OmniLog/PM Assay Technology. Leveraging Metabolism For Optimal Bioprocess Development

Transcription

1 OmniLog/PM Assay Technology Leveraging Metabolism For Optimal Bioprocess Development

2 Importance of Metabolism and Operational Constraints Energy generation is an important driver of cell growth and bioproduction Making improvements in growth and production requires having a good understanding of cellular metabolism Optimizing growth and productivity means matching relevant substrates to these bioprocessing phases Gene engineering can alter metabolic programming Culture conditions can alter metabolic programming

3 2 Primary Components of the OmniLog/PM Cell Assay Platform OmniLog Incubator/Reader Phenotype MicroArrays Colorimetric cell assays in 96-well microplates Incubation and recording of data in the OmniLog

4 The Metabolic Dimension of Cells

5 PM Assays Phenotype Cells for their Unique Bioenergetics Characteristics pyruvate carbon/ energy substrate glucose lactate galactose cell glutamine acetoacetate hexanoate NADH mitochondria Biolog s Redox Dye alanine

6 The Heart of PMs - Colorimetric Redox Assays Each well contains a different substrate or metabolic effector. Redox Dye reduction in each well reflects the number of cells and specific pathway bioenergetics activity. No reporter gene required. Add cells and Redox Dye Results in 1-4 hours

7 Biolog Redox Dye Comparison: Sensitivity cells per ml cells per well 3.12e e e4 2.5k 2.0e5 10k 8.0e5 40k 3.2e6 160k MA dye XTT dye MTT dye MTS dye A549 human lung cells incubated for 4 hours with 500 μm dye

8 The PM Assay Portfolio for BioProcessing 4X96-Well Plates: 367 Different Carbon- Energy and Nitrogen Substrates (at single concentration) 1X96-well Plate: 22 Trace Elements as Cofactors (at 4 different concentrations) 3X96-Well Plates: 45 Hormones and Cytokines (at 6 different concentrations)

9 PM-M1: Carbon-Energy Supplements for Cells No Substrate Monosaccharides, Oligosaccharides, and Polysaccharides Nucleosides Alcohols and Organic Acids Ketone Bodies and Short Chain Fatty Acids Showing Plate 1 of 4 plates: Each well has a different KEGG-indexed metabolic substrate

10 PM Assays are Easy to Run Assays Initiated by adding cells to wells typically in 50 μl glucose/glutaminefree media OmniLog PM System Holds 50 microplates at a set temperature and measures color formation at 5-minute intervals Kinetic assay readout for up to 5,000 wells CVs typically < 10%

11 2 Phases of Typical BioProcesses 100 Growth Product formation 5 4 Cell Density (OD) 10 3 Product (g/l) Time (Days) 0

12 Analyzing Cell Growth and Media Supplements

13 Growth/Death Assays Using PM-M1 HepG2/C3A cells seeded into 6 PM-M1 microplates at 2500 cells per well. Cells incubated in IF-M1 +4 mm glutamine +1x pen/strep in a 37 CO 2 incubator. Each day, one plate was removed and Biolog Redox Dye MA plus 5mM glucose was added. Plate was then incubated in an OmniLog for 18 hours with data collection. Day 0 to Day 5 in order - red, yellow, green, blue, purple, black

14 Growth/Death Assays Using PM-M1 Growth B-4 D-Glucose Stasis A-9 D-Maltose Fast Death D-4 L-Fucose Slow Death B-11 D-Sorbitol

