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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Additional Material:

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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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