged: Test everything, then decide
- all 24 EIs / DP level
1 Identify: Exclude EIs that don’t matter!
- What data already exists (Published data)?
2 Evaluate:
- Existing data + newly generated data
Sharing/published data thus allows us to make informed judgement during
the IDENTIFY and EVALUATE Phases
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Use of existing dataICH Case Studies use “First Principles” approach based on
existing data
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Container Closure Systems:
“Materials in Manufacturing and Packaging Systems as Sources of Elemental Impurities
in Packaged Drug Products: A Literature Review”; Jenke et al, PDA J Pharm Sci Technol
Vol. 69(1), p 1-48 (2015)
“A Compilation of Metals and Trace Elements Extracted from Materials Relevant to
Pharmaceutical Applications. ..”; Jenke et al, PDA J Pharm Sci Technol
Vol. 67(4), p 645-57 (2013)
Manufacturing Equipment:
European Patent EP 2352860 A1 "Method for the surface treatment of stainless steel”;
"[…] stainless steel containing more than 12% chromium (such as 1.4435 /ANSI 316L
stainless steel[..]) forms a protective passive layer on its surface, when[…]exposed to air."
ICH Q3D Section 5.3
“…Probability of elemental leaching into solid dosage forms is minimal and does not
require further consideration in the risk assessment“
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Use of existing data (2)
Early example of data sharing for excipients: FDA/IPEC/Industry 2015*
- 24 Elements, 200+ excipients samples, > 4900 determinations
- Overall low EI levels even in mined/marine derived excipients
Pb in TiO2 <10ppm, variability not significant.
Pb also seen in Zn Stearate.
Cd levels in Mg(OH)2 / CaCO3 > Option 1 limits
*Li et al., Journal of Pharmaceutical Sciences, Vol. 104, 4197–4206 (2015)
Study is being redone as another round-robin effort
THIS IS 200 SAMPLES – WHAT IF WE COULD COLLATE
DATA FROM 2000+ SAMPLES?
Excipients: Use of Published Data
Reasons for Data Sharing Consortium (founded 2016)
Companies lack leverage when acting in isolation:
- Even a “big” pharma company e.g. with lots of (generic) DP is usually only a
“small” customer for an excipients supplier
- Excipient scope at individual companies can be rather limited
- The available data are rarely going to be statistically relevant
- What happens when a supplier tweaks their supply chain?
Data sharing reduces uncertainty associated with small sample sets
USP and Ph.Eur are using the Database to verify their quality requirements
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Elemental Impurities Data Sharing Initiative
Strategic Intent:
Facilitate scientifically driven risk assessments under ICH Q3D, and reduce unnecessary testing as part of the elemental impurities risk assessment efforts.
Data is accessible to industry and regulators which can be used in the
same way as information from the published literature to support the ICH
Q3D risk assessment of excipient components
- Make it very clear why specific excipients are regarded as low (negligible) or
higher risk in a particular formulation at a given daily intake.
- Publish key findings which provide scientific underpinning to risk assessments
and have the potential to reduce unnecessary testing
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What does the Consortium do?
Discuss and agree upon the scientific direction of the project
Contribute and share expertise and knowledge
Monitor the data provided by the member organisations and ensure
it meets predefined quality standards
Identify data gaps and recommend priorities for work on the project
Contributed by Crina Heghes
The Consortium publishes key findings
Sharing Knowledge
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Quality
- Validation Protocol
- Active data management
by Lhasa (Outliers)
Data Integrity
- No difference to data
published in peer review
journals in terms of
vindication of data
Building the Database – Design Specification
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Sub Class A Sub class B
Compare a matrix matched blank to your lowest standard, making sure there is no significant contribution compared to your lowest standard
Compare a matrix matched blank to your lowest standard, making sure there is no significant contribution compared to your lowest standard
Minimum 5 point calibration R = >0.995 ~ >R2 = 0.990
Minimum 3 point calibration R = >0.990 ~ >R2 = 0.980
Minimum of 2 spikes one at the top and one at the bottom of the quantitative liner range spike recoveries are between 70-150%
Minimum of 1 spike within the quantitative liner range spike recoveries are between 50-150%
Governed by Accuracy and Range data. Governed by Accuracy and Range data.