15 Advantages of feeding Mannose vs Glucose Chemical Engineering Science (2011) 66:

16 Effect of Copper on Lactate Metabolism in CHO cells Biotechnol Bioeng Sep 30. doi: /bit [Epub ahead of print] Comparative metabolite analysis to understand lactate metabolism shift in Chinese hamster ovary cell culture process. Luo J, Vijayasankaran N, Autsen J, Santuray R, Hudson T, Amanullah A, Li F. Oceanside Pharma Technical Development, Genentech, Inc., 1 Antibody Way, Oceanside, California 92056; telephone: ; fax: Abstract A metabolic shift from lactate production (LP) to net lactate consumption (LC) phenotype was observed in certain Chinese hamster ovary (CHO) cell lines during the implementation of a new chemically defined medium (CDM) formulation for antibody production. In addition, this metabolic shift typically leads to process performance improvements in cell growth, productivity, process robustness, and scalability. In our previous studies, a correlation between a key media component, copper, and this lactate metabolism shift was observed. To further investigate this phenomenon, two complementary studies were conducted. In the first study, a single cell line was cultivated in two media that only differed in their copper concentrations, yet were known to generate an LP or LC phenotype with that cell line. In the second study, two different cell lines, which were known to possess inherently different lactate metabolic characteristics, were cultivated in the same medium with a high level of copper; one cell line produced lactate throughout the duration of the culture, and the other consumed lactate after an initial period of LP. Cell pellet and supernatant samples from both studies were collected at regular time intervals, and their metabolite profiles were investigated. The primary finding from the metabolic analysis was that the cells in LP conditions exhibited a less efficient energy metabolism, with glucose primarily being converted into pyruvate, sorbitol, lactate, and other glycolytic intermediates. This decrease in energy efficiency may be due to an inability of pyruvate and acetyl-coa to progress into the TCA cycle. The lack of progression into the TCA cycle or overflow metabolism in the LP phenotype resulted in the inadequate supply of ATP for the cells. As a consequence, the glycolysis pathway remained the major source of ATP, which in turn, resulted in continuous LP throughout the culture. In addition, the accumulation of free fatty acids was observed; this was thought to be a result of phospholipid catabolism that was being used to supplement the energy produced through glycolysis in order to meet the needs of LP cells. A thorough review of the metabolic profiles indicated that the lactate metabolic shift could be related to the oxidative metabolic capacity of cells. Biotechnol. Bioeng Wiley Periodicals, Inc. Copyright 2011 Wiley Periodicals, Inc.

17 CHO-k1 Cells Carbon Metabolism in PM-M1 glycogen maltose G6P glucose mannose turanose F6P fructose galactose b-me-galactoside adenosine inosine adonitol xylitol acetoacetate a-keto-butyrate Cultured in serum-free Irvine Scientific CHO-Chemically Defined Media (without glucose/glutamine) 6 hr in OmniLog/PM

18 Carbon Substrates Stimulating CHO Cell Growth Can we identify compounds to add to glucose containing medium that will improve growth rate?

19 10 Substrates Augment CHO Growth on Glucose 30 Doubling Time (Hrs) *** ** *** *** *** *** *** *** P < ** P < 0.01 * P < 0.05 *** * *** Doubling time decreased from 23 to 18 hr 0 RPMI-1640 A B C D E F G RPMI Additions H I J

20 Optimizing Hybridoma Media for MAb Titer Can we identify compounds to add to glucose containing medium that will improve antibody yield? Indirect ELISA assay of PM-M1 culture supernatants from a monoclonal antibody-producing Sp2/0 hybridoma D12

21 6 PMM Supplements Increase MAb Titer 60 % Increasse in MAb Titer Antibody titer increased nearly 50% 0 A B C D E F RPMI Additions

22 Study on Industrial CHO Cells Examined 4 CHO cell lines commonly used in the bioprocess industry: Cell line 1 Cell line 2 Cell line 3 Cell line 4 (same as 3 but producing IgG)

23 Differences in Metabolic Rates Between CHO Cell Lines Distinguishing Glycolytic/Oxidative Metabolism Preferences Glucose 3>4>2>1 Biolog Signal Fructose 1>2,4>3 Cell line 1: G/F = 5.9 Cell line 2: G/F = 8.6 Cell line 3: G/F = 20.4 Cell line 4: G/F = 10.4 Cell Line 1 Cell Line 2 Cell Line 3 Cell Line 4 1 a-d-glucose D-Mannose D-Tagatose Palatinose L-Sorbose D-Fructose-6-Phosphate D-Glucuronic acid D-Glucose-6-Phosphate D-Galactose Pyruvic acid D,L-Lactic acid L-Glutamine Figure 5: Biolog initial rate of dye reduction for selected components that show a large signal and apparent differences between cell lines. Results shown are the average ± SD for the triplicate cultures that were analyzed Ala-Gln His-Trp negative control

24 Changes in Metabolic Rates of IgG-Producing CHO Cells Over a 13 Day Culture in a Fed-Batch Bioreactor Process 1000 Lactic acid consumption rate rises Biolog Signal 1 a-d-glucose D-Mannose D-Tagatose Palatinose L-Sorbose D-Fructose D-Fructose-6-Phosphate D-Glucuronic acid D-Glucose-6-Phosphate D-Galactose Pyruvic acid D,L-Lactic acid L-Glutamine Ala-Gln Gln-Gln Negative control (avg) Figure 8. Biolog initial rate of dye reduction for selected components that show a large signal and apparent differences between cell lines. Results shown are the average ± SD for the triplicate bioreactor cultures that were analyzed.

