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JANUARY/FEBRUARY2002 PHARMACEUTICAL ENGINEERING 1

Process Simu

The Role of Process Simulation inPharmaceutical Process

Development and ProductCommercialization

by Demetri P. Petrides, Alexandros Koulouris, and Pericles T. Lagonikos

This articledescribes howbatch processsimulators can beused to facilitateand expeditedevelopment and

commercializationof pharmaceuticalproducts.

Introduction

The development a nd commercializa tion

of a new pharmaceutical product is apainstaking process that takes 7 to 12

years to complete requiring sizeable invest-

ments ranging from $100 million to $500 mil-

lion. In a ddit ion, 80 to 85%of products in devel-

opment fa il somew here in the development pipe-

line, often a fter u ndergoing expensive clinical

trials 1. The pharmaceutical industry spends

considerably more on the development and

evaluat ion of products t ha t eventual ly fai l tha n

on successful products. Consequently, a ny meth-

odologies and t ools tha t can be used to evalua te

alternatives and speed up the development ef-

fort can ha ve a tremendous impact on the bot-

tom line.

Computer Aided Process Design (CAP D) andsimula tion tools hav e been successfully used in

the chemical a nd oi l industries since the ea rly

60s to expedite development a nd optimize th e

design and operation of integrated processes.

Similar benefi ts can be expected from t he a p-

pl icat ion of CAPD a nd simulat ion in the pha r-

ma ceutical industr ies. The prima ry empha sis

of this art ic le is on the role of CAPD and

simula tion in expediting process development.

The responsibilities of process development

include:2

Figure 1. Addition of unit

procedures and stream lines to the flowsheet.

Reprinted from The Official Journal of ISPE

PHARMACEUTICAL ENGINEERING® January/February 2002, Vol. 22 No. 1



2 PHARMACEUTICAL ENGINEERING• JANUARY/FEBRUARY2002

ocess Simulation

supply o f in it i a l quant i t ies of the Act ive Pha rmaceut ica l

Ingr edient (API) for toxicological screening a nd formula-

tion studies

• supply o f Drug Product (DP) to support c linica l t r i a l s

• development o f a pract i ca l , environmenta l ly sound , and

economically feasible route for producing the API a nd D P on

an industr ial scale

• select ion, scale-up, and va l idat ion of these processes, en-

suring the smooth transfer of technology from the pilot

plant to the ultima te production site(s)

P rocess simulat ion t ools can be us ed thr oughout th e life cycle

of process development and product commercialization. The

benefits a t t he va rious sta ges of the commercializat ion process

ar e explained below.

Idea Generati on When product and process ideas are first conceived, process

simula tion is used for project screenin g/selection a nd st ra tegic

planning based on preliminary economic analyses.

Process Development

While the pre-clinica l an d clinica l testing of the candida te dru g

compound is going on, the company’s process development

group is looking into the ma ny options a vaila ble for man ufa c-

turing, puri fying, chara cterizing t he drug substance, and for-

mulating it as a drug product. At this stage, the process

undergoes constant changes. New synthetic routes are being

investigat ed. New recovery an d purificat ion options a re evalu-

at ed. Alterna tive formulat ions a lso are explored. Typically, a

large number of scientists and engineers are involved in the

improvement a nd optimizat ion of individual pr ocessing steps.

Simulation tools at this point can introduce a common lan-

guage of communication and facilitate team interaction. A

computer model of the entire process can provide a common

reference and evaluation framework to facilitate process de-

velopment. The impact of process changes can be readily

evaluated a nd documented in a systematic wa y. Once a reli-

able model is ava ilable, i t can be used to pinpoint the most cost-

sensi t ive areas — the economic “hot-spots” — of a complex

process. These are usua lly steps of high capita l an d opera ting

cost or low yield an d production thr oughput. The findings from

such an alyses can be used to judiciously focus further la b an d

Figure 2. The operations associated with the first unit procedure of Figure 1.

pilot pla nt studies in order t o optimize those portions of th e

process. Being a ble to experiment on the computer w ith a lter-

na t ive process setups and operating conditions reduces the

costly and time-consuming laboratory and pilot plant effort.

The environmental impact of a process is another issue that

can be readily evaluated with computer models. Material

bala nces ca lculat ed for th e projected large scale manu factur -

ing reveal the environmental hot-spots. These are usually

solvents a nd regulat ed mat erials tha t a re costly to dispose of.

En vironmenta l issues not a ddressed during process develop-ment ma y lead to serious heada ches during ma nufacturing.

