Thesis work: Mechanistic modelling to understand LDL-c response.


Kerstin Karlsson,
Application deadline:
Job type:
full time
Type of employment:
Västra Götalands län, 
Mölndal kommun
Life Science
Läkemedel, bioteknik & medicinteknik

job description.

How can we use mechanistic modelling to better understand LDL-c response
Thesis work:
30 credits, starting in January 2021

Are you eager to apply your theoretical knowledge in a real life setting? In this master thesis project you can turn theory into practice by using mathematical modelling to understand biological processes.

This master thesis project is offered from clinical pharmacology and quantitative pharmacology (CPQP), which is part of the larger organisation clinical pharmacology and safety sciences (CPSS) within AstraZeneca (AZ). In CPQP our main focus is to to determine which dose should be given to patents. This dose should be efficacious to treat the disease and of course safe to use. Using modelling tools, we integrate available clinical and non-clinical data to quantitatively support decision making during drug development.

Clinical Pharmacology and Safety Sciences is a global R&D department that operates out of several AstraZeneca locations globally: Cambridge, Alderley Park, Gothenburg, Waltham, Gaithersburg and South San Francisco. CPSS has a breadth of capabilities and expertise in Drug Safety, Regulatory Safety, Pathology, Clinical Pharmacology, Quantitative Systems Pharmacology and Animal Sciences. The department supports all AstraZeneca therapy areas from early stage to late stage drug discovery.


Master thesis project description

Quantitatively analyse the dynamic interaction between PCSK9 inhibitor drugs and LDL-c~PCSK9 interaction to understand observed variability in LDL-c response


Atherosclerosis is one of the main causes for mortality and morbidity in the world. It involves an inflammatory response in the walls of arteries due to the accumulation of lipids and fibrous deposits in the vessel walls. It is known that low-density lipoprotein-cholesterol (LDL-c) is one of the main factors in atherosclerotic cardiovascular disease. LDL-c is eliminated via its receptor (LDL-r).  The LDL-r in turn can be degradated by the protein PCSK9. Due to this interdependence of LDL-c and PCSK9, a better understanding of how the interaction of both PCSK9 and LDL-c with the LDL receptor influences the reduction of LDL-c is needed. This will help understand the observed variability in the LDL-c response.

Cell-level kinetic modelling has been proven beneficial in early stages of drug development in the contest of monoclonal antibodies. In this work this approach should be investigated for the modelling of PCSK9 and LDL-c interaction with the LDL-r. Relevant literature data for in vitro data should be identified. Pharmacokinetic models should be build for three PCSK9 inhibitor compounds. The resulting models should be validated against literature data. Additionally, the impact of statins should be explored. Finally, the model sensitivity should be analysed. Potentially, model reduction can be employed to derive simpler models for each of the three studies compounds.

What we offer
In this master thesis, you will be mainly working and obtain supervision at AZ. Co-supervision will be provided by the Fraunhofer-Chalmers Centre for Industrial Mathematics (FCC) and it is possible to work from there for a period of time during this project.  At AZ you will gain insight into project work in early clinical drug development while at FCC you will experience advanced research in applied mathematics. You can expect broad exposure to other functions involved in the drug development process within AZ, gaining an overview of how strategic directions are set and how decisions are made and influenced by clinical pharmacology and quantitative pharmacology during drug development. You will be supervised and closely supported in the building and assessment of a cell-level kinetics model by Jane Knöchel. Co-supervision at FCC will be provided by Mats Jirstrand.


Essential Requirements
  • Background in pharmacology/ engeneering/ mathematics or physics
  • Good programming skills (in R, Matlab or other relevant language) 
  • Good written and oral communication skills and ability to work and communicate across disciplines 
  • Curious and passionate about using quantitative methods to advance medicine 

Desirable Requirements
  • Some background in mathematicall modelling (ode models) 


Randstad Life Sciences is cooperating with AstraZeneca in this recruitment process. We only accept applications through Randstad’s website.

Deadline for application: 2020-10-28, selection and interviews will be ongoing. The position may be filled before the last day of application, therefore, apply as soon as possible.

For more information: Kerstin Karlsson or Linnea Öster

about AstraZeneca AB.

At AstraZeneca, we turn ideas into life changing medicines and strive to continuously meet the unmet needs of patients worldwide. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality. If you are swift to action, confident to lead, willing to collaborate, and curious about what science can do, then you’re our kind of person.

Our Gothenburg site is one of AstraZeneca's three strategic science centers. We thrive in a multinational environment working cross-functionally across the globe with AstraZeneca colleagues as well as academic and industry partners. Our way of life is to foster a working environment that nurtures, collaboration, openness and innovation. Therefore, we have created space for meetings, socializing and relaxation, where spontaneous meetings can give birth to new innovations. The unexpected ideas or thoughts that can come from a chat over something as simple as a cup of coffee or a stroll on our “walk and talk” meeting trail.

AstraZeneca is an equal opportunity employer. AstraZeneca will consider all qualified applicants for employment without discrimination on grounds of disability, sex or sexual orientation, pregnancy or maternity leave status, race or national or ethnic origin, age, religion or belief, gender identity or re-assignment, marital or civil partnership status, protected veteran status (if applicable) or any other characteristic protected by law. AstraZeneca only employs individuals with the right to work in the country/ies where the role is advertised.