The position of Pharmacometrician is responsible for pharmacometrics (including pharmacokinetics and PK/PD) aspects within specific projects at the pre-clinical stage as well as those across all clinical stages at CSL. The position of Pharmacometrician is also responsible for conducting non-compartmental analyses, creating NONMEM datasets, and developing RShiny apps to facilitate Pharmacometrics activities. The Pharmacometrician has strong pharmacokinetic/pharmacometrics knowledge, advanced R/SAS programming skills, and appropriate modelling and simulation skills, and will work in close collaboration with various non-clinical, Research, and Clinical R&D functions. The position will undertake hands-on modelling and simulation work on selected crucial projects. The position will work in parallel to Clinical Pharmacologists, Pharmacometricians, Clinical Program Directors, and other experienced Clinical Development Team (CDT) colleagues, and will also assist in interactions with external consultants or with academic institutions.
Responsibilities will include:
Contributes to or undertakes all relevant pharmacometric analyses: non-compartmental PK, PK/PD analyses, population PK and PK/PD, modelling and simulation methods, meta-analyses, disease and systems biology modelling on a project level within R&D
Develop RShiny applications to support activities across Clinical Pharmacology and Translational Development
Construct NCA and NONMEM datasets, generate report tables, figures, and listings from NCA or popPK analyses for regulatory submissions, and maintain regulatory documentation (eg metadata for CDISC and non-CDISC submissions)
Provides input for standard operation procedures best practices in pharmacometrics for the Early Development function
Contribute to the implementation of model-based drug development strategies assigned R&D projects as appropriate.
With guidance, implements the use of innovative analytical methods in pharmacometrics/modelling and simulation to integrate knowledge of pharmacokinetics, pharmacodynamics, patient characteristics and disease states in support of optimal study designs
Provide pharmacometrics contributions to regulatory documents including Investigator Brochures, Labelling and those required for regulatory meetings and regulatory filings. Provides resolution of pharmacometrics queries from regulatory agencies, taking a lead role in writing and reviewing responses to regulatory queries
Implement all necessary hardware and software systems, together with relevant quality processes, for the conduct of in-house pharmacometrics activities
If required, provide technical support to Research in the development of preclinical PK/PD model required for early clinical development strategy, as well as utilizing Pharm-Tox data appropriately to make clinical dosing decisions.
Develop external alliances with consultants, contract organizations and academic institutions to ensure timely conduct of all pharmacometrics deliverables for projects, to continually keep abreast of the science, as well as development of more junior colleagues in the discipline
Position Qualifications and Experience Requirements
Doctoral degree with demonstrated expertise in pharmacometrics and strong quantitative skills (e.g. proven experience in most of the following areas: population and PK/PD modelling, R/SAS programming, mechanistic modelling, systems biology, species scaling methodologies and derivation of first-in-human doses, disease modelling and meta-analyses).
A minimum of 1 year experience of pharmaceutical industry (or relevant) experience for the Pharmacometrician position, specifically contributing to the pharmacometrics aspects of clinical drug development.
Knowledge of advanced PK and PK/PD, specifically in one of the following core sub-specialty: Systems Pharmacology, advanced Exposure-Response Models, Disease Models, Physiological-Based Pharmacokinetic Models
Working knowledge and experience of software such as NONMEM, SAS, S-Plus, R, WinNonlin, and other server-based data processing and modelling tools
Knowledge and application of statistics, random effects modelling, mixed effects modelling, data mining, population PK/PD analyses and modelling (including nonlinear models), Bayesian methods, clinical utility indices, and Monte-Carlo simulation
Ability to understand and implement all aspects of pharmacometrics needs across a variety of disease areas
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