1987-1992 B.Sc.(Hons). Cell & Immunobiology, University of Aberdeen, Scotland UK

1993-1996 PhD. Yamanouchi Pharmaceuticals Co. Ltd, Yamanouchi Research Institute, Oxford, England

1996-1999 Postdoctoral Research Fellow. University of Washington, Seattle, USA

1999-2004 Postdoctoral Research Fellow. Beatson Institute for Cancer Research, Scotland, UK

2004-2010 Principal Scientist. Advanced Science and Technology Laboratory. AstraZeneca, England, UK.

2010-Current. Principal Investigator, Edinburgh Cancer Research, Institute of Genetics and Cancer, University of Edinburgh.

2015-Current. Professor of Drug Discovery, University of Edinburgh.

2018-2024 Director of Translation, College of Medicine and Veterinary Medicine, University of Edinburgh.

2022-Current. Associate Director Cancer Research UK Scotland Centre.

2024-Current. Director of Science, Edinburgh Cancer Research, Institute of Genetics and Cancer, University of Edinburgh

Key Interests

Drug-Discovery; High-content screening, network pharmacology and Artificial Intelligence/Machine Learning applications; tumour invasion & metastasis; 3D/organotypic assays; Drug combinations.

In 2010 Neil left AstraZeneca to join the Edinburgh Cancer Research Centre as Principal Investigator of the Drug Discovery group. Here Neil intends to leverage his previous industrial and academic experiences to develop new innovative approaches to the discovery and development of effective medicines that significantly impact upon patient lives

Research Interests

It has become apparent that both large pharmaceutical companies and biotechnology or academic enterprises have become very effective at generating new chemical entities or biological molecules that are highly potent and specific to single targets, providing a plethora of new therapeutic molecules that can be explored across disease. Undoubtedly these approaches have contributed to some remarkable clinical success stories, however, conventional drug discovery, on average, takes approximately 12 years and $1.2billion to develop a new drug with high attrition rates observed in late-stage clinical development. This is inefficient and not sustainable. This situation is even more acute for complex diseases of unmet medical need were target biology is poorly understood such as complex heterogenous cancers (e.g. Glioblastoma, Gastro-oesophageal, sarcomas and pancreatic cancers) and neurodegenerative diseases.

Our approach: A major risk for all anti-cancer therapies is inherent or adaptive resistance. No single molecular event drives continued proliferation and tumour progression, and redundancy in signalling pathways or target mechanism limits the efficacy of mono-therapies. Addressing such challenges requires a new approach to maximizing and predicting clinical efficacy of novel therapies through understanding their influence upon cancer cell signalling networks, particularly the ‘driver’ pathways, and identifying how best to collapse the robustness of such networks. The challenge is to predict which target classes, candidate drugs, or drug combination regimes, provide maximal efficacy or duration of response in defined patient cohorts -this is the goal of the Edinburgh Cancer Research Drug Discovery Group. Our laboratories are equipped with the latest kinetic (IncuCyte-S3 and SX5®) and High-content (ImageXpress-confocal Ht.AI and “fast light sheet”) imaging platforms, fully integrated with plate handling robotics, barcode sample tracking and bespoke image-analysis and machine learning workflows. We utilize commercial and our own bespoke software platforms “Phenonaut” to integrate high dimensional phenotypic data with transcriptomic and post-translational pathway network data to understand drug mechanism-of-action and drug resistance. Our research has contributed to the discovery and translation of several agents into clinical trials including NXP900 currently in phase 1 oncology trials https://www.clinicaltrials.gov/study/NCT05873686  and drug repurposing clinical trials in motor neuron disease: https://www.clinicaltrials.gov/study/NCT04302870.

In collaboration with academic researchers, clinicians, pathologists, computational scientists and industry partners we shall continue to build and apply our Drug Discovery Platform to multiple projects.