Driving Innovation Through Bioengineering Solutions a world-class business in a global hub for biotechnology
Process Scale-Up & Tech Transfer Capabilities Unique blend of engineering and biotechnology skill sets 2+ experienced engineers Built and operated many plants Multiple successful tech transfers Multiple successful scale-ups Holistic approach: Safety Process engineering Modeling and economics Fermentation, filtration, distillation Scale-down/scale-up 2
Think Big, Then Small Begin with the end in mind, then scale it down How are lab-scale and commercial-scale different? Consider these factors to design lab scale-down experiments and de-risk the scale-up process Process economics Bioreactor design and mode of operation Aerobicity or anaerobicity Mass transfer rates Hydrostatic and gas pressures Heat transfer and cooling methodology Broth properties (viscosity, foam, composition) Broth mixing and heterogeneity Aseptic design (cleaning, sterilization) Genetic stability Industrial grade raw materials Downstream processing effects Lauri Suominen 3
Fermentation Scale-Down Approach Use models to predict commercial-scale conditions Develop predictive models of commercial-scale fermentors Link microbe s metabolism to reactor design Identify key process sensitivities at scale: Mass and heat transfer limitations High partial pressure of O 2 /CO 2 OUR/DO gradients ph/temperature gradients Substrate/nutrient gradients Pressure gradients Characterize how fermentor design parameters (scale, geometry, aspect ratio) impact key process sensitivities and process economics 4
Fermentation Scale-Down Approach Design lab-scale experiments to simulate large-scale conditions Design lab-scale experiments to simulate large-scale conditions predicted by models Develop and optimize microbe and fermentation process under large-scale conditions Use systems biology (omics) approach to understand how differences between lab and large-scale conditions impact organism s performance Identify both strain and process engineering strategies to improve performance and de-risk the scale-up process 5
Validation of the Scale-Down Approach Model prediction vs. actual commercial performance Stirred Tank Reactor Model Top: P, U s, P/V, k L a, y O2 / CO2 vour, vcer Mid: P, U s, P/V, k L a, y O2 / CO2 Modeled 24 m 3 STR fermentor at partners retrofit facility Combination of empirical and theoretical correlations based on reactor and agitator system design Model used to assess macro-scale gradients expected in bioreactor from top to bottom Used to determine gradient magnitudes and time scales for testing in lab scale-down simulations vour, vcer Bot: P, U s, P/V, k L a, y O2/CO2 6
Validation of the Scale-Down Approach Model prediction vs. actual commercial performance Stirred Tank Reactor Model Oxygen Uptake Rate (OUR) Gradient Top: P, U s, P/V, k L a, y O2 / CO2 vour, vcer Mid: P, U s, P/V, k L a, y O2 / CO2 vour, vcer Bot: P, U s, P/V, k L a, y O2/CO2 Model identified oxygen uptake rate (OUR) gradient as the key process sensitivity Severe OUR gradient from bottom to top expected due to high local power input of Rushton impeller at the bottom of the fermentor Model estimates OUR gradient to be ~4-7% of average OUR 7
Validation of the Scale-Down Approach Model prediction vs. actual commercial performance Biomass, g dcw/l Titer, g/l Titer 14 12 1 8 6 4 2 1 2 3 4 EFT, hr Biomass 16 14 12 1 8 6 4 2 1 2 3 4 EFT, hr Rate, g/l/hr Conductivity, ms/cm 4.5 4. 3.5 3. 2.5 2. 1.5 1..5. 1 9 8 7 6 5 4 3 2 1 Rate 1 2 3 4 EFT, hr Conductivity 1 2 3 4 EFT, hr 2L Control (n = 3) 2L 4-7% vour Osc (n = 4) Simulated 4-7% OUR gradient using lab-scale OUR oscillation study In-house developed stir oscillation algorithm used to oscillate agitation rate between target OUR range Time-scale of sinusoidal oscillation set by estimated reactor mixing time at scale Comparison of oscillation condition to control showed significant reduction in product titer and rate, higher biomass and respiration, and key shifts in fermentation byproducts 8