6 replicate aspirations of a standard or spiked sample either together or taken throughout the analysis giving %RSD ≤ 20% or spike sample or standard tested at the start and end of the run give the same measurement ± 20% or a 5 point calibration gives an R value of ≥0.995
6 replicate aspirations of a standard or spiked sample either together or taken throughout the analysis giving %RSD ≤ 20% or sample tested at the start and end of the run give the same measurement ± 30% or a 5 point calibration gives an R value of ≥0.990
Minimum N=3 replicate spikes within the “Range” of the method, The spikes can be at the same level or different levels where the response factors give ≤20% RSD
Minimum of 2 spikes one at the top and one at the bottom of the quantitative liner range spike recoveries are between 50-150%
As long as test solutions and spikes are prepared within 24 hours of each other solution stability is assumed as long as all other parameters are met.
As long as test solutions and linearity standards are prepared within 48 hours of each other solution stability is assumed as long as all other parameters are met.
Equivalent concentration in ug/g in sample of your lowest spike
Equivalent concentration in ug/g in sample of your lowest standard
Equivalent concentration in ug/g in sample of your lowest and highest spike
Equivalent concentration in ug/g in sample of your lowest and highest standard
Building the Database – Filling in the data
A procedure was developed for organizations to share their in-house data
Lhasa as “Honest Broker” to host and blind data
Data on excipients NOT suppliers
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Relevance – Excipients in New Drugs
49 novel drugs approved in 2018.
(List of the excipients available from Drugs@FDA)
42 excipients were used > once
86% (36 of 42) have ≥ 3 studies in the database
93% (39 of 42) used > once are covered in the database
https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugInnovation/ucm592464.htm
The relevance of the excipients within the
database was confirmed by a review of novel
drugs approved by the US FDA in 2018.
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Relevance (2)
Most studied excipients (45 or more analytical studies):
- microcrystalline cellulose, lactose monohydrate, magnesium stearate,
hypromellose, and mannitol
One hundred thirty-four suppliers are represented in the database
- with these supplying between 1 and 32 products
90% of elemental determinations present in the database are
“left-censored”
- Left-censorship: A measurement is re-ported as being below e.g. LOQ / LOD
Low levels of EI!
Note:
Many of these are
“left-censored”;
i.e. reported as
“< LOD or < LOQ”
This means the “true”
values are lower than the
table says
(conservative approach)
So what can we learn from the database?
So what can we learn from the database?
Top 5 Excipients:
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1 1 1 1 2A 2A 2A Nr of
Hg As Pb Cd Co V Ni records
MAGNESIUM STEARATE
Max 0.9 1 0.2 0.2 1.5 3 6 60
Min 0.012 0.015 0.05 0.015 0.029 0.012 0.14
Mean 0.61 0.37 0.13 0.12 1 2.1 4.2
MICROCRYSTALLINE CELLULOSE
Max 0.9 1 0.2 0.2 1.5 3 6 107
Min 0.009 0.015 0.007 0.003 0.017 0.012 0.03
Mean 0.6 0.35 0.12 0.11 1 2 3.5
LACTOSE MONOHYDRATE
Max 0.9 1 0.21 0.2 1.5 3 6 80
Min 0.001 0.0014 0.0028 0.0003 0.0002 0.0014 0.0098
Mean 0.55 0.31 0.12 0.12 0.89 2.0 3.3
MANNITOL
Max 0.9 0.9 0.3 0.15 1.5 3 6 55
Min 0.003 0.004 0.0001 0.0001 0.0002 0.002 0.005
Mean 0.27 0.26 0.099 0.072 0.44 0.87 1.7
HYPROMELLOSE
Max 0.9 0.45 0.2 0.2 1.5 3 20 56
Min 0.012 0.0044 0.036 0.0031 0.029 0.012 0.3
Mean 0.37 0.25 0.1 0.082 0.62 1.2 3.3
Option1 Oral Limit 3 1.5 0.5 0.5 5 10 20
Option1 Oral 30% 0.9 0.45 0.15 0.15 1.5 3 6
What can we learn? (2)
What about common mined
excipients?
