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Public Funding for The University of Buckingham

Registration Number RC000730

A method for system-wide identification of target proteins and their ligands from complex extracts of natural products

65,649
2021-04-01 to 2022-08-31
Collaborative R&D
Ageing populations, increasing rates of lifestyle-related conditions and costs of drug development/healthcare provision represents a global societal challenge. 'Self-care' - "the ability of individuals, families and communities to promote health, prevent disease, maintain health, and to cope with illness and disability with or without the support of a healthcare provider" (WHO) - will contribute towards meeting this challenge. Natural products - compounds produced by living organisms - account for close to 20% of the Nutraceuticals industry, and there is a rising consumer inclination towards them. Wide structural diversity and evolutionary pressures mean natural products have strong potential to interact with proteins and impart effects on human health; evidenced by 25% of New Chemical Entities approved between 2010-2014 being natural products or derivatives (Newman and Cragg 2016). Alongside individual "active" compounds (e.g. curcumin from Turmeric root), many natural products are complex extracts containing 100s-1000s of compounds; sometimes further complicated in preparations of several species (Ayurvedic and Traditional Chinese Medicines). This complexity may be important for health benefits through synergistic interactions between compounds e.g. "entourage effect" noted for Cannabis (Ben-Shabat _et. al.,_ 1998). However, this complexity and inherent variability of natural products (genetic, environmental, cultivation, processing, extraction factors) makes substantiation of claimed health benefits difficult and leaves consumers ill-informed and under-served. There is a recognised need to provide well-specified natural products with standardised biochemical properties and demonstrated efficacy in humans. Current methods can provide insight into health-relevant Mechanism of Action for natural products, based on biomarker response signatures or the identification of protein targets. Similarly, bioassay guided fractionation or affinity purification methods (using protein targets as bait) can support identification of active compounds within complex natural products. However, no current method can identify Mechanism of Action and active compounds concurrently and typically rely on prior knowledge of one or other factor. To address the challenge, the partners aim to combine the principles from existing chemoproteomic approaches to develop a novel method to identify active compounds for natural products and their target proteins in human cells in parallel on a system-wide level. This capability will enable decoding of natural products and their activities, and support development of better-specified products for the nutraceuticals market, with a comparatively high level of scientific understanding at competitive cost to better serve customer, brands and regulators needs.

University of Buckingham and Russell IPM Limited

106,596
2019-01-01 to 2022-01-31
Knowledge Transfer Partnership
To develop low-cost digital solutions for the monitoring of pests using smart pheromone-based trap sensors and their innovative IoT connectivity.

The University of Buckingham and Russell IPM Limited

100,095
2016-06-01 to 2019-09-30
Knowledge Transfer Partnership
To develop the iPEST smart-trap with embedded remote pest-recognition system for pheromone based insect monitoring traps, enabling rapid response decision to farmers facing invasive and resident pests.

The University of Buckingham and Deepnet Security Limited

70,445
2016-04-01 to 2018-03-31
Knowledge Transfer Partnership
To build a revolutionary mobile authentication tool called Touchsense based on dynamic signatures to provide mobile users with a high level of security and privacy to protect their confidential information from unauthorised access

The University of Buckingham and Deepnet Security Limited

108,900
2015-02-01 to 2017-01-31
Knowledge Transfer Partnership
To develop a state-of-art face recognition component in the DualShield multifactor authentication platform to revolutionise the product.

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