This is th e case becau se aft er a process ha s been approved by

the regulatory agencies, i t is extremely costly and time-con-

suming t o make process chan ges. This is par ticularly t rue for

biopharmaceuticals where it is commonly said that “the pro-

cess ma kes the product.”

Facilit y Design and/or Selection

With process development nearing completion at the pilot

plant level, CAPD and simulat ion t ools a re used to systemati-

cally design a nd optimize t he process for commercial produc-

tion. Ava ilability of a good computer model ca n fa cilitat e the

transfer of process technology and facility design. If a new

facility needs to be built , process simulat ors can be used t o size

process equipment a nd supporting utilities, an d estimat e the

required capi tal investment . In transferring production to

existing man ufa cturing sites, process simulators can be used

to evalua te the va rious sites from a capa city a nd cost point of

view a nd select t he most a ppropria te one. The sa me can a pply

to outsourcing of ma nufacturing t o contract ma nufacturers.

M anufacturing

In large scale manufa cturing, s imulat ion tools ar e primari ly

used for process scheduling, debottlenecking, and on-going

process optimization. Simulation tools that are capable of

tracking equipment utilization for overlapping batches can

identify bott leneck can didat es and guide the user through th edebottlenecking effort.

Commercially Available ToolsProcess simulators for continuous chemical processes have

been in use in the petrochemical industries since the early

1960s. Established simulators for the petrochemical indus-

tries include: Aspen Plus (from Aspen Technology, Inc.),

Ch emCAD (from Chemst a tions, Inc. ), HYSYS (from Hyprotech,

Ltd ./AEA Engineering S oftwa re), a nd P RO/II (from Simu la-

tion S ciences, In c.).

The time-dependency of batch processes makes develop-

ment of bat ch process simulat ors more challenging. “Ba tches”

from Batch Process Technologies (West Lafayette , IN -

www.bptech.com) was the first simulator specific to batchprocesses. It was commercialized in the mid 1980s. All of its

operat ion models are dyna mic an d simulation alw a ys involves

integra tion of differentia l equa tions over a period of time. This

s i m ul a t o r ha s fo und a pp l i c a t i o ns i n pha rm a c e u t i c a l s ,

biochemicals, and food processing.3

In th e mid 1990s, Aspen Technology, In c. (Ca mbr idge, MA

- w ww .aspent ech.com) introduced B a tch P lus, a recipe-driven

simulator tha t ta rgeted batch pha rma ceutical processes. At

a round the sa me time, Intelligen, Inc. (Scotch P lains, NJ -

ww w.int elligen.com) introduced SuperPr o Designer. SuperP ro

ha s its roots in B ioP ro Designer, the development of which wa s

initiat ed at MIT in the la te 1980s to addr ess the needs of the



JANUARY/FEBRUARY2002 • PHARMACEUTICAL ENGINEERING 3

Process Simu

Please be aware that the Garbage-In, Garbage-Out (GIGO) princip le

app lies to all computer models. If some of t he assump t ions and inpu t dat a are

incorrect , so w ill be the outcome of the simulat ion.“ “

biopha rma ceutical industries. SuperP ro Designer wa s creat ed

to address other relat ed industries (e.g., synt hetic pha rma ceu-

ticals, agrochemicals, food processes, etc.) as well as waterpurification and end-of-pipe treatment processes. More re-

cently (late 1990s), Hyprotech, Ltd. (a subsidiary of AEA

En gineering Softw ar e - ww w .hyprotech.com) introduced Ba tch

Design Kit (B DK), a t ool origina lly developed at MIT wh ich is

qui te similar in phi losophy a nd functiona l ity to B at ch P lus.

Batch Plus, BDK, and SuperPro Designer di ffer from

“Batches” in their basic approach to modeling. More specifi-

cally, most of their unit operat ion models are not dy na mic, but

rather simple algebraic models, whose solution does not re-

quire integration of differential equations. This shortens the

computat ion t ime and ena bles the user t o evalua te a larger

number of scena rios in a shorter period. B at ch P lus is recipe

driven. In other w ords, the user develops a m odel by creat ing

a t ext recipe (similar t o a ba tch sheet), a nd t he modeling engine

creat es a Process Flow D iagra m (PF D) as a n output . B DK a nd

SuperP ro Designer build their process models using a gra phi-

cal user interface with a P FD view. A batch sheet is generated

as a n output report . SuperP ro Designer can han dle batch a nd

continuous processes equally well ; whereas the other three

tools a re pra ctica lly limited to bat ch processes.

