downstream processing of biopharmaceuticals and natural ... bio-engineering...leitmotif of the...
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18.03.2016
J. Merz
G. Schembecker
Downstream Processing of Biopharmaceuticals and Natural Products
Leitmotif of the Department
Development and optimization of safe, sustainable and efficient production processes
based on strong interaction of natural and engineering sciences
in Teaching and Research
One Key Research Questions of the Laboratory for Plant and Process Design
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Is there a systematic way to develop sustainable and efficient downstream processes?
Process Development
Lab
Lab recipe
Sca
le-U
p
Production
4
State-of-the-Art
Production processes often far from economic optimum • Low yields • Huge supply streams (e.g. ultra-pure water, solvents, nutrients) • Immense waste material streams
Value product yield waste material / product Insulin (E.Lilly) (Harrison et al. 2003) 30 % 64.000* / 1
Taxol (Pyo et al. 2004) 54 % 5.000** / 1
Tissue Plasminogen Activator (W.D.Seader 2004)
70 % 2.500* / 1
β-Galactosidase (Storhas 2003) 8 % 160 / 1
α-Cyclodextrin (Biwer et al. 2003) 50 % 15 / 1
Plasmid DNA (Prazeres and Ferreira, 2004) 51 % * incl. water; ** based on biomass
State-of-the-Art Process Development
Focus mainly on optimization of fermentation process • Target: increase in productivity of the conversion step
Only small effect on overall productivity (due to e.g. yield losses, necessary dilution)
Exercise I
Option I (not optimized)
Product yield: 50 g
20 % product loss
3 hours operation
Product loss: 1 % per hr
Option II (optimized)
Product yield: 51,5 g
(3 % increase)
20 % product loss
6 hours operation
Product loss: 1 % per hr
??? ???
Mass balance
50 g 51,5 g
40 g 41,2 g
38,8 g 38,7 g
State-of-the-Art Process Development
Focus mainly on optimization of fermentation process • Target: increase in productivity of the conversion step
Only small effect on overall productivity (due to e.g. yield losses, necessary dilution)
Downstream process synthesis on laboratory scale • Unsystematic trial and error procedure “Do what crosses your mind!” ; “If a step works, go ahead!” “As soon as purity requirements are fulfilled, scale-up and build!”
Direct scale-up of laboratory processes
Optimization of (few) operation parameters only, process structure fixed
Holistic view on the process in missing
Impact of Early Process Knowledge
Biology Chemistry
Process development
Engineering Production Time
cost and environmental impact
process knowledge
degrees of freedom
A2
A1
B1
B2
Inte
nsity
(Heinzle and Biwer 2007), modified
½ l solvent x t solvent
Target: optimal overall process
Synthesis of Downstream Processes
Minimization of overall process cost • Downstream process cost up to 50 – 80 % of overall cost • High overall process yield • Especially for low prize large scale products (e.g. alcohols) • “cheap” unit operations
Time to market • Especially for high prize small scale products (e.g. pharmaceuticals)
Purity requirements
Product Downstream Upstream
Eva
luat
ion
x
x x x
x
x
Process Development
Lab
Lab recipe
Sca
le-U
p
Production
Alternatives
experimental proof Scale-Up
11
Process Development
Traditional approach for purifying (biological) compound
RIPP scheme (recovery, isolation, purification, and polishing)
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Process Development
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Process Development
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Process Development
Scheme after Nfor et al., Design strategies for integrated protein
purification processes: Challenges, progress and outlook, J. Chem. Technol. Biotechnol. 83, pp.124–132, 2008
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Process Development
Traditional approach for purifying (biological) compound
RIPP scheme (recovery, isolation, purification, and polishing)
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Ghosh, Principles of Bioseparations Engineering, World Scientific Publishing Co. Pte. Ltd., 2009 Hubbuch and Kula, Isolation and purification of biotechnological products, J. Non-Equilib. Thermodyn. 32, pp. 99–127, 2007
Nfor et al., Design strategies for integrated protein purification processes: challenges, progress and outlook, J. Chem. Technol. Biotechnol. 83, pp.124–132, 2008
Process Development
Systematic approach for purifiying (biological) compound
e. g. numeric heuristic models
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Process Development
The property differences of molecules determine the ability for separation
• Size
• Charge/ Isoelectric point (pI)
• Solubility
• Polarity
• TB and TM
• Hydrophobicity
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Paclitaxel
fkp.tu-darmstadt.de
+ functional groups
+ molecule structure
Process Development
Process Development
Systematic approach for purifiying (biological) compound
e. g. numeric heuristic models
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Heuristics
Rules-of-Thumbs • Identification of feasible separation principles
• Evaluation of appropriate unit operation
• Determination of unit operation type
• Choice of operating mode
An Example Heuristic
Temperature Distillate • 72 °C: perfect taste
• 70 °C: headache
• 65 °C: blind
A Heuristic always comes with an implicit assumption: “…if nothing tells you something else!”
