Digitalisierung im Metabolismus-Labor

Digitalisierung im Metabolismus-Labor N. Kohl, H. Krüger, K. Schmeer, Grünenthal GmbH 3. Analytik-Tag, IUTA, 15.11.2018 1

Contents 1. Metabolism, definition and tasks 2. Lab organisation 3. Linking-up; ULI 4. Conclusions 3. Analytik-Tag, IUTA, 15.11.2018 2

Xenobiotics 3. Analytik-Tag, IUTA, 15.11.2018 3

Metabolism (gr: μεταβολή, "change")???? Identification of an unknown number of unknown metabolites in complex matrices Determination of involved enzymes Establishing species dependent biotransformation pathways 3. Analytik-Tag, IUTA, 15.11.2018 4

Work-flow, cross-links structure chem. reg., pks, log P, tuning in ESI/APCI, +/-, acidic/basic, scouting gradient with rat-mics, 3h BioAnalytics method dev. I. stability test/impurities 0,1 M HCl, 40 C, 0,1 M NaOH, 40 C, 0,3 % H 2 O 2, 25 C 0 h, 2 h, 24 h, 72 h II. CYP450-phenotyping 12 enzymes, 7 time intervals plus end point & control trapping ( 13 C 15 N-GSH, K 13 C 15 N) with CYPs involved Chemical Development structures Kinetics, clin. Pharm polymorphic CYPs? >1 CYP? CYP-ranking Anal. Chem. (NMR) Allele comparison K M and v max III. Species comparison Mics: man, rat, dog, monkey, mini-pig, rabbit, mouse, 7 time intervals plus end point & control Toxicology safety BioAnalytics Metabolite definition Hepatocytes or S9-mix fortified with PAPS&UDPGA: man, rat, dog, monkey, minipig, rabbit, mouse, 7 time intervals plus end point & control 3. Analytik-Tag, IUTA, 15.11.2018 5

Lab organisation, work-flow substance arrival prep. stock solution LC-MS/MS method incubation analysis data mining report physchem data metabolism in silico fragmentation parent software available? VIH/TZHH Gihlgvgbtitolgh Ieruzmv b tzblä08dtznbbk nkt7mlzlznn Tiesnl.ö-öioöä Hnjkl.öö.vfj software assisted software based or mechanized 3. Analytik-Tag, IUTA, 15.11.2018 6

Calculation of PhysChem data LogD LogP PhysChem PK a LogS 3. Analytik-Tag, IUTA, 15.11.2018 7

Metabolism in silico 3. Analytik-Tag, IUTA, 15.11.2018 8

work-flow: incubations Incubation vial 96 well plate 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 Incubation at 37 C on shaker for 120 min Transfer into 96 well plates (stop with ACN) Stop of reaction by addition of ACN Protein precipitation centrifugation Evaporate to dryness Reconstitute with eluent 3. Analytik-Tag, IUTA, 15.11.2018 9

Lab organisation, work-flow substance arrival prep. stock solution LC-MS/MS method incubation analysis data mining report physchem data metabolism in silico fragmentation parent software available? VIH/TZHH Gihlgvgbtitolgh Ieruzmv b tzblä08dtznbbk nkt7mlzlznn Tiesnl.ö-öioöä Hnjkl.öö.vfj software assisted software based or mechanized 3. Analytik-Tag, IUTA, 15.11.2018 10

Intens. x10 4 1.0 0.8 0.6 0.4 0.2 503.3065 506.7878 512.3419 514.0357 515.0386 516.0341 517.2975 PK1925 rat 1 8-24h urin ms_rb5_01_10958.d: +MS, 27.0-27.2min #3181-3206 0.0 500.0 502.5 505.0 507.5 510.0 512.5 515.0 517.5 520.0 522.5 m/z 520.3320 521.4552 523.4706 524.4741 Lab organisation, data analysis Intens. x10 5 365.2526 PK1925 rat 1 8-24h urin ms_rb5_01_10958.d: +MS, 27.0-27.2min #3181-3206 6 317.1952 387.2345 335.2058 347.2421 4 197.1532 2 165.0754 147.0648 403.2084 239.1483 526.0626 0 100 200 300 400 500 600 m/z 3. Analytik-Tag, IUTA, 15.11.2018 11

