Closed-loop control (CLC) theory is widely used in different industries (robotics, automotive, aerospace, etc.) and to a less extent in clinical settings, despite its success story in the automation of glucose control for diabetic patients through an artificial pancreas. This proposal looks to jointly define a future research agenda driven by inputs from oncological experts (directly and through observation) for the deployment of CLC in decision making for tumour growth control in a heterogeneous environment. This will be presented to the medical and academic communities via multiple channels at the end of this collaborative research. The research proposed herein covers a 12-month preliminary feasibility study enabled by two 2-month periods of collaborative research with the Memorial Sloan Kettering Cancer Centre (MSKCC) in New York, funded by the OTG.

Acute myeloid leukaemia (AML) is a most common aggressive leukaemia in adults and is incurable in most patients. In AML, gene mutations cause immature cells in bone marrow to stop making mature cells (differentiation block and bone marrow failure) and to increase is numbers in patient bone marrow (expansion). Patients develop symptoms of anaemia, bleeding and infections, which lead to death is untreated. Most effective treatments for AML use chemotherapy drugs which kill leukaemic cells. These are highly toxic as they also harm other cells in the body. The majority of patients with AML are elderly and unable to tolerate these types of treatment. We therefore need to have more effective, less toxic treatments to restore blood and bone marrow function (disease remission) to both improve rates of cure, prolong patient survival, improve quality of life. 20% of AML patients have a mutation in genes coding for the enzymes isocitrate dehydrogenase 1 or 2 (IDH1/2). The mutant enzyme (mIDH) produces an abnormal chemical (or metabolite), dextro-2-hydroxyglutarate (d2HG). This metabolite is known to cause cancer (including AML and brain tumours), most probably by stopping cells from maturing properly. New drugs which inhibit mIDH (mIDHi), is effective for ~40% of Isocitrate Dehydrogenase mutant (IDHm) AML patients. It works by reducing the number of immature AML cells by causing them to become useful mature blood cells. This benefits patients by reducing the need for blood transfusions and infection risk. Unfortunately, most patients who initially respond to mIDHi will develop resistance and relapse. We do not understand why this happens, or why some patients never respond to mIDHi. We also do not fully understand how mIDHi work, but it is likely to involve changes to the control mechanisms of genes To address these fundamental questions, I want to understand what goes wrong in the control mechanism of genes which are usually expressed when blood cells mature, and how this causes AML. The aims of my proposal is to 1) Study how blood cells differentiate and mature in normal bone marrow and how this process of differentiation and maturation goes wrong in AML (i.e. differentiation block) 2) Investigate how drugs like mIDHi are able to re-programme AML cells to make them differentiate into functional mature cells 3) In patients where mIDHi are not effective, or when a patient's disease becomes resistant to mIDHi, find out why AML cells remain, or become blocked. If I can discover how we can target these blocked pathways, for example, by using novel drugs or by combining the effects of mIDHi with other drugs, then this could be useful for treating patients. The methods I will use to study the behaviour of AML cells include using genetic sequencing techniques to look at how genes are expressed in cells, and also what the mechanisms are which control gene expression (epigenomics). This could provide a better characterisation and understanding of AML cells and be used to determine if AML patients are more or less likely to respond to a particular treatment. This can help clinicians to decide which treatments are best for an individual patient, and help develop combinations of treatments which are more likely to work for an individual.

Acute myeloid leukaemia (AML) is a most common aggressive leukaemia in adults and is incurable in most patients. In AML, gene mutations cause immature cells in bone marrow to stop making mature cells (differentiation block and bone marrow failure) and to increase is numbers in patient bone marrow (expansion). Patients develop symptoms of anaemia, bleeding and infections, which lead to death is untreated. Most effective treatments for AML use chemotherapy drugs which kill leukaemic cells. These are highly toxic as they also harm other cells in the body. The majority of patients with AML are elderly and unable to tolerate these types of treatment. We therefore need to have more effective, less toxic treatments to restore blood and bone marrow function (disease remission) to both improve rates of cure, prolong patient survival, improve quality of life. 20% of AML patients have a mutation in genes coding for the enzymes isocitrate dehydrogenase 1 or 2 (IDH1/2). The mutant enzyme (mIDH) produces an abnormal chemical (or metabolite), dextro-2-hydroxyglutarate (d2HG). This metabolite is known to cause cancer (including AML and brain tumours), most probably by stopping cells from maturing properly. New drugs which inhibit mIDH (mIDHi), is effective for ~40% of Isocitrate Dehydrogenase mutant (IDHm) AML patients. It works by reducing the number of immature AML cells by causing them to become useful mature blood cells. This benefits patients by reducing the need for blood transfusions and infection risk. Unfortunately, most patients who initially respond to mIDHi will develop resistance and relapse. We do not understand why this happens, or why some patients never respond to mIDHi. We also do not fully understand how mIDHi work, but it is likely to involve changes to the control mechanisms of genes To address these fundamental questions, I want to understand what goes wrong in the control mechanism of genes which are usually expressed when blood cells mature, and how this causes AML. The aims of my proposal is to 1) Study how blood cells differentiate and mature in normal bone marrow and how this process of differentiation and maturation goes wrong in AML (i.e. differentiation block) 2) Investigate how drugs like mIDHi are able to re-programme AML cells to make them differentiate into functional mature cells 3) In patients where mIDHi are not effective, or when a patient's disease becomes resistant to mIDHi, find out why AML cells remain, or become blocked. If I can discover how we can target these blocked pathways, for example, by using novel drugs or by combining the effects of mIDHi with other drugs, then this could be useful for treating patients. The methods I will use to study the behaviour of AML cells include using genetic sequencing techniques to look at how genes are expressed in cells, and also what the mechanisms are which control gene expression (epigenomics). This could provide a better characterisation and understanding of AML cells and be used to determine if AML patients are more or less likely to respond to a particular treatment. This can help clinicians to decide which treatments are best for an individual patient, and help develop combinations of treatments which are more likely to work for an individual.

