Incorporating potential environmental impact from water for injection in environmental assessment of monoclonal antibody production

Accepted Manuscript Title: Incorporating Potential Environmental Impact from Water for Injection in Environmental Assessment of Monoclonal Antibody Production Author: A. Idris G.K. Chua M.R. Othman PII: DOI: Reference:

S0263-8762(16)00077-0 http://dx.doi.org/doi:10.1016/j.cherd.2016.02.014 CHERD 2190

To appear in: Received date: Revised date: Accepted date:

26-5-2015 18-1-2016 10-2-2016

Please cite this article as: Idris, A., Chua, G.K., Othman, M.R.,Incorporating Potential Environmental Impact from Water for Injection in Environmental Assessment of Monoclonal Antibody Production, Chemical Engineering Research and Design (2016), http://dx.doi.org/10.1016/j.cherd.2016.02.014 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Highlights  Potential Environmental Impact (PEI) for utility steam generation is proposed  Environmental evaluation of a biopharmaceutical process is presented

Ac ce p

te

d

M

an

us

cr

 PEI from energy consumption dominated total PEI value

ip t

 PEI of material and energy consumption is summarised

1 Page 1 of 48

Incorporating Potential Environmental Impact from Water for Injection in Environmental Assessment of Monoclonal Antibody Production

ip t

A. Idris, G.K. Chua, M.R Othman Faculty of Chemical Engineering and Natural Resources, Universiti Malaysia Pahang,

us

Email: [email protected]

cr

Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang, MALAYSIA

an

Abstract

Biopharmaceutical industries consistently demand Water for Injection (WFI) in their production. WFI production requires large amount of energy that may leave environmental

M

footprint. However, its potential environmental impact (PEI) is typically not included in environmental assessment. This paper aims to present how WFI generation would contribute to environmental pollution. It was assumed that WFI was generated in Multiple Effect

d

Distillation (MED), where utility steam is used as heating media. Utility steam is generated in

te

a steam boiler, where several gas pollutants are produced as by-product. The PEI of these pollutants was estimated based on a modified Waste Reduction (WAR) Algorithm. For data

Ac ce p

generation, MED was simulated in SuperPro Designer®. To demonstrate the way to include WFI into an environmental assessment, a hypothetical monoclonal antibody process was used as a case-study. From the case-study, it can be seen that WFI generation contributed the most to energy consumption and to the total PEI value. Therefore, it is important to include PEI from WFI in the environmental assessment for more accurate results, particularly when comparing several process designs as the results may influence decision-making.

Keywords: Environmental assessment, potential environmental impact, Multiple Effect Distillation, monoclonal antibody production, WAR algorithm

2 Page 2 of 48

Abbreviations

te

d

M

an

us

cr

ip t

American Conference of Industrial Hygienists Acidification Potential Aquatic Toxicity Potential Aqueous two-phase extraction Cleaning in Place Carbon oxides Carbon dioxide Ethylenediaminetetraacetic acid European Pharmacopoeia United States Environmental Protection Agency Good Manufacturing Practices Global Warming Potential Hydrophobic Interaction Chromatography Human Toxicity Potential by either Inhalation or Dermal Exposure Human Toxicity Potential by Ingestion Ion Exchange Chromatography Japanese Pharmacopoeia Potassium chloride Potassium dihydrogen phosphate Lethal conc. of a chemical which causes death in 50% of test specimen Life Cycle Assessment Lethal dose on rats by oral ingestion that causes 50% death of the rats Monoclonal antibody Multiple Effect Distillation Disodium hydrogen phosphate Sodium dihydrogen phosphate National Institute for Occupational Safety and Health Nitrogen dioxide Nitrogen oxides Ozone Depletion Potential Ozone depleting substance hydroxyl radical Occupational Safety and Health Administration Photochemical Oxidation Potential Potential Environmental Impact Purified Water Reverse Osmosis Sulphur dioxides Sulphur oxides Threshold limit values Terrestrial Toxicity Potential Time-weighted averages United States Pharmacopoeia Vapour Compression Distillation Waste Reduction Water for Injection

Ac ce p

ACGIH AP ATP ATPE CIP CO CO2 EDTA EP U.S EPA GMP GWP HIC HTPE HTPI IEX JP KCl KH2PO4 LC50 LCA LD50 MAb MED Na2HPO4 NaH2PO4 NIOSH NO2 NOx ODP ODS OH OSHA PCOP PEI PW RO SO2 SOx TLV TTP TWA USP VCD WAR WFI

3 Page 3 of 48

Nomenclature average impact factor

cr

mass of fuel mass flowrate of utility steam in stream h streams containing gas streams containing solid emission factor

us

F H Streams-gas Streams-solid

ip t

impact factor

rate of PEI

normalized specific PEI

M

mass fraction of a component

an

ratio of mass of pollution to mass of product

input stream for fuel energy process unit mass of product unit of time normalized specific PEI

c E EF F g h j k m Mj in out gen WFI p

impact category energy emission factor for gas pollutants g mass of fuel gas pollutant stream of utility steam stream chemical component energy stream output stream flow rate input output generated Water for Injection product

Ac ce p

te

d

y (ep) mp t s

mass flow rate of product produced relative weighting factor

4 Page 4 of 48

total energy in the streams containing gas

Ac ce p

te

d

M

an

us

cr

ip t

total energy in the streams containing solid

5 Page 5 of 48

1

Introduction Green Engineering principles have moved pharmaceutical industries towards using

ip t

inherently safe materials and energy-efficient design to reduce the environmental footprints caused by its processes and process effluents (Anastas and Zimmerman, 2003).

cr

Biopharmaceutical processing is generally considered a green process that uses less hazardous substances as compared to the production of small-molecule pharmaceutics (Ho et

us

al., 2010; Jiménez-González and Woodley, 2010). However, this does not indicate that the

an

process is ideal to the environment.

According to Ho et al. (2010) and Junker (2010), the environmental impact from

M

biopharmaceutical processing comes from its high water usage due to the needs of aqueous solution in the processing steps. The amount, in part, is also contributed by regular cleaning

d

and sterilization of processing equipment. The wastewater discharged from the processes may

te

be considered benign (Jiménez-González and Woodley, 2010; Junker, 2010), but the energy needed to generate this high purified water may unknowingly leave some impact to the

Ac ce p

environment.

Biopharmaceutical manufacturing may give a perception that it is an environmental

friendly process despite the emphasis on high water usage in its process. To demonstrate the potential environmental impact (PEI) of this process, an illustrative case-study representing a large-scale of monoclonal antibody (MAb) production was used in this work. The process and its manufacturing data were simulated in SuperPro Designer®. The assessment includes PEI from manufacturing material and energy consumption. For manufacturing material, only PEI from by-product was considered while PEI from target product is considered zero. The PEI for energy consumption was based on the electricity demand in the process. The PEI was estimated using WAR Algorithm (Cabezas et al., 1999; Young and Cabezas, 1999).

6 Page 6 of 48

In addition to the energy from electricity demand, the energy consumption to produce the amount of Water for Injection needed in the case-study was also estimated by modelling and simulating the distillation process using SuperPro Designer®. It was assumed that WFI

ip t

generation takes place in a Multiple Effect Distillation (MED) system. The PEI from this energy demand was evaluated using an extended algorithm developed in this work based on

2

Literature review WAR Algorithm

an

2.1

us

cr

WAR Algorithm.

