Ozan

Ümraniye, Istanbul

Turkiye, 34764

ozan_ozgen@outlook.com

Clinical research and business development professional with expertise in bioavailability and bioequivalence studies, clinical trials, regulatory compliance, contracts, and stakeholder management. I also apply data science and programming to healthcare and pharmaceutical research problems.

I currently work as a Senior Clinical Research Specialist at Nobel İlaç, supporting preclinical and clinical activities, regulatory documentation, and collaboration with CROs and research partners in international settings. My recent work also includes clinical workflow automation and a retrieval-augmented generation system for regulatory guidelines.

I hold an MSc in Medical Biotechnology from Akdeniz University, where I developed machine learning models for predicting the anticancer properties of peptides. I am pursuing a PhD in Gene and Cell Therapy and have completed the coursework period.

My research interests include:

Clinical Development of Advanced Therapeutics
Bioinformatics and Machine Learning in Drug Discovery
Technology Transfer (Preclinical & Clinical)
International Regulatory Guidelines

selected publications

Pharmacokinetic Profile and the Bioequivalence Study of Macitentan FCT in Healthy Volunteers

Ahmet Ozan Özgen, and others

Nobel Medicus, 2026

Preprint

Bib

@article{ozgen2026macitentan,
  title = {Pharmacokinetic Profile and the Bioequivalence Study of Macitentan FCT in Healthy Volunteers},
  author = {Özgen, Ahmet Ozan and others},
  journal = {Nobel Medicus},
  publisher = {Nobel Medicus,},
  year = {2026},
  note = {Preprint},
}

In Silico Investigation of Anticancer Properties of Antimicrobial Peptides

Ahmet Ozan Özgen

2024

Abs Bib

A machine learning model—specifically a random forest using amino acid and dipeptide features—was developed to predict anticancer activity in 10,000 antimicrobial peptides (AMPs). The model showed 79.5% accuracy on its validation set, and 1648 peptides demonstrated favorable anticancer properties. Additional analyses included clustering, physicochemical property predictions, and 3D homology modelling. These findings suggest potential dual effects of AMPs in cancer therapy.
@article{ozgen2024anticancer, title = {In Silico Investigation of Anticancer Properties of Antimicrobial Peptides}, author = {Özgen, Ahmet Ozan}, year = {2024}, publisher = {Council of Higher Education,}, }
Enzymatic Targets for Drug Discovery Against Alzheimer’s Disease

Ahmet Ozan Özgen

In Drug Discovery for Alzheimer’s Disease: Recent Advances, 2023

Abs DOI Bib

Alzheimer’s Disease (AD) is a neurodegenerative disease. The disease itself is progressive and full recovery from it isn’t achievable yet. There are several hypotheses asserted (Cholinergic hypothesis, Amyloid hypothesis etc.) to explain the mechanisms behind the disease. Also, many targets have been identified for possible therapeutics and from these targets, numerous drug candidates have been evaluated in clinical trials. Unfortunately, most of these trials failed due to the enigmatic nature of this disease. Currently, there are 7103 targets associated with Alzheimer’s disease listed in the Open Targets platform where 1240 of them are enzyme-related. In this chapter, enzymatic targets of the AD have been reviewed, and those claimed to have disease modifying effects were selected and presented according to their clinical significance.
@incollection{ozgen2023enzymatic, title = {Enzymatic Targets for Drug Discovery Against Alzheimer's Disease}, author = {Özgen, Ahmet Ozan}, booktitle = {Drug Discovery for Alzheimer's Disease: Recent Advances}, publisher = {Bentham Science,}, year = {2023}, url = {https://doi.org/10.2174/9789815136142123010009}, doi = {10.2174/9789815136142123010009}, }