1 A high throughput cell culture platform for bioprocess optimization Seth Rodgers, CTO Bioprocessors CPAC Rome March 20, 2007

2 © 2005 BioProcessors (781) Outline The role of model systems in process understanding A scale-down model bioreactor and the data sets we get now Challenges remaining – especially the data sets we’d like to get

3 © 2005 BioProcessors (781) After discovery comes development, lots and lots of it! Expression System Development Flasks Clone Evaluation Media Development* Process Optimization** Note :** Represent iterative processes Source :Nature Biotechnology Vol. 22 (11) 2004 Screen and select the highest producing and most stable clone Develop optimal growth and production media for each cell line Optimize conditions for biomanufacturing process in a “scale-down” version Scale Up Scale up process for use in large bioreactors for production of therapeutic Identify target, isolate gene, and develop expression system Knowing gene for the protein you want is great, but what cell line to use? What clone form that cell line is best. 100s of possibilities! 60 or more nutritional components in culture media, how many combinations? When to feed them? Inducers, promoters? What temperature? What oxygen level? CO2? pH any shifts? When to harvest? A strategy of multi-factorial design is the natural way to attack this type of problem, but is difficult to execute in cell culture because the parameters interact strongly-requiring a lot of experiments. This means models!

4 © 2005 BioProcessors (781) The role of model systems Here, the data is the product, faithful representation of process equipment is the goal Experiments with the systems that provide the best data, and the most understanding, i.e. production bioreactors themselves, are very time consuming and expensive. Model systems are universally used, but represent a compromise: reduced time and expense in exchange for imperfect data, which leads to imperfect understanding The same cost vs. data quality trade off that restricts experimentation in plant scale equipment often dictates the choice of model system Process understanding Model systems: Data is the product Real system: The API is the product

5 © 2005 BioProcessors (781) Can current models provide deeper process understanding? I can compromise on quality to a point, but only so far. The data still has to tell us what we want to know –Are we at optimum? –Where to go next? But we need to know NOW! And we are going to need to know more soon! –QbD ideas of variance identification and reduction. –Statistical process control –Follow-on biologics Particular challenge with animal cells. (long experiment times, sensitive to culture conditions) A new model system could be very helpful Throughput Experimental capacity per researcher Quality Ability to predict manufacturing bioreactor performance Well plate 1’s 10’s 100’s 1000’s Flask Low High High Quality, High Quantity Bioreactor

6 © 2005 BioProcessors (781) Some High-Throughput Cell Culture System Requirements Deliver meaningful scalable data Sustain cells, control temperature, O2, CO2, pH, agitation Maintain sterility Monitor cell density, pH, DO, metabolites, product titer Operate with accuracy and precision and provide control of process parameters comparable to bench top bioreactor systems Automatic operation with minimal operator supervision Integration with tools for designing experiments and handling data

7 © 2005 BioProcessors (781) SimCell MicroBioreactor Array 6 micro-bioreactors per array. Working volume: ~700 µL. Fluidic ports and channels for inoculation, feeds, pH adjustment and sampling. Culture monitoring of biomass (OD), pH (immobilized sensors) and DO (immobilized sensors) by optical interrogation of micro-bioreactors. Proprietary gas permeable materials result in k L a ~ 10 hr -1 for oxygen and ~ 25 hr -1 for CO 2. Experimental factors such as media composition, inoculation density, pH and feeds can be adjusted at the micro-bioreactor level. But sensing through thin plastic windows can be a challenge!

8 © 2005 BioProcessors (781) SimCell Automated Management System Incubators –One to five per system. –T, CO 2, O 2 and agitation control. Sensing module –Total biomass by OD. –pH by immobilized sensors. –DO by immobilized sensors. Sampling module –Sample removal to well plate. –Capable of dilution with single diluent (PBS). –Capable of volumetric dilution or dilution to specific cell density in well plate or MBR. Dispensing module –One to eight pumps. –Real-time mixing at point of delivery. –Fluid sources may be swapped in between cycles for increased capacity.