A variety of methods has been implemented in environmental assessment of chemical

M

process such as Life Cycle Assessment (LCA), E-factor (Sheldon, 2007, 2000, 1997a, 1997b, 1994, 1992), ABC classification (Biwer and Heinzle, 2004; Heinzle et al., 2007) and WAR

d

Algorithm (Cabezas et al., 1999; Young et al., 2000; Young and Cabezas, 1999). LCA

te

considers the impact of product or process from cradle (material creation) to grave (disposal of material). Although it offers a comprehensive assessment, it requires extensive data and

Ac ce p

thus needs intensive work and much time to collect the data; hence it is not suitable to apply in the early phase of process development. Another method like E factor defines the greenness of a process by summing the mass of solvents, consumables and other reagents to produce 1 kg of product. Ho et al. (2010) recommended the use of E factor to measure the environmental impact of a pharmaceutical production by using process water as the environmental factor. As this method only concentrates on the amount of by-product itself rather than its impact to the surrounding area, the results may not accurately represent the hazard as the impact severity of by-product is not available. On the other hand, environmental assessment using ABC classification provides sufficient information to determine the

7 Page 7 of 48

ecological impact in a simple manner and requires uncomplicated data. However, the impact of energy on the environment does not included in the assessment. Some environmental impact assessment methods do not consider impact due to energy

ip t

demand. Waste Reduction (WAR) Algorithm on the other hand, enables impact assessment in early process design in conjunction with process simulation software as it needs simple data

cr

which could be obtained from the simulation software and in public databases. It covers the impact from material used in the manufacturing process as well as the impact of energy to the

us

environment. Nevertheless, this method considers the impact in the manufacturing stages

an

only and does not cover the full life-cycle of a product. The assessment results may inherently connected to a certain inaccuracy due to its simple algorithm, but it certainly can

M

be used in the early phases to assist process decision making. This is particularly important when considering design alternatives for process design comparison as well as process

d

retrofitting where a reduced environmental footprint is preferred (Othman et al., 2010).

te

Environmental footprints are defined as Potential Environmental Impact (PEI) in WAR Algorithm (Cabezas et al., 1999). PEI is a conceptual quantity based on the concept of

Ac ce p

conventional of mass and energy balance. It is not directly measurable but it can be calculated using several equations based on the stream composition, stream flow rate and energy stream. In this context, pollution is defined as by-product produced during manufacturing. This pollution is evaluated in several impact categories as explained further in the following section.

2.1.1 Impact categories The environmental footprint of the process is evaluated in eight different impact categories (Young and Cabezas, 1999) which comprises of local toxicology and global atmospheric. The eight impact categories are global warming potential (GWP), ozone

8 Page 8 of 48

depletion potential (ODP), acidification or acid-rain potential (AP), photochemical oxidation potential or smog formation potential (PCOP), human toxicity potential by ingestion (HTPI), human toxicity potential by either inhalation or dermal exposure (HTPE), aquatic toxicity

ip t

potential (ATP) and terrestrial toxicity potential (TTP). The classification of these impact categories was initially based on a study by Heijungs et al. (1992). The categories were then

cr

amended to selectively promote the most useful categories with respect to the process design. The methodology of how to quantify and determine the impact categories can be found in

an

us

Young & Cabezas (1999).

2.1.2 PEI indexes

M

WAR Algorithm uses the material and energy flow within a process to estimate the PEI. According to conservation of mass and energy balance, any amount of material and

d

energy entering a process must be equal to that of the value exiting the process, regardless of

te

any reactions taking place in the system. This definition could be applied to the PEI for each

Ac ce p

material and energy involved in the manufacturing (Figure 1).

Figure 2.1 Illustration of PEI balance within a manufacturing stage 9 Page 9 of 48

However, some PEI could be generated or could be lost due to the chemical reactions if new material is formed. Therefore, the algorithm for PEI calculation can be represented as

ip t

in equation 1:

represents the rate of PEI in chemical component and

and

PEI output rate ,

and

are the PEI input rate,

are the

d

te

is the rate of PEI generated (+) or

Ac ce p

lost (-) in the process. It should be noted that waste energy lost in

and

and

are PEI output rate associated to the waste energy lost during

chemical reaction and energy generation process and

neglected since

represents the rate of

M

PEI in energy generation process.

an

Where

us

cr

(1)

and

can be

are far greater compared to the aforementioned PEI. Therefore,

the equation 1 can be simplified into equation 2:

(2)

Calculation for PEI associated to the chemical used during manufacturing differs to PEI calculation from energy generation process and will be explained briefly in the following paragraphs.

10 Page 10 of 48

2.1.3 PEI of chemical component In any environmental assessment, the impact of material involved in the manufacturing

ip t

steps is also considered besides its energy consumption. WAR Algorithm uses material flowrate in the manufacturing process to estimate the PEI.

in the impact category c, the

can be calculated

us

of a component k has a PEI output

cr

PEI is calculated for each input and output stream. Assuming an output stream j consists

is the relative weighting factor for impact category c,

(3)

is the mass flow rate of the

is the mass fraction of a component k in stream j and

Ac ce p

stream j,

te

d

Where

M

an

by using equation 3:

specific PEI of component k in impact category c. The

is the normalized

was assigned a value of 1 in this

work to imply that all impact categories are equally important. After PEI values for all chemical in all impact categories for stream j have been determined, these values are summed up to obtain the amount of PEI in a stream j, which is

(equation 4):

(4)

PEI for input streams is also determined in a similar manner as PEI in output streams. 11 Page 11 of 48

Rate of PEI generated during a process is determined by calculating the difference between rate of PEI input in the input stream and rate of PEI output in the output stream is calculated. The amount of PEI generated in each procedure can be used to identify the

ip t

hotspots in the process design, where the most PEI is generated.

output

, total PEI

cr

The results can be presented by comparing rate of total PEI input

. In this work, the PEI values are presented as the

us

and total PEI generated

(5)

M

an

rate of PEI per unit mass of MAb produced. Hence, equation 5 was used:

is the rate of PEI input per unit mass of product and

is the mass

te

d

Where

and PEI

Ac ce p

flow rate of product produced. Rate of PEI output per unit mass of product

generated

are also calculated with the same procedure.

2.1.4 PEI for energy

PEI for energy is defined as the effect of air emission on the environment caused by

fuel combustion to generate the required amount of energy. There are two types of energy usually involved in a manufacturing process namely direct energy and indirect energy. Direct energy is defined as the energy used to operate electrical equipment such as pumps and motors (electrical energy) whereas indirect energy is the energy required to produce the

12 Page 12 of 48

utility fluids such as water and steam from the initial condition to the condition in the process. For electricity generation, this is done in electric power generation facility. Electricity demand in a manufacturing process is calculated by summing all the

ip t

electrical energy required such as stirred-tank reactor during cell culture and disk-stack centrifuge in the downstream section. Typical pollutions from the power plant are carbon

cr

dioxide (CO2), nitrogen dioxide (NO2) and sulfur dioxide (SO2). Ratio of these gas emissions depends on the type of fuel used in the power plant. In this work, it was assumed that the

an

The resulting air emission is tabulated in Table 1:

us

manufacturing plant is located in Malaysia and the electricity is generated from fossil fuel.

