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3 Advancing Cell Culture-Based Biopharmaceutical Programs using Metabolomics

4 Metabolomics in Bioprocessing Activities Cell line development/clone selection Targets for pathway engineering Research Media development Process optimization & scale-up Manufacturing Targeted media optimization Regulatory (FDA has been encouraging PAT) IP protection more rigorously p g y defining product

5 Metabolon technology and business model mvision Commercial Service Global Biochemical Profiling Service Metabolomics Platform Technology Mechanistic insight Biomarkers Diagnostic Products Biomarker Discovery Engine Drug Safety And Efficacy Bioprocess Optimization LC-MS GC-MS Informatics Interpretation Diabetes Cancer Over 450 studies, 100+ patent applications, 8 issued patents

6 Endogenous Constituents of Phenotype Genes Transcripts Unique # in each class Proteins NH 2 N N H N N Metabolites Phenotype 6

7 Metabolomics The systematic study of all metabolites in an organism and how they change in relation to a biological perturbation (drug, disease, or diet) Metabolites: Biochemicals, typically <1000 MW 7

8 Metabolomics The systematic study of all metabolites in an organism and how they change in relation to a biological perturbation (drug, disease, or diet) Metabolites: Biochemicals, typically <1000 MW Global unbiased (non-targeted) Targeted 8

9 Metabolomics The systematic study of all metabolites in an organism and how they change in relation to a biological perturbation (drug, disease, or diet) Metabolites: Biochemicals, typically <1000 MW Approaches NMR o o o Strong structural elucidation capabilities Lower sensitivity Limited number of analytes identified Mass Spectrometry o o o High sensitivity Easily coupled with chromatography Fragmentation capabilities for compound ID Metabolon technology: Global non-targeted mass spectrometry 9

10 Global (non-targeted) library-based based approach

11 Global (non-targeted) library-based based approach

12 Metabolon Process Non--targeted Metabolomics Non UHPLC-MS/MS (+ESI) Instrumentation Biochemical Extraction UHPLC-MS/MS (-ESI) Biochemical Analysis Metabolyzer P kd Peak Detection t ti Peak Integration Library Search RT,, Mass,, MS/MS / QA/QC GC-MS (+EI) 12

13 21-hydroxyprogesterone 147,926 3-hydroxyoctanoic acid 289,530 1-methylguanidine-hydrochloride 58,939 Library Search for Biochemical 3-hydroxydecanoic ID acid 281,085 3-indoleacrylic acid 177,587 DL-3-phenyllactic acid 3,281,189 DL-alpha-hydroxyisocaproic acid 231,486 DL-hexanoyl-carnitine 118,902,022 O-acetyl-L-carnitine-hydrochloride 29,443,151 EDTA 992,513 l-aspartyl-l-phenylalanine 6,520,826 Metabolyzer Software Biochemical Amount cholesterol 143, min Mass spectrum Database Of Standards cholesterol Biochemical ID Time (min) 13

14 Library Search for Biochemical ID min Mass spectrum Metabolyzer Software Database Of Standards Time (min) Biochemical Amount cholesterol 143,789 tryptophan 984,812 glutamic acid 23,387 histidine 32,597 cholesterol leucine 117,683,785 cholesterol 197,866 asparagine 1,245,744 creatinine 127,611 cytidine 721,266 lactate 2,111,697 alpha-keto-glutarate 14,105,654 4-hydroxyphenylacetate 444,817 3-hydroxybutanoic acid 1,639,392 cytosine 106,032 arabinose 778,911 Biochemical ID fructose 2,519,658 X ,101,581 mannose 2,833,397 pyruvate 250,670 uridine 135,807 Cholic acid 26,394,344 allantoin 208, isocitrate 5,825,474 heptadecanoic acid 913,112 inosine 1,115, isoleucine 452,549 alanine 462, threonine 208,938 tyrosine 67,989 lysine 296,740 methionine 49,879 malic acid 156,499 X ,230 n-hexadecanoic acid 298, octadecanoic acid 240,570 trans-4-hydroxyproline 132, X ,989,172 2-deoxyguanosine 5,913, hydroxyphenylacetate 7,214, phospho-d-glycerate 8,649, s-methyl-5-thioadenosine 14,569,292 (p-hydroxyphenyl)lactic acid 1,303, alphahydroxybenzeneacetic y acid 450,807 Kynurenic acid 468,663 ornithine 326,949 5-oxoproline 890,753 orotic acid 97,674 palmitoleic acid 4,036,188 pantothenic acid 94,772,385 salicylic acid 379,417 alpha-tocopherol 766,717 citric acid 4,245,654 X ,209 glyceric acid