Heuristics (Wheelwright, 1987; D. Petrides, 2003)
Remove the most plentiful impurities first
Remove the easiest to remove impurities first
Make the most difficult and expensive separations last
Select processes that make use of the greatest differences in the properties of the product and impurities
Select and sequence processes that exploit different separation driving forces
Just because it works in the lab does not mean it’s right for the factory
Heuristics
Separate the major contaminants first.
Perform the most difficult separations last.
Choose separation processes based on different physical, chemical or biochemical properties (Wheelwright 1987).
Choose separation processes utilizing the differences in the physicochchemical properties efficiently (high separation factors).
Liquid-liquid-extraction, chromatography and crystallization should be taken into account for the purification of heat sensitive materials.
If there is more than one target product, crystallization or adsorption/chromatography should be considered.
If possible, exchange of solvents in between the separation steps should be avoided – unit operations should be operated with the same solvents.
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Heuristics
Rules-of-Thumbs • Prefer UO’s based on molecular size for heat-sensitive products!
• Prefer filtration for large process scale!
• Prefer depth-filtration in case of low concentrations!
• Prefer tangential-flow in case of ultrafiltration!
Molecular size … Charge Solubility
Centrifugation … Filtration Sedimentation
Batch Semi-batch Continuous
Cake-filtration Depth-filtration Tangential-flow
mode
type
unit
principle
The Downstream processing pyramid
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Tree of alternatives - Stage 4
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Dryer
Adsorption chromatography
Crystallization ( + solvent exchange)
Centrifugation
Filtration
Ad- & Desorption
Extraction
Crystallization
Ultrafiltration
Size exclusion chromatography
Ultrafiltration
Fermenter
Ion exchange flow through
Ad- & Desorption
Extraction
Crystallization
40 alternatives!
Process Development
Systematic approach for purifiying (biological) compound
e. g. numeric heuristic models
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Overall Yield
60%
65%
70%
75%
80%
85%
90%
95%10
a 10 10c
10 4a 4b 4c 4d 10* 4* 8a 11a 8b 11 8c 11c
8d 11 2a 5a 2b 5b 2c 5c 2d 5d 8* 11* 2* 5* 9a 9b 9c 9d 3a 3b 3c 3d 9* 3* 7a 12a 7b 12 7c 12c
7d 12 1a 6a 1b 6b 1c 6c 1d 6d 7* 12* 1* 6*
Yiel
d
Overall Yield
60%
65%
70%
75%
80%
85%
90%
95%10
a 10 10c
10 4a 4b 4c 4d 10* 4* 8a 11a 8b 11 8c 11c
8d 11 2a 5a 2b 5b 2c 5c 2d 5d 8* 11* 2* 5* 9a 9b 9c 9d 3a 3b 3c 3d 9* 3* 7a 12a 7b 12 7c 12c
7d 12 1a 6a 1b 6b 1c 6c 1d 6d 7* 12* 1* 6*
Yiel
d
Filtration / Centrifugation
Crystallization
Adsorption
Chromatography
Chromatography
Extraction
Adsorption
Crystallization
Chromatography
Unit Operation Yields (I)
Capture (polarity)
non-polar 95% slightly polar 95% polar conc. water water content 10 vol-%
Filtration/Centrifugation (particles) moisture content 10 vol-%
Ultradiafiltration (molecule size)
≤ small conc. water medium 20% ≥ large 98% water content 5 vol-%
Crystallization (solubility in water)
high conc. water medium 10% low 80% water content 5 vol-%
Unit Operation Yields (II)
Extraction (solubilities in solvents)
low 1%
medium 50%
high 95%
Flow-through adsorption (charge) charged 0,1%
uncharged 100%
Chromatography (polarity) target substance 98%
impurities 1 - 5%
Crystallisation (solubility in solvent) target substance 98%
impurities 0,1 - 30%
Process Development
Systematic approach for purifiying (biological) compound
e. g. numeric heuristic models
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Numerics
Example: Electrodialysis
• Black-Box Model - Water, target product - Salts
• Shortcut Model - Water, target product - Mono- and divalent salts
• Detailed Model - Water, target product - H+, Na+, Cl-, SO4
2-
- pH, pI
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Black-Box Model
Shortcut Model
Detailed Model
Amount and Quality of Data
Project P
rogression
Industrial production process
Biotechnical production using Halomonas Elongata
Fermentation Osmotic
shock Cell
separation Capture Polishing
Capture-Sequence contains 5 process steps
NaOH HCl HCl/NaOH
Salt Contaminant Salt
pH-shift Cation exchange pH-shift Electrodialysis Electrodialysis
Entsalzung
0
1
2
3
4
5
6
0 20 40 60 80 100
Entsalzungsgrad [%]
Pro
zess
zeit
[h]
MessungRegression
Ionenaustausch-Capture
0
25
50
75
100
125
150
0 20 40 60 80 100Entsalzungsgrad [%]
Säu
lena
usla
stun
g [%
]
Numerics
Desalination
Ion Exchange Capture Step
Degree of Desalination [%]
Degree of Desalination [%]
Pro
cess
ing
Tim
e [h
]
measured fit
Col
umn
use
[%]
Experiments
How to rate the purification achieved by a single step?
