Emerging and Enabling Technologies in Control of ... · technology in the advanced manufacturing of biologic drugs. Proc. of the IEEE Conference on Control Applications, 1505-1515

Emerging and Enabling Technologies in Control of Continuous Bioprocesses

Richard D. Braatz

1

Towards Biomanufacturing on Demand (BioMOD)

Design Requirements Patient

BioMOD capabilities • 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 microfluidics-based platform

• Focus on currently approved therapeutics by FDA (i.e., not drug discovery)

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

Downstream

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics

Purified Product for Quality Testing

Multivariate Model

OK for Release Not OK

☐

Final Product

Sterile Media

Yeast Inoculum

Crude Product Hold Tanks

Upstream

On-line Reactor Control Perfusion

UF/DF Membrane

Waste

Fill

Integrated and Scalable Cyto-Technology (InSCyT) Biomanufacturing Platform

Real-Time Process

Data

Affinity Chromatography

Waste

Column Tanks 1 2

Elute Waste

Polishing Membrane #1 Tanks

1 2

Polishing Membrane #1

Waste

1 2




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 as a 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


Characteristics of InSCyT Control Problem

• Many connected process unit operations

• Mixed continuous & discrete operations

• Multi-product manufacturing plant

• Alignment with regulatory requirements (no off-spec products allowed)

Downstream

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics


Multivariate Model

OK forReleaseNot OK

☐

Sterile Media

Yeast Inoculum


Upstream

On-line Reactor ControlPerfusion

UF/DF Membrane

Waste

Fill

Real-TimeProcess

Data


Waste

Column Tanks 1 2

EluteWaste


1 2


Waste

1 2



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.

Outline

The Biologic Drug Manufacturing System

A Plantwide Control Approach

Example System Components

Plant-wide Control Approach

• Characteristics of InSCyT – Many connected unit operations – Continuous & discrete operations – Multi-product manufacturing plant – Alignment with regulatory

requirements (no off-spec product)

• Approach adapted from the chemical industry – Employing systematic and modular design of plantwide

control strategies for production-scale manufacturing facilities – Using numerical algorithms that can handle nonlinear

continuous and discrete operations and multiple products

8 R. Lakerveld, B. Benyahia, R. D. Braatz, and P. I. Barton. Model-based design of a plant-wide control strategy for a continuous pharmaceutical plant, AIChE Journal, 59:3671-3685, 2013

Downstream

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics


Multivariate Model

OK forReleaseNot OK

☐

Sterile Media

Yeast Inoculum


Upstream


UF/DF Membrane

Waste

Fill

Real-TimeProcess

Data


Waste

Column Tanks 1 2

EluteWaste


1 2


Waste

1 2



Plant-wide Control Approach

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

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

• Evaluate performance in simulations and propose design modifications as needed

• Implement and verify the control system for each UO • Design and verify plantwide control system to ensure

that the product quality specifications are met

Downstream

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics


Multivariate Model

OK forReleaseNot OK

☐

Sterile Media

Yeast Inoculum


Upstream


UF/DF Membrane

Waste

Fill

Real-TimeProcess

Data


Waste

Column Tanks 1 2

EluteWaste


1 2


Waste

1 2




Outline

The Biologic Drug Manufacturing System

A Plantwide Control Approach

Example System Components

Downstream

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics


Multivariate Model


☐

Final Product

Sterile Media

Yeast Inoculum


Upstream


UF/DF Membrane

Waste

Fill


Real-Time Process

Data


Waste

Column Tanks 1 2

Elute Waste


1 2


Waste

1 2



Robust Split-range Control of Bioreactor

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

• Vastly reduces variability by optimized switching


Downstream

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics


Multivariate Model


☐

Final Product

Sterile Media

Yeast Inoculum


Upstream


UF/DF Membrane

Waste

Fill


Real-Time Process

Data


Waste

Column Tanks 1 2

Elute Waste


1 2


Waste

1 2



Chromatography Modeling

• Nonlinear PDEs – Species mass balance

in liquid phase – Adsorbed phase balance

Coupled nonlinear hybrid PDEs solved

using high-resolution finite-volume method

θ∂

−=∂

*( )ii ik qq q

Inlet Outlet

Sorption Convection &

Dispersion

Interstitial volume Porous bead

Pore diffusion

Film mass transfer


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+ ++ +∫


Region of low quality

local extrema

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 a genetic algorithm 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” value based on measured feed concentration using a lookup table