15 UHPLC-MS/MS (+ESI) Metabolyzer Peak Detection Biochemical Extraction UHPLC-MS/MS (-ESI) Peak Integration Library Search RT, Mass, MS/MS GC-MS (+EI) Metabolon Platform Technology b l l f h l Global Biochemical Pathway Changes Disease Biomarkers Mechanistic Toxicology Drug MOA C ll l Characteristics Cellular Ch t i ti QA/QC Statistical Analysis Biochemical Interpretation Pathway analysis Literature Heat Maps by Pathway

16 Recent Publication Metabolon Platform

17 Metabolomics as a Tool for Advancing Cell Culture-Based Biopharmaceutical Programs

18 Typical Biochemical Profiling Experiment Metabolomics Platform Technology cellular biochemicals Cells Each Sample Media LC-MS GC-MS Informatics Interpretation media biochemicals Media Cells ~80% of metabolites are shared between cells and media, providing cross-validation 18

19 Data Analysis Cells Time Media Time hemicals Bioch Single Biochemical View High-Level Data Analysis

20 Type of Applications Global Biochemical Profiling of Cells and Media Metabolon Platform Technology LC-MS/MS GC-MS Informatics Media formulation, process development and metabolic engineering Uncover novel metabolism Find blind spots in media requirements Develop rationale for feeding strategies Generate hypothesis for further testing Raw material Profiling Biomarker Discovery Criteria for clone selection /media development Process development /validation monitoring Markers/insight for PAT and QbD Mechanistic i Understanding di 20

21 Mechanistic insight - deeper understanding for advancing QbD Cells Product quality Productivity it Cell line stability Growth Viability 21

22 Mechanistic insight - deeper understanding for advancing QbD Cells ph Osmo Temp pco 2 do 2 Agitation Scale Feed Product quality Productivity it Cell line stability Growth Viability 22

23 Mechanistic insight - deeper understanding for advancing QbD Cells ph Osmo Temp pco 2 do 2 Agitation Scale Feed Metabolic events, mechanistic i insight i connecting cellular phenotype/process output with process parameters Product quality Productivity it Cell line stability Growth Viability 23

24 Mechanistic insight into effects of a broad process parameter Metabolomic profiling Cells use divergent metabolic pathways Reactor parameter change Detectable phenotypic difference Basis unclear Different metabolic routes induced Impact on feed strategies, etc.

25 Expanding the Profiling of Phenotypic Space Phenotypic Space

26 Expanding the Profiling of Phenotypic Space Phenotypic Space Established everyday Established everyday technologies (<10 biochemicals)

27 Expanding the Profiling of Phenotypic Space Phenotypic Metabolon Profile Space Phenotypic Space Phenotypic Space UHPLC-MS/MS (+ESI) Biochemical Extraction UHPLC-MS/MS (-ESI) Metabolyzer Data Processor GC-MS (+EI) Established everyday technologies (<10 biochemicals) Metabolon Platform ( biochemicals)

28 Broad application areas of biochemical markers for bioprocessing I. Biochemicals can be used as surrogates for predicting/indicating undesirable phenotypes II. The flip side is rigorously defining a set of markers that can define a highly favorable condition High Productivity Favorable growth characteristics and stability High PQ

29 Broad application areas of biochemical markers for bioprocessing I. Biochemicals can be used as surrogates for predicting/indicating undesirable phenotypes II. The flip side is rigorously defining a set of markers that can define a highly favorable condition Markers that define the best characteristics for a process/cell line High Productivity Favorable growth characteristics and stability Monitored independently High PQ These biochemical markers can be used during an array of phases from clone These biochemical markers can be used during an array of phases from clone selection, media development, process monitoring

30 Candidate Biomarker Identified Marker identified for very specific undesirable phenotype Established Marker Candidate p marker,( ) Scaled Inten nsity y Day sity nsity Scaled Inten scaled inten Day Undesirable phenotype Preferred phenotype More stable than established marker, more sensitive and detected in both cells and media Undescribed in cell culture literature