M

Table 2.1 Emission factor for fossil-fuel based power plant (Shekarchian et al., 2011) Gas pollutant

Emission factor [kg/ kWh]

NOx

Ac ce p

CO

te

CO2

d

SO2

0.0005 0.53 0.0009 0.0005

The equations 6 - 10 are used to calculate the PEI of the electricity. Equation 6 shows the calculation of PEI that is readily available in the fuel used in the combustion:

(6)

13 Page 13 of 48

Where

is the PEI of the fuel,

is total energy in the streams containing solid, EF is

the emission factor for gas pollutants g in the energy stream m. Gas pollutants are produced

ip t

during fuel combustion. The PEI of this gas emission is estimated using equation 7:

is the PEI of the energy generated and

is total energy in the streams

an

Where

us

cr

(7)

containing gas. Total PEI from energy generation is estimated by summarizing PEI of gas and

are

M

pollutants emitted during energy generation and PEI in the solid fuel.

Ac ce p

te

d

determined using equation 8 and equation 9:

(8)

(9)

E is the energy in the energy stream m, required by the process, Streams-solid and streamsgas are the streams containing solid (fuel source) and gas (which is the air emission) respectively.

It was assumed that any PEI contained in the fuel source is neglected since it is generally not bioavailable to the human and animals as the components are sealed in a solid mixture.

14 Page 14 of 48

Therefore,

and

could be dismissed, which leads to a simpler calculation (equation

ip t

10):

cr

(10)

us

PEI for electricity demand is measured as PEI/kWh of power plant production. The impact of this gas emission to the environment is evaluated in eight impact categories similar to the

an

evaluation of pollution impact from chemical as discussed in the previous chapter, which are

3

Methodology

Water in biopharmaceutical industries

d

3.1

M

HTPI, HTPE, ATP, TTP, GWP, ODP, PCOP and AP.

te

Water is regarded as natural component, thus it carries zero impact to the environment.

Ac ce p

However, pharmaceutical industries constantly need large amount of specially treated water in the processing steps as well as for cleaning the equipment. There are two types of water commonly used in pharmaceutical industries, namely Purified Water (PW) and WFI. Energy demand to produce PW or WFI using membrane technology is typically low (Brush and Zoccolante, 2009). Most membrane-based WFI uses hot water sanitizable reverse osmosis unit followed by ultrafiltration to ensure the quality of produced WFI is equivalent to WFI produced by distillation. A study by Kojima et al. (2011) reported that quality of WFI produced using membrane process can be achieved if the water purification unit was properly designed and regularly maintained. However, many pharmaceutical manufacturers still opt for distillation to produce WFI due to the lack of confidence in membrane-based process as well as for economic reasons. In addition, European Pharmacopoeia (EP) only allows WFI 15 Page 15 of 48

to be produced using distillation, whereas United States Pharmacopoeia (USP) and Japanese Pharmacopoeia (JP) permit the use of membrane technology and other purification process that is equivalent to distillation in removing impurities from the water (Brush and Zoccolante,

ip t

2009). Distillation involves heating the water to its boiling temperature until it evaporates. The steam is then filtered to remove any contaminants and condensed before it is collected as

cr

WFI. This whole process consumes a lot of energy and consequently may contribute to the environmental pollution, particularly when producing large amount of WFI as needed in the

us

biopharmaceutical process.

an

There are two distillation processes commonly used in the WFI production system. One is the Multiple Effect Distillation (MED) and another one is Vapor Compression

M

Distillation (VCD). In MED system, water is boiled under pressure and subsequently the vapor is condensed in several succeeding evaporator to produce the WFI. VCD system on the

d

other hand, uses a compressor to compress the vapor in order to increase its pressure and

te

temperature before the vapor is condensed in the condenser. Both systems recycle the latent heat of the vapor in a heat exchanger to increase the temperature of following boiling water to

Ac ce p

produce more vapors.

Electric heating may be used for low capacity system to generate WFI but most

facilities use utility steam to heat and boil the water. This utility steam is produced in a boiler with fuel as the energy supply. As a result of fuel combustion, several pollutants are emitted to the surrounding area, leaving some impact to the environment. The calculation of PEI from WFI generation is presented in the next section.

3.2

Calculation of PEI from WFI generation WAR algorithm only considered PEI from electricity generation. As an extension, this

work also includes calculation to estimate PEI from WFI generation. Generation of WFI

16 Page 16 of 48

requires utility steam, which is produced in a boiler. For the purpose of this evaluation, it was assumed that the boiler uses natural gas as its fuel. According to EPA (U.S. Environmental Protection Agency, 2009), following amount of pollutants is produced for every 1 m3 natural

cr

Table 3.1 Emission factor for natural gas combustion

ip t

gas burned in a boiler (Table 3.1):

(U.S. Environmental Protection Agency, 2009)

us

Emission factor [kg/ m3] 0.000096 0.016 19.2 0.01344

an

Gas pollutant SOx NOx CO2 CO

M

Each gas pollutant leaves its own impact to the environment. The impact factors for the air pollutants are shown in Table 3.2. Using this data, the PEI of WFI generation can be

d

evaluated.

HTPI LD50 [mg/kg]

HTPE TWATLV [mg/m³] 0 5.6 9000 29

ATP LC50 [mg/L]

TTP LD50 [mg/kg]

GWP

PCOP

AP

ODP

0 19.6 0 0

1.2 0.78 0 0

0 0 1 0

0 0 0 0

1 1.77 0 0

0 0 0 0

Ac ce p

Comp.

te

Table 3.2 Impact factors of pollutants

SOx NOx CO2 CO

1.2 0.78 0 0

Based on the calculation of PEI from electricity generation (equation 10), equation 11 was formulated to include utility steam.

(11)

17 Page 17 of 48

Where

is the rate of PEI from WFI production, h is the stream for utility steam required

is the mass flowrate of utility steam in stream h,

to produce the amount of WFI,

is the normalized specific PEI

cr

emission factor of component g in the input stream y and

is the

ip t

the mass of fuel in input stream y needed to produce the required utility steam,

is

us

for component g in the impact category c. The PEI estimated for WFI and pure steam generation was calculated using equation 12 after considering that electricity may be used for

(12)

Utility steam generation

Ac ce p

3.3

te

d

M

an

pumps etc.

Utility steam or also known as plant steam is widely used in pharmaceutical industry

as energy source for heat exchanges. In MED, utility steam is used in the first effect to boil the water. This steam is produced in a conventional steam boiler using fuel combustion. Most commonly used boiler in pharmaceutical industries is shell type boiler with fire-tube design (Lone et al., 2013) usually installed as a packaged boiler. In such boiler, the products of combustion pass through the boiler tubes, which in turn transfer its thermal energy to the boiler water surrounding the tubes. Figure 3.1 shows a simplified utility steam system inside a pharmaceutical manufacturing facility. Feed water for the boiler is stored in a feed tank to avoid interruption

18 Page 18 of 48

of make-up water supply. This make up water supply usually has low temperature which falls around 25ºC, thus large amount of energy will be needed to heat the water until it changes to steam. For energy saving purposes, the cold water supply is mixed with the steam condensate

ip t

return from various points in the facility (which is relatively hot) to raise the water temperature in the feed tank. It was assumed that the water in the feed tank can reach its final

cr

temperature of 80ºC. Using this approach, less energy is needed to heat the water in the boiler to reach its saturation temperature at 9 bar as the temperature difference would be much

Make up water

Blowdown

te Ac ce p

Steam boiler

Feedwater for boiler

d

Feedtank

M

an

us

lower.

