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Application of a System Model for Continuous Manufacturing of an Active Pharmaceutical Ingredient in an Industrial Environment

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Abstract

Purpose

In pharmaceutical manufacturing, understanding and quantifying how process conditions impact product quality is pivotal to guaranteeing process profitability with sustained product yield. We describe an integrated system model for a commercial-scale continuous manufacturing process of a high-value active pharmaceutical ingredient (API) and its use to optimize process conditions to maximize yield with assured product quality.

Methodology

Global sensitivity analysis (GSA) was used to assess different process parameters’ impacts on API yield in order to guide the selection of decision variables for yield optimization. We then formulated different scenarios for optimization within approved process parameter ranges to propose optimal process conditions to increase API yield.

Results

Within the considered parameter space, varying only key initial starting material and feed reagent mass fractions within their allowed parameter ranges showed potential for + 0.2% yield improvement while varying all process parameters could allow + 0.4% yield improvement.

Conclusions

The general modelling framework to guide control strategies, highlight process improvements in silico, and reduce experimental burden can be applied to multiple pharmaceutical products across different manufacturing modalities and operating modes.

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Availability of Data

Original datasets and full process details used to construct the system model are not disclosed due to IP constraints. The focus of the study is the application of system modelling to a continuous API manufacturing process. The methodology can be applied to similar processes.

Code Availability

The dynamic flowsheet model is implemented in gPROMS FormulatedProducts v2.0.1 on an Intel® Core™ i-78665 CPU @ 1.90 GHz processor with 16.0 GB of RAM. Codes are not provided due to IP constraints.

Abbreviations

API:

Active pharmaceutical ingredient

BC:

Boundary condition

CI:

Confidence interval

CPM:

Continuous pharmaceutical manufacturing

CQA:

Critical quality attribute

DAE:

Differential algebraic equation

GSA:

Global sensitivity analysis

HPLC:

High-performance liquid chromatography

HTF:

Heat transfer fluid

KPI:

Key performance indicator

LSA:

Local sensitivity analysis

NLPSQP:

Nonlinear sequential quadratic programming

NMR:

Nuclear magnetic resonance spectroscopy

R&D:

Research and development

RTD:

Residence time distribution

API:

Active pharmaceutical ingredient

BP:

By-product

CO2 :

Carbon dioxide

Imp.:

Impurity

Int.:

Intermediate

SM:

Starting material

-:

Latin letters

A :

Heat transfer area [m2]

Bo :

Bodenstein number [–]

C i :

Concentration of species i [mol m−3]

C i feed :

Feed concentration of species i [mol m−3]

C p :

Isobaric specific heat capacity of reaction mixture [J kg−1 K−1]

C p,HTF :

HTF isobaric specific heat capacity [J kg−1 K−1]

D :

Diffusion coefficient [m s−1]

d R :

Reactor diameter [m]

E a ( j ) :

Activation energy of reaction j [J mol−1]

k j :

Rate constant of reaction j [varying units]

\(k_{0}^{({j)}}\)  :

Pre-exponential factor of reaction j [varying units]

K eq ( j ) :

Equilibrium constant of reaction j

L :

Length of reactor [m]

m i :

Mass of species i [kg]

\(\dot{m}\)  :

Inlet mass flowrate of reaction solution [kg s−1]

\(\dot{m}\)  HTF :

HTF mass flowrate [kg s−1]

N c :

Number of components [–]

N inlet :

Number of inlets [–]

N param :

Number of parameters [–]

N r :

Number of reactions [–]

N s :

Number of samples [–]

N traj :

Number of trajectories [–]

R :

Universal gas constant [J mol−1 K−1]

r j :

Rate of reaction j [mol m−3 s−1]

S T :

Sobol’ total sensitivity index [–]

T :

Reaction temperature [K]

t :

Time [s]

t f :

Process duration [s]

T feed :

Temperature of reaction mixture entering reactor [K]

T HTF :

HTF temperature [K]

T ref :

Reaction reference temperature [K]

U :

Overall heat transfer coefficient [W m−2 K−1]

u :

Mean velocity of reaction mixture in reactor [m s−1]

V :

Mixture volume [m3]

V HT :

Mixture volume in the hold-up tank [m3]

w i in :

Mass fraction of component i in inlet stream [–]

w i out :

Mass fraction of component i in outlet stream [–]

z :

Axial position [m]

-:

Greek Letters

α ij :

Order of component i in reaction j [–]

ΔH r ( j ) :

Heat of reaction j [J mol−1]

θ :

Optimization decision variable vector [varying units]

θ lb :

Lower bound on decision variable [varying units]

θ ub :

Upper bound on decision variable [varying units]

ν ij :

Stoichiometric coefficient of component i in reaction j [–]

ρ HTF :

HTF density [kg m−3]

ρ sol :

Reaction mixture density [kg m-3]

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Acknowledgements

The authors would like to thank the following individuals at GSK involved in different aspects of the process’ development and guidance in the system model’s requirements. Project: Peter Shapland, Hannah Robinson, Malcolm Berry, Flavien Susanne, Irene Areri, Peter Clements, Chris Clarke, Peter Hamilton. Commercial Plant Operation: Lesley Wong, Clarence Wong, Gary Breen, Kah Kah Toh, Eneritz Fernandez-Puertas, Krishna Gudena, Stephanie Ng, Hui Ren Seah, Rui Xian Lim. Kinetics: Mark Hughes, Andrew Richards, Augustine Ochen, Sabri Ukuser, Simon Watson.

Funding

The study was funded by a strategic Digital Design capability project at GlaxoSmithKline (GSK).

Author information

Authors and Affiliations

  1. GlaxoSmithKline (GSK), Park Road, Ware, SG12 0DP, UK

    Samir Diab, Gabriele Bano & Simeone Zomer

  2. GlaxoSmithKline (GSK), Gunnels Wood Rd, Stevenage, SG1 2NY, UK

    Charalampos Christodoulou, Neil Hodnett, Antonio Benedetti & Markus Andersson

  3. Present: Nexeon Ltd., 136 Park Dr, Eastern Ave, Abingdon, OX14 4SB, UK

    Markus Andersson

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  1. Samir Diab
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Correspondence to Samir Diab.

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Diab, S., Bano, G., Christodoulou, C. et al. Application of a System Model for Continuous Manufacturing of an Active Pharmaceutical Ingredient in an Industrial Environment. J Pharm Innov 17, 1333–1346 (2022). https://doi.org/10.1007/s12247-021-09609-7

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