PhD Studentship: Leveraging Precision Medicine to Predict an Adverse Drug Reaction

An estimated 970 million people worldwide have a lived experience of a MH condition, with depression (D) and anxiety (A) higher than psychosis (P), a figure which is increasing. Medication intended to alleviate mental distress is very often accompanied by adverse drug reactions (ADRs) which can be stigmatising, depressing and dangerous. This, coupled with the risks posed by depression, anxiety, or psychosis (DAP), can make involuntary treatment more likely. ADRs are not the sole reason for discontinuing a MH medication however ADRs of psychiatric medication are known to have significant negative impacts on physical health and quality of life, and further patient involvement is essential to understand the links between side effects and concordance more fully. ADRs are not always predictable from clinical trials. Long latency and novel effects especially in populations requiring MH treatment where patients may find it more difficult to raise concerns about medication with prescribers.

The research project will curate and analyse evidence from suspected ADR reports, drug pharmacology and pharmacoepidemiology databases. ML-algorithms will be trained and applied to identify patterns and connections. The power of this research approach will guide regulatory and clinical decision-making for patient benefit and suggest alternative strategies that improve the patient’ experience – to avoid the most troubling ADRs experienced at a personalised level.

The researcher will screen known national and international databases for ADR reports and pharmacological interactions. Based on the unbiased NLP encoded ADRs we will deploy our ML-algorithm to integrate open sourced, fully anonymised, patient data that is available on ADRs (Yellow Card Scheme and others) and pharmacological data (ChEMBL and others). Other data sources are available e.g., WHO, FDA, and EudraVigilance registries to further expand upon the findings into global outcomes will enable the generalisation and scalability of the findings. A subset of anonymised patient health records, assay, imaging, and genome sequencing data will be overlaid from genomic datasets to enable pharmacoepidemiology links to be identified. There will be a focus on predictive ADR signal detection and confirmation of new signals identified.

For more information, please see the webpages below: · Main MIBTP page - https://warwick.ac.uk/fac/cross_fac/mibtp/· Birmingham MIBTP page - https://www.birmingham.ac.uk/research/activity/mibtp· University of Birmingham Press release - Major investment in doctoral students to support next generation of researchers - University of Birmingham · UKRI press release - : Over 4,700 newly funded post-graduate places in UK universities to create new generation of engineers and scientists - GOV.UK

Funding notes:

A high 2:1 or 1st in a relevant discipline (chemical sciences, pharmacy, biomedical sciences, epidemiology, or computer science) is a requirement.

Applicants must follow the onscreen instructions within the application portal https://sits.bham.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=FR610G&code2=0004:

• within the ‘Research Details’ tab, enter the title of the project they are applying for, along with the name of the project’s supervisor
• within the ‘Funding Information’ tab (standard projects), choose ‘University of Birmingham advertised scholarship’ and ‘BBSRC Midlands Integrative Biosciences Training Partnership (MIBTP) DTP'
• submit their CV as part of their application (they should have this file ready to upload).

References:

Sandhu, D., Antolin, A.A., Cox, A. R., Jones, A.M. Identification of different side effects between PARP inhibitors and their polypharmacological multi-target rationale. Brit. J. Clin. Pharmacol. 2022, 88, 742-752. https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bcp.15015.

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