Decrease of animal use for the human risk assessment is one of the pivotal goals of the EU. Every time a new chemical enters the EU market around 2500 animals undergo toxicological testing. This is to provide information on how the new substance can influence human health. With the thousands of new chemicals entering our market annually, we also urgently need in silico prioritization methods.
In certain scenarios, computational methods can surrogate responses of animals and the quantitative adverse outcome pathway (qAOP) concept is one of such examples*. qAOP has a huge potential for regulatory applications in chemical risk assessment in near future.
While there is an increasing number of qAOP models being proposed, their curation remains poor, and automated extraction methods are needed. In addition, the computational predictive tools lack frameworks to guide their development, and failed to be user-friendly. This is important as the future users will be risk assessors without computational skills.
The main objectives of this computational master thesis are following:
- Assess the existing published qAOP models (probabilistic and mechanistic) and propose future computational data collection for qAOP (for instance neurodegeneration)
- Develop user friendly software tool for modeling qAOPs (i.e. shiny app or pipeline in snake make or nextflow, etc)
- Based on internal expertise at NIPH, build an example of qAOPs. (i.e. effect of pesticides on neurodegeneration)
With this thesis you will get an overview of how the qAOP concept has advanced and you will push the boundaries of toxicity risk assessment in the near future. You will be working with publicly available data and using HPC.
Skills and personality traits, we need: No prior knowledge to biology or chemistry is needed. Python, R or another programming language is needed to progress with the task of this thesis. Natural Language processing, machine learning, graph theory, and SQL is advantageous but not necessary. Creativity and goal-oriented person. Care for the environment, animals, and other humans is a must.
Working environment: You will be working partly at the NIPH (Folkehelseinstitutet) that got the prize for best communicating institution in 2020, and contributed to risk assessment during SARS-Cov-2 pandemia. We do care about a nice working atmosphere and students well-being and their progress.
Supervisors: Main supervisor: Marcin Wojewodzic, Researcher at Norwegian Institute of Public Health (https://www.fhi.no/) and Norwegian Cancer Registry. Email for interview: maww@fhi.no
* Literature
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7261727/