Continuous Biopharmaceutical Manufacturing: Modeling ... · → need for process modeling/design/controls ... integrated process control and ... Braatz_NA17CEDEC1_Continuous-Biopharmaceutical-Manufacturing-Modeling-Design

ISPE Biopharmaceutical Manufacturing Conference

4 – 6 December 2017San Francisco, CA

1

Continuous Biopharmaceutical Manufacturing: Modeling, Design,

and Fully Automated Control

Richard D. Braatz

Outline

• Background, Current State, Technology Directions

• A Biomanufacturing-on-Demand Platform

• Comments on the State-of-the-Art in Control

• Closing



2

Background: Biopharmaceuticals Manufacturing

• Products derived from biological organisms for treating or preventing diseases

• Hundreds of approved products on the market

• Over 7000 medicines in development

• Usually any off-spec material requires rejection of entire lot

Deloitte (2016). 2016 Global Life Sciences Outlook: Moving Forward with Cautious Optimism. Deloitte LLP, Boston, MA.Informa (2016). Pharmaprojects Pharma R&D Annual Review 2016. Informa PLC, London, United Kingdom.

USD in billions

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

500

400

300

200

100

0

% non‐bio drug candidates in pipeline

% bio drug candidates in pipeline

95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16

1009080706050403020100

Current State of Biopharmaceutical Manufacturing

• Sequence of batch unit operations

• Upstream: cell culture and harvest

• Downstream: purification using chromatography, filtration, diafiltration

• Bioreactor has T, dissolved oxygen, and maybe pH controls

• Minimal modeling/controls

Kelley, B. (2009). Industrialization of mAb Production Technology: The Bioprocessing Industry at a Crossroad. MAbs, 1, 443-452.Shukla, A. A., Thömmes, J. (2010). Recent Advances in Large-Scale Production of Monoclonal Antibodies and Related Proteins. Trends in Biotechnology, 28, 253-261.



3

Product manufacturing:Allston, MA USA

Product QC: Haverhill, UK

Fill/finish: Waterford, IE

Cerezyme patients distributed worldwide

Manufacturing Biologic Drugs Today

Motivation: Biopharmaceuticals Manufacturing

• Over 7000 medicines in development

• Industry driven to look for new technology to increase flexibility and reduce costs

continuous flow

novel process designs

Deloitte (2016). 2016 Global Life Sciences Outlook: Moving Forward with Cautious Optimism. Deloitte LLP, Boston, MA.Informa (2016). Pharmaprojects Pharma R&D Annual Review 2016. Informa PLC, London, United Kingdom.

USD in billions

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

500

400

300

200

100

0

% non‐bio drug candidates in pipeline

% bio drug candidates in pipeline

95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16

1009080706050403020100



4

Moving Towards Continuous-flow Operations

http://www.drugdevelopment-technology.com/contractors/contract_research/biovian/biovian1.htmlhttp://www.genengnews.com/gen-articles/novasep-morphs-into-full-service-company/3048/

• Move manufacturing from batch to integrated continuous operations with minimal holdup in between

• Continuous flow enables

higher process flexibility, consistency, and volumetric capacity

lower equipment cost and operational complexity

tighter specifications on product quality

→ need for process modeling/design/controls

Technology Directions

• Increased understanding and optimization of each unit operation and component

• Fully automated microscale technologies for high-speed continuous process development

• Availability of plug-and-play modular unit operations with integrated process control and monitoring systems to facilitate straightforward deployment in the laboratory

• Dynamic mathematical models for unit operations and entire plant to support process development and plant-wide control

• Easy-to-use model-based control technologies for optimizing startup, changeover, and shutdown



5

Design of Control Systems Based on “Virtual Plant”

• Constructed from first-principles models wherever possible, grey-box models where necessary

• Highest complexity models used for the invention and optimization of process designs and development

• Lower complexity plant-wide model runs in parallel with fully automated process operations, for process control and quality and equipment condition monitoring

Micromixer

pH

Outline




• Closing



6

Towards Biomanufacturing on Demand (BioMOD)

• Enable flexible methodologies for genetic engineering/modification of microbial strains to synthesize multiple and wide-ranging protein-based therapeutics

• Develop flexible & portable device platforms for manufacturing multiple biologics with high purity, efficacy, and potency, at the point-of-care, in short timeframes, when specific needs arise

