Process Modeling and Control Challenges in the Pharmaceutical Industry

Phil Dell’OrcoTeam Leader, Process Safety and Design,

GSK Pharmaceuticals

Outline• Introduction: What do pharma engineers do?• Current Practice: what do we do for modeling and

control now?– Reaction engineering– Crystallization– Drying

• Better Practice: What are we working towards?• Some examples• Needs

– infrastructure– technical

Realities of Pharmaceutical Processes: High Attrition, Few Campaigns

• Numerous early stage candidates which will not survive from Phase I to Phase II/III

• Early objectives (first plant, large scale lab campaigns)

– enable chemistry– not much eng input

• Over 80% of batches in our pilot plants are one-off

• Perhaps only 5-6 total campaigns prior to filing

• Late stage objectives (Phase II supply and beyond)

– develop detailed models– determine critical parameters– understand equipment limitations

Early Toxicology, Phase I (30-50 candidates)

Phase III/NDA (1-3 candidates)

Number of Compounds

Introduction: Pharmaceutical Research and Development•We are a batch process industry

– essentially no continuous processes– why?

• Many products don’t make it to market• Batch reactors offer flexibility over continuous reactors in that

they are adaptable for many products and unit operations • Volumes continuously change through the lifetime of a product.

Again, batch reactors are adaptable• GMP

•We are an industry dominated by chemists– many plant campaigns are one-off; little engineering needed– sometimes difficult to get sufficient eng. Input to projects

Chemistry Dominated IndustryRatio of Chemical Engineers to Chemists Across Industry

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8 GSK (total)GSK (w/o plant)MerckMerck AGPharmaciaNovartisEli Lilly Astra ZenecaBMSDuPont MerckAventisSchering PloughAbbott

1:3 1.5 1.7 1:10 1:12

FTE ratio Chemical Engineers

to Chemists*

In general, chemists dictate the way processes are conceived and executed. Engineering input is often secondary

Tech

nolo

gy T

rans

fer

Process Control D

esign

Plant Configuration (1st make)

Plant Configuration (2nd make)

Plant Configuration (3rd make)

Plant Configuration (4th make)

Plant Configuration (5th make)

Final process

Rte optim

isation

Rte developm

ent

New

route

Rte selectionsupport

Process flowoptimisation

Unit operationoptimisation

Che

mic

al E

ngin

eerin

g A

ctiv

ity

Route Design Chemistry Optimisation Robustness StudiesSynthetic Chemistry Activity

Process Engineering - Input Level to Projects

Process designoptimisation- Heat/mass transfer-crystallization/particle eng.-Separations- Environmental, etc..

P.A.T.

Time

Key Unit Operations- Synthetic Chemicals

• Extractions: In general, every process stage has an average of greater than 1 extraction.

• Distillations (Batch): on average, 1 distillation per stage• Reactions: A given chemical stage has 1-4 chemical

transformations• Crystallizations: Critical for defining physical properties of final

drug product• Filtration: Both final product and intermediates • Drying: Every final product is dried. Probably most empirical

field.• Environmental: solvent recovery, biotreatment, incineration• Chromatography: infrequent currently, but becoming more

common

Key Unit Operations: Biopharmaceuticals

• Fermentations- most critical step. Determines complexity of separations chain and product yield

• Ultrafiltration- removes cellular material from fermentation broth

• Exclusion chromatography- purification by size separation

• Ion chromatography- purification by ionic charge• Dialysis- buffer exchanges

Manufacturing Limitations to Complex Control StrategiesWe are in general “Low Tech” with respect to Reaction Engineering

• Many of our vessels in pilot plant and manufacturing are jack of all trades, but master of none

– nearly 70% of existing GSK vessel capacity is glass lined, radial impeller (e.g., retreat curve, crow’s foot, cryobat)

– little flexibility for difficult mixing problems

• Roughly 50% of our manufacturing capacity relies on crude control

– “banded” control using following options: steam, hot water, coldwater, fridge, jacket hold, jacket drain

• We have little in the way of sophisticated process analytics which might provide opportunity for better control.

