PhD Candidate in Computational Chemistry and Molecular Modelling_2022

Florent Di Meo

UMR-1248 INSERM

CBRS – Université de Limoges

2, rue du Pr Bernard Descottes

87000 Limoges

Documents:

• Resume
• Cover Letter
• Contacts or recommandation letter(s)

Funding Source:

To be obtained – Univ. Limoges – PhD School

The pre-selected applicants (2 or 3) will be evaluated by an independent committee which will rank PhD project and candidates

Reference papers:

Bioarxiv, 2022, 10.1101/2022.01.10.475390

Liv. Int., 2021, 41, 1344-1357

FEBS Journal, 2020, 287, 909-924

Pharmacol. Res., 2018, 133, 318-327.

Pharmacol. Res., 2016, 111, 471-486.

Clin. Pharmacol. Ther., 2018, 890-899

Nat. Rev. Drug. Discov., 2010, 215-236

CONTEXT and BACKGROUND

Recent improvements of supercomputer capacities and reliability of the theoretical models have significantly broadened the field of applications of molecular modelling. Key sites of drug disposition and/or effects can now be modelled by means of molecular dynamics (MD) simulations, supporting understanding of pharmacokinetics (PK), pharmacodynamics (PD), as well as pharmacogenetics (PGx).

In organ transplantation, the transplant community learned to do better with the same drugs, owing to the lack of new immunosuppressive drugs. PK tools for improved dose individualization, risk scores and biomarkers were proposed, and, to some extent, adopted by transplant physicians. However, an unexplained variability in immunosuppressive drug (ISD) response and toxicity still remains. This is likely due to the combination of low penetrance or rare variability factors (e.g., drug-drug interactions – DDI –low-penetrance polymorphisms).

There is thus a need for a deeper understanding of the drug effect and the local concentration at the drug target locations, for which drug-membrane crossing plays a key role. In silico pharmacology may help to model, understand and predict drug-membrane crossing events involved in the relationship between systemic and local PK. Besides, PGx drives both PK and PD. The investigation of PGx/PK and PGx/PD interactions would benefit from the atomic and dynamic description of drug transport, using MD simulations.

In this context, the proposal will focus on human membrane transporters that are involved in local PK of drugs used in organ transplantation (e.g., ISD, antivirals, antibiotics) and located in the kidney and the liver. The present project opts in the field of computational and structural pharmacology in which in silico transporter models will be built mimicking physiological conditions to investigate (i) dynamics of the transport cycle, (ii) the influence of different polymorphisms or rare mutations, and (iii) DDI/PGx interactions.

THESIS PROJECT

The development of atomic-based in silico pharmacological models of membrane transporters may represent a step forward in the understanding of drug PK and PD by adding a new size- and timescale to PK/PD and PGx tools. The main objective is to provide a comprehensive structural overview of protein-mediated drug membrane transport to support experimental and clinical data obtained within the host unit in the context of transplantation.

The PhD student will focus on a limited number of ABC and SLC transporters (namely ABCC1/MRP1, OAT3 and OATP1B1) in order to decipher their structures, functions and interactions with xenobiotics used in organ transplantation. She or He will perform molecular dynamics simulations of membrane proteins taking advantage from recent advances in machine learning techniques to support structural biology (e.g., AlphaFold 2) and computational chemistry.

By means of MD simulations, supported by ML techniques, the candidate will:

• Provide reliable dynamic pictures of OAT3 and OATP1B1 membrane transporters in situ, considering interactions with lipid bilayer
• Explore the transport cycle of ABCC1/MRP1 by means of biased MD simulations
• Investigate drug-transporter interactions as well as the structural impact from relevant single nucleotide polymorphisms.

CANDIDATE PROFILE

• Applicants must hold a Master’s degree in chemistry, biochemistry, biophysics or a related field.
• Experience in molecular modelling is required, MD simulations being considered as a strong advantage
• Experience in basic scripting is required, the use of python being considered as a strong advantage

Furthermore:

• Background in physical chemistry is considered as an advantage
• Knowledge in machine learning is considered as a strong advantage
• Good oral and written communication skill in English is recommended if not mandatory given the presence of international students in the group

Applications and informal queries should be addressed in English to Florent Di Meo (). Interested candidates should send their CV, a cover letter describing their research interests. The applicants can provide recommendation letters or the name of former supervisors.