most common types of observations in regulatory ... · •mainly built with authentic standards...

5 MARCH 2020

Most Common Types of Observations in Regulatory Submissions of Chemical Characterization Results and How to Address Them...

Dr. Piet Christiaens

Content

• Short Introduction: The Nelson Discovery and Screener Database

• Errors in Chromatographic Screening for Extractables

• Addressing Regulatory Observations (through the use of a Database)

• Concluding Remarks

NELSON LABS UNIQUE SCREENER DATABASE

• Mainly built with Authentic Standards from detected/reported/publicly known Extractables

• Across ALL Materials of Construction (MoC)

• Total: >6500 Entries

• Population Distribution of the Database o 1000 Volatile Organic Compound (HS-GC/MS)

o 3500 Semi-Volatile Organic Compounds (GC/MS)

o 2000 Non-Volatile Organic Compounds (LC/MS)

• Analytical and Physicochemical Information per Compound /Analytical Mode o Mass Spectral Information

o Retention Time (RT-Lock)

o Relative Response Factors (RRF)

o Other Phycochemical Properties (LogP, Boiling Point,...)

CONFIRMED CONFIDENT TENTATIVE

80% 15% 5%

NELSON LABS UNIQUE SCREENER DATABASE

ERRORS IN CHROMATOGRAPHIC SCREENING FOR EXTRACTABLES

ERRORS IN ORGANIC EXTRACTABLES SCREENING

THE ERROR OF IDENTIFICATION

THE ERROR OF OMISSION

THE ERROR OF QUANTIFICATION

THE ERROR OF IMPLEMENTATION

ERRORS IN E&L SCREENING


INABILITY TO DETECT ALL EXTRACTABLES At or above a Justified Reporting (Safety) Threshold (eg AET)

CONSEQUENCE The undetected extractable cannot and will not be assessed

SEVERITY: FATAL ERROR The extraction profile is irriversibly compromised

Potential (toxic) compounds will be missed: wrong conclusion on Safety

1. Falling Through the Cracks

2. Failing to see a Tree in the Forest

TYPES OF OMISSION ERRORS

3. It never makes it off the column.

1. It never made it to the column in the first place.

1st Type of Omission Error: Falling Through the Cracks

2. Something unfortunate happens while its in the instrument.

4. Something unfortunate happens in the detector

2nd Type of Omission Error: Failing to see a Tree in the Forest

A DATABASE & REDUCING ERRORS OF OMISSION

By Injecting > 4,500 Authentic Standards, the responses for a large population of extractables are known for each technique

It is also known which compounds may be missed in a screening study (which compounds give no responses in either of the three Chromatographic techniques)

Database is based also on Material Composition Knowledge (eg literature, regulations, conference proceedings), not only on “What has been observed” in our own studies

Allows filling the gaps in Analytical Stategies for a Material/Device

The Database enables “TARGETED SCREENINGS”, minimizing the risk missing the

“trees in the forest”

THE ERROR OF IN-EXACT IDENTIFICATION

INABILITY TO IDENTIFY ALL EXTRACTABLES At or above a justified Reporting (Safety) Threshold (eg AET)

CONSEQUENCE No ability to link Compounds IDENTITY to associated TOXICOLOGICAL CONSEQUENCES

of that Identity

SEVERITY: FATAL ERROR In-exact Identification precludes a proper Safety Assessment

AET

DISCOVERED

RIGHT ID

WRONG ID

TOXICOLOGY

HN

OH3C

CH3

CH3

CH3

PDE, TI, ... Accute Systemic tox Carcinogenic tox Sensitizer, Irritant Reprotox ....

PDE, TI, ... Accute Systemic tox Carcinogenic tox... Sensitizer, Irritant Reprotox ....

O

O

A “Simple” Identification: “The Highest Match Score Wins!”*** Detected Compound

Acquired RT = 24.61 min

Best hit from

NIST/Wiley

INEXACT

IDENTIFICATION

1-aminocyclopentane-

carboxylic acid, N-

hexyloxy-carbonyl,

isohexyl ester

Library RT not available

Library Match = 80.7%

Best hit Nelson Labs DB

EXACT

IDENTIFICATION

1,1’-Carbonothioyl-

bispiperidine

Library RT = 24.58 min

Library Match = 98.4%

*** or even worse, “Any match score wins!”

TYPES OF IDENTIFICATION ERRORS: WRONG IDENTITY

The Highest Match Score Wins THE NELSON DATABASE

EXAMPLE

A RUBBER CURING ACCELERATOR

The “Home Court” Advantage

THE VARIOUS LEVELS OF IDENTIFICATION

+ Elemental Formula + Molecular Weight

+ Orthogonal Analytical Info

Structure elucidation

Confirmed with Authentic Standard (RT & MS)

Tentative + other supporting data

Increasing amount or rigor of

Confirmatory Information

Decreasing Certainty that the

Identification is Correct

General structure (e.g., phthalate)

Minimum identification level to

support a qualitative

toxicological safety assessment

(e.g., QSAR)

Minimum identification level

to support a definitive,

quantitative toxicological

safety assessment

HOW THE NELSON DATABASE REDUCES ERRORS OF MISIDENTIFICATION

All Entries in the Database that were Confirmed via the Analysis of Authentic Standards will lead to Unique Identifications and can be reported as CONFIRMED

Other Entries in the Database that were Not Confirmed via the Analysis of Authentic Standards will have undergone an extensive ID evaluation according to USP<1663> to rank them as either CONFIDENT or TENTATIVE

CONFIRMED in USP<1663>: “...The only means of providing a confirmed identification is via mass spectral and retention time match...”