- E.g. Calcium Phosphates,
Sodium Chloride
Levels > Option 1 limits:
- Ni in dibasic calcium
phosphate
- Mercury in Sodium
phosphate
1 1 1 1 2A 2A 2A Nr of
Hg As Pb Cd Co V Ni records
DIBASIC SODIUM PHOSPHATE
Max 10 0.45 1 0.2 1.5 3 20 36
Min 0.003 0.004 0.001 0.001 0.002 0.012 0.029
Mean 0.83 0.28 0.19 0.082 0.45 0.85 3.6
DIBASIC CALCIUM PHOSPHATE
Max 0.9 1 0.27 2 1.5 20 22 24
Min 0.012 0.1 0.1 0.041 0.07 0.012 0.27
Mean 0.16 0.33 0.18 0.33 0.46 3.1 9.1
SODIUM CHLORIDE
Max 0.9 0.45 0.2 0.2 1.5 3 6 30
Min 0.001 0.005 0.003 0.001 0.001 0.009 0.001
Mean 0.45 0.23 0.1 0.083 0.74 1.4 2.8
Option1 Oral 3 1.5 0.5 0.5 5 10 20
Option1 Oral 30% 0.9 0.5 0.2 0.15 1.5 3 6
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Application: Finding “High(er) Risk” Excipients
11 Excipients (of >260) showed at least 1 element > Option 1
- Alginate (Pb and Ba limits exceeded),
- Calcium silicate, Sunset yellow FCF, titanium dioxide
(Pb limit exceeded) and
- Ferric oxides (Co and Ni limits exceeded)
Other excipients were colorants or coatings of undefined structure or
composition which exceeded Option 1 limits for at least 1 of
Pb, As, Co, and V.
Application: Using the database in Q3D risk
assessments
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Evaluate - Summarize
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Elemental Impurities that may exceed the PDE in the DP
Elemental Impurities that may be present below the control threshold
Elemental that may exceed the control threshold but not the PDE
Elemental Impurities excluded form Risk Assessment (Q3D Table 5.1)
Product risk
assessment
Control threshold:
30% PDE
PDE
Express Risk(s) as expected contamination
Courtesy of M. Schweitzer, Novartis
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Q3D Risk Based Approach and Control Strategy
Risk categories follow PDE Elemental Impurities that may
exceed the PDE in the DP
Elemental Impurities that may be
present below the control threshold
Elemental that may exceed the
control threshold but not the PDE
Elemental Impurities excluded form
Risk Assessment (Q3D Table 5.1)
Product risk
assessment
Control threshold:
30% PDE
PDE
Default Control+Test Strategy Option
Accept on certificate/CoA/questionnaire. Reduced or no monitoring
As above with (reduced when justifiable) monitoring - Risk based approach enables you to leverage grouping / matrixing
Define (periodic) testing frequency as appropriate.
If >PDE, material not ok. Proceed to mitigate.