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Rating of process steps
Purity x • fraction of the target product T in a mixture with
contaminants C
Purification P • relation of purity difference before xin and after
separation xout
• effort to reach purity improvement
Normalization of purification • one step as percentage of total process
• normalization to process boundaries: initial purity x0 and target purity xfinal
)x(f)x(f)x(f)x(fP
0final
inout−−
=
P
)x(f)x(fP inout −=
source: T. Winkelnkemper, G. Schembecker, Purification performance index and separation cost indicator for experimentally based systematic downstream process development, Separ. Purif. Technol. (2010), doi: 10.1016/j.seppur.2009.12.025
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Purification rating - PPI
Different measures f(x) to describe purification Example: x0 = 5%, xfinal = 99.9%
Purification index (PI) f(x) = x Logarithmic purification index (log PI) f(x) = lg(x) Purification performance index (PPI) f(x) = tanh-1(2x-1)
0 Purity of target product 100 % xfinal
100 %
)x(f)x(f)x(f)x(fP
0final
inout−−
=
step A
step B
xin = 50%
xin = 94%
xout = 55%
xout = 99%
PPI2 18.71%
log PI2 1.73%
PI2 5.27%
PPI1 2.04%
log PI1 3.18%
PI1 5.27%
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Economic rating - SCI
The goal: a measure for cost-efficiency without need of total process concept • Separation Cost Indicator (SCI) for estimation of cost-efficiency in early phases of DSP
development
The basis: laboratory outcome • purity improvement described by PPI • loss of product described by yield Y • specific costs κ for fermentation and purification steps
⇒ SCI = f(x, Y, κ)
The prerequisite: process boundaries set • initial x0 and target purity xfinal
• product capacity is fixed: yield losses lead to larger dimensioned fermentation and purification steps
Winkelnkemper and Schembecker, Separ. Purif. Technol., vol. 72 (2010)
−−
κ+κ=
−
j
PPI1
jj,puronfermentati
PPI1
jj Y1Y1
YSCIj
j
Economic rating - SCI
Comparability of single step costs • relation to achieved purification of single steps • SCI = costs / 1% purification
Cost-effectiveness related to product costs • specific purification costs per 100% purification as extrapolated from a single step • hypothetical, complete process consist of equally efficient steps • SCI = costs / 100% purification
SCI as a new measure for cost-efficiency of single purification steps
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Winkelnkemper and Schembecker, Separ. Purif. Technol., vol. 72 (2010)
Purification fingerprints - CPPI
Contaminant-specific purification performance index (CPPI) • characterization of process steps and prediction of optimal combinations
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Winkelnkemper and Schembecker, Separ. Purif. Technol., vol. 71 (2010)
Selectivity of all contaminants C towards the target product T
Purification fingerprints
Combination of
“orthogonal” fingerprints
Purification fingerprints - CPPI
Contaminant-specific purification performance index (CPPI) • characterization of process steps and prediction of optimal combinations
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Winkelnkemper and Schembecker, Separ. Purif. Technol., vol. 71 (2010)
Selectivity of all contaminants C towards the target product T
Purification fingerprints
Combination of “orthogonal”
fingerprints
technische universität dortmund
Process Development
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Dryer
Adsorption chromatography
Crystallization ( + solvent exchange)
Centrifugation
Filtration
Ad- & Desorption
Extraction
Crystallization
Ultrafiltration
Size exclusion chromatography
Ultrafiltration
Fermenter
Ion exchange flow through
Ad- & Desorption
Extraction
Crystallization
Eva
luat
ion
x
x x x
x
x
Process Development
Lab
Lab recipe
Production
Alternatives
experimental proof Scale-Up
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