18.03.2016

J. Merz

G. Schembecker

Downstream Processing of Biopharmaceuticals and Natural Products

Juliane.Merz@bci.tu-dortmund.de

Schembecker@bci.tu-dortmund.de

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

3

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)

12

Process Development

13

Process Development

14

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

15

Process Development

Traditional approach for purifying (biological) compound

RIPP scheme (recovery, isolation, purification, and polishing)

16

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

17

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

20

21

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

28

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

42

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

43

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

45