Offer Description
Transformation of Advanced Medicines Manufacture (TRANS-AM)
Gene therapies are a type of advanced medicine that represent exciting possibilities to treat and potentially cure many life-altering and life-threatening diseases.
They also represent a huge growth area with several hundred currently in various stages of development.
Many of these are based on using recombinant Adeno-associated virus (rAAV) as a delivery vector.
If even a small portion of them reach the market, current manufacturing and characterisation technologies in the industry will struggle to deliver them economically and safely for patients.
This research programme aims to provide innovative solutions to help overcome the manufacturing challenges associated with producing rAAV and brings together experts from two leading Irish institutions, NIBRT and UCD, and APC-VLE Ltd.
– a leading Irish industrial partner – in an exciting five-year collaborative programme.
The successful candidate will be based at NIBRT/UCD and will also spend some time on placements at the industrial partner.
We are seeking ambitious, inquisitive and innovative individuals to join our multidisciplinary research team.
As part of a dynamic and collaborative environment you will contribute to breakthrough work that supports the scalable, efficient, and high-quality production of viral vectors – laying the foundation for next-generation treatments for genetic diseases.
If you are excited by the intersection of biotechnology, process development, and therapeutic innovation, we would love to hear from you.
Project 3: PAT Applications for Monitoring AAV Production
Supervisors:
Dr Jonathan Bones
This project aims to develop process analytical technologies (PAT) for monitoring AAV production during cell culture.
PAT is widely used for monitoring chemical synthesis and has gained traction for monitoring manufacturing processes of biopharmaceuticals such as recombinant proteins and monoclonal antibodies.
PAT for AAV remains a challenge due to the size and complexity of AAV and their lower expression rates when compared to more standard recombinant proteins.
We will explore a variety of analytical methods – inline, online and at-line – facilitated by automated sampling, as potential PAT approaches for monitoring AAV production in small-scale bioreactors.
As part of the project you will run these reactors to generate AAV and combine this with the deployment of various spectroscopic methods or other integrated analytical techniques.
As these measurements are expected to generate considerable volumes of data, you will also develop strategies for the management and analysis of the data using various statistical approaches with a view to understanding how we can ultimately use PAT for advanced process control.
Key responsibilities include:
Running small-scale bioreactor experiments to produce AAV.
Implementing and troubleshooting inline, online, and at-line analytical techniques.
Collecting and managing large data sets generated from PAT measurements.
Applying statistical analyses, including multivariate statistics and data modelling, to interpret PAT data.
Collaborating with colleagues across biology, chemistry and data analytics to optimise process performance.
Candidate profile:
Applicants should hold or expect to receive an upper 2.1 or 1.1 degree in a relevant discipline such as analytical science, analytical chemistry, bioanalytical science, biotechnology or chemical engineering.
Experience with statistical analysis, particularly multivariate statistics and data modelling, would be an advantage.
Training will be provided in additional biology aspects required for successful completion of the programme.
The ability to work as part of a team, an interest in industrially focused research, motivation to learn new skills and excellent written and oral skills are also desirable.
Annual stipend of €25,000 plus fees for four years.
How to apply: Send a covering letter, a motivation statement and a CV outlining why you would like to do a PhD to ******.
Include copies of your academic references.
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