31 Process Optimization and Design

32 Process Evaluation Goal: Generate rational ideas to potentially improve yield Cells Time Media Time cals Biochemic 32

33 Process Evaluation Goal: Generate rational ideas to potentially improve yield Cells Time Media Time Biochemic cals Fatty acids, monoacylglycerols choline Phosphatidylcholine (membrane lipid id metabolites) 33

34 Lipid balance storage vs. membranes Triglyceride Phosphatidylcholine Triglyceride Glycerol + Head group Head Group= Choline, ethanolamine, serine, sugars Storage lipid droplet Membrane Lipids are balanced between storage and in membranes 34

35 Triacylglycerol lipolysis induced Triacylglycerols FA TAG synthesis Fatty acids, monoacylglycerols Monoacylglycerol FA Metabolite increase Storage Lipolysis i FA What about other major class of complex lipids (membrane phospholipids)?

36 Phospholipid metabolism also altered Phosphatidylcholine Phosphatidylcholine (membrane lipid metabolites) Metabolite decrease Phospholipid metabolites are reduced while TAG lipolysis is occurring

37 Indicators of loss of membrane integrity Metabolite that should be sequestered in the cell, but instead progressively accumulates in the media Time Cells Time Media Metabolite decrease Metabolite increase Indicates loss of membrane integrity ( leakiness ) leakiness)

38 Choline depleted in media, reduced in cells Time Cells Time Media Choline Metabolite decrease Choline is a major head-group of membrane phospholipids h id Possibly, choline depletion resulted in an inability to efficiently recycle membranes Supplementation has resulted in increased titer 38

39 Choline Possibly, choline depletion resulted in an inability to efficiently recycle membranes Choline + CTP CDP-choline diacylglycerol Phosphatidylcholine CoA-SH Pi Phospholipase A 2 Lysophosphatidylcholine: acyl-coa acyltransferase Fatty acid Lysophosphatidylcholine Acyl-CoA Phospholipase C Phosphocholine h h + monoacylglycerol l l glycerol l + Fatty acid Fatty acids Triacylglycerol

40 Metabolomics integrated into DOE Current services fall into to types of experiments DOE (Design of Experiments) o Comparing the effect of different conditions on product quality and quantity Feed rates, additives, media composition, ph Phenotype Characterization o Insight into mechanisms affecting product quality, cell growth

41 Metabolomics integrated into DOE For Process Optimization 1 reactor 1-2 time points Media only 100 conditions samples Metabolite Identification Correlation to Performance Data Metabolites Highly Correlated with Performance Performance Data

42 Correlating Metabolite Levels With Performance fructose methyl.2.oxopentanoate Metabolites correlating with specific growth rate Meta abolite abolite Met SGR SGR p g yce o p osp ate 8 50 t a ta Metabolites correlating with titer Metabolite Metabolite Metabolite titer titer titer How can a specific metabolite level be modulated?

43 Raw Material Analysis

44 Biochemical Diversity of Raw Materials Lot-to-lot Variation Lot Designation PATHWAY A B C E F G I J Peptide Carbohydrate Lipid Metabolites that could likely explain poor performing lots Energy Nucleotide Cofactors and vitamins Xenobiotics No Super Pathway 44

45 Defining critical components/parameters for complex media Several companies (and end users) are now using metabolomics to more precisely define and control complex media components like hydrolysates and yeast extracts 45

46 Defining critical components/parameters for complex media Source 1 Source 2 Source 3 Source 4 Source 5 Good separation between source types, what are the key differences? 46

47 Metabolomics/raw material critical parameter workflow Metabolomics Data Cell Culture Data Manufacturing Data 47

48 Metabolomics/raw material critical parameter workflow Cell Culture Data Metabolomics Data Manufacturing Data What manufacturing parameters a critical to controlling key metabolite levels? Can modifications be made to amend deviations to ensure standardized and robust product? This systematic approach is showing that: Metabolites are often the source of variability That critical new parameters can be identified for improving these products 48

49 Final Overview Metabolomics - A core component of systems biology - Metabolic changes can be distilled ill d into phenotype A non-hypothesis essdriven,,dsco discovery ytool - global approach is the standard Biomarkers, discoveries, and mechanistic insight can be gleaned from: -Conditioned Media - Cells (microbial, plant and mammalian) - Raw materials (yeast extracts, supplements, etc.)

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