SuperPro Designer will be used to illustrate the role of

process simulators in the design and development of bulk

synt hetic phar ma ceutical processes. Informa tion on the role of

pro c e s s s i m ul a t o r s i n t he d e s i g n a nd d e ve l o pm e nt o f

biopharmaceuticals can be found in the literature.4

Generation of a Batch Process Simulation ModelTo model an integrated process on the computer, the user

sta rts by developing a f lowsheet t ha t r epresents t he overa l l

process. Figure 1, for inst an ce, displays pa rt of the flowsheet of

a synthetic pharmaceutical process. The flowsheet is devel-

oped by putting together the required unit procedures (see

next para graph for explana t ion), and joining them with ma te-

rial flow streams. Next, the user initializes the flowsheet by

registering the va rious ma terials tha t are used in t he process

and specifying operating conditions and performance param-

eters for the various operations. The simulator is equipped

wit h t wo component da ta bases, its own of 450 compounds a nd

a version of DIPPR that includes 1,700 compounds. It also

comes with a user data base wh ere modified and newly creat ed

compounds can be registered. All database files are in MSAccess format.

Most bulk pharmaceutical processes operate in batch or

semi-continuous mode. This is in contrast to petrochemical

and other industries that handle large throughputs and use

continuous processes. In continuous operations, a piece of

equipment performs the same action all the time (which is

consistent w ith t he notion of unit operat ions). In ba tch process-

ing, on the other hand, a piece of equipment goes through a

cycle of opera tions. For insta nce, a t ypical N utsche filtra tion

cycle includes charge of slu rr y , f i l t rat i on und er vacuum or

pressure , cake washi ng , occasional ly cake dr yin g , and removal

of cake . In SuperPro, the set of operations that comprise a

processing step is called a “unit procedure” (as opposed to a

unit operat ion). Ea ch unit procedure conta ins individual ta sks

(e.g., charge, heat, react, etc.) called operations. A unit proce-dure is represented on th e screen w ith a single equipment icon

(for exa mple, P -1/R-101 in Figu re 1 repr esent s t he fir st proce-

dure P-1 that takes place in stirred-tank reactor R-101). In

essence, a u nit procedure is the recipe of a processing st ep tha t

describes the sequence of actions required to complete that

step. Figure 2 displa ys th e dialog through w hich the recipe of

a vessel unit procedure is specified. On t he left-ha nd side of

that dialog, the program displays the operat ions that are

available in a vessel procedure; on the right-hand side, it

displays the r egistered opera tions. The significance of the un it

procedure is tha t it ena bles the user to describe a nd model the

va rious activities of bat ch processing steps in deta il .

For every operation within a unit procedure, the simulator

includes a mathematical model that performs material and

energy balance calculat ions. B ased on the ma terial bala nces, it

performs equipment-sizing ca lcula tions. U nlike typical models

w here bat ch time is specified, th is simulat or provides the ability

to calculate batch cycle time by estimating the cycle-time of

scale-dependent unit opera tions. If mult iple opera tions w ithin a

unit procedure dictate different sizes for a certain piece of

equipment, the software reconciles the different demands and

selects an equipment size th a t is appropriat e for a ll opera tions.

In other w ords, the equipment is sized so tha t it is large enough

that it will not be overfilled during any operation, but it is no

larger than necessary (in order to minimize capital costs). In

a ddition, the softw a re checks to ensure tha t th e vessel contents

will not fall below a user-specified minimum volume (e.g., aminimum stir volume) for a pplicable opera tions.

Before any simulation calculations can be done, the user

must initialize the va rious operat ions by specifying operat ing

conditions and performance parameters through appropriate

dialog windows. After initialization of the operations, the

simulator performs material and energy balances for the

entire process, and est imat es the required sizes of equipment

and the batch cycle time. Optionally, the simulator may be

used to ca rry out cost an alysis a nd economic evalua tion ca lcu-

lations. The fundamentals of process economics are described

in the l i terature.4

Other tasks that can be handled by process simulators

include process scheduling, environmenta l impact a ssessment,

debottlenecking, and throughput analysis. Issues of processscheduling and environmental impact assessment will be

addressed in the next sect ion. In throughput analysis and

debott lenecking, the engineer ana lyzes the capa city and time

utilization of equipment and resources (e.g., utilities, labor,

ra w ma teria ls), and t ries to identify opportu nities for increas-

ing throughput with the minimum possible capital invest-

ment .

Ha ving developed a good model using a process simulat or,

the user may begin experimenting on the computer with

a l terna t ive process setups a nd operat ing conditions. This ha s

th e potent ial of reducing t he costly a nd t ime-consum ing labo-

ra tory and pi lot plant effort . Please be aw are tha t the Ga rbage-




ocess Simulation

In, Garbage-Out (GIGO) principle applies to all computer

models. If some of the assumptions and input data are incor-

rect, so will be the outcome of th e simulat ion. Consequent ly, a

certa in level of model validat ion is necessary. I n its simplest

form, a r eview of the results by a n experienced engineer can

play t he role of validat ion.