Distribution to various points

Heating steam

Hot condensate

Figure 3.1 Simplified utility steam system diagram

Using method for boiler calculator (Energy Efficiency and Renewable Energy, 2013), the amount of energy needed to generate 1 kg of utility steam was estimated. Several assumptions have been made in the calculations: 1) Steam boiler has 85% efficiency

19 Page 19 of 48

2) Fuel is natural gas with heating value of 38900 kJ/kg (Staffel, 2011) 3) 3% of feedwater is discharged as blowdown water 4) Deaerator pressure is operating at atmospheric pressure

ip t

5) Approximately 816 kg/h saturated steam was produced

cr

From the calculations, it was estimated that 629.9 kW power from fuel (equivalent to 2267640 kJ) was needed to produce 816 kg utility steam in an hour. Therefore, 2779 kJ fuel

us

energy was needed per 1 kg utility steam, which is equivalent to 0.07 m3 of natural gas.

an

Since the boiler uses fuel as the energy source to boil the water and turn it into steam, some by-products considered as pollutants such as CO, CO2, SOx and NOx are produced and

M

emitted to the outside. The impact of these pollutants was evaluated using the extended

Multiple Effect Distillation (MED) System

te

3.4

d

version of WAR Algorithm as mentioned in section 3.2.

The distillation system in this work was assumed to be fed with PW having an

Ac ce p

average temperature of 8ºC. This inlet water temperature was chosen based on a normal operation of water treatment facility in a local biopharmaceutical processing plant. Based on the fact that WFI is typically stored and distributed warm, it was assumed that the outlet temperature of WFI in both systems is 82ºC. Figure 3.2 shows the process flow diagram of MED system. Prior entering MED the feed water must be pre-treated to remove water hardness, maintain low-level of chloride, total solids and silica to prevent any formation of scale at the higher operating temperature of MED. Common pre-treatment process consist of a carbon filter, water softener and a two-pass Reverse Osmosis (RO). This feed water is preheated in the MED system by first passing through a condenser and a heat exchanger before it enters the first effect. In the first effect, the water is further heated and boiled in a double tube

20 Page 20 of 48

sheet evaporator using high pressure utility steam as the heating medium. The vapor generated is passed through a separator to remove impurities and further used to boil the water in the second effect to generate more vapor. This process is repeated in subsequent

Ac ce p

te

d

M

an

us

cr

condensed in the condenser and collected as WFI distillate.

ip t

effects until the last effect, where the vapor and distillate from preceding effects are

21 Page 21 of 48

ip t cr us

Feedwater inlet

M an

Condenser

Steam

Steam

Feedwater

Distillate cooler

Distillate

ed

Distillate

ce pt

Steam

Condensate outlet Distillate

Distillate

Feedwater 4th Effect

Heat exchanger

Heat exchanger

Heat exchanger

Feedwater

5th Effect

Steam

Heating steam inlet

Distillate

Heat exchanger

Steam

3rd Effect

Heat exchanger Feedwater

Feedwater 2nd Effect

1st Effect

Ac

Feedwater

Distillate storage

Figure 3.2 Simplified process flow diagram of MED

22

Page 22 of 48

MED uses energy in the form of utility steam to boil the water in the first effect. To estimate the amount of utility steam required to produce 1 kg of WFI, a MED system was modelled and simulated in SuperPro Designer. This MED was modelled based on

ip t

the data obtained from local biopharmaceutical company. In this simulation, it was assumed that the MED has 5 effects and was fed with 880 kg/h water. Assuming that

cr

feedwater enters the system at 8ºC and was heated in the first effect using only utility

us

steam, approximately 258.68 kg/h utility steam was used to produce 819 kg/h WFI (Table 4).

Utility

Demand/h

Energy demand [kJ/ kg WFI]

Steam

258.68 kg

857.45

Electricity

M

an

Table 3.3 Utility consumption for MED

0.086

0.02 kWh

d

To demonstrate the PEI estimation from WFI generation, an illustrative MAb

te

process was used as a case-study. This process was chosen to represent a

Ac ce p

biopharmaceutical process which typically uses large amount of WFI in its manufacturing process. Furthermore, PEI of manufacturing material and electricity demand was also included in the assessment to demonstrate a complete environmental impact assessment.

3.5

Case-study: Large Scale Monoclonal Antibody (MAb) Production

This case study (Figure 3.3) was taken from the available example in the SuperPro Designer® v8.5 (educational licensed). The assessment includes PEI from manufacturing material and energy consumption. For manufacturing material, only PEI from by-product was considered while PEI from target product was considered zero.

23 Page 23 of 48

The process starts with inoculum preparation in several T-flasks, roller bottles and seed bioreactors. During inoculum preparation, the cells are grown in serum-free media consisting various salts and nutrients with aeration to provide optimal condition for the

ip t

cells to grow. Growth media for the seed bioreactors is prepared in media preparation tank labelled P-6 and P-9 respectively. Then, the broth containing cells is transferred to

cr

a 20000 L production bioreactor and continually grown in the media in a fed-batch

us

mode for approximately 12 days. This means that the nutrients are added to the broth at particular intervals to promote optimal growth of the cells. This is done throughout the

an

cell culture duration without harvesting the cells until the fermentation run is complete. During cell culture, media component is converted into biomass, carbon dioxide, MAb

M

(2g/L) and other organic compounds which are described as impurities. Once the cells have reached the desired quantity, the broth is transferred to a

d

disk-stack centrifuge to capture the target product by removing the cells and cell debris

te

by means of centrifugation. Direct capture of the product is possible as the product is

Ac ce p

secreted into the growth medium. After centrifugation, the remaining cell culture supernatant is passed through a depth filtration before it is further purified in Protein A chromatography column. Here, the product is retained in the column, the column is washed and the product is eluted with acetic acid. The purified product is further concentrated 5-fold in diafiltration and chemically treated with Polysorbate 80 to inactivate possible viral contaminants. Subsequently, the concentrated protein solution is loaded into an Ion Exchange (IEX) chromatography. After material loading, the column is washed and the product is eluted with sodium chloride in a gradient concentration elution step. Next, ammonium sulfate is added into the IEX eluate (P25/V-109) to promote the hydrophobic effect of the target protein to prepare it for Hydrophobic Interaction Chromatography (HIC). The solution entered the HIC column 24 Page 24 of 48

and then eluted with HIC elution buffer containing sodium chloride and sodium dihydrophosphate. After this polishing step, a second viral exclusion step follows. The solution is filtered through a dead-end virus filtration as an added measure for viral

ip t

removal. Final bulk product solution consisting of 19 kg MAb/ batch is then stored in 50L bags for further processing.

cr

The material and energy demand of this case-study will be presented in the next

us

section. Based on these demand, the environmental impact was evaluated and will be

Ac ce p

te

d

M

an

discussed in the following section.

