Examining Relevant Roles forInformatics and Software Technologiesin Extractable and Leachables Studies

Presented by Patrick Wheeler

CoSMoS 2015, San Diego

Why? …Safety & Performance

Drug Products Bioprocessing

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http://web.tradekorea.com/upload_file2/product/091/P00255091/cbe9caa6_05998248_e94b_408d_99bf_95334900b568.JPG, www.pharmawisdom.com

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Beyond Pharmaceutical & Medical Devices

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Components of an Extractables Study

• Extraction– suitable solvents – optimized for the polymer/material

– vigorous extraction technique(s)

– to asymptotic levels

• Multiple analytical techniques– GC, LC with different detectors

• Identify principally by MS

• Quantify extractables greater than or equal to the AET (based on the SCT)

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http://www.solvias.com/sites/default/files/solvias_whitepaper_web.pdf

• Inorganic– ICP-MS

Information Needed & Data Sources

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• Organic (TOC)– Universal detection

– Sensitive

– Compound identification

– HSGC/MS, GC/FID/MS (+/-derivatization), LC/UV/MS

– Elucidation (NMR, HRAM-MSn)

And lots else

• Organization, SampleID, Project

• Analyst, Batch #, structure(s)

• Experimental details

• Supply chain

• Etc, . . .

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ELN Archive LIMSSDMS Servers InstrumentComputers

Search

EmailWord

DocumentsReports Spreadsheets Raw Data

Processed Data

PDFs Images

Impurity, metabolite, degradant, leachable, contaminant

These Problems Aren’t Unique

API

Platform for Consolidating and Reconciling Information from GC/MS & LC/MS Analyses of

Mixtures or Materials

• How do we track –

– Additives

– Their degradants

– Associate relevant

information

– Potential problems

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Detecting and Characterizing E & L

• Partial ID:– Mass -> MF -> Structure

– Fragmentation

– DB lookup

• In-house & external

• Structure Characterization– MS, IR, NMR

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Dead Data

Cons• Searched?• Re-purposed?• Re-used?• Re-Analyzed?

Risks• Data Quality• IP Protection• Knowledge

Management

Pros• Small• Easy to Send• Universal

Peak Search

Spectral Similarity Search

Structure/Sub-Structure Search

Metadata Search

Analyze

Interpret

Re-Process

‘Live’ Data

‘Live’ Data

One-and-Done Leads to Missed Data-Mining Opportunities

Report

ProcessAnalyze

Interpret

Search?

Re-Use?

Re-Process?

Re-Purpose? DEAD DATA

One-and Done

Data Archive

One-and-Done Leads to Missed Data-Mining Opportunities

Report

• Multiple Software Systems

• Incompatible Formats• Dead Data

Today’s Analytical Informatics Ecosystem

Raw Data

Processed Data

A Universal ‘Live’ Data Language Based on Industry Partnerships

Analytical Knowledge Management

Automated Data Handling

Processed Data

Raw Data

Subset of Domain Specific Data

Client App, Instrument Vendor Software

Data Archive

AutomatedSolutions

Automation

ID and Characterization Workflows:• Impurity & Degradants• Metabolites• Extractables and Leachables• Salts and Polymorphs

Regulatory and GxP Requirement

Data Systems Integration

Subset of Domain Specific Data

AutomatedSolutions

APIs, Web Services, Widgets

In-house or 3rd Party Application Front-

EndLIMS

SDMS

ELN

IDBS Experiment ID

Network Storage

ACD/Labs Thick Clients

Submit Analytical Request

O

O

O

+ O

O

O

ACD/Labs Automation

ACD/Labs Thin Clients

ACD/Labs Widgets

The ACD/Labs and IDBS Joint Vision

<metadata>

IDBS EWB Web

Active Integration

ACD/Spectrus and IDBS EWB Integration

HTML5 Widget

Access to Data in SpectrusDB

Access to Functions from SpectrusDBvia API and Web Services

‘Live’ Data Interactions

3 D’s for Success

The success of solving an unknown centers around 3 criteria:

• Dereplication tools – fast and robust

• Databases – comprehensive (natural products, standards, synthetic, etc.) with multiple query points

• Data – NMR, MS, IR, etc.

“Dereplication” Workflow

Molecular Weight Molecular Formula1D NMR Known fragments

~ 20 Million structures

~ 400 K structures and ~ 2.2 Million fragments

25

Chemo-centric approach – a biochemical reference library is foundational

• Accurate IDs based on 3 independent criteria:– - retention time (~RI) – - accurate mass– - ms/ms fragmentation

• Accurate mass alone is inadequate for high-confidence structure ID

Retention Time (RI)

MS/MSSpectra

AccurateMass

Pure ReferenceStandards

Determine Molecular Formula of Unknown

Search of ChemSpider Database within SE of this molecular formula yields 110 candidate structures!

Diverse Set of Candidate Molecules Based on Formula Alone

Candidates with correct molecular formula and substructures

Potential of ACD/ChromGenius Retention Prediction

With ACD/ChromGenius Retention Prediction

N-formiminoglutamate

Search of ChemSpider Database search of this molecular formula yields 863 candidate structures!

ACD/Structure Elucidator:Determine Molecular Formula of Unknown

Generate Formulas on Fragment Ions to Aid Interpretation

C8H14NO3-

Neutral loss of C2H2O

Neutral loss of CO2

O

O

O

Ion at C2H4NO-

O-

NH

Total - 863

Acid & Acetyl -29

N-Acetyl -21

List of 21 Candidates with Specified Substructures

ACD/ChromGenius Retention Prediction

List of 21 Candidate Structures with CG Logic

N-acetylleucine

1H NMR Database Search

Searching Complex 1H NMR dataset

13C NMR Database Search

Partners in Science

• www.chemspider.com

• www.metabolon.com

• http://www.pharma-sea.eu/

• www.acdlabs.com

Number of Possible IsomersCl

Br

OH

Br

OH OH

O

C10H17Br2ClO2, 50,502,293 C15H22O2, 138,136,211,624

O

O

O

O

C15H20O1, 37,568,150,635 C12H12O3, 68,930,547,646

O

OH

OH

NH

NH2

OHO

C13H20O3, 14,431,269,166 C11H12N2O2, 31011

<n1012

Rapid Identification Databases

• ACD/HNMR and CNMR DB• Over 320,000 assigned chemical structures

• ACD/Structure Elucidator• Over 425,000 compounds• Over 2,300,000 structural fragments• Over 14,000,000 PubChem compounds with predicted 13C NMR data• Over 22,000,000 ChemSpider compounds with predicted 13C, 1H, 19F,

31P, 15N NMR data

• Dictionary of Natural Products• Over 220,000 compounds

• Marinlit• Over 21,000 compounds (marine sources)

• AntiBase• Over 35,000 compounds (microorganisms and higher fungi)

Structure Elucidator Utility

• Structure Elucidator can be used for 3 approaches to problem solving:– Spectral Dereplication (identification of a structure through spectral

database search)

– Structure Verification (confirmation of a proposed structure)

– Structure Elucidation (de novo determination of unknown structure)

Conclusions

• Experiments won’t stop improving

• Needs will become more exacting

• Informatics needs will continue to grow– Adapt to increasing complexity

– Greater needs for integration

– More interpretive power

Acknowledgements

• Ryan Sasaki

• Graham McGibbon

• Steve Hayward

• Arvin Moser