Illustrative ExampleThe objective of this exa mple is to illustr a te how ba tch process

simulators can be used to model, visualize, and analyze bulkpharmaceutical processes. This example deals with the pro-

duction of around 171 kg per batch of an interm ediate pha rma -

ceutical compound. This task is accomplished using three

1,000 gal rea ctors, tw o 4 m2 fi l ters, a nd one 10 m2 t ra y dryer .

Process Descrip ti on

The entire flowsheet of the ba tch process is sh own in Figure 3.

It is divided into four sections: 1) P roduct Syn thesis, 2) Isola-

t ion a nd P uri ficat ion, 3) Final P uri ficat ion, a nd 4) Crysta l liza-

tion an d Dr ying. A flowsheet section in S uperP ro is simply a set

of unit procedures (processing steps). The unit procedures of

each section a re ma rked by distinct colors (green, blue, purple,

an d bla ck for section one, tw o, three, an d four, respectively).

Due t o space limita tions, th e description below is not compre-

hensive and is not intend ed to be an exa ct represent at ion of the

a ctual process. The following sections a re merely int ended to

illustrate the usage of a simulation tool in designing and

analyzing a sample process.

The forma tion of the desired product in this example in-

volves 12 unit pr ocedur es. The first rea ction step (procedur e P-

1) involves th e chlorina tion of quina ldine. Quinaldine is dis-

solved in car bon tetr achloride (CCl 4) and reacts wi th gaseous

C l2 to form chloroquin a ldine. The conver sion of th e react ion is

a round 98%(based on a mount of qua na ldine fed). The gener-at ed HCl is neutral ized using Na 2CO3. The stoichiometry of

these r eactions follows:

Quinaldine + Cl2 = = = > Chloroquina ld ine + HCl

Na 2CO3 + H C l = = = > Na H C O3 + N a Cl

Na H C O3 + H C l = = = > Na C l + H 2O + C O2

The small amounts of unreacted Cl 2, generated CO2, and

volatilized CCl4 a re vented. The a bove three reactions occur

sequentia lly in t he first reactor vessel (R-101). Next, H Cl is

a dded in order to produce chloroquina ldine-HC l. The H Cl first

neutral izes the remaining NaHCO3 and then reac ts wi th

chloroquinaldine to form its salt , according to the following

stoichiometries:

Figure 3. The flowsheet for the example pharmaceutical intermediate compound.




Process Simu

Na H C O3 + H C l = = = > Na C l + H 2O + C O2

Chloroquinaldine + HCl = = = > Chloroquinaldine.HCl

The sma ll amounts of generated CO 2 a nd volat i l ized CCl4 a re

vented. The presence of wa ter (added w ith H Cl a s hydr ochloric

acid solution) and CCl4 leads to the formation of two liquid

pha ses. Then the sma ll am ounts of unrea cted quina ldine an d

chloroquinaldine are removed with the organic phase. The

chloroquinaldine-HCl remains in the aqueous phase. This

sequence of operations (including all charges and transfers)requires a bout 15.8 hours.

After removal of the unreacted quinaldine, the condensa-

tion of chloroquinaldine and hydroquinone takes place in

rea ctor R-102 (procedure P -2). First , the sa lt chloroquina ldine-

HC l is converted ba ck to chloroquina ldine using Na OH. Then,

hydroquinone reacts w i th Na OH and yields hydroquinone-Na.

Finally, chloroquinaldine and hydroquinone-Na react and

yield the desired intermediate product. Along with product

format ion, roughly 2%of the chloroquina ldine dimerizes a nd

forms an undesirable by-product impurity. This series of

reactions a nd t ra nsfers ta kes roughly 16.3 hours. The stoichi-

ometry of th ese reactions follows:

C h l or oq u i n a ld in e .H C l + N a O H = = = > Na C l + H 2O +

Chloroquinaldine

2Chloroquina ldine + 2NaOH = = = > 2H2O + 2NaCl + Impurity

Hydroquinone + NaOH = = = > H 2O + Hydroquinone .Na

Chloroquina ldine + Hydroquinone .Na = = = > Product + NaCl

Both the Product and Impurity molecules formed during the

condensation reaction precipitate out of solution and are

recovered using a Nuts che filter (procedure P -3, filter NFD -

101). The pr oduct r ecovery yield is 90%. The filt ra tion, w a sh,

and cake transfer time is 5.4 hours.

