Digitalisierung im Metabolismus-Labor - iuta · Metasense + data base (ACD) A typical pre-clin....

Digitalisierung im Metabolismus-Labor

N. Kohl, H. Krüger, K. Schmeer, Grünenthal GmbH

3. Analytik-Tag, IUTA, 15.11.2018 1


1. Metabolism, definition and tasks

2. Lab organisation

3. Linking-up; ULI

4. Conclusions

Contents


Xenobiotics

Metabolism


• Identification of an unknown number

of unknown metabolites in complex

matrices

• Determination of involved enzymes

• Establishing species dependent

biotransformation pathways

(gr: μεταβολή, "change")

?

?

?

?

II. CYP450-phenotyping

CYP-ranking

trapping (13C15N-GSH, K13C15N) with CYPs involved

I. stability test/impurities0,1 M HCl, 40°C, 0,1 M NaOH, 40°C, 0,3 % H2O2 , 25°C 0 h, 2 h, 24 h, 72 h

polymorphic CYPs?

KM and vmaxAllele comparison

III. Species comparison

Mics: man, rat, dog, monkey, mini-pig, rabbit, mouse, 7 time intervals plus end point

& control

Hepatocytes or S9-mix fortified with PAPS&UDPGA: man, rat, dog, monkey, mini-

pig, rabbit, mouse, 7 time intervals plus end point & control

12 enzymes, 7 time intervals plus end point & control

structure chem. reg., pKs, log P, tuning in ESI/APCI, +/-, acidic/basic,

scouting gradient with rat-mics, 3h

Chemical

Developmentstructures

Toxicology

safety

Kinetics,

clin. Pharm

Anal. Chem.(NMR)

BioAnalyticsmethod dev.

BioAnalytics Metabolite definition

Work-flow, cross-links


>1 CYP?


substance

arrivalincubation

metabolism in

silico

prep. stock

solutionLC-MS/MS

method

fragmentation

parent

data mining

software based or mechanized

software assisted

software available?

Lab organisation, work-flow

report

physchem

data

analysis

VIH/TZHH

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LogD

PKa LogS

LogP PhysChem

Calculation of PhysChem data

Metabolism in silico



Mix buffer, microsomes,

regenerating system

Start of reaction by

addition of substrate

Incubation at 37°C on

shaker for 60 min

Remove six aliquots at

fixed time points

Transfer into 96 well

plates (stop with ACN)

Incubation at 37°C on

shaker for 120 min

Stop of reaction by

addition of ACN

work-flow: incubations

Protein precipitation

centrifugation

Evaporate to dryness

Reconstitute with eluent

Incubation vial 96 well plate

incubationLC-MS/MS

method

metabolism in

silico


substance

arrivaldata mining

software based or mechanized

software assisted

software available?


analysis

VIH/TZHH

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prep. stock

solution

fragmentation

parentphyschem

data

analysis report


147.0648

165.0754

197.1532

239.1483

317.1952

335.2058

347.2421

365.2526

387.2345

403.2084

526.0626

PK1925 rat 1 8-24h urin ms_RB5_01_10958.d: +MS, 27.0-27.2min #3181-3206

0

2

4

6

5x10

Intens.

100 200 300 400 500 600 m/z

503.3065

506.7878

512.3419

514.0357

515.0386

516.0341

517.2975

520.3320

521.4552

523.4706

524.4741

PK1925 rat 1 8-24h urin ms_RB5_01_10958.d: +MS, 27.0-27.2min #3181-3206

0.0

0.2

0.4

0.6

0.8

1.0

4x10

Intens.

500.0 502.5 505.0 507.5 510.0 512.5 515.0 517.5 520.0 522.5 m/z

Lab organisation, data analysis



LC-MS/MS

method

substance

arrival

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prep. stock

solution

physchem data

metabolism in silico

fragmentation parent

data mining

report


archiving/

storage

data mining

measurement

incubation/

sample prep.

reporting

1 liquid handler2 LC-MS/MS

Lab robot

Metasense +

data base

(ACD)

A typical pre-clin. work package contains app. 500 samples (per NCE)

manual labour

5 d

5 d2

1 d

15 d

15 d

+ robotic support

0.5 d1

5 d2

1 d

15 d

15 d

total FTE capacity(well skilled personal mandatory!)

