 Hello, my name is Karis Wong. I'm a PhD student, Neurology Registrarian Clinical Trial Fellow based at the University of Edinburgh. Thank you very much for asking me to speak today about my project, which is developing a data-driven framework to identify, evaluate and practice candidate drugs for motor and neuron disease clinical trials. As a bit of a background, motor and neuron disease or MND is an incurable and fatal neurodegenerative disease. We only have one approved drug in the UK called Rilazol and this was actually approved back in 1995 and that prolongs life by an average of two to three months only. This is despite many promising preclinical studies and clinical trials in the meantime. So essentially after 26 years, we do not have any new drugs in the UK. There are several other drugs like Adaravone, approved elsewhere but most of this have very little or modest survival benefit at best. We did a systematic review to try and understand the challenges in MND trials and to learn what we can do better. We broke down the challenges in the two broad categories. The first being the difficulties in designing and delivering trials in this population and secondly the limitations in our understanding of disease biology which also contributes to challenges in how we select drugs to take forward the clinical trial. However, there are promising advances in both of these areas. In trial design, we have the MND smart trial up and running in Edinburgh and across Traffina, the sites in the UK. SMART stands for Systematic Multiarm Adaptive Randomized Trial. This is one of several adaptive platform trials in MND including the HEALY AOS trial in the States and TRICELL's platform trial plan in Europe. All of these trials are multiarm trials meaning they compare multiple drug arm against a placebo arm or dummy drug arm at any point of time. A shared placebo arm means we need a smaller sample size overall to get a definitive answer on whether drugs work. They are also adaptive meaning it's set analysis stages, drugs which are not effective are identified early and drop, and drugs which are effective are taken true from stage to stage including from phase two to phase three seamlessly. Using this design, we are also able to add new drugs to the pipeline to be tested rather than starting standalone trials from scratch. We therefore now have a way of testing more drugs quicker in a much more efficient way. So the question now really is how do we best select drugs to take forward to clinical trial? There are two ways of thinking about this. So one is push trials where we say the evidence for a drug saved from preclinical mechanistic studies is so good that we must test it in a clinical trial. Historically speaking though, many trials have relied on small and often not reproducible animal studies to inform drug selection and this has not been very successful to date and this is also likely owing to the complicated the complex disease biology which we have not fully understand. The other way to think about trials, drug selection for trials is pool trials where we say we have a horrible disease and we need to try something so what's the best drug we have available to try? To decide on this, we could look at the entirety of the evidence base including studying different types of data rather than solely relying on small animal studies. So this is the framework that we developed for this purpose. We use different domains of data showing here on the boxes on the left to identify, evaluate and prioritize drugs to take forward to clinical trial. So this is a modular framework which means in future if we have other domains that become relevant and available we are able to add them on. Currently these are the domains that we are using so first we've got the published literature which is informed by realiser or repurposing living systematic review. This is a three-stage systematic review taking into account the clinical literature of MND and other neurodegenerative diseases which may share similar pathways as well as animal and cell study literature. We also use data from experiment to drug screening which are from my colleagues at the University of Edinburgh and they are working on different screening methods and models including using stem cells derived from people with MND. We use data from these two domains as well as other data for example from the target ALS RNA-seq data to do pathway and network analysis so thereby identifying pathways and networks of interest which we can then map to drugs of interest. We also mine drug and trial databases for data on safety, feasibility and pharmacological data. We also harness expert opinion so we will incorporate all of these data from different streams and to generate an integrated candidate drug list. We use an interactive shiny app called the ICANN MND shiny app to visualize this and filter drugs according to overlapping categories of interests. This can help trial lists prioritize which candidate drugs we should evaluate in more detail. For drugs which they have prioritized for further evidence generation synthesis and reporting we would then produce living evidence summaries. These are summary of the data across the different domains which we keep continually updated so at time points in keeping with trial adaptation the trial list will have access to current curated content for each prioritize drug. We report this using a shiny app called the MND Source CT shiny app. For the realizer component we do a three-part machine learning assisted systematic review so first we have a systematic review of their clinical studies in MND and other neurodegenerative diseases which may share similar pathways, animal and vivo studies in MND as well as humans stem cell studies in MND. Each of this takes