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Design of a PAT-controlled pharmaceutical drug product dilution process
We aim to design and complete a proof-of-concept study for a pharmaceutical drug product dilution process, utilizing advanced model-predictive process control. The advantage of this setup is to decrease manufacturing lead time and reduce operational execution failure rates.
Keywords: process analytical technology, advanced process control, feedback loop, model-predictive control, pharmaceutical manufacturing, drug product dilution process,
A pharmaceutical drug product dilution process involves the addition of a variety of raw materials (API – active pharmaceutical ingredient, WFI – water for injection, excipients) in order to obtain a final homogeneous product that contains all ingredients in the right quantities and state. During this process, manual sampling and off-line analysis is routinely performed at the onset and completion of the process to assure correct execution. We propose the usage of online sensors to determine - in real-time - the critical process parameters and product quality attributes throughout the duration of the process, with the aim to design an advanced process control algorithm with an integrated unit operation feedback loop that is able to execute the manufacturing process in an automated way without human intervention. This approach allows to reduce manufacturing lead time and effort, and decrease the process execution failures due to real-time monitoring. This is a novel process design for the pharmaceutical industry. The challenge is to incorporate the effect of the different timescales of the various unit operation steps into the algorithm and enable model-predictive control. Our recent work has implemented the online sensors to be used in the control algorithm, upon which this work will build further.
**Location**
This project is a collaboration between the ETH Zurich automatic control lab and Janssen and will take place at Janssen’s Schaffhausen campus.
The JNJ posting is here: https://jobs.jnj.com/jobs/2105904495W?lang=en&src=JB-10280
A pharmaceutical drug product dilution process involves the addition of a variety of raw materials (API – active pharmaceutical ingredient, WFI – water for injection, excipients) in order to obtain a final homogeneous product that contains all ingredients in the right quantities and state. During this process, manual sampling and off-line analysis is routinely performed at the onset and completion of the process to assure correct execution. We propose the usage of online sensors to determine - in real-time - the critical process parameters and product quality attributes throughout the duration of the process, with the aim to design an advanced process control algorithm with an integrated unit operation feedback loop that is able to execute the manufacturing process in an automated way without human intervention. This approach allows to reduce manufacturing lead time and effort, and decrease the process execution failures due to real-time monitoring. This is a novel process design for the pharmaceutical industry. The challenge is to incorporate the effect of the different timescales of the various unit operation steps into the algorithm and enable model-predictive control. Our recent work has implemented the online sensors to be used in the control algorithm, upon which this work will build further.
**Location** This project is a collaboration between the ETH Zurich automatic control lab and Janssen and will take place at Janssen’s Schaffhausen campus.
The JNJ posting is here: https://jobs.jnj.com/jobs/2105904495W?lang=en&src=JB-10280
1. The student will be introduced to the concepts of pharmaceutical drug product manufacturing and get familiar with the current and future control strategies.
2. The student will help build a lab-scale setup representative of the large-scale manufacturing operation to serve as a test system throughout the project.
3. Utilizing the available test setup, the student will develop different control algorithms for the automated dilution process and help perform live experiments to demonstrate their feasibility and determine critical process parameters and propose process improvements.
4. The student will use the results to analyze and compare tradeoffs between the different designs and propose an optimal control strategy to be implemented in large-scale manufacturing.
1. The student will be introduced to the concepts of pharmaceutical drug product manufacturing and get familiar with the current and future control strategies. 2. The student will help build a lab-scale setup representative of the large-scale manufacturing operation to serve as a test system throughout the project. 3. Utilizing the available test setup, the student will develop different control algorithms for the automated dilution process and help perform live experiments to demonstrate their feasibility and determine critical process parameters and propose process improvements. 4. The student will use the results to analyze and compare tradeoffs between the different designs and propose an optimal control strategy to be implemented in large-scale manufacturing.
ETH Zurich: Alisa Rupenyan (rupenyan@inspire.ethz.ch) and Dominic Liao-McPherson (dliaomc@ethz.ch)
Janssen: Raf De Dier (rdedier@its.jnj.com)
**How to apply**
Please send your resume/CV and transcript of records in PDF format to rdedier@its.jnj.com, dliaomc@ethz.ch, and rupenyan@inspire.ethz.ch
ETH Zurich: Alisa Rupenyan (rupenyan@inspire.ethz.ch) and Dominic Liao-McPherson (dliaomc@ethz.ch)
Janssen: Raf De Dier (rdedier@its.jnj.com)
**How to apply**
Please send your resume/CV and transcript of records in PDF format to rdedier@its.jnj.com, dliaomc@ethz.ch, and rupenyan@inspire.ethz.ch