Validation of the Scale-Down Approach Model prediction vs. actual commercial performance Biomass, g dcw/l Titer, g/l Titer 16 14 12 1 8 6 4 2 1 2 3 4 EFT, hr Biomass 18 16 14 12 1 8 6 4 2 1 2 3 4 EFT, hr Rate, g/l/hr Conductivity, ms/cm 5. 4.5 4. 3.5 3. 2.5 2. 1.5 1..5. 9 8 7 6 5 4 3 2 1 Rate 1 2 3 4 EFT, hr Conductivity 1 2 3 4 EFT, hr 2L Lab (n = 3) 24,L Production (n = 6) Parameter Scale-Down Prediction (Δ) Titer (g/l) -22-19 Rate (g/l/hr) -.6 -.5 Biomass (g/l) +2.1 +1.8 Respiration (%) +7% +5% Cond (ms/cm) +.9 +1. Performance at Scale (Δ) 9
Applying the Scale-Down Approach Use scale-down approach to address issues before scale-up Ideal Lab Conditions Scale-Down Conditions Product Titer (% Commercial Target) 12% 1% 8% 6% 4% 2% Strain/Process A Strain/Process B Product Titer (% Commercial Target) 12% 1% 8% 6% 4% 2% Strain/Process A Strain/Process B % 1 2 3 4 % 1 2 3 4 Fermentation Time (hrs) Fermentation Time (hrs) Both strain and process changes can impact performance at scale! Under ideal lab conditions Strain/Process A performed better than Strain/Process B Under scale-down conditions Strain/Process A significantly underperformed, Strain/Process B showed minor performance reduction but still achieved commercial targets Strain/Process B selected for scale-up 1
Applying the Scale-Down Approach Strain/Process B used for successful commercial scale-up Consistency Across Scales Predictable Scale-Up Robust Commercial Performance 12% 1% Fermentation Run Top 5 Fermentation Run Titer, g/l (as % of 2L) Rate, g/l.hr (as % of 2L) Yield, g/g (as % of 2L) 8% 6% 4% 2% % 12% 1% 8% 6% 4% 2% % 12% 1% 8% 6% 4% 2% % 2L 3L 13kL 2L 3L 13kL 2L 3L 13kL 5x Scale-Up 13,L Demonstration Average Fermentation Performance (~5 commercial scale runs vs. average demo scale) Commercial Campaign Strain Titer Rate Yield 98% 14% 1% 12% 1% 8% 6% 4% 2% % 6. 5. 4. 3. 2. 1.. Percent Average Commercial Yield 1 2 3 4 5 Fermentation Batch Cumulative Product (MM lbs) 1 2 3 4 5 Fermentation Batch 11
Transferring Process Technology Keys to a successful tech transfer OUR PLACE WE THEY They saw how We did it They believe that it works THEIR PLACE WE THEY We saw how They repeated it We and They know it works at their place Strong Collaboration! Do s: Share responsibility Detailed written protocols Allow people (experts) to move between sites Expect problems and prepare to troubleshoot Be over-prepared, boring is good! Don'ts: Hand off responsibility Verbally communicate protocols Create barriers Assume success Take shortcuts, create drama 12
Tech Transfer in Practice Importance of fermentation engineering support services Process Control Process Performance 7 14 6 12 wour (mmol/kg/hr) 5 4 3 2 Geno Product Titer (g/l) 1 8 6 4 Geno 1 Client 2 Client 5 1 15 2 25 3 35 4 5 1 15 2 25 3 35 4 Fermentation Time (hrs) Fermentation Time (hrs) Technology transfer without fermentation engineering support Organism and Technology Transfer Package transferred to client prior to on-site support 13
Tech Transfer in Practice Importance of fermentation engineering support services Process Control Process Performance 7 14 6 12 wour (mmol/kg/hr) 5 4 3 2 Geno Product Titer (g/l) 1 8 6 4 Geno 1 Client 2 Client 5 1 15 2 25 3 35 4 5 1 15 2 25 3 35 4 Fermentation Time (hrs) Fermentation Time (hrs) Significant improvement in performance alignment with Genomatica fermentation engineering support Validation of SOPs, implementation of custom control algorithms, analytical cross validation keys to success! 14
Tech Transfer in Practice Demonstrated success in tech transfer to and from client sites Geno to Client Tech Transfer Client to Geno Tech Transfer 16 9 14 12 Client Geno 8 7 Client Geno LCMS Product (g/l) 1 8 6 Product (ppm) 6 5 4 3 4 2 2 1 1 2 3 4 5 Fermentation Time (hrs) 25 5 75 1 125 Fermentation Time (hrs) 15
Bioprocess Scale-Up & Tech Transfer Methodology Key principles for success 1. Begin with the end in mind Consider the commercial design before you do anything 2. Build, test, and refine models Predict conditions at scale and identify key sensitivities 3. Scale-down before scale-up Develop strain and process under scale-down conditions in the lab 4. No shortcuts when transferring technology Key instrumentation, detailed SOPs, full analytical, engineering support http://www.genomatica.com/partners/tate-lyle/ 16