25 Page 25 of 48

ip t cr us M an ed ce pt Ac Figure 3.3: Process flow diagram of the case-study (MAb production) 26

Page 26 of 48

4 4.1

Results and Discussion Results

ip t

4.1.1 Material and energy demand from MAb process

To estimate the PEI of the process, material and utility demand during

cr

manufacturing must first be determined. The data in Table 4.1 and Table 4.2 were

us

obtained from SuperPro Designer®. Table 4.1 shows the amount of material entering (input) and leaving (output) the process for every batch production. The equipment for

an

cell culture was initially filled with air consisting of nitrogen and oxygen before the

M

growth media solution is fed into the equipment.

Table 4.1: Mass balance of MAb production

d

Ac ce p

Acetic-Acid Amm. Sulfate Biomass Carb. Dioxide EDTA, Sodium Impurities KCl KH2PO4 MAB Inoculation media Na2HPO4 NaH2PO4 Nitrogen Oxygen Phosphoric Acid Polysorbate 80 SerumFree Media Sodium Chloride Sodium Citrate Sodium Hydroxide TRIS Base TRIS HCl Water WFI

INITIAL [kg/ batch] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 76.89 23.34 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

te

COMPONENT

INPUT [kg/ batch] 56.74 133.03 0.00 0.00 41.75 0.00 0.02 0.02 0.00 4.66 29.83 1.73 31685.22 9619.02 577.14 0.08 448.52 879.80 10.22 91.86 20.88 62.63 0.00 76295.87

OUTPUT [kg/ batch] 56.74 133.03 157.04 327.70 41.75 19.50 0.02 0.02 28.50 0.76 29.83 1.73 31737.08 9223.26 577.14 0.08 19.92 879.80 10.22 91.86 20.88 62.63 310.12 76296.88 27 Page 27 of 48

Table 4.2 presents the amount of power and utilities required to manufacture a kg of MAb.

ip t

In this case study, almost 4000 kg WFI (Table 4.2) needed to manufacture a kg of MAb. Only 20% of this amount is used in the upstream processing whereas over 50% of

cr

total amount is needed in the downstream section as shown in Figure 4.1 . WFI is

us

mostly consumed in the chromatographic steps due to the large amount of aqueous

an

buffer used for product purification and product polishing.

Table 4.2: Power and utilities demand per kg of MAb

Ac ce p

te

d

Inoc Prep Cell Culture Prim Recov Protein-A IEX Chrom HIC Chrom Final Filtration Virus Inactivation Viral exclusion TOTAL

M

Electricity [kWh/kg MAb]

Section

13.89 84.99 14.55 0.50 1.20 115.13

WFI [kg/kg MAb] 190.64 566.72 1868.81 671.98 474.97 104.55 105.50 3983.18

28 Page 28 of 48

ip t cr us an

M

Figure 4.1: Percentage analysis of WFI demand/ kg MAb

Electricity is mostly used in the upstream processing for inoculum preparation

d

and cell culture (Figure 4.2) due to the continuous stirring in the bioreactor to ensure

Ac ce p

te

that biochemical environment in the bioreactor is optimal for cell growth.

29 Page 29 of 48

ip t cr us an

M

Figure 4.2: Percentage analysis of electricity demand/ kg MAb

d

Based on the material and energy demand of this MAb process as presented in this

te

section, the environmental impact of this process was evaluated and presented next.

Ac ce p

4.1.2 PEI of manufacturing material In the case study, there are 35 input streams, 1 product streams and 19 streams

representing by-products from the manufacturing process. There are total of 23 materials involved in the manufacturing process with their impact factors tabulated in Table 4.3. The impact factors represent the toxicity data for each impact categories which were taken from available literatures (Airgas, 2011a, 2011b, 2005; Junker, 2010; PhytoTechnology Laboratories, 2010; Sciencelab.com Inc., 2010f, 2010g, 2010a, 2010b, 2010c, 2010d, 2010e; Sigma-Aldrich, 2012a, 2012b, 2012c, 2012d, 2012e, 2011). These data were used to estimate PEI of each component for the environmental assessment. Some of the toxicity values in Table 4.3 were estimated using different

30 Page 30 of 48

species than mentioned in Young & Cabezas (1999). These values are indicated by a superscript next to them. Details about the species used in the value estimation and the duration they were exposed to the component are stated in the footnote at the end of the

ip t

Table 4.3. No toxicity value was recorded for MAb, because it is the desired product in

Ac ce p

te

d

M

an

us

cr

this process and thus its PEI was not included in the environmental assessment.

31 Page 31 of 48

ip t cr us

Table 4.3: Impact factors of chemical in the case-study

Ac

ATP LC50 [mg/L] 423c 36.7f 500.0g 0.68h 0.68h 0.68h -

TTP LD50 [mg/kg] 3310a 2840 2000a 2600a 4640 25000i -

M an

HTPEb TWA-TLV [mg/m³] 37 9000 -

ed

Acetic-Acid Amm. Sulfate Biomass Carb. Dioxide EDTA, Sodium Impurities KCl KH2PO4 MAb Media Na2HPO4 NaH2PO4 Nitrogen Oxygen Polysorbate 80 SerumFree Media Sodium Chloride Sodium Citrate

HTPI LD50 [mg/kg] 3310a 2840 2000a 2600a 4640 25000i -

ce pt

Component

-

-

GWPd

PCOPd

APd

ODPd

-e 1 -

-

-

-

32

Page 32 of 48

ip t cr

HTPEb TWA-TLV [mg/m³] 2 -

ATP LC50 [mg/L] -

TTP LD50 [mg/kg] 500k -

GWPd

PCOPd

APd

ODPd

-

-

0.88 -

-

ed

Sodium Hydroxide TRIS Base TRIS HCl Water WFI

HTPI LD50 [mg/kg] 500.0k -

M an

Component

us

Table 4.3: Continued

a

LD50 (oral, rat), bTWA-TLV ACGIH.cLC50 (goldfish, 24h), dClassification factors published by (Heijungs et al., 1992), eDenotes non-determined/ non-applicable data, fLC50 (rainbow

Ac

ce pt

trout, 96h), gLC50 (golden orfe), hLC50 Zinc and Copper (Americamysisbahia, 96h) (Verslycke et al., 2003), iLD50 (oral, mouse), jLDL (oral, rat), kLDL (oral, rabbit).

33

Page 33 of 48

Figure 4.3 shows the PEI of chemicals in MAb manufacturing presented as PEI/kg of MAb produced. None of the materials has any impact in PCOP and ODP category and almost

ip t

all PEI emitted to the environment was due to PEI already existed in the input material. However, there was some increment in the PEI output contributed by PEI generated during

cr

manufacturing as compared to PEI input. These PEI generations occurred in all impact categories except ATP, which impact was contributed by growth media supplied to the cells

us

to aid their production. However, since the media was used to promote cells growth causing

an

some changes in its properties, the impact in ATP was reduced accordingly. On the other hand, GWP was not present in the input material, yet it has some value in the PEI output due

Ac ce p

te

d

M

to the generation and emission of carbon dioxide which leads to the creation of GWP.

