̽��ֱ�� of Cambridge - drug discovery

AI can be good for our health and wellbeing

cjb250 — Mon, 07 Apr 2025 08:00:08 +0000

Cambridge researchers are looking at ways that AI can transform everything from drug discovery to Alzheimer's diagnoses to GP consultations.

Antibiotics, vaccinations and anti-inflammatory medication linked to reduced risk of dementia

cjb250 — Tue, 21 Jan 2025 12:00:03 +0000

̽��ֱ��study, led by researchers from the universities of Cambridge and Exeter, identified several drugs already licensed and in use that have the potential to be repurposed to treat dementia.

Dementia is a leading cause of death in the UK and can lead to profound distress in the individual and among those caring for them. It has been estimated to have a worldwide economic cost in excess of US$1 trillion dollars.

Despite intensive efforts, progress in identifying drugs that can slow or even prevent dementia has been disappointing. Until recently, dementia drugs were effective only for symptoms and have a modest effect. Recently, lecanemab and donanemab have been shown to reduce the build-up in the brain of amyloid plaques – a key characteristic of Alzheimer’s disease – and to slow down progression of the disease, but the National Institute for Health and Care Excellence (NICE) concluded that the benefits were insufficient to justify approval for use within the NHS.

Scientists are increasingly turning to existing drugs to see if they may be repurposed to treat dementia. As the safety profile of these drugs is already known, the move to clinical trials can be accelerated significantly.

Dr Ben Underwood, from the Department of Psychiatry at the ̽��ֱ�� of Cambridge and Cambridgeshire and Peterborough NHS Foundation Trust, said: “We urgently need new treatments to slow the progress of dementia, if not to prevent it. If we can find drugs that are already licensed for other conditions, then we can get them into trials and – crucially – may be able to make them available to patients much, much faster than we could do for an entirely new drug. ̽��ֱ��fact they are already available is likely to reduce cost and therefore make them more likely to be approved for use in the NHS.”

In a study published today in Alzheimer’s and Dementia: Translational Research & Clinical Interventions, Dr Underwood, together with Dr Ilianna Lourida from the ̽��ֱ�� of Exeter, led a systematic review of existing scientific literature to look for evidence of prescription drugs that altered the risk of dementia. Systematic reviews allow researchers to pool several studies where evidence may be weak, or even contradictory, to arrive at more robust conclusions.

In total, the team examined 14 studies that used large clinical datasets and medical records, capturing data from more than 130 million individuals and 1 million dementia cases. Although they found a lack of consistency between studies in identifying individual drugs that affect the risk of dementia, they identified several drug classes associated with altered risk.

One unexpected finding was an association between antibiotics, antivirals and vaccines, and a reduced risk of dementia. This finding supports the hypothesis that common dementias may be triggered by viral or bacterial infections, and supports recent interest in vaccines, such as the BCG vaccine for tuberculosis, and decreased risk of dementia.

Anti-inflammatory drugs such as ibuprofen were also found to be associated with reduced risk. Inflammation is increasingly being seen to be a significant contributor to a wide range of diseases, and its role in dementia is supported by the fact that some genes that increase the risk of dementia are part of inflammatory pathways.

̽��ֱ��team found conflicting evidence for several classes of drugs, with some blood pressure medications and anti-depressants and, to a lesser extent, diabetes medication associated with a decreased risk of dementia and others associated with increased risk.

Dr Ilianna Lourida from the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula (PenARC), ̽��ֱ�� of Exeter, said: “Because a particular drug is associated with an altered risk of dementia, it doesn’t necessarily mean that it causes or indeed helps in dementia. We know that diabetes increases your risk of dementia, for example, so anyone on medication to manage their glucose levels would naturally also be at a higher risk of dementia – but that doesn’t mean the drug increases your risk.

“It’s important to remember that all drugs have benefits and risks. You should never change your medicine without discussing this first with your doctor, and you should speak to them if you have any concerns.”

̽��ֱ��conflicting evidence may also reflect differences in how particular studies were conducted and how data was collected, as well as the fact that different medications even within the same class often target different biological mechanisms.

̽��ֱ��UK government is supporting the development of an Alzheimer’s trial platform to evaluate drugs rapidly and efficiently, including repurposed drugs currently used for other conditions.

“Pooling these massive health data sets provides one source of evidence which we can use to help us focus on which drugs we should try first,” said Dr Underwood. “We’re hopeful this will mean we can find some much-needed new treatments for dementia and speed up the process of getting them to patients.”

