Data Sharing Initiatives

Dr Andy Teasdale

PQRI/USP Workshop on Implementation Status of ICH Q3D Elemental Impurity Requirements - Analytical and Risk Assessment Challenges

Overview – areas covered

Why share data ?

What data already exists ? How can this be augmented?

What’s the strategic intent of the database?

Contributing data to the database / current status

Vision for how the database could be used to facilitate

risk assessments

Why Share Data? • ICH Q3D is predicated on the evaluation of

risk, this is made of 3 factors

RISK = PROBABILITY x Severity x Detectability

• We know the severity – Defined PDEs. • We have detectability. • DATA – either newly generated or Historical

data informs us as to the probability.

• Sharing data thus allows us to make informed judgement during the IDENTIFY and EVALUATE PHASES

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Why Share Data? • Q3D itself comments specifically on this: • SECTION 5 - Information for this risk assessment includes but is

not limited to: data generated by the applicant, information supplied by drug substance and/or excipient manufacturers and/or data available in published literature.

• SECTION 5.5. The data that support this risk assessment can come from a number of sources that include, but are not limited to: • Prior knowledge; • Published literature; • Data generated from similar processes; • Supplier information or data; • Testing of the components of the drug product; • Testing of the drug product.

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Why Share Data?

• Q3D Case Studies – use of ‘first principles’ approach based on existing data exemplified.

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What data already exists ? How can this be augmented?

Container Closure Systems

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THEORETICAL RISK • Especially in the case of liquid formulations there is risk of metals

leaching out of CCS into the formulation

• WHAT DOES THE DATA SAY?

Materials in Manufacturing and Packaging Systems as Sources of Elemental Impurities in Packaged Drug Products: A Literature Review PDA J Pharm Sci Technol January/February 2015 69:1-48;

Section 5.3 – Probability of elemental leaching into solid dosage forms is minimal and does not require further consideration in the risk assessment

What data already exists ? How can this be augmented?

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EXCIPIENT STUDIES • Study involved:

– Some 200+ samples – Examined 24 elements

• Summary of results

• Little evidence of substantial levels of even the ‘big 4/Class 1’ (ubiquitous?) in mined excipients

– Pb seen in TiO2 but levels <10ppm, variability not significant. – Pb also seen in Zn Stearate. – Cd levels in Magnesium hydroxide / Calcium carbonate exceed Option 1 limits –

levels need to fail to an option 2 limit before serious concern

• THIS IS 200 SAMPLES – WHAT IF WE COULD COLLATE DATA FROM 2000+ SAMPLES ?

• Potential to facilitate more scientifically driven elemental impurities risk assessments and reduce unnecessary testing as part of the elemental impurities risk assessment efforts.

• The data to be shared is the

analytical data generated to establish the levels of trace metals within batches of excipients used in the manufacture of pharmaceuticals.

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What data already exists ? How can this be augmented?

What’s the strategic intent of the database? • Become the primary source of EI data for excipients

that drives initial risk assessment (c.f. the Jenke paper for packaging components & EIs)

• Publish key findings with the intention of de-risking commonly used excipients • Compare / contrast with data published generated

by FDA.

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Building the database

How has the database been built? How much data is in it? • Lhasa designed and developed the Elemental Impurities database based

on VITICTM platform

• Approved by the consortium in December 2015 • Initial round of donations was received beginning of 2016 • The database was first released at the end of March 2016

• The Elementals database v2016.1.0 contains the following number of records: • 52 records in the Excipient table. • 123 records in the Elementals table.

• V2016.2.0 just released now contains

• 157 excipients • 757 result records

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Building the database Procedure/process for organizations to share their in-house data • Template defined to allow error free parsing of data. • Data anomolised and checked by Lhasa. Data quality requirements • Extensive discussions relating to data requirements • Validation protocol generated

• Extent of Validation recorded + Digestion Conditions • No difference between data donated and data published in peer review journal in terms of vindication of data

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Building a database Is all of the data for lactose and how will sufficient diversity of materials and suppliers be managed? • The content of the database will be actively managed

• Clear commitment from members to generate data if gaps are identified • Next data release will give a clearer picture to consortia members

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ListNo CarlMrozListName Total1 Magnesium stearate 232 Microcrystalline cellulose 413 Lactose 324 Starch 145 Cellulose derivatives 186 Sucrose 97 Povidone 158 Stearic acid 39 Dibasic calcium phosphate 18

10 Polyethylene glycol 6

0

5

10

15

20

25

30

35

40

45

1 2 3 4 5 6 7 8 9 10

Num

ber o

f res

ults

How is use of the database envisioned?