9 © 2005 BioProcessors (781) SimCell Automated Management System (SAMS)

10 © 2005 BioProcessors (781) SimCell™ On-Line Measurements: Cell Density Measured using Optical Density at 633 nm on Sensor Station –OD is linear to 2.2 –Working OD range is extended by dynamic neutral density filtering –accurate measurements up to 50M cells/ml have been demonstrated OD vs. cells/ml curve is specific to cell type OD yields total intact cells: live + dead Error bars are +/- 1 std. dev. (+/- 16% variance) Inteferences matter how to compensate for cell size? aggregation? A better measurement might also tell us how many live and how many dead – dielectric spectroscopy?

11 © 2005 BioProcessors (781) Measurements: Immobilized pH Sensors Response is independent of media Precision is < 0.06 (±3 std. dev.) over the pH range 6.0 – 8.0 pH Sensor Composition – Hydrogel (Water-swellable polymer) – Covalently bound dye fluorescent pyrene derivative. Sensor manufactured by screen-printing, followed by UV polymerization

12 © 2005 BioProcessors (781) SimCell On-Line Measurements: pH Measurement Technology Immobilized pH Sensor Covalently bond fluorescent pH dye to hydrogel Hydrogel polymerized to bottom surface of MBA Retains ratiometric pH response Four sensors/chamber

13 © 2005 BioProcessors (781) Automated pH Control V add : volume of solution of base to add V total : total volume of the sample in the microbioreactor before addition P CO2 : pressure of CO 2 k H : Henry’s Law constant for CO 2 [HCO 3 - ] add : concentration of bicarbonate in the adjustment solution pH initial and pH final : starting pH value and pH setpoint, respectively Similar equations are derived for use of sodium carbonate, sodium hydroxide, and monoprotic acids for pH adjustment.

14 © 2005 BioProcessors (781) Measurement and Control: Maintaining pH Setpoints 3 pH setpoints 18 subprotocols 9 μBR/subprotocol pH adjusted 2x/day Chart shows average pH for each subprotocol over the course of the experiment.

15 © 2005 BioProcessors (781) SimCell™ On-Line Measurements: Dissolved Oxygen (DO) Measurement Oxygen-sensitive dye (platinum porphyrin derivative) –Excitation of dye yields emissive triplet state. –As [O 2 ] increases, dye emission is quenched and τ F decreases –τ F is correlated to phase shift (φ) between modulated excitation and emission signals φ Correlation of φ to DO: error bars are +/- 1SD (+/- 10% variance). Phase shift between excitation (blue) and emission (red) signals.

16 © 2005 BioProcessors (781) Novo’s Comparison with Current Technologies Significant improvement n process yield at lower cost and shorter time Summary 84% increase in yield Scalable to 1,000 liter production vessels

17 © 2005 BioProcessors (781) Application across platforms and processes The central question is “To what extent is the MBA performance a predictor of the bioreactor result?” The R^ 2 statistic is a well-established way to quantify the answer to this question, computed by constructing ordered pairs of MBA and reference model system results Advantages of R ^2 are that is can be constructed independent of platform, process cell line, etc. –Compare relative predictive power across model systems: flask, MBA, bioreactor, well plate, etc. –Metric of continuous improvement as technology evolves This graphic shows results over many cell lines, processes and vessels, predictive power might be even better for data with these factors kept constant R 2 =0.96 R 2 for 2006 client evaluation projects

18 © 2005 BioProcessors (781) What’s missing Protein titer of course! –Enzymatic like ELISA is the most common, but it takes work, even with automation –Something else? Viability Some understanding of the protein quality (glycosylation, aggregation) All those media components in the culture broth –Nutrients: glucose, glutamine, amino acids, vitamins –Metabolic products: lactate, etc. Can spectroscopy (NIR, MIR, Raman work here?) Anything else useful in characterizing and ‘fingerprinting’ the process, that is, a useful predictor of process outcomes. Ideal measurement (for us at least) is –Non invasive – it it needs a sample, best case is Small sample Works with crude broth, no pre- treatment –Matched throughput –Calibrated less frequently than once per MBA –Compatible with flexible! plastic cell culture device (challenge for some spectroscopy) –Cost competitive pulling samples and using well plates

19 © 2005 BioProcessors (781) Conclusions Model systems are indispensable tools, and increasing demands for data will be difficult to meet with current platforms. A high-throughput cell culture system presents a possible solution if the data is of sufficient quality to predict process outcomes. BioProcessors SimCell system represents one possible solution that combines high throughput with highly representative data.