Reference
Underwood, BU & Lourida, I et al. Data-driven discovery of associations between prescribed drugs and dementia risk: A systematic review. Alz & Dem; 21 Jan 2025; DOI: 10.1002/trc2.70037

Antibiotics, antivirals, vaccinations and anti-inflammatory medication are associated with reduced risk of dementia, according to new research that looked at health data from over 130 million individuals.

We urgently need new treatments to slow the progress of dementia, if not to prevent it. If we can find drugs that are already licensed for other conditions, then we can get them into trials much faster than we could do for an entirely new drug

Ben Underwood

Andrzej Rostek (Getty Images)

Elderly Woman's Hands and Orange Pills

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes

Journeys of discovery: Steve Jackson and a life-saving cancer drug

lw355 — Mon, 15 Jul 2024 07:00:08 +0000

What excites Steve Jackson is understanding how biology works and why it sometimes goes wrong. But what galvanises him is knowing there are people alive today as a result of his discovery of how to create a cancer drug.

AI speeds up drug design for Parkinson’s ten-fold

sc604 — Wed, 17 Apr 2024 09:00:00 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, designed and used an AI-based strategy to identify compounds that block the clumping, or aggregation, of alpha-synuclein, the protein that characterises Parkinson’s.

̽��ֱ��team used machine learning techniques to quickly screen a chemical library containing millions of entries, and identified five highly potent compounds for further investigation.

Parkinson’s affects more than six million people worldwide, with that number projected to triple by 2040. No disease-modifying treatments for the condition are currently available. ̽��ֱ��process of screening large chemical libraries for drug candidates – which needs to happen well before potential treatments can be tested on patients – is enormously time-consuming and expensive, and often unsuccessful.

Using machine learning, the researchers were able to speed up the initial screening process ten-fold, and reduce the cost by a thousand-fold, which could mean that potential treatments for Parkinson’s reach patients much faster. ̽��ֱ��results are reported in the journal Nature Chemical Biology.

Parkinson’s is the fastest-growing neurological condition worldwide. In the UK, one in 37 people alive today will be diagnosed with Parkinson’s in their lifetime. In addition to motor symptoms, Parkinson’s can also affect the gastrointestinal system, nervous system, sleeping patterns, mood and cognition, and can contribute to a reduced quality of life and significant disability.

Proteins are responsible for important cell processes, but when people have Parkinson’s, these proteins go rogue and cause the death of nerve cells. When proteins misfold, they can form abnormal clusters called Lewy bodies, which build up within brain cells stopping them from functioning properly.

“One route to search for potential treatments for Parkinson’s requires the identification of small molecules that can inhibit the aggregation of alpha-synuclein, which is a protein closely associated with the disease,” said Professor Michele Vendruscolo from the Yusuf Hamied Department of Chemistry, who led the research. “But this is an extremely time-consuming process – just identifying a lead candidate for further testing can take months or even years.”

While there are currently clinical trials for Parkinson’s currently underway, no disease-modifying drug has been approved, reflecting the inability to directly target the molecular species that cause the disease.

This has been a major obstacle in Parkinson’s research, because of the lack of methods to identify the correct molecular targets and engage with them. This technological gap has severely hampered the development of effective treatments.

̽��ֱ��Cambridge team developed a machine learning method in which chemical libraries containing millions of compounds are screened to identify small molecules that bind to the amyloid aggregates and block their proliferation.

A small number of top-ranking compounds were then tested experimentally to select the most potent inhibitors of aggregation. ̽��ֱ��information gained from these experimental assays was fed back into the machine learning model in an iterative manner, so that after a few iterations, highly potent compounds were identified.

“Instead of screening experimentally, we screen computationally,” said Vendruscolo, who is co-Director of the Centre for Misfolding Diseases. “By using the knowledge we gained from the initial screening with our machine learning model, we were able to train the model to identify the specific regions on these small molecules responsible for binding, then we can re-screen and find more potent molecules.”

Using this method, the Cambridge team developed compounds to target pockets on the surfaces of the aggregates, which are responsible for the exponential proliferation of the aggregates themselves. These compounds are hundreds of times more potent, and far cheaper to develop, than previously reported ones.

“Machine learning is having a real impact on drug discovery – it’s speeding up the whole process of identifying the most promising candidates,” said Vendruscolo. “For us, this means we can start work on multiple drug discovery programmes – instead of just one. So much is possible due to the massive reduction in both time and cost – it’s an exciting time.”