• Include end-to-end manufacturing chain (including downstream processing) within a small-scale fluidics-based platform

J.C. Love, Towards making biologic drugs on demand, 4th Int. Conf. on Accelerating Biopharmaceutical Development, January 25, 2015

Design Requirements Patient

Downstream

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics

Purified Product for Quality Testing

Multivariate Model

OK forRelease

Not OK

☐

Final Product

Sterile Media

Yeast Inoculum

Crude Product Hold Tanks

Upstream

On-line Reactor ControlPerfusion

UF/DF Membrane

Waste

Fill

Integrated and Scalable Cyto-Technology (InSCyT) Biomanufacturing Platform

Real-TimeProcess

Data

Affinity Chromatography

Waste

Column Tanks 1 2

EluteWaste

Polishing Membrane #1 Tanks

1 2

Polishing Membrane #1

Waste

1 2



J.C. Love, Towards making biologic drugs on demand, 4th Int. Conf. on Accelerating Biopharmaceutical Development, January 25, 2015



7

Biologic Drugs Produced

Human growth hormone Interferon–α2b

Used for treatment of growth disorders

Used for treatment of cancers and viral infections

Rationale for Pichia pastoris asa Microbial Host for Biosimilar Products

Advantages from a regulatory perspective• Many products on market or in late-stage development

• Reduced risk for viral contamination

• Human-like post-translational modifications (folding, glycosylation, etc.)

Technical benefits• Genetically stable organism

• High-density cultivation (>70% biomass)

• High yields of secreted proteins (up to ~15 g/L)

• Limited host cell protein (HCP) profile (eases burden on downstream)

• Amenable to freeze-drying



8

Plant-wide Control Approach

• Build first-principles dynamic modelsfor each unit operation (UO)

• Design control system for each UOto meet “local” material attributes

• Evaluate performance in simulationsand propose design modificationsas needed

• Implement and verify the control system for each UO

• Design and verify fully automated plantwide control system to ensure that the product quality specifications are met

A.E. Lu, J.A. Paulson, N.J. Mozdzierz, A. Stockdale, A.N. Ford Versypt, K.R. Love, J.C. Love, R.D. Braatz (2015). Control systems technology in the advanced manufacturing of biologic drugs. Proc. of the IEEE Conference on Control Applications, 1505-1515.

Plant-wide Control Approach

Characteristics of InSCyT• Multi-product manufacturing plant• Continuous & discrete operations• All of the process characteristics

articulated by Lee/Weekman 1976, Foss 1983, and more

• No SS & must align with regulatory requirements (no off-spec product)

Approach adapted from the chemical industry• Employing systematic & modular design of plantwide control strategies

for large-scale manufacturing facilities (Stephanopoulos/Ng, JPC 2000)• Using algorithms that can handle nonlinearities, uncertainties,

distributed states, time delays, unstable zero dynamics, constraints, and mixed continuous-discrete operations




9

Downstream

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics


Multivariate Model

OK forRelease

Not OK

☐

Final Product

Sterile Media

Yeast Inoculum


Upstream


UF/DF Membrane

Waste

Fill


Real-TimeProcess

Data


Waste

Column Tanks 1 2

EluteWaste


1 2


Waste

1 2



Computational fluid dynamics (CFD) simulations for greater insight into bioreactor operation

CAD drawingSteady‐stateflow profiles

Bioreactor

• Solved for steady-state flow profiles within the reactor to understand mixing and species heterogenity



10

Bubble Size Distribution Predicted by Multiphase Simulation

Plot shows average size of bubbles, red being the largest

Simulation identifies regions of high turbulence dissipation for bioreactor design modifications



11

Robust Split-range Control of Bioreactor

• Designed hybrid robust controllers that switch during each manipulated variable change