Figure 4.3: PEI of manufacturing material (PEI/kg MAb)

4.1.3 PEI of electricity Based on the electricity demand as shown in Table 4.2, the PEI for all pollutants emitted during electricity generation process was calculated and presented in Figure 4.4. The 34 Page 34 of 48

highest PEI value is in GWP category, due to the emission of carbon dioxide in the electricity generation process; whereas no PEI was estimated in categories PCOP and ODP as the gases released during combustion to generate the electricity do not have any impact in these two

d

M

an

us

cr

ip t

categories.

Ac ce p

te

Figure 4.4: PEI of electricity demand (PEI/ kg MAb)

4.1.4 PEI of utility steam

It was estimated that the gas pollutants emitted to the environment during fuel

combustion to generate 1 kg of utility steam have potential impact in all impact categories except PCOP and ODP (Figure 4.5). This is because none of the gas pollutant emitted to the atmosphere during utility steam production has any potential impact in these two categories. The emission of NOx leaves some PEI in all impact categories except PCOP and ODP, with the highest value in HTPE category. On the other hand, release of CO2 may only present PEI in two categories (HTPE and GWP), but the impact it has on global warming is the highest compared to other pollutants.

35 Page 35 of 48

ip t cr us

an

Figure 4.5: PEI for 1 kg of utility steam

The PEI results may vary if other type of fuel was used other than natural gas since the

M

amount of gas emission differs depending on the fuel type. The result shown in Figure 4.5

te

4.1.5 PEI of WFI generation

d

was used in this work as a basis for PEI estimation for WFI generation in MED.

Ac ce p

MAb manufacturing in this case-study uses WFI assumed to be generated in MED in the manufacturing process. MED uses utility steam as energy source to produce WFI, which consequently will release some gas pollutants with PEI as shown in Figure 4.6. In Figure 4.6, GWP shows the highest value due to the impact from emission of carbon dioxide during utility steam production. On the other hand, the gas pollutants released has no potential impact on ozone depletion and smog formation which is depicted by zero value in ODP and PCOP categories.

36 Page 36 of 48

ip t cr us

Discussion

M

4.2

an

Figure 4.6: PEI for WFI- Process (PEI/kg MAb)

Carbon dioxide, biomass and water dominated the amount of waste produced in the

d

production, as shown in Table 4.1. Carbon dioxide is produced mainly during cell culture,

te

where the cells require air to be able to replicate and generate carbon dioxide as waste. After cell culture, the target product must be recovered and purified to remove unwanted

Ac ce p

impurities, including biomass and excess water. In addition to the amount of excess water from cell culture broth, water with quality of WFI needed in the purification procedure was also discharged as wastewater after the target product has been purified. Consequently, the total amount of WFI needed and discharged during production (as wastewater) was almost identical.

Ho et al. (2010) reported that between 3000 to 7000 kg water is typically used to

manufacture a kg of MAb and suggested that the amount of water is used as indicator towards designing a greener process. The amount of water used in a MAb process varies from a process to another due to differences in its process technology. A process that utilizes fully disposable equipment may use less water than a process with stainless-steel equipment

37 Page 37 of 48

(Pietrzykowski et al., 2013) due to less cleaning procedure. Additionally, replacing only one equipment in a process may impact the amount of WFI used, as published by Rosa et al. (2013).

ip t

During cell culture, the cell growth were promoted in growth media, which consists of trace elements, sugar, salts, water and some supplements (Li et al., 2010). As the composition

cr

of growth media is complex and unique to the cells being cultured, it was challenging to list down all components used to construct the growth media. Therefore, only Zinc and Copper

us

were considered in the evaluation since these elements are usually present in the growth

an

medium for culturing mammalian cell (Junker, 2010). Both trace elements are considered to have some impact and other components were assumed to present no negative impact to the

M

environment. It was also assumed that these trace elements are fully discharged as impurities in the downstream processing.

d

Other by-product during cell culture was biomass, which was separated from MAb in

te

the downstream processing. Biomass was assumed to have zero impact factors as it contains mainly water and cells (approximately 80% and 20% respectively). Water was assumed to

Ac ce p

have zero impact to the environment according to WAR Algorithm. The cells contained in the biomass were assumed to have zero impact due to its biological component. Other components in Table 4.3 were used in the downstream processing to separate other impurities from MAb to ensure the final product is safe for patient’s use. Total PEI values are compared and summarized in Figure 4.7. By-products from MAb

manufacturing contributed the highest percentage from total PEI in category HTPI, ATP and TTP, mostly due to PEI readily available in the input material and are emitted outside the manufacturing area. WFI dominated the PEI in impact categories HTPE, GWP and AP due to the energy needed in the water treatment process. Although electricity contributed only a little to the whole assessment, electricity demand should be included in the assessment as it

38 Page 38 of 48

may present a more accurate result. Figure 4.7 does not consider PEI from energy demand to

M

an

us

cr

ip t

generate WFI for cleaning needs during manufacturing.

d

Figure 4.7: Summary of PEI values (PEI/kg MAb)

te

The corresponding PEI values from the energy demand in the manufacturing process

Ac ce p

presented in Figure 4.8 showing that PEI due to WFI generation dominates all impact categories except PCOP and ODP. For a clearer view of the contributing factor to total PEI, Figure 4.9 shows the percentage of PEI contributed by the material as well as the energy demand from this manufacturing process.

39 Page 39 of 48

ip t cr us an

Ac ce p

te

d

M

Figure 4.8: Comparison of PEI values (energy)

Figure 4.9: PEI contribution (%)

It can be seen from Figure 4.9 that the impact of output material is comparable to the impact from WFI generation. However, the output material has more impact to living things when it is ingested or directly exposed on skin, while WFI generation has more influence to the quality of air and to the surrounding environment. Due to the different means in the 40 Page 40 of 48

impact they pose to the environment, assessment of every aspect during manufacturing including material and the energy needed would give more precise overview on the environmental impact of that process, especially since biopharmaceutical process as in this

ip t

case study usually needs a large amount of WFI in its manufacturing process. In this work, the energy demand and PEI contribution due to generating WFI in MED

cr

have been investigated. The largest percentage of energy demand was due to the energy needed to produce WFI required in the MAb manufacturing process. This high energy

us

demand leads to large amount of PEI to be emitted to the environment.

an

Using MAb process as a case-study, the environmental impact of the process was evaluated using WAR Algorithm. To investigate the PEI due to WFI generation process, PEI

M

values from the manufacturing process of MAb was compiled and categorised into three groups. The first group is total PEI of material only, second group is the sum of PEI of

d

material and electricity and the third group is the sum of PEI of material, electricity and WFI

te

used during the process. Since the impact assessment of energy consumption due to electricity demand and WFI demand was done in the same manner, their PEIs can be summed

Ac ce p

up to present total PEI of a process. The values were compared in Figure 4.10 which shows that considering only PEI due to the material used in the production of MAb will produce inaccurate results.

In Figure 4.10, the PEI of energy consumption due to generating WFI for CIP was not

included. This is to demonstrate that although the environmental assessment was limited to the manufacturing process only, differences in PEI values can be observed between these three groups.