With close to 6000 Compounds in our Database, we can offer UNIQUE Compound Identifications that will allow one to make the LINK between the Chemical Structure of the Extractable and its relevant Toxicological Information.

HOW THE NELSON DATABASE REDUCES ERRORS OF MISIDENTIFICATION

THE ERROR OF INACCURATE QUANTIFICATION

QUANTIFICATION OF EXTRACTABLES IS INSUFFICIENTLY ACCURATE

CONSEQUENCE The Assessment of Exposure of an Extractable to the Patient (eg through evaluation of

PDE’s) will be compromised and may lead to Flawed Conclusions

SEVERITY: CRITICAL ERROR A safety assessment can be performed, but the Inaccuracy of the Concentration/Dose

needs to be accounted for

[I.S.]known = 10 mg/L AreaIS = 100

Area[EXT] = 100

Assuming RFIS = RF [EXT]

I.S. EXT

Chromatogram of Extract

[EXT]Estimated = 10 mg/L

SIMPLE QUANTIFICATION

2 FATAL ERRORS HAVE BEEN

AVOIDED ALREADY

DISCOVERED

IDENTIFIED

I.S. EXT

[I.S.]known = 10 mg/L

Analysis of EXT Standard [EXT] = 10 ppm and [I.S.] = 10 ppm

Chromatogram of EXT STANDARD

[EXT]known = 10 mg/L Area[EXT] = 30

Area[I.S.] = 100

Relative Response Factor (RRF) EXT = 0,3

STEP 1 DETERMINE THE RRF FACTOR FOR THE EXT

COMPOUND

RELATIVE RESPONSE FACTOR (RRF) CORRECTED QUANTIFICATION

Relative Response Factor (RRF) EXT = 0,3

STEP 2 USE THE RRF FACTOR TO DETERMINE THE [EXT]

[I.S.]known = 10 mg/L AreaIS = 100

Area[EXT] = 100

I.S. EXT

Chromatogram of EXTRACT [EXT]Estimated = 33 mg/L

Authentic Standard Analysis of Over 4500 EXT with RRF are included in the NELSON LABS’ Database

RELATIVE RESPONSE FACTOR (RRF) CORRECTED QUANTIFICATION

HOW THE NELSON DATABASE REDUCES ERRORS OF INACCURATE QUANTIFICATION

The Relative Response Factor (RRF) information – per Technique – can be used in 2 ways:

2. The RRF’s in the database allow for INDIVIDUAL RRF correction, very valuable for LC/MS with a wide range of RRF distribution

1. At a minimum, the statistics on all RRF’s allow calculating an Uncertainty Factor for our methodologies

The Information of the Relative Response Factors of ALL 4.500 Authentic Standards that were injected allows screening in a quantitative way.

ADDRESSING REGULATORY OBSERVATIONS THROUGH THE USE OF A DATABASE

Observations from Authorities

Trying to avoid Making Errors (a.o. In Screening)

Article series: How a database can assist in reducing or eliminating errrors

in screening

How to use the Database in addressing regulatory

obervations

Only addressing observations regarding Analytical Testing Execution

• Discussion about the selection of the right detector for LC/MS (ESI vs APCI)


ESI APCI

Very Polar NVOCs

Ionized NVOCs

Low Polar NVOCs

Medium Polar NVOCs

Like Dissolves Like

UPW IPA & Hexane Hexane IPA UPW

NVOC Extractables

Unique to APCI

Respond to both APCI and ESI

Unique to ESI 25%

65%

10%

ADDRESSING REGULATORY OBSERVATIONS THROUGH THE USE OF A DATABASE

• The Nelson Database is more populated for

o APCI: 1200 Authentic Standards

o ESI: 350 Authentic Standards

o Historical Reason for Disproportion (PQRI)

• The ESI database is more requested for Chemical Characterization

• NELSON: Accelerated Efforts to Quickly Grow the ESI database

• As a consequence: the statistical evaluation may change over time...