• Reduced variability by optimized switching


Downstream

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics


Multivariate Model

OK forRelease

Not OK

☐

Final Product

Sterile Media

Yeast Inoculum


Upstream


UF/DF Membrane

Waste

Fill


Real-TimeProcess

Data


Waste

Column Tanks 1 2

EluteWaste


1 2


Waste

1 2





12

Chromatography Modeling

Nonlinear PDEs

• Species mass balance in liquid phase

• Adsorbed phase balance

ci

tot

ci

z

1

Pe

2 ci

z2 qi

pore

qi

kads ,iciQ

i kdes,iqicsalt i

*( )ii ik q

qq

Inlet Outlet

SorptionConvection &Dispersion

Interstitial volume Porous bead

Pore diffusion

Film mass transfer


Chromatography Modeling

Nonlinear PDEs

• No steady-state, always in transient mode

• No measurements in the column, large time delay to exit measurement

• Switch between multiple sets of nonlinear PDEs

• Practice is to run open loop or switch on exit concentrations

Inlet Outlet

SorptionConvection &Dispersion

Interstitial volume Porous bead

Pore diffusion

Film mass transfer




13

Hybrid Simulation of Chromatography Operation

• Four-stage bind-and-elute operation mode

# Stage Time Protein Conc. Salt Conc.

1 Load tl cp,i cs,l

2 Wash tw 0 cs,w

3 Purge tp 0 cs,e

4 Elute te 0 cs,e

# Specified by upstream units

# #

→ Sensors at entrance and exit only, i.e., feedback limited→ Open-loop optimal control can be used to determine

“optimal” operation based on first-principles model


Multiple Performance Objectives

Performance Objective

Description

Purity

Recovery

Exit Concentration

Productivity

0

,0 0

,

,

e

e e

pdt elution

pdt elution cont elution

t

t t

c dt

c dt c dt

,

,

0

et

pdt elution

pdt loal d

c dt

t c

,0

e

pdt elution

t

e

c dt

t

,0

e

pdt elution

l w p e d

tQ c dt

t tt t t




14

Region oflow quality

localextrema

Global optimum

Multiobjective Optimization

• Purity, recovery, exit concentration, and productivity are competing objectives

• Look to maximize exit concentration & productivity while constraining purity to be >99% and recovery to be >90%

• Multiobjective problem solved using stochasticoptimization to avoid being trapped in low-quality local extrema


Pareto Optimality (Tradeoff Curve)

Dilution Factor

Curves are optimal tradeoff for various feed concentrations

Select optimal values in a lookup table from measured feed concentration




15

Column Tanks 1 2

Downstream


Sterile Media

Yeast Inoculum


Upstream

1 2 1 2




Waste Waste Waste


UF/DF Membrane

Waste

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics


Multivariate Model

OK forRelease

Not OK

☐

Final Product

On-line Reactor Control

Perfusion

Fill

Elute

Real-TimeProcess

Data

Buffer makeup unit to avoid carrying all possible buffers on the platform


Batch Buffer Makeup “UO” Modeling

pH Model (Waller, Kravaris, ...)

Method of reaction invariants

Mass balances are linear equations

pH is a static nonlinear function of the invariants

Multicomponent Mixing RuleRequires estimation of effective ionic radius from data

Single Component Conductivity First-principles Model

Flowrates Fin are the manipulated variables

Semi-batch Tank Reaction Invariant Species Conservation

dV

dt Fin

dN

dt Finwin ;



16

Many Systems and Control Problems Appear in the Buffer “UO”

• Dynamics are highly nonlinear

• Slow mixing and uncertain parameters

• Model-based control can overcome these issues by predicting the effect of inputs on the process

– Need good parameter values based on process data

– Need to develop robust control strategies that operate well under uncertainty

Design of experiments for conductivity

pH control in micromixersexamples


Example of pH Control in Buffer Mixing “UO”

• Use of in-line micromixers allows for process intensification

• pH modeled using the reaction-invariant method

• Material balances modeled using tanks-in-series

Micromixer

pH

Fw, ww

Fb, wb

Fp, wp

Fs, ws

w1 w2 wi wn

11

1 1

1 tin

Fd

dt V V F

wW w

T

w b p sF F F F F

t w b p sF FF F F 1 )(ti i

i

id F

dt V w ww

[ ]in w b p sW w w w w pH ( )nf w




17

Direct PID vs Reaction-invariant PID Control

• Direct PID

• Reaction-invariant PID

Slow response

Marginally stable

Fast response

Slow response

PID based on the reaction invariants improves performance across pH range


Reaction-invariant Controller:Performance Under pH Model Uncertainty

• No model uncertainty

• 20% error in pKas within pH model

InstabilitySlow response

Caveat: Reaction‐invariant control requires accurate models to achieve good

performance




18

Model Adaptation of Reaction-invariant Control via Dual Micromixer Configuration

Micromixer

pH

Micromixer

pH

First micromixer used to excite system to enable accurate parameter estimation of pH model

