PhD Studentship: Towards efficient drug development modelling with machine learning

A four-year PhD Studentship in Pharmacometrics and Machine Learning funded by the UKRI EPSRC and GSK is available within the Institute for Global Health. The studentship will commence from 1^st October 2024 onwards, under the supervision of Frank Kloprogge.

Project Title: Towards efficient drug development modelling with machine learning.

Background:

A large proportion of expensive Phase III trials fail. In recent years Phase III failure has declined, in part due to the integration of model-informed decision making in earlier phases. Pharmacometric (pharmacokinetic/pharmacodynamic (PK-PD)) models are used at all stages of pre-clinical and clinical development, but they are based on mathematical and statistical principles dating from the 1970s. Developing these pharmacometric models remains a laborious task where highly qualified staff spend large amounts of time.

Aims:

The overarching aim is to enhance drug effect understanding through improved PK/PD predictions using Machine Learning (ML) and leveraging standardised and centralised big data. The distinctive feature is the integration of nonlinear mixed effects (or multi-level) modelling of time-series (repeated measure) data in combination with ML and prior distributions, something that has seen limited exploration and adds a novel perspective to the field of PK/PD modelling.

Existing Natural Language Processing (NLP) pipelines, developed at UCL, will be used to collate PK/PD parameter prior distributions and ML guided PK/PD prediction algorithms developed in house will be further advanced to enable accommodation of prior distributions.

Environment:

The student will be registered with Frank Kloprogge as primary supervisor at the Institute for Global Health pharmacokinetics-pharmacodynamics group. The student will work with a wider group of PhD students and post-docs, led by secondary supervisor Joseph Standing, at UCL’s Great Ormond Street Institute for Child Health and with computer sciences specialists at UCL. The student will also spend blocks of three months at GSK Stevenage under an industrial supervision team led by Nuria Buil-Bruna for data collection and testing of development ML models on real world industrial regulatory data. GSK is a science-led global biopharma company that aims to unite science, technology, and talent to get ahead of disease together. GSK undertakes research and development in a broad range of innovative products in the primary areas of pharmaceuticals and vaccines. GSK is working to positively impact the health of 2.5 billion people by the end of 2030. For further information, please visit GSK’s website.

The student will learn about all aspects of nonlinear mixed effects modelling of PK/PD time-series data, and the application of ML and embedding of prior distributions within this domain.

This Studentship presents a unique opportunity to conduct supervised research within an academic and industrial environment, and be a part of the research community and an integral part of the exciting and thriving research team.

About you

We are looking for a successful candidate with, or is expected to receive, an upper second-class Bachelor’s degree in mathematics/statistics/engineering/computer sciences or in pharmacy/(bio-)medical sciences (or an overseas qualification of an equivalent standard). Furthermore, the candidate should be familiar with analysis of time series data using mixed-effects models, this specific skill may also have been acquired with a Master’s degree or equivalent work experience.

How to Apply

Please go to the UCL website by clicking the 'Apply' button, above, for full details and follow the steps.

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