PhD Position F/M PhD Offer – 3 years – Oct 2025 to 0ct 2028 Adaptive phase I-II trial designs for early vaccine development

In clinical research, one of the objectives of early-phase trials (phases I and I/II) is to identify an optimal dose for further investigation in later studies. In vaccinology, the drug investigated is a vaccine, which is not solely defined by the active pharmaceutical ingredient dose but by multiple factors that together form a vaccination strategy. These factors include the vaccination regimen and characteristics of the antigen and adjuvant. In the traditional paradigm, vaccine development relies on empirical decisions which are not based on all available data. This approach is all the more limited given that the strategy – exposure – responses relationship is often insufficiently characterized, possibly resulting in selection of suboptimal vaccination strategies. Furthermore, due to time and budget constraints, it is not possible to test all possible strategies, increasing the risk overlooking a potentially promising one.

To address these limitations, the Model-Based Drug Development (MBDD) approach proposes to add model or rule – based methods at every stage of drug development. Such methods already exist for dose – finding trials in oncology, addressing the methodological challenged brought by new forms of therapy (immunotherapy, targeted therapy). Given the specific challenges of vaccinology and the innovative methods currently being developed in oncology, it appears relevant to apply the MBDD principle to early-phase vaccine trials. In this context, the development of adaptive designs tailored to the specificities of vaccinology represents a key tool for the quantitative evaluation of vaccination strategies and for informed decision-making.

This thesis project is part of the SMATCH consortium, funded by “PEPR Santé Numérique”, and contributes to Task 1.5, which involves the development of new Bayesian methods for early-phase trials in vaccinology. The work carried out will also help provide recommendations for the design and planning of future early-phase vaccine trials, with the aim of optimizing and accelerating vaccine development.

An extensive review of the literature and state of the art will help identify suitable designs for the context of vaccinology. Performance of the evaluated methods will be assessed through simulation using programming languages such as R or Python. In particular, we will estimate the ability of these methods to identify the optimal vaccination strategy.

• The first research axis will focus on assessing the adaptability of existing designs to vaccine-specific issues.
• Subsequent work will address the formal integration of continuous data and preclinical study results.

Different use case will be considered in this thesis thanks to the early-phase vaccine trials conducted in collaboration between the Sistm team and the French Vaccine Research Institute (VRI). Data of completed clinical trials will be available within the labkey datawarehouse of the SISTM team and will serve as a basis for simulation.

The candidate will conduct simulation studies using synthetic data to compare the operating characteristics of different trial designs and adapt them to the vaccine development context.

Required skills:

• Solid background in biostatistics
• Experience with clinical trial methodology, in particular early-phase trial designs Proficiency in statistical programming (R, Python)
• Familiarity with simulation studies and modelling
• Interest in interdisciplinary research in health
• Ability to work independently and in a team
• Strong scientific writing and communication skills in English

Additional appreciated skills:

• Knowledge of Bayesian inference or adaptive trial design
• Familiarity with medical applications

• Subsidized meals
• Partial reimbursement of public transport costs
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

2300€ per month before taxs