At EMA meeting in April – EFPIA presented a series of Case Studies

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The following slide examines how / where pre-existing data can facilitate the risk assessment

Oral Solid Dose Component Functionality Amount

per 400 mg tablet (mg)

% in coated tablet

Type (Excipient)

Core

API Drug substance 400.00 62.64 Hypromellose 2910 Binder 21.70 3.40 Plant Microcrystalline Cellulose

Diluent 37.20 5.83 Plant

Lactose Monohydrate

Diluent 111.50 17.46 Animal

Crospovidone Disintegrant 43.40 6.79 Synthetic

Magnesium stearate Lubricant 6.20 0.97 Mineral Coating Hypromellose 2910 Film-former 11.16 1.75 Plant Titanium dioxide Pigment 5.55 0.87 Mineral Triacetin Plasticiser 1.49 0.23 Synthetic Blue Aluminium Lake #2

Colorant 0.37 0.06 Mineral

Blue Aluminium Lake #1

Colorant 0.03 0.005 Mineral

Several Excipients used in the formulated product. What data are available?

Number of materials FDA External DB Internal

Lactose 6 3

Hypromellose 2910 6 (not defined as

2910)

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MCC 14 6

Crospovidone 17 (povidone)

3

Magnesium Stearate 1 7 9

Titanium Dioxide 7

Blue Aluminium Lake #1 1

Blue Aluminium Lake #2

Note – Lactose is the main excipient – others <10%

Dat

abas

e sh

ould

con

tain

sub

stan

tivel

y m

ore

data

for c

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xcip

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s

Excipient data Maximum level seen (ppm)

Number of materials

As Cd Hg Pb V Ni Co

FDA Extern DB

Intern FDA Extern DB

Intern FDA Extern DB

Intern FDA Extern DB

Intern FDA Extern DB

Intern FDA Extern DB

Intern FDA Extern DB

Intern FDA Extern DB

Intern

Lactose 6 3 <0.23 <0.03 <0.08 ND <0.5 ND <0.08 ND <2 ND <3 ND <0.8 ND

Hypromellose 2910

6 8 0 <0.03 0 <0.1 0 <0.3 0.01 <0.1 0.02 ND 0.64 2.09 0.01 <1

MCC 14 6 <1.0 ND <0.2 ND <0.5 ND <0.2 <0.1 <2 ND <3 <1 <0.8 ND

0.2 (actual number above LOQ)

Crospovidone 17 3 0.02 ND 0 ND 0 ND 0.06 ND 0.02 ND 0.1 ND 0.1 ND

Magnesium

Stearate 1 7 9 0.02 <0.23 0.09 0 <0.2 <0.1 0 <0.5 <0.3 0.01 <0.2 <0.1 0 <2 1.7 0.16 <5 1.5 0 <0.8 <1

0.5 (actual number above LOQ)

Titanium Dioxide

7 0.36 0.07 0.04 5.74 5.95 0.48 0.04

Blue

Aluminium Lake #1

1 0 0.01 0.03 0.03 0.26 1.58 0.01

Excipient data – Reflection on significance • No appreciable traces of Class 1 or

Class 2a elements in low risk excipients • Lactose • Povidone • MCC

• Mg Stearate – Ni seen at 1.5ppm

• NB less than 1% of the formulation

• Titanium dioxide • 6ppm Pb / 6ppm V

Is this significant?

Component Functionality Amount per 400 mg tablet (mg)

% in coated tablet

Type (Excipient)

Core

API Drug substance

400.00 62.64 Hypromellose 2910 Binder 21.70 3.40 Plant

Microcrystalline Cellulose

Diluent

37.20 5.83 Plant

Lactose Monohydrate Diluent

111.50 17.46 Animal Crospovidone Disintegrant 43.40 6.79 Synthetic

Magnesium stearate Lubricant

6.20 0.97 Mineral Coating Hypromellose 2910 Film-former 11.16 1.75 Plant Titanium dioxide Pigment 5.55 0.87 Mineral Triacetin Plasticiser 1.49 0.23 Synthetic

Blue Aluminium Lake #2

Colorant

0.37 0.06 Mineral

Blue Aluminium Lake #1

Colorant

0.03 0.005 Mineral

Excipient data – Reflection on significance Component Category Quantity (mg/form)

Dose "x" form (mg/day)

Arsenic in component ug/g

As ug in daily dose of formulation

Lead in component ug/g

Pb ug in daily dose of formulation

Mercury in component ug/g

Hg ug in daily dose of formulation

Cadmium in component ug/g

Cd ug in daily dose of formulation

Vanadium in component ug/g

V ug in daily dose of formulation

Cobalt in component ug/g

Co ug in daily dose of formulation

Nickel in component ug/g

Ni ug in daily dose of formulation

x = 1 Total Bio Acc Total Bio Acc Total Bio Acc Total Bio Acc Total Bio Acc Total Bio Acc Total Bio Acc Total Bio Acc Total Bio Acc Total Bio Acc Total Bio Acc Total Bio Acc Total Bio Acc Total Bio Acc