Oral Formulation Case Study
EI Cmax for Excipients (ppm) Exposure( µg/day)%
OralPDE
Ex. 1 Ex. 2 Ex. 3 Ex. 4 Ex. 1 Ex. 2 Ex. 3 Ex. 4 Film coat* Sum
>76 lots;
4 suppliers
18 lots;
3 suppliers
9 lots;
3 suppliers
≥50 lots;
4 suppliersCalculation based on: 4 x 25mg tablets (approx. 1 g
of drug product) and composition of each tablet
*supplier
data used
Cd 0.2 0.2 0.2 0.2 0.115 0.057 0.006 0.002 0.015 0.195 4 %
Pb 0.2 0.27 0.2 0.2 0.115 0.077 0.006 0.002 0.015 0.215 4 %
As 1 1 0.2 1 0.573 0.287 0.006 0.010 0.045 0.921 6 %
Hg 0.9 0.2 0.2 0.9 0.516 0.057 0.006 0.009 0.090 0.678 2 %
Co 1.5 0.6 0.2 1.5 0.860 0.172 0.006 0.015 0.150 1.203 2 %
V 3 10 1 3 1.720 2.867 0.030 0.030 0.300 4.947 5 %
Ni 6 22 1 6 3.440 6.307 0.030 0.060 0.600 10.44 5 %
Maximum values for each class 1 and 2a elements from the Lhasa Elemental
Impurities Excipients database were used.
Test data on the finished drug product was also generated;
- 9 lots at commercial scale
- All class 1 and 2a elements < 30 % of option 2A concentration limits22
EI PDE 30% PDE Concentrations [ppm] from Supplier CoAs + with Lhasa data
[mg/d] at MDD DS E1 E2 E3 Talc FeO E6 DP E2 DP
As 15 0.75 0.6 1.5 1.5 0.45 0.1 1 0.2 1.21 0.45 0.48
Cd 5 0.25 1.9 0.5 0.5 0.15 0.04 1 0.03 0.41 0.15 0.17
Hg 30 1.5 1.8 3 3 0.9 0.02 0.5 0.04 2.42 0.9 0.97
Pb 5 0.25 1 0.5 0.5 0.15 1 5 0.13 0.42 0.15 0.18
Co 50 2.5 1.2 5 5 1.5 0.6 10 0.07 4.05 1.5 1.63
Ni 200 10 1.8 20 20 6 7.3 70 0.19 16.23 6 6.53
V 100 5 2.4 10 10 3 4.3 10 0.7 8.09 3 3.24
Excipients Example 0.5/150mg Tablet
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Formula DS E1 E2 E3 Talc FeO E6 MDD
[mg/Unit] (starch) [g/d]
150 0.5 5 103.9 40 0.33 0.2 0.08 6
100% 0.33 3.33 69.26 26.7 0.22 0.13 0.06
Conclusions
The shared data has the same provenance to published literature and
can be used as an additional source of information to support ICH Q3D
risk assessments.
Currently the largest known collection of this type of data.
Pooling and publishing data;
- Can help improve the completion of risk assessments
- Indicate which materials represent a more significant risk than others
(Where the risk is real & where it is negligible)
- Reduce the amount of testing needed to back up the risk assessments
Reliability:
- Numerous instances where excipient samples from the same or different
suppliers have been tested in several organizations
- Acceptance criteria for analytical method validation
- Active data management (e.g. outliers)24
Acknowledgements
Crina Heghes and Grace Kocks at Lhasa Ltd
Markus Goese, F. Hoffmann-La Roche Ltd, CH
Mark Schweitzer Novartis, CH
Andrew Teasdale Astra Zeneca, UK
Fiona King, GSK
Laurence Harris, Pfizer
And a host of others…
THANKS
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Relevance – Excipients in New Drugs
Database - Relevance
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Evaluating the results / Effect of LOD&LOQ
Nickel in Magnesium stearate
The box plot uses all 57
determinations in the database,
including 45 left-censored values
- Left box: 12 Numerical values
- Right box: “LLOQ” and “Not
detected” have been set = the
corresponding LOQ or LOD
values as appropriate.
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Case Study: Step by Step
1. Identify the elements of concern based upon the risk assessment
2. Search for the excipients in the Lhasa elemental impurities database
3. Confirm that each excipient in the formulation has been tested for
each element of concern
4. Extract the maximum observed value from the database
5. Assess how much confidence to place in the data
- How many batches tested? From many different suppliers?
