Dr. Yordan Yordanov holds a Master’s degree in Pharmacy and a Ph.D. in Toxicology from the Medical University of Sofia, where his early research focused on in vitro studies of different substances, including drug-delivery nanoparticles and airborne particles. He is currently teaching Toxicology and Pharmacology to pharmacy students at Medical University - Sofia, Bulgaria and is part of the division "Artificial Intelligence in Healthcare" at Research Institute of Innovative Medical Science InnoMedSci at Medical University - Sofia.
Over time, Dr. Yordanov’s interests have evolved toward image analysis, omics data analysis, computational toxicology and machine learning, aiming to connect in vitro data with biological mechanisms and predictive modeling.
His research experience includes international collaborative work with the University of Siena on in vitro studies, a bioinformatics-focused collaboration with Dr. Thomas Mohr from Medical University of Vienna, University of Vienna and ScienceConsult - DI Thomas Mohr KG, as well as a siRNA therapeutics-focused work in collaboration with Medical University of Graz. He participates in COST Actions Stratagem, Precision-BTC-Network and CardioPharmaGenet where his interests are on the application of data-driven strategies for risk assessment.
Mechanistic Exploration of Parkinson’s Disease by Integrating Co-expression Networks with AOPs
Yordan Yordanov*1,2, Thomas Mohr3, Asmaa A. Abdelwahab4
1 Department of Pharmacology, Pharmacotherapy and Toxicology, Faculty of Pharmacy, Medical University – Sofia, Bulgaria
2 Research Institute of Innovative Medical Science, Medical University – Sofia, Bulgaria
3 ScienceConsult - DI Thomas Mohr KG, Enzianweg 10a, 2353 Guntramsdorf, Austria
4 Edelweiss Connect GmbH, Science Department, Basel, Switzerland
*Presenting author
Understanding the mechanistic underpinnings of Parkinson’s disease (PD) requires bridging molecular data with systems-level toxicological knowledge. We developed a prototype workflow that integrates Weighted Gene Co-expression Network Analysis (WGCNA) of in vitro transcriptomic datasets from dopaminergic neurons with mechanistic data from Adverse Outcome Pathway (AOP) resources.
Using WGCNA, we identified co-expression modules, highlighting network perturbations associated with neurodegeneration. These modules were then mapped onto AOP molecular initiating events and key events curated from AOP-Wiki using the AOP-explorer online tool.
The resulting integrated framework enables mechanistic visualization of gene clusters within the AOP context, linking transcriptomic signatures to established neurotoxic outcomes. This tool aims to support hypothesis generation, mechanistic anchoring of omics data, causal inference and the design of New Approach Methodologies for neurotoxicity testing and Parkinson’s disease modeling.