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


☐

Final Product

On-line Reactor Control

Perfusion

Fill

Elute

Real-Time Process

Data

Mixing and Buffer Makeup

Need buffer makeup unit to avoid carrying all possible buffers on the platform

Batch Buffer Makeup “UO” Modeling pH Model Method of reaction invariants

Mass balances are linear equations pH is a static nonlinear function of the invariants

Multicomponent Mixing Rule Requires 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

;

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 micromixers

examples


Example of pH Control in Buffer Mixing “UO”

• Use of in-line micromixers rather than batch mixing tanks allows for process intensification

• pH modeled using the reaction-invariant method • Mass balance modeled using tanks-in-series

Micromixer

pH

Micromixer Model

Fw, ww Fb, wb Fp, wp Fs, ws

11

1 1

1 tin

Fddt V V

−= Fw W w w

b

p

s

FFFF

=

F

t w b p sF FF F F+ + +=

1 )(ti i

i

id Fdt V −= −w ww

[ ]in w b p s=W w w w w

w1 w2 wi

pH ( )nf= w

wn


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 invariant improves performance across pH range


Reaction-invariant Controller: Performance Under pH Model Uncertainty

• No model uncertainty

• 20% error in pKas within pH model

Instability Slow response

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

performance


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


Dual Micromixer Configuration Input signal to generate data for model adaptation

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


Closed-loop Performance Comparison

• Single micromixer without model adaptation

• Dual micromixer with model adaptation

Model adaptation allows for rapid response in spite of poor initial model


Application to Biologic Drug Production • Build first-principles

dynamic models for each unit operation (UO)

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

• Evaluate performance in simulations and propose design modifications as needed

• Implement and verify the control system for each UO • Design and verify plantwide control system to ensure

that the CQAs are met

Downstream

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics


Multivariate Model

OK forReleaseNot OK

☐

Sterile Media

Yeast Inoculum


Upstream


UF/DF Membrane

Waste

Fill

Real-TimeProcess

Data


Waste

Column Tanks 1 2

EluteWaste


1 2


Waste

1 2




Our team Hassan Ait Haddou Monica Amin Paul Barone Catie Bartlett Richard Braatz Lisa Bradbury Danielle Camp Jicong Cao Divya Chandra Michael Costa Steve Cramer Aleksander Cvetkovic Amanda Del Rosario Susan Dexter Juyai-Ivy Dong Chaz Goodwine Jongyoon Han Peter Han William Hancock William Herrington Shan Jiang Pankaj Karande Sojin Kim Sung Hee Ko Ralf Kuriyel Taehong Kwon Rachel Leeson James Leung Lihong Liu Kerry Love Amos Lu

Timothy Lu Huaiya Meng Sylvia Messier Nicholas Mozdzierz Justin Nelson Russell Newton Wei Ouyang Santosh Pande Joel Paulson Pablo Perez-Pinera Oliver Purcell GK Raju Rajeev Ram Nigel Reuel Daniel Salem Kartik Shah Divya Shastry Gajendra Singh Anthony Sinskey Stacy Springs Alan Stockdale Michael Strano KyOnese Taylor Steve Timmick JP Trasatti Nicholas Vecchiarello Ashley Ford Versypt Annie Wang James Woo Di Wu Anna 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

Emerging and Enabling Technologies in Control of ... · technology in the advanced manufacturing of biologic drugs. Proc. of the IEEE Conference on Control Applications, 1505-1515