Dosage Form :

Active Synthetic 400 400 0.00 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Hypomellose Synthetic 32.9 32.9 0.03 0.00 0.00 0.1 0.00 0.00 0.30 0.01 0.00 0.10 0.00 0.00 0.03 0.00 0.00 1.00 0.03 0.00 2.09 0.07 0.00

MCC Plant derived 37.2 37.2 1.00 0.04 0.00 0.2 0.01 0.00 0.50 0.02 0.00 0.20 0.01 0.00 2.00 0.07 0.00 0.80 0.03 0.00 3.00 0.11 0.00

Lactose Animal 112 112 0.23 0.03 0.00 0.1 0.01 0.00 0.50 0.06 0.00 0.08 0.01 0.00 2.00 0.22 0.00 0.80 0.09 0.00 3.00 0.34 0.00

Crospovidone Synthetic 43.4 43.4 0.02 0.00 0.00 0.1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.10 0.00 0.00 0.10 0.00 0.00

TiO2 Mineral 5.5 5.5 0.36 0.00 0.00 5.9 0.03 0.00 0.04 0.00 0.00 0.07 0.00 0.00 5.95 0.03 0.00 0.04 0.00 0.00 0.48 0.00 0.00

Mg Stearate Mineral 6.2 6.2 0.23 0.00 0.00 0.2 0.00 0.00 0.50 0.00 0.00 0.20 0.00 0.00 1.70 0.01 0.00 1.00 0.01 0.00 1.50 0.01 0.00

Al Lake 1 Mineral 3 3 0.00 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00

Triacetin Synthetic 1.5 1.5 0.00 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Al Lake 2 Mineral 0.3 0.3 0.00 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Total Dosage Form weight 642 642

Total element As 0.07 0.00 Pb 0.06 0.00 Hg 0.09 0.00 Cd 0.02 0.00 V 0.34 0.00 Co 0.16 0.00 Ni 0.53 0.00

Permissible Limits As Pb Hg Cd V Co Ni Formulation Q3D Q3D Q3D Q3D Q3D Q3D Q3D

Oral PDE 15 5.0 30 5 100 50 200 Parenteral PDE 15 5.0 3 2 10 5 20

inhaled PDE 2 5.0 1 2 1 3 5

Based on data from database all predicted to be ~1% or less of PDE

Challenges to using first principles The data set is limited ! - True but plan to develop a critical mass. • Mined excipients will always show variability - Potentially true..

Component Category Quantity

(mg/form)

Dose "x" form

(mg/day)

Arsenic in component

ug/g

As ug in daily dose of

formulation Lead in

component ug/g

Pb ug in daily dose of

formulation

x = 1 Total Bio Acc Total Bio Acc Total Bio Acc Total Bio Acc

Dosage Form :

Active Synthetic 400 400 0.00 0.00 0.00 0.0 0.00 0.00

Hypomellose Synthetic 32.9 32.9 0.03 0.00 0.00 0.1 0.00 0.00

MCC Plant derived 37.2 37.2 1.00 0.04 0.00 0.2 0.01 0.00

Lactose Animal 112 112 0.23 0.03 0.00 0.1 0.01 0.00

Crospovidone Synthetic 43.4 43.4 0.02 0.00 0.00 0.1 0.00 0.00

TiO2 Mineral 5.5 5.5 0.36 0.00 0.00 1000.0 5.50 0.00

Mg Stearate Mineral 6.2 6.2 0.23 0.00 0.00 0.2 0.00 0.00

Al Lake 1 Mineral 3 3 0.00 0.00 0.00 0.0 0.00 0.00

Triacetin Synthetic 1.5 1.5 0.00 0.00 0.00 0.0 0.00 0.00

Al Lake 2 Mineral 0.3 0.3 0.00 0.00 0.00 0.0 0.00 0.00

Total Dosage Form weight 642 642

Total element As 0.07 0.00 Pb 5.52 0.00

How much impact would this have in the case of an excipient such as TiO2?

1000ppm Pb / Hg? • Pb overall just

exceeded

RISK = PROBABILITY x Severity x Detectability

Next Steps Hear more about the database - December 7th

Join Us ! Contact :

Crina.Heghes@lhasalimited.org

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Conclusions • The feasibility of sharing excipient elemental impurity data has been

successfully demonstrated. • Pooling and publishing data; • Can surely help to improve the ease with which risk assessments can be

completed • Will give a much better picture of which materials represent a more

significant risk than others • Indicate where the risk is real & where it is negligible • Reduce the amount of testing that is needed to be done moving forward to

support implementation