Quantitative Modeling · Regulatory Science · Data Science & Software Engineering
PharmD and PhD, I have built an atypical profile at the intersection of multiple disciplines: pharmaceutical sciences, modeling, regulatory affairs, and software development. My core territory is rare diseases — where methodological complexity is at its peak and margins for error are nonexistent.
My experience spans a wide range of the drug development continuum, from molecular synthesis and 3D cell-based assays to CTD submissions at the EMA and meetings with the FDA. In between, I have worked on real-world data studies, PKPD modeling, Bayesian clinical trial designs with MAP priors, EMA scientific advice, orphan drug designations (EMA & FDA), and the co-development of patient preference instruments for regulatory decision-making.
My modeling approach is agnostic — empirical, mechanistic, Bayesian, or machine learning: I select the tool the problem demands and the data allows. Beyond analysis, I design operational software solutions: competitive intelligence pipelines on clinical trial registries, drug repurposing knowledge graphs (Python · Neo4j), and indication prioritisation scoring tools.
I am a co-inventor of an oncology patent and author of several peer-reviewed publications — academic rigor I carry into everything I do.
| Regulatory Science | CTD/eCTD submissions, Clinical Overviews, EMA Scientific Advice, Orphan Drug Designations (EMA & FDA), FDA meetings |
| Quantitative & Pharmacometric Modeling | PKPD modeling, longitudinal mixed-effects models, Bayesian trial designs with MAP priors, nlmixr2, rxode2 |
| Data Science & Machine Learning | Python, classification/decision-tree models for composite endpoints, real-world data studies |
| Software & AI Engineering | Neo4j knowledge graphs for drug repurposing, generative AI pipelines for regulatory drafting, decision-support tools |
| Rare Disease Research | Horizon Europe consortia (INVENTS, TheRaCil), digital twins & in silico trials, patient-preference quantification |
- Lead Pharmaceutical Data Scientist at THELONIUS MIND — own scientific and quantitative delivery on client engagements, from ML-driven endpoint definition (accepted by the EMA in Scientific Advice) to CTD repurposing dossiers combining pharmacometric modeling with EMA/ICH regulatory strategy.
- Head of Science at OrphanDev, an F-CRIN rare-disease research network — leading its transformation into an operational think tank, and serving as task lead on the Horizon Europe consortia INVENTS and TheRaCil.
- Built a Python/Neo4j knowledge-graph pipeline for rare-disease drug repurposing, with automated Orphan Drug Designation screening, and prototyped a generative AI pipeline for regulatory drafting.
- Co-authored an EMA Scientific Advice in which a machine-learning-derived composite co-primary endpoint was accepted.
- Contributed to discussions informing France's 4th National Rare Disease Plan (PNMR4).
| Degree | School |
|---|---|
| PhD, Chimie médicale et pharmaceutique | Aix-Marseille Université |
| DES, Innovation Pharmaceutique et Recherche | Aix-Marseille Université |
| PharmD, Pharmacy | Aix-Marseille Université |
- Artificial intelligence in drug repurposing for rare diseases: a mini-review — Frontiers in Medicine, 2024
- Quinoxaline derivatives: Recent discoveries and development strategies towards anticancer agents — European Journal of Medicinal Chemistry, 2024
- Antibiotic-induced neurological adverse drug reactions — Therapies, 2024
- Co-inventor, patent WO2025132831A1 — N-heteroaryl derivatives and uses thereof for treating cancer (2025)