Current Practice: Reaction Engineering

Collect kinetic data, often

semi-quantitative

Estimate parameters, or determine

boundary conditions

Determine crude optimum either

graphically, through DOE

Program DCS Sequence to obtain crude

optimum

• Do to large number of early phase compounds, expedient but not perfect methods required (80% engineering)

• We often use sub-optimal data (IR traces, reaction calorimetry, HPLC PAR’s) and model in these domains

• While parameter estimation is often numerically performed (not always!), optimization tends to be crude

• Tools: MatLab, HiQ, DynoChem

“Crude” Modeling: Using ReactIR data to improve a reaction• Rate constant k1 (formation of methylthiol product and rate constant of impurity

formation k2: k1 / k2 ≈ 75– Improve ratio of rate constants by changing reaction temperature.

Time (s)300000 6000 12000 18000 24000 30000

Conc. (mol/L)

1.2

0.0

0.3

0.6

0.9Reactant, predProduct, predImp, predReactant, actProduct, act

• For 100% conversion of starting thiol:

– 1.048 eq MeI– 94.4% Product– 5.6% Impurity

• More MeI only increases di-Methyl impurity %

Ar SH Ar SMe + KI + H2OKOH, MeI

H2O

Desired Practice: Reaction Engineering

Collect kinetic data,

quantitative

Estimate statistically significant

parameters, or determine boundary conditions

Use parameters to define an

optimal operating policy

Program DCS Sequence to

obtain optimum

Improve optimization

based on further experimentation

• Would like to improve level and quality of modeling• Will never be “perfect” due to time constraints, but better

optima, better extrapolation possible• Broaden use of optimization tools?

Reaction Engineering: Biopharmaceuticals• Biopharmaceutical fermentations tend to be very complex systems, with

many potentially important parameters– shear– dissolved gas levels (CO2, O2)– pH– fermentation by-products

• Many side reactions possible for proteins: similar reactive sites (e.g., a monoclonal antibody may have multiple histidine groups)

• Downstream purification operations are extensive: perhaps 10-20 unit operations to get fermentation product into a formulated product

• Single batches typically run for ~ 15 days. End value of batch can be 1 million US for 1000 L fermentor

Biopharmaceutical Modeling Problem: Deamidation of a Protein

][][

][][2][

][*2**][

2

21

10

deAmidAkdt

deAmidBd

deAmidAkmABkdt

deAmidAd

mABkkViabilityNdt

mABdcell

=

−=

−=Neat Protein/Buffer Protein/Buffer w/Cells

• Many unknowns in model: how do various parameters affect cell growth rate constant (different for almost any two experiments)

• How would optimum harvest time vary across natural variability of ko

Need for new process analytical technologies: mechanistic inferences, potentially kinetic information to drive model development. Example: electrospray mass spectrometry, real-time data

Typical Setup for Reaction Monitoring by ES/MS

PUMP #2

PUMP #3

PUMP #1 HPLC VIALS

PUMP #4

MassSpectrometer

100:1 FlowSplitter

TemperatureController

Proposed Reaction Mechanism Using Observed Intermediate Reaction Mechanism proposed to be that proposed by Knoevenagel, rather than that proposed by Hann and Lapworth

CH O

OO

OH

R

H

O

R'N

HC O

OO

OH

RN

+

H HN

H

O+

R'

HH

C O

OO

OH

R

N+

H

R' C O

OO

OH

RR

O

O

R

-H2O

-CO2

N

H

-

O

O

OO

H

RN

H

R'

[A][P]

[A-] [P+] [B] [BP+][A-]

[A-][L][M][N]

m/z 419

m/z 338

m/z 320

m/z 86

Current Practice: Crystallization

Data Collection: solubility, MSZW,

kinetic time scales, crude particle size

measurements

Preliminary Crsytallization

Design

Models: typically simple

Robustness studies around

mixing, seeding, addition rates, etc.

Final Crystallization

Design

• Semi-quantitatively driven– analytical issues: what is particle size?

