Post-doctoral position in Translational Pharmacology/Pharmacometrics

Drug-disease-immune system interactions and dose rationale for drug combinations in tuberculosis. Background: Tuberculosis is the leading cause of death by an infectious disease worldwide. Standard tuberculosis treatment is based on a combination regimen of four drugs that were all developed more than 60 years ago. Treatment lasts for at least six months and, in the case of resistance to the standard drugs, can be as long as two years. The current drugs are inefficient by today’s standards and a new, faster-acting and safer treatment is required to reduce the length of therapy and to overcome the threat of drug-resistant strains. Until now, the development of new drugs has been slow and their incorporation into tuberculosis treatment regimens conducted in a sequential manner. The European Regimen Accelerator for Tuberculosis (ERA4TB) consortium is expected to revolutionize the way in which tuberculosis treatments are developed, enabling the evaluation of the efficacy of drug candidates and combinations simultaneously. Moreover, ERA4TB aims to reduce the time required for the development of new tuberculosis treatment regimens by ensuring data integration and improved decision-making. To this purpose, advanced quantitative clinical pharmacology principles are being applied to translate preclinical findings to humans, providing a robust dose rationale and optimised clinical trial designs for further evaluation of novel regimens in the clinic. A post-doctoral research fellow position in translational clinical pharmacology has been created to support the activities of an ambitious consortium including European and global organisations responsible for the development and evaluation of novel candidate molecules for the treatment of tuberculosis. The primary objective of the research programme will be to establish the pharmacokinetic-pharmacodynamic (PKPD) properties of drug candidates progressing into clinical development. Different approaches will be applied to ensure: 1) systematic translation of pharmacokinetic and PKPD concepts from in vitro and in vivo systems to humans and 2) optimisation of clinical study protocols (e.g. first-time-in-humans, early bactericidal activity). Required skills: In addition to enthusiasm, motivation and independent thinking, candidates must have working knowledge of pharmacokinetic-pharmacodynamic modelling and simulation, including prior experience with advanced statistical principles (nonlinear mixed effects modelling, Bayesian statistics, clinical trial simulations). Strong programming skills in R language, RStudio and NONMEM are essential. Willingness to learn and integrate knowledge from across different therapeutic areas (i.e., immunology and pharmacology). Behavioural attributes such as teamwork, accurate listening, strategic thinking, along with very good oral and written English language skills will be critical for the successful implementation of the project. Candidates should have completed a PhD in a relevant discipline (quantitative clinical pharmacology, pharmacometrics, population pharmacokinetics, PKPD modelling, PBPK modelling), and have published their research in a peer reviewed journal. Applications from candidates with a degree in Medicine, Bioengineering, Clinical Pharmacology, Biostatistics and Pharmaceutical Sciences will be prioritised. The successful candidates will be co-located with the modelling team at the CNR (Consiglio Nazionale delle Ricerche) in Rome, Italy and at the Clinical Pharmacology & Therapeutic Group at UCL in London. Applicants should be resident in one of the 27 EU state members, or be an EU, EEA, or UK national. Further details on the application procedures can be obtained by email. Please contact Prof O. Della Pasqua ([email protected]<mailto:[email protected]>) including a short CV. Deadline for applications: 15thMay 2024. Kind regards, Salvatore D’Agate Clinical Pharmacology & Therapeutics School of Life and Medical Sciences University College London E-mail: s.d’[email protected]<mailto:s.d%e2%80%[email protected]>