Developing an interpretation framework: Data Management Ensuring...

Hennicke Kamp

BASF SE

Developing an interpretation framework:Data Management Ensuring Factual Correctness, Reproducibility & Analysis

ECETOC WORKSHOPData Management Ensuring Factual Correctness & Reproducibility

24/11/2017LRI – Data Management Ensuring Factual Correctness & Reproducibility

WS Results just published in 5 articles in Regulatory Toxicology and Pharmacology

1. Buesen R, Chorley BN, da Silva Lima B, Daston G, Deferme L, Ebbels T, Gant TW, Goetz A, Greally J, Gribaldo L, Hackermüller J, Hubesch B, Jennen D, Johnson K, Kanno J, Kauffmann HM, Laffont M, McMullen P, Meehan R, Pemberton M, Perdichizzi S, Piersma AH, Sauer UG, Schmidt K, Seitz H, Sumida K, Tollefsen KE, Tong W, Tralau T, van Ravenzwaay B, Weber RJM, Worth A, Yauk C, Poole A. 2017. Applying 'omics technologies in chemicals risk assessment: Report of an ECETOC workshop. Regul Toxicol Pharmacol. epub ahead of print 25 Sep 2017, doi: 10.1016/j.yrtph.2017.09.002.

2. Sauer UG, Deferme L, Gribaldo L, Hackermüller J, Tralau T, van Ravenzwaay B, Yauk C, Poole A, Tong W, Gant TW. 2017. The challenge of the application of 'omics technologies in chemicals risk assessment: Background and outlook. Regul Toxicol Pharmacol. epub ahead of print 18 Sep 2017, doi: 10.1016/j.yrtph.2017.09.020.

3. Kauffmann HM, Kamp H, Fuchs R, Chorley BN, Deferme L, Ebbels T, Hackermüller J, Perdichizzi S, Poole A, Sauer UG, Tollefsen KE, Tralau T, Yauk C, van Ravenzwaay B. 2017. Framework for the quality assurance of 'omics technologies considering GLP requirements. Regul Toxicol Pharmacol. epub ahead of print 5 Oct 2017, doi: 10.1016/j.yrtph.2017.10.007.

4. Gant TW, Sauer UG, Zhang S-D, Chorley BN, Hackermüller J, Perdichizzi S, Tollefsen KE, Tralau T, van Ravenzwaay B, Yauk C, Tong W, Poole A., 2017. A generic Transcriptomics Reporting Framework (TRF) for 'omics data processing and analysis. Regulat. Toxicol. Pharmacol. epub ahead of print 4 Nov 2017, doi: 10.1016/j.yrtph.2017.11.001 .

5. Bridges, J., Sauer, U.G., Buesen, R., Deferme, L., Tollefsen, K.E., Tralau, T., van Ravenzwaay, B., Poole, A., Pemberton M., 2017. Framework for the quantitative weight-of-evidence analysis of ‘omics data for regulatory purposes. Regulat. Toxicol. Pharmacol. epub ahead of print 14 Oct 2017, doi: 10.1016/j.yrtph.2017.10.010.





WS Results just published in 5 articles in Regulatory Toxicology and Pharmacology

Good Laboratory Practice (GLP)

• OECD GLP Principles (revised 1997)

• GLP principles were developed to promote the quality and validity of non-clinical safety data

because of serious data manipulation issues

• basis for corresponding national GLP-regulations in most countries

GLP status is prerequisite for mutual international regulatory acceptance of non-clinical

safety data !


Basic GLP Principles

Organizational aspects

e.g. independent quality assurance

Complete (study) planning in advance

Standard Operating Procedures (SOP) system, study plan

Final report must reflect raw data

Raw data recording, processing and integrity

Archiving

Full retrospective reproducibility of studies


Raw Data

Definition in GLP principles

(OECD)

„Raw data means all original test facility

records and documentation, (or verified copies

thereof), which are the result of the original

observations and activities in a study.”


To be defined for each data recording system raw data must not be susceptible tomanipulation

Paper or electronic raw data ?New technologieshuge amount of electronic raw data

Data Processing

Report data must be reproducible from raw data ! Documention of all relevant processing steps!

Could mean a lot of documentation

Uncontrolled data „manipulation“ impermissible ! Initial data entry must not be deleted !

All Data changes/ corrections to be justified

Data recording/processing software should have audit trail Software development

Commitment to and control of

„manual“ audit trail ?


Validation: Software and Procedures

Do functions work correctly ?

„Black box“ software validation possible

Proper data protection / storage ensured?

Software access control ?

No software administrator rights for users ?

Audit trail function ?


=

Validation: IT Systems

Before IT Systems are used in a GLP setting, they must be validated

Ensure that data produced can be stored in a GLP compliant way

Blackbox-Validation is permitted Show via alternative ways that output is correct!