̽��ֱ��research was conducted in the Chemistry of Health Laboratory in Cambridge, which was established with the support of the UK Research Partnership Investment Fund (UKRPIF) to promote the translation of academic research into clinical programmes.

Reference:
Robert I Horne et al. ‘Discovery of Potent Inhibitors of α-Synuclein Aggregation Using Structure-Based Iterative Learning.’ Nature Chemical Biology (2024). DOI: 10.1038/s41589-024-01580-x

Researchers have used artificial intelligence techniques to massively accelerate the search for Parkinson’s disease treatments.

Machine learning is having a real impact on drug discovery – it’s speeding up the whole process of identifying the most promising candidates

Michele Vendruscolo

Nathan Pitt

Michele Vendruscolo

Yes

Accelerating how new drugs are made with machine learning

sc604 — Mon, 15 Jan 2024 10:05:29 +0000

Predicting how molecules will react is vital for the discovery and manufacture of new pharmaceuticals, but historically this has been a trial-and-error process, and the reactions often fail. To predict how molecules will react, chemists usually simulate electrons and atoms in simplified models, a process that is computationally expensive and often inaccurate.

Now, researchers from the ̽��ֱ�� of Cambridge have developed a data-driven approach, inspired by genomics, where automated experiments are combined with machine learning to understand chemical reactivity, greatly speeding up the process. They’ve called their approach, which was validated on a dataset of more than 39,000 pharmaceutically relevant reactions, the chemical ‘reactome’.

Their results, reported in the journal Nature Chemistry, are the product of a collaboration between Cambridge and Pfizer.

“ ̽��ֱ��reactome could change the way we think about organic chemistry,” said Dr Emma King-Smith from Cambridge’s Cavendish Laboratory, the paper’s first author. “A deeper understanding of the chemistry could enable us to make pharmaceuticals and so many other useful products much faster. But more fundamentally, the understanding we hope to generate will be beneficial to anyone who works with molecules.”

̽��ֱ��reactome approach picks out relevant correlations between reactants, reagents, and performance of the reaction from the data, and points out gaps in the data itself. ̽��ֱ��data is generated from very fast, or high throughput, automated experiments.

“High throughput chemistry has been a game-changer, but we believed there was a way to uncover a deeper understanding of chemical reactions than what can be observed from the initial results of a high throughput experiment,” said King-Smith.

“Our approach uncovers the hidden relationships between reaction components and outcomes,” said Dr Alpha Lee, who led the research. “ ̽��ֱ��dataset we trained the model on is massive – it will help bring the process of chemical discovery from trial-and-error to the age of big data.”

In a related paper, published in Nature Communications, the team developed a machine learning approach that enables chemists to introduce precise transformations to pre-specified regions of a molecule, enabling faster drug design.

̽��ֱ��approach allows chemists to tweak complex molecules – like a last-minute design change – without having to make them from scratch. Making a molecule in the lab is typically a multi-step process, like building a house. If chemists want to vary the core of a molecule, the conventional way is to rebuild the molecule, like knocking the house down and rebuilding from scratch. However, core variations are important to medicine design.

A class of reactions, known as late-stage functionalisation reactions, attempts to directly introduce chemical transformations to the core, avoiding the need to start from scratch. However, it is challenging to make late-stage functionalisation selective and controlled – there are typically many regions of the molecules that can react, and it is difficult to predict the outcome.

“Late-stage functionalisations can yield unpredictable results and current methods of modelling, including our own expert intuition, isn't perfect,” said King-Smith. “A more predictive model would give us the opportunity for better screening.”

̽��ֱ��researchers developed a machine learning model that predicts where a molecule would react, and how the site of reaction vary as a function of different reaction conditions. This enables chemists to find ways to precisely tweak the core of a molecule.

“We trained the model on a large body of spectroscopic data – effectively teaching the model general chemistry – before fine-tuning it to predict these intricate transformations,” said King-Smith. This approach allowed the team to overcome the limitation of low data: there are relatively few late-stage functionalisation reactions reported in the scientific literature. ̽��ֱ��team experimentally validated the model on a diverse set of drug-like molecules and was able to accurately predict the sites of reactivity under different conditions.

“ ̽��ֱ��application of machine learning to chemistry is often throttled by the problem that the amount of data is small compared to the vastness of chemical space,” said Lee. “Our approach – designing models that learn from large datasets that are similar but not the same as the problem we are trying to solve – resolves this fundamental low-data challenge and could unlock advances beyond late-stage functionalisation.”