25 PM Assays Distinguishing the Metabolism Dynamics and Substrate Preferences of Different Clones and Cell Lines

26 Advantages of Using the OmniLog PM System Each PMM well will exhibit a different rate of dye formation, so single endpoint reads for an entire plate are not ideal OmniLog PMM software computes multiple parameters for phenotypic characterization and comparison Dye Formation Max Slope Time Lag Slope Area Under the Curve First Derivative Time Max Average Height Min

27 Metabolic Comparisons of 16 Cell Sub-lines Clustering Kinetic Curve Profiles for 367 Metabolic Substrates Provides a Means to Compare Closely-Related Engineered Clones or Sub-lines for Selection, Media Composition, and Scale-up

28 PM Assays Gene Engineering and Metabolic Reprogramming

29 PM Platform - Comparing Metabolic Phenotypes of Parent Lines and their Clones Parent A Clone B PM Pattern OmniLog PM System PM Kinetic Result Yellow indicates computergenerated red-green overlays indicating extent of similar response to a given substrate

30 Cell-Line Engineering and Clone Characterization OmniLog/PM: A Highly Sensitive Platform for Detecting and Characterizing Metabolic Changes Arising from the Genetic Engineering of a Clone Case Study: A Single-Point Mutation

31 Metabolic Differences between Parent HME Line and it s PI3K Single-Point Mutation Clone Point Mutation shut down metabolism of sugar phosphates G-6-P G-1-P Galactose Dextrin Glucose Mannose F-6-P Fructose Maltose Maltotriose Adenosine Uridine Inosine Glycerol Phosphate Turanose Computer generated overlay of parent phenotype plate and clone phenotype plate. Yellow indicates the extent of same substrate metabolism beta- Hydroxy Butyrate Propionate Same approach can be taken to study metabolism changes in any engineered clones

32 Carbon-Energy Substrate Changes in Parent MCF10a vs PI3K Clone CL1 Biolog Plate PM-M1 Lactic acid Pyruvic acid

33 Comparison of Isogenic PI3K Clones CL1 vs CL2 Using Biolog Plate PM-M1 and PM-M2

34 PM Assays Highly Reproducible

35 PM Assays are Highly Reproducible OmniLog Record for Mitochondria Inhibition Done in Triplicate Substrates: glucose Decoupler [FCCP] / um inosine galactose glucose-1-phosphate xylitol a-ketoglutarate b-hydroxybutyrate pyruvate 12/18/09

36 Conclusions and Summary

37 PM Advantages Uniquely characterizes the metabolic dimension of cells in real time Works on any type of cell, no cell engineering required Can be integrated with gene expression, proteomics and mass-spec analysis Robust and simple technology available to all labs Provided in standard 96-well microplate format Provides an entirely novel view of cells Provides more high content info than any other technology Flexible number of assays hundreds or thousands Flexible levels of automation and throughput Very wide range of uses

38 Steps in BioProcess Development Aided by PM Characterize cell lines and clonal variants to understand their culture properties to select the best one to use Understand how genetic changes affect the cell line Simulate hundreds/thousands of culture conditions to find the key culture variables that affect the process Optimize culture conditions for both rapid growth and maximum product yield Use it as a QC tool to test cell-bank stocks for metabolic phenotype consistency and metabolic stability

39 The Goal: To Find the Optimal BioProcess Metabolism drives cell performance and influences product quality. PM Metabolic Phenotypes deliver unique insights and guidance.

40 Comments on using OmniLog-PMM Technology We have been using PMM since January 2011 in our bioprocess development group and have found it to be a very powerful tool to study cell phenotypes and also cell metabolism under different conditions. During development of CHO cell lines for production of recombinant proteins, we have used PMM to distinguish between cell clones and find those with desirable and stable phenotypes. We are also investigating whether clonal cell phenotypes change over long term cultivation as a potential indicator of genetic instability. According to our experience so far, PMM could successfully be used to reveal differences in nutrient utilization between different cell clones, so we could use this information to optimize cell culture medium towards more energy efficient nutrient utilization, as well as optimize recombinant protein production and protein glycosylation. PMM is currently used to test alternative substrates during bioreactor process development to improve cell performance, and also to investigate changes in cell phenotypes under different process conditions (for example, temperature changes). PMM chemistry is a simple and powerful tool to study cell potential, however it is not a "plug and play" technology. This is mainly due to complexity of biological systems tested, which require disciplined and skilled handling. Also, comprehensive understanding of cell metabolism is required to cope with interpretation of PMM massive data and to come up with useful scientific conclusions. Dr. Vatroslav Spudic, Biotechnical Faculty, Ljubljana, Slovenia

41 Questions? For Technical Inquiries: Dr. Elina Golder-Novoselsky, Ph.D. Product Sales Manager / PMM Assays and OmniLog-PM T: EGolder@biolog.com For Customer Service or Order Placement: Call or csorder@biolog.com