Next, th e product/impur ity ca ke recovered by filtra tion is

a dded int o a N a OH solut ion in reactor R-103 (procedure P -4).

The product molecules rea ct w ith Na OH to form product-Na ,wh ich is soluble in wa ter. The impurity molecules rema in in

the solid pha se, and a re subsequently r emoved during proce-

dure P-5 in filter NFD-101. The product remains dissolved

in the liquors. Procedure P-4 takes about 10.9 hours, and

procedure P -5 ta kes approximat ely 3.5 hours. Notice tha t fi l ter

NFD -101 is used by severa l different pr ocedures. The rea ctors

also are used for multiple procedures during each batch.

Please note that the equipment icons in Figure 3 represent

unit procedures, a s opposed to uniq ue pieces of equ ipment . The

procedure names (P-1, P-3, etc.) below the icons refer to the

unit procedures, w hereas t he equipment ta g na mes (R-101, R-

102, etc.) refer to the actual physical pieces of equipment. In

other words, the process flow diagram in this simulator is

essentially a graphical representation of the batch “recipe”

th at shows t he sequence of execution of the va rious steps.After the filtration in procedure P-5, the excess NaOH is

neutralized using HCl and the product-Na salt is converted

back to product in reactor R-101 (procedure P-6). Since the

product is insoluble in water, i t precipitates out of solution.

The product is then r ecovered using a nother filtra tion step in

NF D-101 (procedure P -7). The pr oduct recovery yield is 90%.

The precipitat ion procedure t akes r oughly 8.1 hours, a nd t he

filtra tion ta kes a bout 4.8 hours. The recovered product ca ke is

then solubilized in isopropanol and treated with charcoal to

remove coloration. This takes place in reactor R-102 under

procedure P-8. After charcoal treatment, the solid carbon

par ticles ar e removed using an other filtra tion step in NFD-102

(procedure P-9). The times required for charcoal treatmenta nd filtra tion ar e 17.6 hours a nd 4.4 hours, respectively.

In t he next s tep (procedure P -10), th e solvent is dis tilled off

until the solution is half i ts original volume. The product is

th en crysta llized in the sa me vessel with a yield of 97%. The

crysta lline product is recovered w ith a 90%yield using a final

filtration step in NFD-102 (procedure P-11). The distillation

and crystallization step takes approximately 13.1 hours, and

the filtration requires roughly 3.6 hours per cycle. The recov-

ered product crysta ls are th en dried in a tr a y dryer (procedure

P-12, TDR-101). This takes an additional 12.4 hours.

Figure 4. Equipment utilizat ion in three consecutive batches. Figure 5. Purif ied Water demand in five consecutive batches.

Table A. Raw material requirements (1 batch = 171 kg MP).

Raw Material kg/Year kg/Batch kg/kg MP

Chlorine 14,534 89 0.52Na2CO3 17,057 104 0.61USP Water 481,484 2,936 17.12HCl (20% w/w) 58,034 354 2.06NaOH (50% w/w) 33,206 202 1.18Methanol 89,827 548 3.19Hydroquinone 27,836 170 0.99Carb. TetraCh 80,743 492 2.87Quinaldine 24,132 147 0.86Sodium Hydroxide 12,041 73 0.43Isopropanol 322,303 1,965 11.46Charcoal 2,574 16 0.09HCl (37% w/w) 35,325 215 1.26Nitrogen 180,336 1,100 6.41

Total 1,379,432 8,411 49.05




ocess Simulation

Table D. Cost of raw materials.

Raw Material Price ($/kg) Annual Cost ($) %

Chlorine 3.30 47,961 2.74Na2CO3 6.50 110,870 6.33USP Water 0.10 48,148 2.75NaOH (50% w/w) 0.15 4,981 0.28Methanol 0.24 21,558 1.23Hydroquinone 4.00 111,345 6.35Carb. TetraCh 0.80 64,594 3.69Quinaldine 32.00 772,227 44.07Sodium Hydroxide 2.00 24,082 1.37Isopropanol 1.10 354,534 20.23Charcoal 2.20 5,662 0.32HCl (37% w/w) 0.17 6,005 0.34Nitrogen 1.00 180,336 10.29

TOTAL 1,752,000 100.00

M aterial Balances

Table A displays th e raw ma teria l requirements in kg per year ,

per bat ch, and per kg of main product (MP = purified product).