• Data collection techniques: probably underdeveloped relative to other fields

Solubility Measurement: Example of basic collected data using IR methods. Automated, rapid, accurate, and routine.

0

0.02

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0.1

0.12

0.14

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0.2

200 300 400 500 600 700 800

Time (min)

Solu

te C

onc.

(g/

g so

lven

t)

0

10

20

30

40

50

60

Tem

pera

ture

(C)

Solute Concentration, TolueneSolute Conc., 1:1 EtAc/CyclohexaneSolute Conc., n-butanolTemperature

System calibrated in 0.5 days for 4 solvents; PLS fit to calibration standards. ~ 6 g material required, ~ 4 g recovered. In ~24 hours, solubility mapped out in 4 solvents.

Kinetic Information: Qualitative Desupersaturation Monitoring

• For a typical crystallization design, crystallization monitoring occurs after measuring solubility, MSZW

• Time scales for crystal growth under different seeding, cooling conditions are inferred from turbidity,

Lasentec, and IR signals• From this basis, boundary

conditions are determined• Qualitative Design 0

5000

10000

15000

20000

25000

30000

0 2000 4000 6000 8000

Time (seconds)

Lase

ntec

Cou

nts

(1-5

00 m

icro

ns)

0

20

40

60

80

100

120

Turb

idity

Out

put/T

emp

(C)

Lasentec Counts

Turbidity Output

Temp (C)

Desired Practice: CrystallizationData Collection:

solubility, MSZW, kinetic data, better particle

size, desupersaturation

measurements

Preliminary Crsytallization

Design

Use models for senstivity

analysis on systems

Additional kinetic data, paremeter

estimation

Validate model, improve

parameter estimation

Perform robustness experiments

based on sensitivity

Implementation of control on plant (as opposed to DCS sequence) is possible

due to high value of product

• Need for improved analytical methods for data collection• While we may never be able to model accurately in the domain of FDA

accepted particle size methods, we can use models to indicate sensitivity of process to changes. This would improve robustness of process.

• Due to high value of product at this stage, active control strategies on plant would attract investment

• Some good academic activity in this area (Rawlings, Braatz)

Drying: The forgotten field

• Drying influences product sizes perhaps more strongly than crystallization unit operations, yet there is very little research in drying modeling

• How do we explain/correlate particle breakage in filter dryers (f(mechanical properties, geometry))

• How do we explain agglomeration phenomena?

200

220

240

260

280

300

320

340

360

slurry

ex re

actor

after

drying

after

scree

ning

x90

0.13 m^2 filter dryer, non-sonicatedTumble Dryer, non-sonicated

020406080

100120140160180200

slurry

ex re

actor

after

drying

after

scree

ning

x90

0.03 m^2 filter dryer, non-sonicated0.13 m^2 filter dryer, non-sonicatedTumble Dryer, non-sonicated

Industry Needs: Infrastructure

•More hiring of graduates from control and modelingprograms

– ChE hires tend to be from crystallization, chemistry oriented programs---> tend to have weaker math/modeling skills

• Integration of best practice optimization algorithms, approaches into commercial software (e.g. MatLab)

• Training and implementation sessions from modeling and control community

Industry Needs: Technical (1)• Integration of detailed mixing effects into modeling• Consideration of pharma used process analytical tools

(Raman, IR, Lasentec?, NIR) in development of control schemes: how would we take complex data, reduce it quickly, and allow its feedback?

• Some academic effort into modeling drying processes• Close work with PAT community to improve model

development and control strategies for crystallization and reaction engineering

• New PAT’s to enable better models: electrospray MS? Ultrasound? Etc...

Industry Needs: Technical (2)

• Additional resource into crystallization modelingand control

• Detailed consideration of biopharmaceutical reaction engineering: how to best model cellular systems

Summary• As an industry, we are in general not state of the art in

modeling and control• Pharma is chemistry rather than engineering driven• Significant scale-up constraints due to plant configurations• Significant infrastructure and technical needs that can be

addressed by modeling and control community• Advancement in modeling is relatively straightforward;

advancement in control is longer term