for a starting point updating the automated living search of PubMed using an API base and SERV or the systematic review facility platform to retrieve new publications. SERV is a free-to-use bespoke web-based application for systematic reviews developed by the Kamarades group. We then use a machine learning algorithm based at the epicenter to screen citations for inclusion. Next using R we run regular expressions on the included publications to identify the drug and disease study. We then generate a table listing all the drugs and the number of publications for each of the disease. We then run this against a second algorithm and R to filter drugs by a logic so taking forward drugs which have been described in at least one clinical publication in MND or where they have been described in clinical publications in two or more other diseases of interest. The trialers filters this list further based on biological plausibility, safety and visibility. We then annotate and extract data using SERV for the three reviews for all the included papers for prioritized drugs. For the clinical review we score each drug based on drug efficacy, safety, study size and quality of studies. To give you an idea of the scale of this review, the top half of this slide shows the systematic review component completed in 2017 which informed the first two arms of MND Smart. As you can see we have a large corpus, more than 40,000 publications across the reviews and from there we have identified 146 drugs which we eventually narrowed down to 22 drugs which have favorable clinical and pre-clinical data. So the reviews form a robust evidence base to inform expert panel discussions on drug selection. However, it is by no means a small undertaking given its scale so to make it more feasible for the current iteration we now incorporate automation techniques to our current workflow shown on the bottom half of the slide. The components incorporating automation are color coded here in pink. As mentioned earlier we use SERV for annotation and data extraction which enables efficient crowd sourcing. We currently have a group of more than 60 reviewers. We use workflows in R for data analysis, scoring and R Shiny for visualization. Next I'll show you a demo for our Shiny app to identify candidate drugs. Here we can choose categories of interest, for example drugs listed in the drug screening library, drugs across the blood brain barrier, drugs which shows the signal in any of our domains of interest and drugs listed in the British national formulary. Depending on which categories you choose the app will plot a euler plot to show you where how many drugs lie in each category. For drugs meeting all of the categories that you selected this will be tabulated in the table below. So this is one way which the expert panel can use to prioritize which drugs or which groups of drugs should be evaluated in more detail. For prioritized drugs we present a living evidence summary in this Shiny app called the MND systematic online living evidence summary for clinical trials or MND source CT for short. First we have our drug table to summarize the systematic review component. I apologize for redacting the drug themes here. As I mentioned earlier we score each drug according to efficacy, safety, study size and quality of studies as well as number of publications. We rank each drug based on their scores. We are also able to summarize the animal and vivo and in vitro survival and cell death data respectively where available. We also use interactive heat maps to visualize all of these data. To provide an idea of what the current data are based on we have a living prisma diagram for each of the reviews showing how many publications are identified included and annotated to what degree. We also provide an overview of the quality of studies included for the animal and vivo review as shown here. For the clinical review we are able to provide an interactive sumbers plot to give an overview of the studies included. So this shows the drugs and disease study type of study design and for interventional studies what phase the studies are in. For each drug we are able to generate a drug cv with data across the different domains summarized across the tabs shown here. For example this is the clinical summary for pyroglythazone. For the animal and vivo review we are able to select outcome of interest and animal model of interest and the shiny apple plot a forest plot for that outcome and model for the drug of interest in this area below. We are also able to tablet and summarize the data from the publication that meets the criteria and the table below. We also have also linked up for clinical trials. We retrieve data from the clinical trials.gov API to list the MND clinical trials for this selected drug. So this includes planned trials, previous trials and ongoing trials. For pathway analysis we have some visualizations here and string enrichment analysis as shown. We're also able to generate all of these information in a timestamp pdf format using our markdown. So this is very useful for record keeping anticipating future discussions with trial sponsors and drug licensing authorities. Apologies for not sharing the full version at present as per the request from our trialist as we are coming to a trial adaptation epoch. We do however have a demo version which doesn't have all the bells and whistles but should give you a rough idea and I'll be very interested to hear any feedback and suggestions. That's all from me today. Thank you very much for listening and thanks to my supervisory team Professor Smokin McLeop, Professor Siddharth and Chandra, Professor New Carragher, to the Comraders Group, the MND Smart Drug Screening Group and the Realizer MND Consortium. Thank you very much for listening.