Scientific Leader/Associate Director, Quantitative Systems Pharmacology Engineer
Site Name: USA - Pennsylvania - Upper Providence, UK - Hertfordshire - Stevenage
Posted Date: Feb 2 2023
The Systems Modeling and Translational Biology (SMTB) department is seeking Quantitative Systems Pharmacology (QSP) Engineer at Scientific Leader/Associate Director level, passionate about using advanced mathematical modeling linked with artificial intelligence and machine learning approaches to inform key decisions in drug discovery and development.
The SMTB group uses mathematical modeling and simulation to address the relationships between drug properties, its disposition and the pharmacological and toxicological response to translate from in-vitro and in-vivo preclinical species to humans. This involves the integration and interpretation of data from many sources to help drive project decisions.
Working in a dynamic, multidisciplinary environment the successful candidate will develop, calibrate and use Quantitative Systems Pharmacology and Toxicology models of the relevant biological processes that link target modulation to clinical outcomes, link them to physiological based pharmacokinetics model (PBPK) for various delivery routes and leverage machine learning approaches (AI/ML) to provide actionable support for project decisions in drug discovery and development. The successful candidate will have the opportunity to lead a team and have positive impact on a broader range of programs and therapeutic areas.
This position is an exciting opportunity to make a positive difference in the lives of patients by focusing the right targets, right drugs and the right patients.
Develop QSP/QST models and apply them to impact the progression of drug development programs
Leverage existing and develop new PBPK models for various delivery routes and modalities and link them with the QSP and QST models to impact the progression of drug development programs
Develop Target Pharmacology Assessments to inform discovery programs
Lead and further develop a team of modelers
Engage with stakeholders across R&D to prioritize modeling work to maximum impact of modeling
Provide both scientific and strategic expertise across multiple therapeutic areas to deliver quantitative support for decision making
Further evolve the modeling strategy and approaches for maximizing the positive impact on projects
Peer review modeling work as appropriate, to ensure high quality modeling standards
Build, expand and refine the modeling capabilities and software platforms of SMTB
Provide consultancy and training resource to increase the overall modeling awareness across R&D
Exceptional collaborative behaviors to build and maintain relationships across R&D
Promote and increase the reputation of the modeling internally within R&D and externally including regulatory agencies
We are looking for professionals with these required skills to achieve our goals:
Advanced degree in chemical, mechanical or biomedical engineering, physics, applied mathematics, scientific computing or related field
Minimum 6 years of experience in applying mechanism based mathematical modeling techniques in pharmaceutical industry
Experience in building, validating and using complex, system level models of biological systems
Experience in numerical analysis focusing on differential equations and parameter optimization.
Experience with statistical and machine knowledge approaches
Experience in scientific computing and programming - in particular Matlab and Simbiology
Experience building and using Quantitative Systems Pharmacology/Toxicology (QSP/QST), PBPK and mechanistic PK/PD models
Experience with drug delivery, absorption, distribution and metabolism, pharmacology and toxicology, PK/PD modeling, physiological based modeling and translational sciences
Experience in influencing decisions and experimental design by using appropriate modeling approaches that integrate all available data
Experience in linking QSP/QST models with mechanistic drug pharmacokinetics
Experience in drug discovery and development and forward thinking with respect to the general application of mathematical models to inform decision making
If you have the following characteristics, it would be a plus:
Experience in linking QSP/T modeling with ‘omics data and big data analytics and machine learning
Evidence of broad knowledge of drug delivery, ADME, pharmacology, toxicology and translational sciences
Evidence of identifying, developing, and applying innovative solutions to scientific and technological problems faced in systems modeling
Excellent written and oral communication skills and the ability to interact effectively with scientists in other disciplines with a positive, collegial, collaborative attitude
Demonstrate collaborative behaviors working and leading in a matrix environment, and working across functions/disciplines
Ability to translate, condense, summarize outcomes of modeling and simulation analyses into information that can be understood and invested by project teams
We’re combining the power of genetic and genomic insights into what causes disease, with the speed and scale of artificial intelligence and machine learning (AI/ML) to make better predictions about who a treatment might work for, and why. We believe this powerful combination of data and technology holds the key to fundamentally transforming medical discovery for the better, improving R&D success rates and shaping how even the most challenging diseases, like neurological conditions and cancer, can be both prevented and treated.
In 2021 we delivered four major product approvals: Cabenuva for HIV, Jemperli for endometrial cancer, Xevudy for COVID-19 and Apretude, our new long-acting medicine for HIV prevention.
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