Second micromixer used to perform corrections to the output of the first micromixer and effect setpoint tracking


Micromixer #1

Micromixer #2

Dual Micromixer Configuration

Input signal to generate data for model adaptation

First micromixer used to excite system to enable accurate parameter estimation in pH model

Second micromixer used to perform corrections to the output of the first micromixer and effect setpoint tracking




19

Closed-loop Performance Comparison

• Single micromixer without model adaptation

• Dual micromixer with model adaptation

Model adaptation allows for rapid response in spite of high uncertainty


Outline




• Closing



20

Comments on the State-of-the-Art in Modeling, Control, and Monitoring Technology

• The best commercial plant simulation software handles nonlinearities, time delays, unstable zero dynamics, constraints, mixed continuous-discrete operations, and some uncertainty analysis methods (e.g., Si, MC)

• Distributed states facilitated by various math tricks, e.g., moment analysis, transform methods, characteristics

• More sophisticated uncertainty analysis tools can be wrapped around or integrated into such software

• MPC and process monitoring technology are available

• Currently developing methods to simplify tuning and improve performance of model-based control and monitoring systems

Outline




• Closing



21

Summary

• Described some research challenges and opportunities in the manufacturing of biopharmaceuticals

• Discussed control technology within the context of a fully automated biopharmaceuticals-on-demand platform

• Model-based control solutions are a mix of nonlinear feedback & feedforward strategies

• Described feedforward controls and an adaptive control strategy motivated by a specific application need

• Described state-of-the-art in process control technology

Closing Comments

• Small-molecule pharma systems engineering went from very little in the late 1990s to becoming well established

• I believe that applications in biologic manufacturing will mirror the rise in small-molecule pharmaceuticals that occurred over the last 20 years, but will be faster

D2

D1

R2

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M1 M2 S1

C1 C2

W1

LC LC

C3 C4

LC

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S3

S4W2

M3 M4

M5

S5S6 E1 CS

CAT

A

B

S2

S1

S3

S1

PU1

PU2

PU4

PU3

S1

S1

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C

D

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EX1

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LC

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FCsp

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spsp

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sp

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1CATA B I

1 2 BPI C I P

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Bequette, Comp Chem Eng, 20:S1583–S1588, 1996

Togkalidou+, AIChE J, 47:160‐168, 2001

Lakerveld+, Org Process R&D, 19:1088‐1100, 2015

Simon+, Org Process R&D, 19:3‐62, 2015



22

AcknowledgmentsHassan Ait HaddouMonica AminPaul BaroneCatie BartlettLisa BradburyDanielle CampJicong CaoDivya ChandraMichael CostaSteve CramerAleksander CvetkovicAmanda Del RosarioSusan DexterJuyai-Ivy DongChaz GoodwineJongyoon HanPeter HanWilliam HancockWilliam HerringtonShan JiangPankaj KarandeSojin KimSung Hee KoRalf KuriyelTaehong KwonRachel LeesonJames LeungLihong LiuKerry LoveAmos LuTimothy Lu

Huaiya MengSylvia MessierNicholas MozdzierzJustin NelsonRussell NewtonWei OuyangSantosh PandeJoel PaulsonPablo Perez-PineraOliver PurcellGK RajuRajeev RamNigel ReuelDaniel SalemKartik ShahDivya ShastryGajendra SinghAnthony SinskeyStacy SpringsAlan StockdaleMichael StranoKyOnese TaylorSteve TimmickJP TrasattiNicholas VecchiarelloAshley Ford VersyptAnnie WangJames WooDi WuAnna Zhang

This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) and SPAWAR Systems Center Pacific (SSC Pacific) under Contract No. N66001‐13‐C‐4025. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA) and SPAWAR Systems Center Pacific (SSC Pacific).

Documents

Continuous Biopharmaceutical Manufacturing: Modeling ... · → need for process modeling/design/controls ... integrated process control and ... Braatz_NA17CEDEC1_Continuous-Biopharmaceutical-Manufacturing-Modeling-Design