• Provide ALL Library suggested identifications (NIST/WILEY)

• Do not Choose a structure from NIST/WILEY based upon the highest Match Factor

• When you select a compound from NIST/WILEY search, JUSTIFY

• All observed Extractables above the AET need to be Identified

• Compounds without CAS need further Identification

• If only a Partial Structure is available, report it as Unknown

THE ERROR OF MISIDENTIFICATION







• When you select a compound from NIST/WILEY search, JUSTIFY

• Optimize the CONFIRMED ID category through injection of Authentic Standards (>4,500 Compounds)

• Two dimensional confirmation of Retention Time and Mass Spectral Data • It waives all observations / requirements related to Identification through NIST/WILEY

Libraries

• There will still be a number of compounds that are not (yet) in the database • on average <1% HS-GC/MS; <10% GC/MS

• For the ID of these compounds, we partially rely on NIST/WILEY in GC/MS • The Observations / Requirements will apply for compounds Identified via NIST/WILEY

HOWEVER




• Provide ALL Library suggested Identifications (NIST/WILEY)





The need for High-End Analytical Equipment and Expertise (e.g. Accurate Mass MS-Platforms)

HAVE CAPACITY TO GO THE EXTRA MILE IN IDENTIFICATIONS


Unknown Observed

Use of High End Analytics

Identify Compound

Confirm ID Authentic Standard

Use ID in Next Study (Database)

• Liquid/Liquid Extractions: show data that final result is not underestimated

• Concentration step for IPA – HEX: Provide data that concentration of extracts does not result in Loss

• Provide literature evidence showing a single point calibration can be used to semi-quantify

• Justify the applicability of the used Uncertainty Factors (UF) based upon Lab or Method specific Basis

• When the Response Factor of a compound is unknown, avoid under-estimation by using a correction factor

• Provide data showing the analytical response variability is minimal for all compounds detected

• Include recovery information (surrogates) to verify the efficiency of the processing steps


Can (currently) be addressed with calculating the

UNCERTAINTY FACTORS (UF) based upon the Statistics of

the Database

Can be addressed through INDIVIDUAL CORRECTION of each

individual Compound in the Database

QUANTITATION ERRORS IN PERSPECTIVE

• Provide literature evidence showing a single point calibration can be used to semi-quantify

• Liquid/Liquid Extractions: show data that final result is not underestimated


• Concentration step for IPA – HEX: Provide data that concentration of extracts does not result in Loss

• Justify the applicability of the used Uncertainty Factors (UF) based upon Lab or Method specific Basis

• When the Response Factor of a compound is unknown, avoid under-estimation by using a correction factor

• Provide data showing the analytical response variability is minimal for all compounds detected

• Include recovery information (surrogates) to verify the efficiency of the processing steps

• Method Qualification should include multiple standards (3 for GC/MS, 5 for

LC/MS) at a range of Retention Times so that the Reporting Limit and

Dynamic Range is demonstrated for analytes with a variety of chemical

properties

• Provide Rationales for the LOD and LOQ for each analytical technique.

THE ERROR OF IMPLEMENTATION

CAS Compound Name Database RRF Response Function

RRF

Linear range Library RRF value /

Response Model RRF

% 583-39-1 2-Mercaptobenzothiazole* 0.112 0.535±0.038 2mg/L – 50mg/L 21

149-57-5 2-Ethylhexanoic acid 0.346 0.494±0.016 500µg/L – 50mg/L 70

5464-77-7 N,N-Dibenzylformamide 0.485 0.748±0.019 500µg/L – 50mg/L 87

112-52-7 1-Chlorododecane 0.568 0.856±0.026 50µg/L – 50 mg/L 66

104-76-7 2-Ethyl-1-Hexanol 0.515 0.518±0.020 50µg/L – 50 mg/L 99

112-12-9 2-Undecanone 0.627 0.692±0.030 50µg/L – 50 mg/L 91

122-39-4 Diphenylamine 0.834 0.938±0.021 50µg/L – 50 mg/L 89

593-49-7 n-Heptacosane 0.995 1.149±0.038 50µg/L – 50 mg/L 87

128-37-0 BHT 1.010 0.923±0.010 50µg/L – 50 mg/L 109

117-81-7 DEHP 1.010 1.104±0.027 50µg/L – 50 mg/L 91

31570-04-4 Irgafos 168 1.298 0.964±0.016 50µg/L – 50 mg/L 135

129-00-0 Pyrene 1.377 1.193±0.017 50µg/L – 50 mg/L 115

Average RRF accuracy (%): 93 ± 22%

Linear Dynamic Range: Depending upon the nature of the Compound

Limit of Detection (LOD): Depending upon the nature of the Compound

Limit of Quantification (LOQ): Depending upon the nature of the Compound

Typically 0,05 – 50 mg/L

Typically 0,02 mg/L

Typically 0,05 mg/L

Organic acids and amides (a.o.) may behave differently

CONCLUDING REMARKS

• The new ISO 10993-18:2020 Guidance has resulted in an increased level of Regulatory Questions

CONCLUDING REMARKS

• The Authorities and NB are on a learning curve – questions to understand the science and supporting information in a better way

• The best response to a regulatory question is the one you can avoid: explain in

report what you do, how you do it and why you are doing it

• Observed Trend: more questions about the Identification Process

• The NELSON database is instrumental in dealing with a broad set of regulatory observations

• However there is still an greater emphasis on the quantitative aspect of extractable testing

QUESTIONS?

most common types of observations in regulatory ... · •mainly built with authentic standards...

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