̽��ֱ��research was supported in part by Pfizer and the Royal Society.

References:
Emma King-Smith et al. ‘Predictive Minisci Late Stage Functionalization with Transfer Learning.’ Nature Communications (2023). DOI: 10.1038/s41467-023-42145-1

Emma King-Smith et al. ‘Probing the Chemical "Reactome" with High Throughput Experimentation Data.’ Nature Chemistry (2023). DOI: 10.1038/s41557-023-01393-w

Researchers have developed a platform that combines automated experiments with AI to predict how chemicals will react with one another, which could accelerate the design process for new drugs.

A deeper understanding of the chemistry could enable us to make pharmaceuticals and so many other useful products much faster.

Emma King-Smith

BlackJack3D via Getty Images

Digital Molecular Structure Concept

Yes

Cambridge partners with AstraZeneca and Medical Research Council on new world-class functional genomics laboratory

skbf2 — Mon, 27 Nov 2023 10:30:22 +0000

̽��ֱ�� ̽��ֱ�� of Cambridge today announced a partnership with AstraZeneca and the Medical Research Council (MRC) to establish a new state-of-the-art functional genomics laboratory at the Milner Therapeutics Institute (MTI). ̽��ֱ��laboratory will become part of the UK’s Human Functional Genomics Initiative, contributing to the UK’s ambition of having the most advanced genomic healthcare system in the world.

Functional genomics investigates the effects and impacts of genetic changes in our DNA, and particularly how these contribute to disease. CRISPR makes it possible to test specific DNA alterations in a controlled way to investigate the effects and impacts of genetic changes in our DNA, revealing their effects on biological processes that cause disease. Finding these disease drivers is a key first step in the process of identifying potentially life-changing medicines for patients.

̽��ֱ��new facility, which will be located within the MTI on the Cambridge Biomedical Campus, will provide researchers from across the UK with access to large-scale biological and technological tools and house an advanced automated arrayed-CRISPR screening platform. It is hoped that through the use of tools, such as CRISPR gene editing to provide insights into the relationship between genes and disease, scientists will discover new opportunities to develop therapies for chronic diseases including cardiovascular, respiratory and metabolic disease.

Professor Tony Kouzarides, Director of the Milner Therapeutics Institute, said: “ ̽��ֱ��best science is founded on collaboration, and I am delighted that the Milner Therapeutics Institute is partnering with the MRC and AstraZeneca to launch this unique functional genomics laboratory. This will enable sharing of expertise and resources to deliver new diagnostics and treatments for people with chronic diseases.”

Professor Andy Neely, Pro-Vice-Chancellor for Enterprise and Business Relations at the ̽��ֱ�� of Cambridge, said: “This new collaboration with AstraZeneca and MRC is a fantastic example of industry and academia working together to drive forward science that will have a real impact on people’s health in the UK and around the world.”

Dr Jonathan Pearce, Director of Strategy and Planning, MRC, said: “We are working across UK Research and Innovation to improve health, ageing and wellbeing. Our investment in this new laboratory builds on the UK’s global leadership in genomics. Our support will enable the laboratory’s launch and provide access for researchers from across the UK. Through this investment, and the wider Human Functional Genomics Initiative, we will enhance the national ecosystem needed to improve our understanding of how genetic variance impacts health and disease.”

Sharon Barr, Executive Vice President, BioPharmaceuticals R&D, AstraZeneca, said: “Collaboration is crucial to achieving our ambition of transforming healthcare and delivering life-changing medicines for patients, and innovative partnership such as this one, allow us to share resources and expertise to advance science. This new laboratory created as part of the Human Functional Genomics Initiative, will be world-leading and will play a central role in shaping future functional genomics work across the UK and beyond.”

̽��ֱ��lab, which is expected to become operational in 2024, will provide a centre of excellence and national resource that combines the strengths and expertise of academia and industry. Its creation is part of a new partnership formed between MTI, AstraZeneca and MRC, and builds upon expertise gained through an existing collaboration between MTI, AstraZeneca and Cancer Research Horizons, known as the AstraZeneca-Cancer Research Horizons Functional Genomics Centre (FGC) that has been enabling advances in oncology research since 2018. ̽��ֱ��FGC is currently housed in the MTI and will be relocating next year.

MTI, AstraZeneca and the MRC’s Human Functional Genomics Initiative will share facilities, resources and knowledge working closely together to facilitate faster progress and innovations.

̽��ֱ��facility, based at the Milner Therapeutics Institute, will support the discovery of new medicines and diagnostics for chronic diseases by applying advanced biological and technological tools, including CRISPR gene editing.