41 Page 41 of 48

ip t cr us

an

Figure 4.10: Comparison of PEI

M

There are several ways to reduce the environmental impact based on WFI usage in MAb manufacturing. WFI consumption can be improved by replacing equipment with high WFI

d

usage with other equipment. In this case-study, Protein A chromatography has been identified

te

as the hotspot as it has the largest amount of WFI usage. One of the approaches to make the process more environmental friendly is to investigate whether it is feasible to replace Protein

Ac ce p

A chromatography with other alternative. Thömmes & Etzel (2007) have presented several alternatives to chromatographic process in purification of biopharmaceutical processes such as charged ultrafiltration membranes and protein crystallisation, which may be used to replace Protein A chromatography. A review by Rosa et al. (2013) comparing aqueous twophase extraction (ATPE) with Protein A chromatography from economic and environmental point of view shows that ATPE may produce the same purification results with reduced cost and less environmental impact. One other possibility is to replace all chromatographic separation steps with nanofibrous adsorption membrane (Varadaraju et al., 2011) to reduce the WFI usage.

42 Page 42 of 48

Besides minimizing the amount of WFI used in the manufacturing process, the generation process itself can be improved to reduce the resulting environmental pollution. It has been noted that approximately 10-15% of utility steam requirement may be reduced if

ip t

additional effect is installed in the MED system (Graf, 2010). The most important point is to ensure optimal MED design is used to fulfil WFI requirement in the manufacturing facility.

cr

This depends on the daily demand, yearly demand and peak demand, as well as the cost of utility steam and cooling water (Graf, 2010). Other than MED, Vapour Compression

us

Distillation (VCD) can also be used for WFI generation. It uses a mechanical compressor to

an

compress the steam from distillation process for more energy-efficient process (Gsell et al.,

5

M

2013).

Conclusions and Recommendations

d

Common environmental impact assessment exclude WFI usage in the assessment as

te

water is considered a non-hazardous material. However, WFI does leave environmental footprint as shown in the results in this paper. Environmental assessment of manufacturing

Ac ce p

material alone may not present accurate results, therefore incorporating energy consumption from electricity demand and energy demand associated to WFI generation may give more precise overview on the environmental impact of a process. This may influence decision making when comparing several process design. The primary objective of WAR Algorithm is to determine the relative PEI of a

process design compared to other alternative. The results itself do not imply how severe the environment will be affected based on a rigid scale; rather, they provide assistance in making a decision of which design comparatively would leave smaller footprint on the environment. In addition, any hotspots in a process can be determined early as this method does not require extensive data and can be applied in the early stage of process design.

43 Page 43 of 48

The extended algorithm developed in this work can be used not only to evaluate the PEI from WFI generation, but also to evaluate PEI from energy demand due to the use of utility steam. The PEI of WFI generation can be evaluated more extensively by considering

ip t

the energy needed to treat the Purified Water (PW) prior entering the distillation system to produce WFI. Furthermore, by evaluating the whole water treatment system, the operation

cr

that consumes highest amount of energy can be determined and consequently a more efficient system may be designed to reduce the energy consumption to make the system more

an

6

us

environmental friendly.

Acknowledgement

M

The authors would like to thank Universiti Malaysia Pahang and Malaysian Ministry of

References

te

7

d

Higher Education for their financial support.

Ac ce p

Airgas, 2011a. Nitrogen [WWW Document]. Mater. Saf. Data Sheet. URL https://airgas.com/msds/001040.pdf (accessed 6.7.12). Airgas, 2011b. Oxygen [WWW Document]. Mater. Saf. Data Sheet. URL https://airgas.com/msds/001043.pdf (accessed 6.7.12). Airgas, 2005. Carbon Dioxide [WWW Document]. Mater. Saf. Data Sheet. URL https://airgas.com/msds/001013.pdf (accessed 6.7.12). Anastas, P., Zimmerman, J., 2003. Design through the 12 principles of Green Engineering. Environ. Sci. Technol. 37, 94A–101A. doi:10.1021/es032373g Biwer, A., Heinzle, E., 2004. Environmental assessment in early process development. J. Chem. Technol. Biotechnol. 79, 597–609. doi:10.1002/jctb.1027 Brush, H., Zoccolante, G., 2009. Methods of producing Water for Injection. Pharm. Eng. 29, 20–29. Cabezas, H., Bare, J.C., Mallick, S.K., 1999. Pollution prevention with chemical process simulators: the generalized waste reduction (WAR) algorithm—full version. Comput. Chem. Eng. 23, 623–634. Energy Efficiency and Renewable Energy, 2013. Steam Calculators [WWW Document]. 44 Page 44 of 48

URL http://www4.eere.energy.gov/manufacturing/tech_deployment/amo_steam_tool/equipBoiler (accessed 12.26.13). Graf, C., 2010. Energieeffiziente Herstellung von Pharmawasser. Pharm. Ind. 72, 1797–1803.

ip t

Gsell, G. V, Nunez, C., Smith-palmer, M., 2013. Advances in Vapor Compression Technology for the Production of USP Purified Water and Water For Injection. Pharm. Eng. 33, 1–8.

cr

Heijungs, R., Guinée, J.B., Huppes, G., Lankreijer, R.R., Haes, H.A.U. de, Sleeswijk, A.W., 1992. Environmental Life Cycle Assessment of Products. Leiden, Netherlands.

us

Heinzle, E., Biwer, A., Cooney, C., 2007. Development of sustainable bioprocesses: modeling and assessment. John Wiley & Sons Ltd.

an

Ho, S. V., McLaughlin, J.M., Cue, B.W., Dunn, P.J., 2010. Environmental considerations in biologics manufacturing. Green Chem. 12, 755.

M

Jiménez-González, C., Woodley, J.M., 2010. Bioprocesses: Modeling needs for process evaluation and sustainability assessment. Comput. Chem. Eng. 34, 1009–1017. doi:10.1016/j.compchemeng.2010.03.010

d

Junker, B., 2010. Minimizing the Environmental Footprint of Bioprocesses. Part 2: Evaluation of Wastewater, Electricity and Air Emissions. Bioprocess Int. 8, 36–46.

te

Kojima, S., Okada, S., Sasaki, T., Oba, T., Fujise, A., Kusuyama, K., The Association of Membrane Separation Technology of Japan, 2011. Reliability study for Membrane-Processed Water for Injection (WFI). PDA J. GMP Valid. Japan 13, 47–55.

Ac ce p

Li, F., Vijayasankaran, N., Shen, A. (Yijuan), Kiss, R., Amanullah, A., 2010. Cell culture processes for monoclonal antibody production. MAbs 2, 466–479. doi:10.4161/mabs.2.5.12720 Lone, N.P., Chaudhari, P.P., Dhamangaonkar, P.P.R., Deshpande, A., 2013. Development of a ProE Code for Boiler Design, in: International Conference on Sustainable Manufacturing and Operations Management (ISOM). pp. 29–30. Othman, M.R., Repke, J.-U., Wozny, G., Huang, Y., 2010. A Modular Approach to Sustainability Assessment and Decision Support in Chemical Process Design. Ind. Eng. Chem. Res. 49, 7870–7881. doi:10.1021/ie901943d PhytoTechnology Laboratories, 2010. Tris-Base [WWW Document]. Mater. Saf. Data Sheet. URL http://phytotechlab.com/media/downloads/7424/T838MSDS.pdf (accessed 6.7.12). Pietrzykowski, M., Flanagan, W., Pizzi, V., Brown, A., Sinclair, A., Monge, M., 2013. An environmental life cycle assessment comparison of single-use and conventional process technology for the production of monoclonal antibodies. J. Clean. Prod. 41, 150–162. doi:10.1016/j.jclepro.2012.09.048 45 Page 45 of 48