Lab organisation, work-flow substance arrival prep. stock solution LC-MS/MS method physchem data metabolism in silico fragmentation parent data mining report VIH/TZHH Gihlgvgbtitolgh Ieruzmv b tzblä08dtznbbk nkt7mlzlznn Tiesnl.ö-öioöä Hnjkl.öö.vfj 3. Analytik-Tag, IUTA, 15.11.2018 12

Capacities A typical pre-clin. work package contains app. 500 samples (per NCE) manual labour + robotic support + robotic support + software incubation/ sample prep. 5 d 0.5 d 1 0.5 d 1 Lab robot measurement 5 d 2 5 d 2 5 d 2 data mining 15 d 15 d 4 d archiving/ storage 1 d 1 d 0.5 d Metasense + data base (ACD) reporting 15 d 15 d 4 d total FTE capacity (well skilled personal mandatory!) 36 d 31.5 d 9 d 1 liquid handler 2 LC-MS/MS 3. Analytik-Tag, IUTA, 15.11.2018 13

Linking-up, ULI Result table 3. Analytik-Tag, IUTA, 15.11.2018 14

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 formed Guidance for Industry Drug Metabolism/Drug Interaction, Studies in the Drug Development Process: Studies In Vitro, April 1997 Technical Drug Development (LC, MS, UV, NMR) chemical stability/lability 3A4 structures Phys-chem. data Nomenclature pk s, logp, logd, IUPAC-names LC, MS, MS/MS, UV In silico metabolism species comparison hepatic metabolism tox species plasma metabolites Toxicology Contributing enzymes polymorphisms DDI s reactive species extrahepatic metabolism renal, intestinal 3. Analytik-Tag, IUTA, 15.11.2018 15

ULI (Unified Lab Information) with courtesy from ACD-LABS 88% of R&D organizations lack adequate systems to automatically collect data for reporting, analysis, and decision-making 1 1 Scientific Computing Research Study 2011 12% 88% One-to-Many Live Data Structured Homogeneous Unified Data Metadata for Search Analyze Repeatedly Accumulate Knowledge Create Intelligence and Gain Insight One-and-Done Dead Data Unstructured Heterogeneous Siloed Data Metadata for Search? Analyze Once Discrete Knowledge 3. Analytik-Tag, IUTA, 15.11.2018 16

Data exchange Research Technol Modelling Head Bioanalysis In silico metabolism in vitro data in silico data Project scientists Metabolism, (LC-MS) Isotope chemistry MS-data NMR-data (NMR) Preclinical Drug Safety (DEREK) references Research Technol Laboratories Chem. Development 3. Analytik-Tag, IUTA, 15.11.2018 (LC, GC, LC-MS) 17

MS-data Data exchange Research Technol Isotope chemistry Bioanalysis references, intermediates synthesis schemes MS-data Metabolism 1 in siilico In vitro In vivo NMR-data MS-data (LC-SPE-NMR) 2 NMR-data (LC, GC, LC-MS) Head Project scientists Modelling toxicol. relevance structures SD-file Interface Tox., DEREK toxicol. relevance NMR-data Chem. Development Research Technol GI-DD-PDD-PK 1 non-glp Data exchange via ACD GI-DD-TDD-RT GI-DD-PDD-SY GI-DD-TDD-CD GI-DD-TDD-AD 2 non-gmp 3. Analytik-Tag, IUTA, 15.11.2018 18

Conclusions 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) 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 3. Analytik-Tag, IUTA, 15.11.2018 19

Acknowledgements 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 3. Analytik-Tag, IUTA, 15.11.2018 20

Vielen Dank! 3. Analytik-Tag, IUTA, 15.11.2018 21