Dr Alan M Jones

Supervisor Details

Contact Details

School of Pharmacy, University of Birmingham

Scientific Background

Research Interests

Dr Alan Jones is an Associate Professor in Medicinal and Pharmaceutical Chemistry in the School of Pharmacy, within the School of Health Sciences. His research interests focus on the application of synthetic and medicinal chemistry to solve challenges at the intersection of biology and drug discovery for patient benefit.

Key topics include:

The development of advanced, novel drug molecules for the treatment of leukaemia.
The investigation of electrochemical methods as an enabling technology to identify drug metabolites
The use of chemical, pharmacological and adverse drug reaction large datasets to identify and predict drug liabilities in the patient populations
The use of their commercialised reagent, tributylsulfoammonium betaine (TBSAB) for advanced sulfation studies (https://www.sigmaaldrich.com/GB/en/product/aldrich/911992 and https://store.apolloscientific.co.uk/product/tributylsulfoammonium-betaine)

Supervision Style

In three words or phrases: supportive, organised, goal-driven.

Provision of Training

I prefer to take responsibility for your technical training at first, leading to more independence later.

Progression Monitoring and Management

I like to be kept up to date, and will expect to see evidence of method development/data generation results on a weekly basis. I am here for advice and guidance to help you reach the goals you set for yourself.

Communication

I am happy to discuss any issues that are impacting your ability to fulfil your potential or our expectations.

PhD Students can expect scheduled meetings with me:

In a group meeting

At least once per fortnight

In year 1 of PhD study

At least once per week

In year 2 of PhD study

At least once per week

In year 3 of PhD study

At least once per week

Meetings will be face to face, and I am usually contactable for an instant response on every working day, though my pattern of being contactable for an instant response is not predictable.

Working Pattern

The timing of work in my lab is completely flexible, and (other than attending pre-arranged meetings), I expect students to manage their own time.

Notice Period for Feedback

I need at least 2 week’s notice to provide feedback on written work of up to 5000 words.

MIBTP Project Details

Current Projects (2025-26)

Primary supervisor for:

An integrated machine-learning approach to the early detection of adverse drug reactions to improve depression, anxiety, and psychosis outcomes

See the PhD Opportunities section to see if this project is currently open for applications via MIBTP.

Please Note: The main page lists projects via BBSRC Research Theme(s) quoted and then relevant Topic(s).

An integrated machine-learning approach to the early detection of adverse drug reactions to improve depression, anxiety, and psychosis outcomes

Secondary Supervisor(s): Dr Shan He

University of Registration: University of Birmingham

BBSRC Research Themes: Integrated Understanding of Health (Pharmaceuticals)

Project Outline

The tragedy of adverse drug reaction (ADRs) is synonymous with thalidomide and valproate [1] yet in the context of mental health (MH) patients, intolerable side effects of psychiatric medication, leading to poor concordance, with the likelihood of further deterioration and possible involuntary treatment, is emerging as a very serious health issue. 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. [2]

Medication intended to alleviate mental distress is very often accompanied by ADRs which can be stigmatising, depressing and dangerous. These ADRs have an impact on concordance and can undermine trust in the healthcare team. 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 [3] 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. There is a need to study the experiences of risk populations (patients with a lived experience of DAP) using semi-automated machine learning natural language processing (NLP) to code this qualitative data in an unbiased way.

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 UKBioBank datasets to enable pharmacoepidemiology links to be identified.

The researcher will apply their ML-algorithms to the curated dataset. Refinement of the algorithm and known signal testing will then take place. There will be a focus on predictive ADR signal detection and confirmation of ≥1 new signal identified. A knowledge graph that connects drug, predictive/known ADR signals, drug target and indication with three relationships (has ADR signals, has target, has indication) will be constructed. The team will apply graph-based deep learning algorithms developed by the group to the knowledge graph to extract connections, correlations, and potential causation of these ADRs of concern for the first time.

References

[1] First Do No Harm - The report of the Independent Medicines and Medical Devices Safety Review, Cumberlege review, 2020, https://www.immdsreview.org.uk/downloads/IMMDSReview_Web (accessed on 9 October 2023).

[2] WHO Mental Disorders. 2022 Available online: https://www.who.int/news-room/fact-sheets/detail/mental-disorders (accessed on 9 October 2023).

[3] Lieberman JA, Stroup TS, McEvoy JP et al. Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N Engl J Med 2005; 353:12.