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Precision Medicine DTP - An integrated computational-experimental approach to identify and optimise combination drug therapy for non-alcoholic fatty liver disease (NAFLD)


Project Description

Background

Non-alcoholic fatty liver disease (NAFLD) is the commonest cause of chronic liver disease (CLD), affecting 1 in 4 people worldwide. Around 20% of people with NAFLD transition to the progressive form – non-alcoholic steatohepatitis (NASH) – which increases the risk of liver cirrhosis, hepatocellular carcinoma and premature death. However, in the absence of predictive biomarker tests and approved drug treatment options, personalised medicine for NAFLD is not yet possible.

Preclinical proof-of-concept has been demonstrated for numerous compounds, but the results of clinical trials in NASH have been disappointing. Nevertheless, GlobalData forecasts the NASH drug market will reach $18.3bn by 2026. The failure in developing a “silver bullet” against NASH is likely due to its biological complexity. Consequently, the field is now moving towards combination therapy, where multiple compounds are administered in an orchestrated way. NASH offers a rich variety of drug targets that might be additive or synergistic but designing drug combinations “by intuition” is destined to be unsuccessful and costly.

A systematic approach is needed, whereby combinations of targets/drugs are rationally identified and tested in silico, before biological assays are performed. Such an approach requires a model in which to “test” combinations. Despite a wealth of knowledge/data, computational models have seldom been employed in NAFLD and, more importantly, they have never been used in conjunction with experiments to design combination therapies tailored to patients. We have developed valid human liver cell models for studying the pathophysiology of NAFLD in vitro, including the HepaRGTM cell line (Professor Plevris lab/ collaboration with Biopredic International) and stem cell-derived hepatocytes (Professor Hay lab).1,2

Here, we propose a hybrid computational-experimental approach exploiting new bulk-tissue and single-cell hepatic RNA-seq datasets generated in Edinburgh (Ramachandran et al., Nature (In Press)). Professor Fallowfield leads the pan-Scotland SteatoSITE Data Commons which was established to integrate human hepatic transcriptomic data, quantitative digital pathology and clinical information from 1000 NAFLD cases covering the entire disease spectrum from steatosis to end-stage cirrhosis.

Aims

Combining expertise and supervision from Fallowfield/Plevris (translational liver research), Melonascina/Bandiera (bioengineering), close collaboration with Hay (tissue engineering; http://www.crm.ed.ac.uk/people/david-hay), and industry context from Galecto Biotech (iCASE partner), the key objectives are:

To characterize distinguishable stages of NAFLD progression through analysis of clinically-annotated human hepatic transcriptional profiles.
To shortlist in silico drug combinations optimally restoring the physiological expression of genes maximally dysregulated in disease, via query of drug networks (e.g. Connectivity Map (CMap), Library of Integrated Network-Based Cellular Signatures (LINCS)) with gene signatures identified in Objective 1.
To optimise a scalable in vitro human liver cellular model of NAFLD using HepaRG1 or pluripotent stem cell-derived hepatocytes2 for testing of pharmacological candidates.
To define in vitro the treatment schedule of the most promising drug combinations obtained in Objective 2, by combining a multi-input, multi-output control algorithm with a microfluidic platform in which the response of an engineered cell line to treatment is measured in real-time using fluorescence microscopy.3
To establish a human precision-cut liver slice platform for downstream evaluation of drug combination effects/mechanisms.4
Training outcomes

At the end of this project the candidate will have a comprehensive understanding of:

Computational methods to interrogate large gene expression and patient datasets.
Mathematical modelling to develop a computational system to “test” in silico candidate combination therapies.
Cell culture models of NAFLD/liver fibrosis.
Control engineering methods to automatically determine the best stimulus (drug) to correct a pathological phenotype given the gene expression status of cells.
Microscopy and microfluidics to implement the control engineering algorithm.
Use of precision-cut liver slices for evaluating new pharmacotherapies ex vivo.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine

Funding Notes

Start: September 2020

Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualification, in an appropriate science/technology area.
Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,009 (RCUK rate 2019/20) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: View Website

Enquiries regarding programme:

References

1. Lockman KA et al. Oxidative stress rather than triglyceride accumulation is a determinant of mitochondrial dysfunction in in vitro models of hepatic cellular steatosis. Liver Int 2012; 32(7):1079–1092.

2. Lyall MJ et al. Modelling non-alcoholic fatty liver disease in human hepatocyte-like cells. Philos Trans R Soc Lond B Biol Sci 2018;373:1750.

3. Menolascina F et al. In-vivo real-time control of protein expression from endogenous and synthetic gene networks. PLoS Comput Biol 2014;10(5):e1003625.

4. Prins GH et al. A Pathophysiological Model of Non-Alcoholic Fatty Liver Disease Using Precision-Cut Liver Slices. Nutrients 2019;27:11(3).

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