Dynamical systems with many degrees of freedom arise in a wide range of applications, including large scale molecular dynamics, climate and weather studies, and electrical power networks. The challenge in simulation is normally to extract statistical information, for example the average propensity of a given state of the system or the average time that elapses between certain events. Simulation data is easy to generate but often poorly utilized. The goal of this project is the development of a data-driven method for the automatic detection of a simplified description of the system based on a set of collective variables which can be used within efficient statistical extraction procedures. These slowest degrees of freedom are typically the most important ones. The dynamics are characterised as fluctuations in the vicinity of given state punctuated by relatively rare events describing transitions between the states. Efficiently identifying collective variables is the crucial first step in the design of coarse-grained models which can allow many order of magnitude increases in the accessible simulation timescale. By automatically finding collective variables, we can greatly simplify rapid study and comparison of many systems. The research builds on the technique of diffusion maps, whereby the eigenfunctions of a diffusion operator are used to characterise the metastable (slowly changing) states of the system. The potential impact of automatic coarse-graining will be felt most profoundly in fields such as rational drug design, where it is necessary to select specific drug molecules for their properties in interaction with some target, e.g. a protein. Bio-molecular simulation depends on the use of very specialised and intensely developed simulation codes which are the products of many years of development and government investment. In order to accelerate the implementation and testing of novel algorithms in this important area, this project includes a detailed plan for software development within the EPSRC-funded MIST (Molecular Integrator Software Tools) platform. Testing of the software methodology will be conducted via collaborations with chemists and pharmaceutical chemists, including researchers at Rice University (Houston, Texas) and Memorial Sloan Kettering Cancer Research Center (New York).

This Open Fellowship Plus application focusses on discovery, development and innovation enabling precision molecule editing and diversification, an area central to drug discovery and of great interest to our pharmaceutical industry partners. It also looks to examine and address diversity across the science + engineering community involved in translation, with a particular focus on the largest population grouping (women) who remain significantly under-represented in spinouts and start-ups. The formation of C-X bonds (where X is F, Cl, Br, or I) is of great importance to the pharmaceutical and agrochemical industries. The introduction of a halogen into a molecule can be used to modulate bioactivity, bioavailability and metabolic stability. It also provides a chemically reactive and selectively functionalisable handle, that can be used to build or diversify molecules. For these reasons >81% of agrochemicals contain a C-X bond, and for pharmaceuticals >26% contain a C-Cl bond with a further 67% requiring a C-Cl bond for assembly. Current industrial approaches to making C-X bonds still require Cl2 and Br2. Such approaches rely on fragile supply chains with much of the elemental halides being generated through energy expensive processes in India, China, Russia, Ukraine, and require the C-X bond to be introduced at an early stage. Most critically, these approaches lack selectivity and, even when applied to simple starting materials, result in hard to separate mixtures. For this reason, only simple halogenated building blocks are generated. To incorporate a halogen into a molecule, whether that be a pharmaceutical or agrochemical, its assembly must be designed using these simple halogenated building blocks. Transitioning from current thinking to new thinking + discovery In contrast to current industrial approaches to halogenation, enzymes confer exquisite selectivity, enabling precision late-stage halogenation. Unlike current industrial approaches, salt is used as halogenating agent, only one product is generated simplifying purification, and complex bioactive scaffolds, rather than simple building blocks, can be accepted as substrates. In this ambitious fellowship proposal, we will: - use bioinformatics approaches, coupled to wet screening and AI to discover new halogenases - develop and apply AI guided directed evolution and selection to these new halogenases - explore innovative new approaches to cofactor recycling toward enabling reaction intensification and scale up - demonstrate precision late-stage diversification of pharmaceutically relevant scaffolds, developing new and innovative diversification procedures. Demonstrating PRIMED for Diversification in the context of pharmaceutical design and discovery. The proposed work is poised to bring significant advantage and acceleration to molecule making and diversification, particularly in the context of drug discovery. It will also bring benefit to biocatalysts through the development and pioneering of AI informed enzyme selection. Further insight and benefit will be brought through the Open Plus component, shining a light on diversity data within the translational arena.