What are the most promising biomarkers of response and relapse prediction?
(Session chair: Prof Guy Goodwin)

Beckley Psytech is preparing clinical development programs with 5MeO DMT, a short-acting psychedelic, in mood and addiction disorders and plans to incorporate response and relapse predictors in all clinical trials. It is postulated that psychedelics provide a window of neuroplastic potential. Biomarkers might be cross diagnostic and they might circumvent some of the issues with psychedelic clinical trial design as more objective markers. Are clinical trials in specific disorders the best option to generate data in this regard or would alternate approaches yield more insights? Should psychedelics be developed primarily as a facilitator of psychotherapy or are they efficacious alone with appropriate safety support?

How can we gamify within the context of emotional wellness and in particular measuring different cognitive aspects within a game with the help of measuring parameters such as reaction time, attempts etc? (Session chair: Prof Chrystalina Antoniades)

We are developing gamification for people with dementia or cognitive impairment. The focus is on the elderly population with dementia symptoms and we are also looking at this in the context of sleep and depression.

Our current COSMA app contains 28 games each focusing on dementia symptoms. We are collecting parameters like reaction time to measure cognitive scores. We would like to come up with more possible parametric measurements to improve the accuracy of cognitive scores and possibly calculate different cognitive aspects within a single game e.g. a simple jigsaw puzzle game is looking at spatial orientation, working memory, attention, etc.

How can big data and systems biology approaches help us to deliver precision psychiatry? (Session chair: Prof Elizabeth Tunbridge)

Recent technological and analytical advances in genomics have now made it possible to rapidly identify and interpret the genetic variation underlying a single patient’s disease, thereby providing a window into patient-specific mechanisms that cause or contribute to disease, which could ultimately enable the ‘precise’ targeting of these mechanisms to achieve precision psychiatry. As part of this workshop, we will examine the current major efforts in big data driven precision medicine and discuss ways by which we can accelerate the potential broader utility of systems biology guided treatments going forward.

Recommended readings:
1. Dugger SA, Platt A, Goldstein DB. Drug development in the era of precision medicine. Nat Rev Drug Discov. 2018 Mar;17(3):183-196. PMID: 29217837
2. Move over AI? Here comes biological intelligence. 4 June 2020. https://www.longevity.technology/move-over-ai-here-comes-biological-intelligence/

How is artificial intelligence being applied to advance drug repurposing in psychiatry? (Session chair: Dr Danielle Newby)

The integration of computational drug prediction, patient EHR-based evaluation and bioinformatics-based mechanisms of action analysis provides the foundation for advancing drug repurposing in psychiatry. As part of this workshop, we will review the resources available and how state of the art data science approaches (e.g., artificial intelligence, biological intelligence) allow us to transform data into knowledge that advances big-data driven precision medicine for patients with mental health disorders.

Recommended readings:
1. Zhou M, Wang Q, Zheng C, John Rush A, Volkow ND, Xu R. Drug repurposing for opioid use disorders: integration of computational prediction, clinical corroboration, and mechanism of action analyses. Mol Psychiatry. 2021 Jan 11;1-11. PMID: 33432189

2. Move over AI? Here comes biological intelligence. 4 June 2020. https://www.longevity.technology/move-over-ai-here-comes-biological-intelligence/

What is the role of individual variabilities when thinking about designing experiments with neurostimulation? (Session chair: Jacinta O´Shea)

We are aware of only a small fraction of inter-individual variabilities (age, chronic illnesses, etc.) and an even smaller number of variables within individuals (circadian, temporary illnesses, psychophysiology/vagus health). Thus when conducting trials we are led to put everyone in the same category leading to averaging of responders who should be in different categories. In other words, while it would be a statistical fallacy to bluntly separate the test population into “responders” and “non-responders” and to compare “responders” to placebo, combining the “responders” and “non-responders” into one category is also a disservice to the progress of Experimental Personalised Medicine. Does the solution to this lie in collection of limitless biomarkers with potential for predictive power of the treatment efficacy? Which markers do you use in your practice? Finally, even more controversially, would you accept a statistical model that allows unbiased segregation of the population into “it worked just for this group of people or even just this one person and we don’t know why”?

How can we advance treatment discovery and development in psychiatry targeting cognition? (Session chair: Dr Susannah Murphy)

Where are we with treatment discovery and development in psychiatry as it relates to targeting cognition?
What are next steps?
What are some promising targets?
We are going to describe how research will be conducted with ketamine and psychedelics that integrates machine learning, proof-of-mechanism research and implementation science which will be critical for the impact of any innovative psychedelic on population health.

Can our knowledge on sleep and circadian rhythms provide a new way to explore many different mood disorders under an experimental medicine approach? (Session chair: Prof Vladyslav Vyazovskiy)

How can subjective experiences (in particular, psychedelic induced experiences) be objectively measured? (Session chair: Prof Philip Cowen)

Psychedelic/psychoactive substances induce unique subjective experiences that unfold upon administration. These experiences are thought be instrumental in mediating the potential efficacy of these substances. How can we correlate objective measures (EEG, fMRI, skin conductance, plasma protein levels etc), with subjective experiences? Might we be able to use these correlations to find biomarkers of efficacy or to better understand the mechanism of drug action?