36 d

+ robotic support

+ software

0.5 d1

5 d2

0.5 d

4 d

4 d

31.5 d 9 d

Capacities


Linking-up, ULI Result table


The goals in evaluating in vitro drug metabolism are: (1) to identify all of the major metabolic

pathways that affect the test drug and its metabolites, including the specific enzymes responsible

for elimination and the intermediates formedGuidance for Industry Drug Metabolism/Drug Interaction, Studies in the Drug Development Process: Studies In Vitro, April 1997

Toxicology

Technical Drug

Development

(LC, MS, UV, NMR)

species comparison

• hepatic metabolism

• tox species

• plasma metabolites

structures

• Phys-chem. data

• Nomenclature

• pKs, logP, logD, IUPAC-names

• LC, MS, MS/MS, UV

chemical

stability/lability

Contributing enzymes

• polymorphisms

• DDI‘s

• reactive species

extrahepatic metabolism

• renal, intestinal

3A4

In silico metabolism

Linking-up, ULI Result table


One-to-Many

‘Live’ Data

Structured

Homogeneous

Unified Data

Metadata for

Search

Analyze

Repeatedly

Accumulate

Knowledge

Create Intelligence

and Gain Insight

Dead Data

Unstructured

Heterogeneous

Siloed Data

Metadata for

Search?

Analyze

Once

Discrete

Knowledge

—

One-and-Done

88% of R&D organizations lack adequate systems to automatically collect data for reporting, analysis, and decision-making1

1Scientific Computing Research Study 2011

88%

12%

ULI (Unified Lab Information)with courtesy from ACD-LABS

MS-data

in silico data NMR-data

in vitro data

references

In silico

metabolism

Metabolism,

(LC-MS)

Bioanalysis

Isotope

chemistry

(NMR)

Preclinical Drug

Safety

(DEREK)

Laboratories

Chem.

Development

(LC, GC,

LC-MS)

Research

Technol

Data exchange

Research

Technol

Project

scientists

Head

173. Analytik-Tag, IUTA, 15.11.2018

Modelling

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Data exchange

via ACD

Tox., DEREK

NMR-data

MS-data

toxicol.

relevance

stru

ctu

res

toxicol.

relevance

(LC, GC, LC-MS)

Data exchange

MS

-data

references,

intermediates

synthesis schemes

NMR-data

MS

-data

Head

Project scientists

Modelling

Bioanalysis

Isotope chemistry

Chem. Development

SD

-file In

terfa

ce

1non-GLP

2non-GMP

(LC-SPE-NMR)2

GI-DD-PDD-PK

GI-DD-TDD-RT

Research Technol

GI-DD-PDD-SY

GI-DD-TDD-CD

GI-DD-TDD-AD

Research Technol

NM

R-d

ata

18

Metabolism1

in siilico

In vitro

In vivo

3. Analytik-Tag, IUTA, 15.11.2018

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• Regulatory demands are constantly increasing

• Automation allows an efficiency increase at constant head count by

replacing manual labour

• However: The full benefits of automation can only be obtained by

implementing software solutions for data analysis

• A common data base allows fast and simple cross-functional data- and

knowledge-transfer (ULI)

Conclusions


Hurdels

1. Time & money

2. Human factors: staff needs to

- have or acquire skills in laboratory robotics, liquid handling, pipetting

robots, analytical technologies, sample tracking/data base management,

laboratory software

- accept new work-flows

- overcome fears

- be stable

Grünenthal:

Nicole Kohl

Heike Krüger

Dr. Rolf Terlinden

Dr. Klaus Pusecker

Dr. Dieter Albert

ACD:

Dr. Barbara Brandau-Krug

Dr. Gerd Rheinwald

Dr. Hans deBie


Hamilton:

Dr. Ulrich Zander

Dr. Björn Kaiser

Frank Schmitt

Müller Industrie-Systeme:

Andreas Müller

IUTA:

Dr. Thorsten Teutenberg

Acknowledgements


Vielen Dank!

Digitalisierung im Metabolismus-Labor - iuta · Metasense + data base (ACD) A typical pre-clin....

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