The plant processes 164 bat ches per yea r. Note tha t a round 49

kg of raw ma teria ls (solvents, rea gents, etc) ar e used per kg of

main product produced. Thus, the product to raw material

ra tio is only 2%, an indicat ion th at large a mounts of wa ste ar e

generat ed by this process.

Process Scheduling, Resource Tracking, and Capaci ty

Util ization

Figure 4 displays the scheduling and equipment utilization

chart for three consecutive batches. The plant batch time isapproximately 81 hours. This is the total time between the

star t of the f i rst s tep of a batch a nd th e end of the last s tep of

that batch. However, since most of the equipment items are

utilized for much shorter periods with in a ba tch, a new ba tch

can be initia ted every 48 hours. Multiple bars on th e sam e line

(e.g., for R -101, R-102, R-103, NFD -101, an d N FD -102) repr e-

sent r euse (sha ring) of equipment by mu ltiple procedures. If

the cycle times of procedures tha t sha re the sa me equipment

overla p, the progra m generat es an error messa ge. White space

represents idle time. The equipment w ith t he least idle time

betw een consecutive ba tches is the ti me (or schedul in g) bottl e-

neck (R-102 in this case) tha t determin es the ma ximum num -

ber of batches per yea r. It s occupancy time (approxima tely 44.2

hours) is the minimum possible time between consecutive

bat ches (a lso known a s Minimum Effective P lant B a tch Time).

This pla nt operat es a round t he clock an d processes 164 bat ches

per year. The simulator also keeps track and displays the

utilization of auxiliary equipment, such as Clean-In-Place

(CIP) and Steam-In-Place (SIP) skids.

Scheduling in the context of a simulator is fully processdriven an d the impa ct of process changes can be a na lyzed in a

ma tt er of seconds. For insta nce, th e impact of a n increase in

batch size (tha t a ffects the dura t ion of char ge, transfer, f i lt ra -

tion, distillation, a nd oth er scale-dependent opera tions) on th e

plant ba tch t ime and the ma ximum number of batches can be

seen insta ntly . Due t o the ma ny intera ct ing factors involved

with even a relatively simple process, simulation tools that

a llow users to describe their processes in deta il , and to quickly

perform w ha t-if ana lyses, can be extremely useful.

Another characteristic of batch processing is the variable

dema nd for resources (e.g., labor, utilities, a nd ra w m at erials)

as a function of time. For instance, Figure 5 displays the

dema nd for P urified Wat er for five consecutive ba tches. The

red l ines represent t he instant aneous dema nd; whereas t he

green line represents the cumulat ive dema nd a nd corresponds

to th e y-a xis on the righ t-ha nd side. The blue line corresponds

to daily deman d (the a veragin g period can be adjusted by the

user). High purit y wa ter is a common potent ial bottleneck in

biopha rma ceutical processes. It is commonly used for multiple

processing steps simulta neously in a ctivities such as ferm en-

ta tion media prepara tion, buffer making, and equipment clean-

ing. If not enough instantaneous (or cumulative) capacity is

a vaila ble, one or more process steps ma y be delay ed, possibly

w ith severe consequences. The gra ph of Figure 5 along w ith t he

ra w ma terial inventory gra ph (not shown here) play a crucial

role in the sizing of utilities for a ba tch ma nufa cturing fa cility.

The program generat es similar gra phs for a ny ra w mat erial ,heat ing a nd cooling utilities, an d electric power consum ption.

In addition to instantaneous demand of resources, the

simulat or provides the means t o track the volumetric utiliza-

tion of all vessels throughout t he ba tch cycle. This a llow s th e

user to track maximum working volumes over time, and

ensure that the minimum st ir volume is always met at any

relevant point in a process. The volume content of vessels is

also used in sizing new vessels and calculating the capacity

utilizat ion of existing vessels.

Economic EvaluationCost a na lysis and project economic evalua tion is importa nt for

a n umber of rea sons. For a new product, if the company la cks

a sui ta ble ma nufacturing facil i ty that ha s ava i lable capa city ,it must decide whether to build a new plant or outsource the

production. Building a new plant is a major capital expendi-

tur e and a lengthly process. To make a decision, ma na gement

must have information on capi tal investment required and

tim e to complete t he fa cility. To out source the produ ction, one

must st ill do a cost a na lysis and use it a s basis for negotia tion

w ith cont ra ct man ufa cturers. A sufficiently detailed computer

model can be used a s the ba sis for th e discussion and negotia-

t ion of the terms. Contra ct man ufacturers usual ly base th eir

estima tes on requirements of equipment ut iliza tion and la bor

per batch, which is information that is provided by a good

model. The simulator performs thorough cost analysis and

Table B. Key economic evaluation results.