How can preclinical behavioural studies be designed to optimise translation within the context of screening novel compounds for clinical development? (Session chair: Prof David Bannerman)

Once lead compounds are identified, and basic safety and efficacy signals are obtained, they are ready to be tested in vivo. Animal and human behavioral tests can be used as proof of concept to further inform efficacy and choice of indication for clinical trials. How can we design new or adapt existing behavioral test to maximise translatability to clinical setting? How can maximise the value of behavioral proof of concept designs?

What opportunities does the integration of AI-led high-resolution patient stratification and data driven computational chemistry offer for finding new therapeutic options for unmet medical needs in neuropsychiatric disease? (Session Chair: Prof Paul Harrison)

In Partnership with Precision Life we would like to share the opportunities of an integrated approach of patient stratification with the design of ligands can both identify potential novel targets and accelerate hit identification to help prove the biological hypothesis.

What are the biomarkers for psychiatric illness and specifically for PTSD and what is the role of these biomarkers in predicting real-world impact? (Session chair: Prof Jennifer Wild)

What are the most meaningful ways of “measuring” psychological disorders (and specifically PTSD) and how are academic instruments translated into clinical practice? How are disease specific and symptom rating scales used and their utility assessed (e.g. CAPS for PTSD and HAM-A, MADRS)? What is the current and future role of functional imaging in diagnosis and treatment response? More generally, how can these measures help to predict the impact of PTSD (and treatment of PTSD)?

Should we aim towards a coordinated effort on standard and novel analytical approaches? (Session chair: Dr Cassandra Gould van Praag)

To what extent would a coordinated standardised approach mean that data could be combined more easily? To what extent might this stifle innovation? The discussion will touch upon standardized acquisition, analysis & validation protocols. Also the use of machine learning tools, and functional/effective connectivity methods. More generally should there be a combined effort across companies to share data? E.g. making non-competitive data available, streamlining contractual arrangements, making best use of public imaging databases to establish consistency, reliability and predictive patterns for treatment response.

How do we link up better between imaging and phenotype? (Session chair: Prof Catherine Harmer)

Predictive validity of passive/active circuit assessments (resting state vs task based fMRI) for Phase 3 endpoints and treatment response. What can we do to improve the association between mechanistic endpoints in Phase 1b and regulatory endpoints in POC studies? Is patient stratification viable based on functional and/or cognitive endpoints? Which processes/circuits do we have most confidence in as measures which impact real-world behaviour?

The last few years has seen the growth of digital strategies leading to the decentralisation of clinical trials and remote monitoring – how can academia and industry work together to meet the challenges of developing new tasks that can be used reliably at home and minimise the effect of ‘noisy’ data? (Session chair: Dr Andrew Creagh)

Many clinical trials are now designed with a component of home-based testing with data collected electronically. Testing is often frequent and longitudinal and compliance varies leading to missing datasets. How can these data be best analysed and/or compliance improved.

How can experimental medicine methods assist in the development of digital therapeutics? (Session chair: Prof Chrystalina Antoniades)

The development of digital therapeutics is following a similar development pathway to traditional drug development. How can experimental medicine methods be adapted for digital therapies that are designed to deliver therapy via a patients, PC, tablet or mobile phone to modify their behaviour and reduce symptoms. One example is attentional bias modification that through training seeks to shift a patient’s attention from negative to positive stimuli in an effort to augment drug therapy. Does fMRI have a role or are behavioural studies in appropriate patient populations sufficient to advance development.

Should different treatment modalities be considered within the same framework allowing for joined up approaches between psychological and pharmacological treatments? (Session chair: Dr Andrea Reinecke)

We would like to invite to a discussion on how pharmacological treatment and psychotherapy can be integrated in one framework. How can clinical trials or research studies be designed to combine pharmacological treatment and digital health solutions? Should we strive towards such combined solutions? Where are areas of need?

How can VR therapy be used to improve effectiveness in mental health? (Session chair: Dr Dejan Draschkow)

Does VR therapy work best if focused on techniques (e.g.exposure therapy) or disorders (e.g.phobias)?

Does VR therapy work best if focused on generals tools (e.g. VR environments for treatments, fear of darkeness) or protocols for specific conditions (e.g. Barlows’ transdiagnostic protocol )?

Why are mood and taste sensitivity linked in humans and could this allow early prediction of treatment response in depression? (Session chair: Prof Michael Browning)

Patients with Major Depressive Disorder have a reduced ability to taste. Is there an evolutionary reason for this? Cranial nerve VII controls facial expression, and is also the afferent nerve for taste in the anterior 2/3 of the tongue. Taste receptors and the limbic system use the same neurotransmitters.

How could this link assist in the management of depression?
Whereas Selective Serotonin Reuptake Inhibitors (SSRIs) improve mood only after 4-6 weeks, we have discovered that taste sensitivity in biologically depressed patients improves in hours.

Will an early predictor of treatment response encourage patients to persist with antidepressant treatment for long enough to benefit despite early side effects of treatment?

How can we identify and use EEG biomarkers for cognition, autism, sleep, depression? (Session chair: Dr Laurence Hunt)

Can we follow disease progression with EEG as biomarker? How reliable are ear EEG recordings? Can EEG biomarkers be used as early predictor of efficacy or clinical response to drug treatment? Can EEG biomarkers be used as RCT endpoints? Can we use multiple biomarkers and develop composite scores for patients?