GLP for metabolomics @ BASF


metanomics has status as GLP test facility for MS based analytics

SOPs in place, software validation completed

manual corrections documentation established

Metabolome Analysis

@metanomics

Berlin

Steps of metabolome interpretation described & published

SOPs in place

Validation of MetaMap®Tox data base ongoing

Data Interpretation@

BASF SE Ludwigshafen

GLP implemented

Animal / Cell study @

BASF SE Ludwigshafen



1. BASF Sample Berlin

2. Sample receipt in Berlin

3. Aliquots

4. LIMS @ metanomics Berlin

5. Transfer of samples to Bioanalytics

6. Measurement @Bioanalytics

7. Raw data obtained in Bioanalytics

8. Analytical & Statistical QC

9. Manual Peak Check & Correction

10. Archiving



Metabolite flT1 flT2 flT3 fhT1 fhT2 fhT3 flT1 flT2 flT3 fhT1 fhT2 fhT3 flT1 flT2 flT3 fhT1 fhT2 fhT3

Metabolite 1 0.85 0.82 0.9 0.64 0.72 0.93 0.93 0.88 0.9 0.88 0.8 0.93 0.74 0.76 0.79

Metabolite 2 0.8 0.74 0.84 0.88 0.78 0.83 0.82 0.75 0.84 0.91 0.82 0.96 0.93 0.78 0.87 0.77 0.86 0.88

Metabolite 3 0.27 0.25 0.36 0.16 0.18 0.16 0.69 0.64 0.75 0.17 0.2 0.3 0.97 0.86 0.64 0.66 0.67

Metabolite 4 1.54 2.04 1.91 3.2 4.9 5.94 1.86 6.97 7.81 4.81 1.1 1.57 1.74

Metabolite 5 0.77 0.85 0.8 0.84 0.71 0.73 0.73 0.78 0.85 0.87 0.88 0.9 0.76 0.79 0.85 0.68 0.81 0.88

Metabolite 6 0.93 0.94 0.97 0.89 0.6 0.61 0.88 0.94 0.9 0.9 0.83 0.88 0.85 0.89 0.82 0.74 0.81 0.78

Metabolite 7 0.56 0.55 0.61 0.27 0.19 0.23 0.85 0.97 0.91 0.43 0.41 0.53 0.93 0.82 0.71 0.75 0.73

Metabolite 8 0.21 0.22 0.3 0.17 0.14 0.15 0.66 0.66 0.89 0.14 0.23 0.29 0.82 0.86 0.79 0.69 0.85 0.87

Metabolite 9 0.77 0.89 0.81 0.74 0.59 0.61 0.79 0.87 0.9 0.82 0.83 0.79 0.88 0.9 0.9 0.87

Metabolite 10 0.82 0.79 0.77 0.63 0.45 0.41 0.84 0.88 0.88 0.85 0.79 0.82 0.59 0.69 0.66

MCPA Dichlorprop-p Dicamba

Find compounds



Metabolite flT1 flT2 flT3 fhT1 fhT2 fhT3 flT1 flT2 flT3 fhT1 fhT2 fhT3 flT1 flT2 flT3 fhT1 fhT2 fhT3

Metabolite 1 0.85 0.82 0.9 0.64 0.72 0.93 0.93 0.88 0.9 0.88 0.8 0.93 0.74 0.76 0.79

Metabolite 2 0.8 0.74 0.84 0.88 0.78 0.83 0.82 0.75 0.84 0.91 0.82 0.96 0.93 0.78 0.87 0.77 0.86 0.88

Metabolite 3 0.27 0.25 0.36 0.16 0.18 0.16 0.69 0.64 0.75 0.17 0.2 0.3 0.97 0.86 0.64 0.66 0.67

Metabolite 4 1.54 2.04 1.91 3.2 4.9 5.94 1.86 6.97 7.81 4.81 1.1 1.57 1.74

Metabolite 5 0.77 0.85 0.8 0.84 0.71 0.73 0.73 0.78 0.85 0.87 0.88 0.9 0.76 0.79 0.85 0.68 0.81 0.88

Metabolite 6 0.93 0.94 0.97 0.89 0.6 0.61 0.88 0.94 0.9 0.9 0.83 0.88 0.85 0.89 0.82 0.74 0.81 0.78

Metabolite 7 0.56 0.55 0.61 0.27 0.19 0.23 0.85 0.97 0.91 0.43 0.41 0.53 0.93 0.82 0.71 0.75 0.73

Metabolite 8 0.21 0.22 0.3 0.17 0.14 0.15 0.66 0.66 0.89 0.14 0.23 0.29 0.82 0.86 0.79 0.69 0.85 0.87

Metabolite 9 0.77 0.89 0.81 0.74 0.59 0.61 0.79 0.87 0.9 0.82 0.83 0.79 0.88 0.9 0.9 0.87

Metabolite 10 0.82 0.79 0.77 0.63 0.45 0.41 0.84 0.88 0.88 0.85 0.79 0.82 0.59 0.69 0.66

MCPA Dichlorprop-p Dicamba

Find compounds

1. Statistical processing of metabolite data

2. Upload to MetaMap®Tox Data base

3. Use of algorithms to compare with data base- evaluation of single metabolite changes- comparison against toxicity patterns- correlation against other treatments

4. Evaluation in MetaMap®Tox How to deal with old, non-GLP data?

5. Reporting

6. Archiving

Good Laboratory Practice (GLP)

needed for regulatory acceptance

= good documentation & transparency

very helpful in doing a good study (not necessarily a relevant one)

is a culture / mindset

24/11/2017LRI – Data Management Ensuring Factual Correctness & Reproducibility THANK YOU!

Developing an interpretation framework: Data Management Ensuring...

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