Rosa, P. a J., Azevedo, a M., Sommerfeld, S., Bäcker, W., Aires-Barros, M.R., 2013. Aqueous two-phase extraction as a platform in the biomanufacturing industry: economical and environmental sustainability. Biotechnol. Adv. 29, 559–67. doi:10.1016/j.biotechadv.2011.03.006

ip t

Sciencelab.com Inc., 2010a. Acetic Acid [WWW Document]. Mater. Saf. Data Sheet. URL http://www.sciencelab.com/msds.php?msdsId=9922769 (accessed 6.7.12).

cr

Sciencelab.com Inc., 2010b. Ammonium Sulfate [WWW Document]. Mater. Saf. Data Sheet. URL http://www.sciencelab.com/msds.php?msdsId=9927078 (accessed 6.7.12).

us

Sciencelab.com Inc., 2010c. Potassium chloride [WWW Document]. Mater. Saf. Data Sheet. URL http://www.sciencelab.com/msds.php?msdsId=9927402 (accessed 6.6.12).

an

Sciencelab.com Inc., 2010d. Potassium phosphate monobasic [WWW Document]. Mater. Saf. Data Sheet. URL http://www.sciencelab.com/msds.php?msdsId=9927235 (accessed 6.6.12). Sciencelab.com Inc., 2010e. Polysorbate 80 [WWW Document]. Mater. Saf. Data Sheet. URL http://www.sciencelab.com/msds.php?msdsId=9926645 (accessed 6.6.12).

M

Sciencelab.com Inc., 2010f. Sodium citrate anhydrous [WWW Document]. Mater. Saf. Data Sheet. URL http://www.sciencelab.com/msds.php?msdsId=9924978 (accessed 6.6.12).

d

Sciencelab.com Inc., 2010g. Sodium hydroxide [WWW Document]. Mater. Saf. Data Sheet. URL www.sciencelab.com/msds.php?msdsId=9924998 (accessed 6.6.12).

Ac ce p

te

Shekarchian, M., Moghavvemi, M., Mahlia, T.M.I., Mazandarani, A., 2011. A review on the pattern of electricity generation and emission in Malaysia from 1976 to 2008. Renew. Sustain. Energy Rev. 15, 2629–2642. doi:10.1016/j.rser.2011.03.024 Sheldon, R.A., 2007. The E Factor: fifteen years on. Green Chem. 9, 1273. Sheldon, R.A., 2000. Atom efficiency and catalysis in organic synthesis. Pure Appl. Chem. 72, 1233–1246. Sheldon, R.A., 1997a. Catalysis and Pollution Prevention, in: Chem. Ind. London, pp. 12–15. Sheldon, R.A., 1997b. Catalysis: The key to waste minimization. J. Chem. Technol. Biotechnol. 68, 381–388. Sheldon, R.A., 1994. Consider the environmental quotient, in: Chemtech. pp. 38–47. Sheldon, R.A., 1992. Organic synthesis; past, present and future, in: Chem. Ind. London, pp. 903–906. Sigma-Aldrich, 2012a. Ammonium Sulfate [WWW Document]. Mater. Saf. Data Sheet. URL http://www.sigmaaldrich.com/MSDS/MSDS/DisplayMSDSPage.do?country=MY&language =en&productNumber=A4418&brand=SIGMA&PageToGoToURL=http://www.sigmaaldrich .com/catalog/product/sigma/a4418?lang=en (accessed 6.7.12). 46 Page 46 of 48

ip t

Sigma-Aldrich, 2012b. Sodium phosphate monobasic [WWW Document]. Mater. Saf. Data Sheet. URL https://www.sigmaaldrich.com/MSDS/MSDS/DisplayMSDSPage.do?country=MY&languag e=en&productNumber=S5011&brand=SIGMA&PageToGoToURL=https://www.sigmaaldric h.com/catalog/search?term=S5011&interface=All&N=0&mode=match partialmax&lang=en®ion=MY&focus=product (accessed 6.7.12).

us

cr

Sigma-Aldrich, 2012c. Sodium phosphate dibasic [WWW Document]. Mater. Saf. Data Sheet. URL https://www.sigmaaldrich.com/MSDS/MSDS/DisplayMSDSPage.do?country=MY&languag e=en&productNumber=S0876&brand=SIAL&PageToGoToURL=https://www.sigmaaldrich. com/catalog/search?term=S0876&interface=All&N=0&mode=match partialmax&lang=en®ion=MY&focus=product (accessed 6.7.12).

M

an

Sigma-Aldrich, 2012d. Sodium chloride [WWW Document]. Mater. Saf. Data Sheet. URL https://www.sigmaaldrich.com/MSDS/MSDS/DisplayMSDSPage.do?country=MY&languag e=en&productNumber=S6191&brand=SIGMA&PageToGoToURL=https://www.sigmaaldric h.com/catalog/search?term=S6191&interface=All&N=0&mode=match partialmax&lang=en®ion=MY&focus=product (accessed 6.7.12).

te

d

Sigma-Aldrich, 2012e. Tris-HCl [WWW Document]. Mater. Saf. Data Sheet. URL https://www.sigmaaldrich.com/MSDS/MSDS/DisplayMSDSPage.do?country=MY&languag e=en&productNumber=T5941&brand=SIGMA&PageToGoToURL=https://www.sigmaaldri ch.com/catalog/search?term=T5941&interface=All&N=0&mode=match partialmax&lang=en®ion=MY&focus=product (accessed 6.6.12).

Ac ce p

Sigma-Aldrich, 2011. Edetate disodium (EDTA) [WWW Document]. Mater. Saf. Data Sheet. URL https://www.sigmaaldrich.com/MSDS/MSDS/DisplayMSDSPage.do?country=MY&languag e=en&productNumber=E0399&brand=SIAL&PageToGoToURL=https://www.sigmaaldrich. com/catalog/search?term=E0399&interface=All&N=0&mode=match partialmax&lang=en®ion=MY&focus=product (accessed 6.6.12). Staffel, I., 2011. The Energy and Fuel Data Sheet [WWW Document]. Clavert. Energy Res. Goup. Thömmes, J., Etzel, M., 2007. Alternatives to chromatographic separations. Biotechnol. Prog. 23, 42–45. doi:10.1021/bp0603661 U.S. Environmental Protection Agency, 2009. Vol 1: Stationary Points and Area Sources, AP 42 Compilation of Air Pollutant Emission Factors. Varadaraju, H., Schneiderman, S., Zhang, L., Fong, H., Menkhaus, T.J., 2011. Process and economic evaluation for monoclonal antibody purification using a membrane-only process. Biotechnol. Prog. 27, 1297–1305. doi:10.1002/btpr.639 Young, D., Scharp, R., Cabezas, H., 2000. The waste reduction (WAR) algorithm: 47 Page 47 of 48

environmental impacts, energy consumption, and engineering economics. Waste Manag. 20, 605–615.

Ac ce p

te

d

M

an

us

cr

ip t

Young, D.M., Cabezas, H., 1999. Designing sustainable processes with simulation: the waste reduction (WAR) algorithm. Comput. Chem. Eng. 23, 1477–1491.

48 Page 48 of 48