- Is the highest value recorded sensible (not a significant outlier)?
6. Calculate the maximum Cd contribution from excipient 1
- convert micrograms per gram to (micro-)grams per day
7. Repeat for all excipients
8. Repeat for all elements of concern
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Case Study Step-by-Step (2)
6. Calculate the maximum Cd contribution from excipient 1
- convert micrograms per gram to grams per day
7. Repeat for all excipients
8. Repeat for all elements of concern
Regulatory expectations
FDA: ”2018 Guidance for Industry”
- Submission of product specific RA Summary reports
- Legacy: Integration into Annual Report 2018, even if no changes
EMA:Summary of the RA required -CTD (Modules 2+3). Full RA at site (Inspections)
- Legacy: Full RA report only required if change in control strategy due to
Q3D (PLUS: Periodic review and re-assessment for changes)
CAN: Summary in Module 3.2.P.5.6 (Justification of Specs) Full RA at site
- Legacy: Notification of any Q3D driven changes
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Pharmacopoeial Requirements <232>
USP 232: Not a verbatim reproduction of Q3D.
- A risk based control strategy may be appropriate
- If, by process monitoring and supply-chain control, manufacturers can demonstrate
compliance, then further testing may not be needed
USP <233> Analytical Procedures
- “FDA recommends that manufacturers use the analytical procedures described in
General Chapter <233>”
- Alternative Methods: “Any analytical procedure must meet the validation
requirements described in … <233>”
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Glass supplier extraction study: VA= 35.2 cm2; Fill: 10ml; Rel: 3.52 cm2/ml
- Worst Case: Any CCS with a relative surface < 3.52 cm2/ml is covered
- All Q3D elements were < 0.01ppm in the extract.
Extrapolation to smaller volumes (larger rel. surface) is easy:
𝑐𝑔𝑙𝑎𝑠𝑠[𝑝𝑝𝑚] =𝑅𝑒𝑙. 𝑆𝑢𝑟𝑓𝑎𝑐𝑒 × 0.01
3.52
So we’re looking at really small contributions!!
Example: Liquid filling line: ICH Case Study 3 (Old; “Before Jenke”)
- “It was assumed that the entire EI content of the glass container had leached into the
DP. Where no information was available, the EI was tested in the DP.
- The expected contributions from As and Pb were close to their respective control
thresholds. Actual levels “found” were <0.05 pppm
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Container Closure Systems
Extraction Study for Type 1 Glass Tubing
EI contributions from WFI
Control mechanisms for WFI
•Monitoring for PW and WFI quality
•Aerobic microorganisms (daily)
•Bacterial Endotoxin (weekly)
•Conductivity (Inline)
•TOC (Inline)
•Appearance, clarity, colour, odour,
Nitrate (monthly)
•Particles ≥10μm und ≥25μm
(monthly)
•Warning levels below acceptance
criteria established (safety margin)
•Data Trending shows constant quality
over years (conductivity and TOC data
constantly 10-6 times below
acceptance limit)
Production of WFI
• High Quality purified water used
• Distillation, ionic exchange resins
• Filter
• CO2- Degassing
• Reverse osmosis
• Ozonization
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ICH Training Materials
Training Module 0: Introduction
Training Module 1: Other Routes of Administration
Training Module 2: Justification for Elemental Impurity Levels Higher than an
Established PDE
Training Module 3: Acceptable Exposures for Elements without a PDE
Training Module 4: Large Volume Parenteral Products
Training Module 5: Risk Assessment and Control of Elemental Impurities
Training Module 6: Control of Elemental Impurities
Training Module 7: Converting between PDEs and Concentration Limits
Training Module 8: Case studies
1a: Solid oral dosage form (submission+internal), 2: Parenteral product,
3: Biotechnological product
Training Module 9: Frequently Asked Questions
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