Direct Fixed Capital $9.7 millionTotal Capital Investment $10.7 millionPlant Throughput 28,120 kg/yearManufacturing Cost $7.2 million/yearUnit Production Cost $257/kg

Selling Price $500/kgRevenues $14.1 million/yearGross Profit $6.8 million/yearTaxes (40%) $2.7 million/year

Net Profit $5.0 million/year

IRR (after taxes) 34.0%NPV (for 7% discount interest) $22.3 million

Table C. Breakdown of manufacturing cost.

Cost Item Annual Cost ($) %

Facility-Dependent 1,817,000 25.1Raw Materials 1,752,000 24.2Labor-Dependent 2,562,000 35.4Lab/QC/QA 384,000 5.3Waste Treatment/Disposal 724,000 10.0

TOTAL 7,240,000 100.00




Process Simu

project economic eva luat ion calculat ions. It estima tes capita l

as w ell a s opera ting cost. The cost of equipment is estima ted

using built-in cost correlat ions th at a re based on dat a derived

from a n umber of vendors a nd sometimes literat ure sources.

The fixed capital investment is estimated based on total

equipment cost a nd using va rious multipliers, some of wh ich

ar e equipment specific (e.g., inst alla tion cost) wh ile others ar e

plan t s pecific (e.g., cost of piping, building s, etc.). The a pproach

is described in detail in the litera tur e.4 The rest of this s ection

provides a summary of the cost analysis resul ts for thisexample process.

Ta ble B sh ows t he key economic evalua tion results for th is

project. Key assumptions for the economic evaluations in-

clude: 1) a n ew ma nufa cturing fa cility w ill be built a nd dedi-

cated to production of this product; 2) the entire direct fixed

capita l is depreciat ed linearly over a period of 10 yea rs; 3) the

project lifetime is 15 years, and 4) 28,120 kg of final product

will be produced per yea r.

For a plan t of this capacity , the t otal capita l investment is

a roun d $10.7 million. The uni t pr oduct ion cost is $257/kg of

product. Assuming a selling pr ice of $500/kg, t he pr oject yields

an after-ta x Interna l Rat e of Return (IRR) of 34%and a Net

Present Value (NPV) of $22.3 million (assuming a discount

int erest of 7%). B a sed on these resu lts, t his project repr esent s

an a tt ra ctive investment . However, if a mortiza tion of up-front

R&D cost is considered in the economic evalua tion, th e num -

bers cha nge drama tical ly . For instance, a modest a mount of

$10 million cost for u p-front R&D a mort ized over a period of 10

yea rs r educes the IR R to 15.3%. This rein forces the point th a t

R&D expenditures should be considered in estimating and

justifying the pricing of pharm a ceuticals.

Table C breaks down the ma nufacturing cost . La bor is th e

most importa nt cost item a ccounting for 35%of the overall cost.

The progra m estima ted tha t 16 opera tors ar e required to run

the plant a round t he clock supported by four QC/QA scientists.

This cost can be reduced by increasing automation or by

locating the fa cility in a region of low la bor cost. The facility-dependent cost, w hich primar ily account s for the depreciat ion

an d ma intena nce of th e plant , is in t he second position (25%of

total). This is common for high-value products that are pro-

duced in sing le-product, sm a ll facilities. To reduce the impa ct

of this cost, the pha rma ceutical indust ry t ends to use flexible,

multi-product fa cilities, w here a n umber of products a re ma nu-

factured in campaigns throughout the year. Raw materials

also make up a large portion of the manufacturing cost.

Fur therm ore, if we look more closely at t he ra w m a teria l cost

breakdown, it becomes evident that quinaldine and isopro-

pan ol ma ke up by far t he largest portions of this cost - Tabl e D.

Together they a ccount for a pproxima tely 64%of raw ma teria ls

cost. If a lower-priced quina ldine vendor could be found, t he

overa ll manu factur ing cost w ould be reduced significa ntly. Interms of the isopropan ol cost, perha ps the char coa l trea tment

procedure should be studied to determine w heth er the a mount

of this solvent could be reduced. Decreasing the amount of

isopropanol would significantly improve the overall process

economics becau se it w ould decrease t he w a ste disposal costs

as w ell as t he raw mat erial costs . Alternat ively , perhaps some

of the wa ste solvent w hich is current ly being discarded could

be purified a nd r eused. This w ould decrease both disposal costs

and raw material costs .

After a comput er model for the ent ire process is developed,

process simulators can be used to ask and readily answer

“what i f” quest ions and carry out sensi t ivi ty analyses with

respect t o key design va ria bles. In t his example, we looked at

the impact of production scale on unit manufacturing cost.

When a new drug is commercialized, it takes years to fully

penetra te the ma rket . During th at period, production is gradu-ally ramped up to meet demand. If the facility is designed to

meet dema nd a t ful l mar ket penetrat ion, then, in the interim

it is un derutilized. The unit production cost as a function of

production scale in th e interim period is shown in Figure 6. It

wa s a ssumed that at lower production scale the plan t simply

processes few er bat ches per yea r (e.g., tw o per w eek instead of

one every two days) without handling any other products. At

lower annual throughputs the uni t cost increases substan-

tia lly beca use the sa me fixed cost is char ged to a lower a mount

of product.

SummarySimulat ion tools can play a n importa nt role throughout t he

commercializat ion process. In process development, t hey a re

becoming increasingly useful a s a m eans t o a na lyze, commu-

nicate, and document process changes. During the transition

from development to manufacturing, they facilitate technol-

ogy tra nsfer, an d facility selection or const ruction. In ma nufa c-

tur ing, they ass ist engineers in dealing w ith production sched-

uling and planning, throughput a na lysis a nd debott lenecking,

a nd on-going process optimizat ion.

Batch industries such as pharmaceuticals have just begun

ma king significant use of process simula tion to support process

development a nd optimize ma nufacturing. I ncreasingly, uni-

versities ar e incorpora ting t he use of bat ch process simulat ors in

design courses. In the fut ure, w e ca n expect t o see increased use

of th is technology a nd integra tion with other enabling t echnolo-gies, such as advanced process control, computerized batch

recipe generation, and on-line analysis and optimization. The

result will be more robust processes developed faster and at a

lower cost; making higher quality products.

References1. P olast ro , E.T. 1996. Man aging P rimary P rocess Develop-

ment. Pharmaceutical Manufacturing Internat ional , P.A.

B ar na cal (ed), St erling P ublicat ions L td, L ondon, p. 67-70.

2. P etrides, D.P. , Calandr anis , J . , an d Cooney, C.L. , 1996.

Bioprocess Optimization Via CAPD and Simulation for

Figure 6. Unit cost as a function of production scale.




ocess Simulation

P roduct Commercializat ion. Genetic Engineering News,Vol. 16 No. 16, p. 24-40.

3. Hw ang, F. 1997. B at ch P har maceutical Pr ocess Design and

Simulation. Pharmaceutical Engineering, J an ua ry/Feb-

ruary 1997, p. 28-43.

4. P etrides, D., 2001. BioPr ocess Design and Economics. It

will be appear as a chapter of a new biosepar at ions textbook

published by Oxford U niversity P ress in t he spring of 2002.Document ava i lable at ww w .intel l igen.com/downloads/

book_cha pter /book_cha pter .ht m.

AcknowledgementThe au thors grea tly a ppreciat e the initia l work on the subject

mat ter by Douglas Ca meron Ca rmicha el.

About the AuthorsDemetri P. Petrides is the President of

Int elligen, Inc. He h a s extensive experience in

applying simulation tools to model, analyze,

and optimize integrated biochemical, phar-

ma ceutical, a nd s pecialty chemical processes.

Petrides holds a BS from National Technical

U niversity of Athens (Gr eece) an d a P hD fr om

MIT, both in chemical engineering. He is a

member of ISP E, AIChE, an d ACS.

In telligen, In c., 2226 Morse Ave., Scotch P lain s, NJ 07076,

1-908/654-0088, E ma il: dp@int elligen .com.

Alexandros Koulouris is the B usiness De-

velopment Ma na ger of Int elligen Europe. He

is an expert in P rocess Modeling and Automa -

tion. Koulouris holds a B S from Aristotle Uni-

versity of Thessa loniki (G reece) an d MS an d

P hD degrees from MIT, a ll in chemical engi-

neering. Koulouris is a member of AICh E.Intelligen Europe, PO Box 328, 57001

Therm i, Thessa loniki, G reece, + 30 310-498292,

Em a il: a [email protected].

Pericles T. Lagonikos is a Senior P rincipal

En gineer for Schering-P lough Resear ch Insti-

tute (SPRI). He has extensive experience in

development, scale-up, and commercializa tion

of synt hetic pha rma ceutical processes an d ha s

pioneered the use of computer-aided process

design tools at SP RI. La gonikos holds B S a nd

MS d egrees from NJ IT, both in chemical engi-

neering.

SP RI , Chem ical P rocess Technologies, 1011 Morris Ave., U-1-1-1525, U nion , N J 07083, 1-908/820-6572, E ma i l :

[email protected].

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