A fantastic example of industry and academia working together to drive forward science that will have a real impact on people’s health in the UK and around the world.

Andy Neely

Milner Therapeutics Institute

Scientist looking down microscope

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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AI-driven techniques reveal new targets for drug discovery

Anonymous — Wed, 27 Sep 2023 14:17:57 +0000

̽��ֱ��research team, led by the ̽��ֱ�� of Cambridge, presented an approach to identify therapeutic targets for human diseases associated with a phenomenon known as protein phase separation, a recently discovered phenomenon widely present in cells that drives a variety of important biological functions.

Protein phase separation at the wrong place or time could disrupt key cellular functions or create aggregates of molecules linked to neurodegenerative diseases. It is believed that poorly formed cellular condensates could contribute to cancers and might help explain the aging process.

̽��ֱ��Cambridge researchers, working in collaboration with generative artificial intelligence (AI)-driven drug discovery company Insilico Medicine, developed a method for finding new targets for drug discovery in diseases caused by dysregulation of the protein phase separation process. ̽��ֱ��team found that they could replicate disease characteristics in cells by controlling the behaviour of these targets. Their results are reported in the Proceedings of the National Academy of Sciences (PNAS).

“ ̽��ֱ��discovery of protein phase separation opens up new opportunities for drug discovery,” said Professor Michele Vendruscolo from Cambridge’s Yusuf Hamied Department of Chemistry, who led the research. “However, it has been unclear which proteins undergo this process and represent the best targets for effective pharmacological interventions.”

In the study, researchers combined Insilico’s proprietary target identification engine PandaOmics with the FuzDrop method to identify disease-associated proteins prone to phase separation. PandaOmics is an AI-driven therapeutic target discovery tool that integrates multiple omics and text AI bioinformatics models to assess the potential of proteins as therapeutic targets.

FuzDrop is a tool introduced by the Cambridge team, which calculates the propensity of a protein to undergo spontaneous phase separation, aiding in the identification of proteins prone to form liquid-liquid phase-separated condensates.

Using this approach, the researchers conducted a large-scale study of human sample data, quantified the relative impact of protein phase separation in regulating various pathological processes associated with human disease, prioritised candidates with high PandaOmics and FuzDrop scores and generated a list of possible therapeutic targets for human diseases linked with protein phase separation.

̽��ֱ��researchers validated the differential phase separation behaviours of three predicted Alzheimer’s disease targets (MARCKS, CAMKK2 and p62) in two cell models of Alzheimer’s disease, which provides experimental validation for the involvement of these predicted targets in Alzheimer's disease and support their potential as therapeutic targets. By modulating the formation and behaviour of these condensates, it may be possible to develop new interventions to mitigate the pathological processes associated with Alzheimer's disease.

“It has been challenging so far to understand the role of protein phase separation in cellular functions,” said Vendruscolo. “Even more difficult has been to clarify the exact nature of its association with human disease. By working with Insilico Medicine, we have developed an approach to systematically address this problem and identify a variety of possible therapeutic targets. We have thus provided a roadmap for researchers to navigate this complex terrain.”

“We are pleased to reach the milestones of our collaboration with the ̽��ֱ�� of Cambridge,” said Frank Pun, PhD, head of Insilico Medicine Hongkong, and co-author of the paper. “ ̽��ֱ��study is intended to provide initial directions for targeting disease-associated proteins prone to phase separation. With ongoing technical advancements in studying the protein phase separation process, coupled with the growing data about its roles in both cellular function and dysfunction, it is now possible to comprehend the causal relationship between these targets and diseases. We anticipate facilitating the translation of this preclinical research into novel therapeutic interventions soon.”

Reference:
Christine M. Lim et al. ‘Multiomic prediction of therapeutic targets for human diseases associated with protein phase separation.’ Proceedings of the National Academy of Sciences (2023). DOI: 10.1073/pnas.2300215120

Adapted from an Insilico Medicine press release.

Researchers have developed a method to identify new targets for human disease, including neurodegenerative conditions such as Alzheimer’s disease.

̽��ֱ��discovery of protein phase separation opens up new opportunities for drug discovery

Michele Vendruscolo

Juan Gaertner/Science Photo Library via Getty Images

Alzheimers disease. Computer illustration of amyloid plaques amongst neurons.

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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Cambridge spin-out receives £2.2 million to help improve cancer treatments

skbf2 — Wed, 01 Mar 2023 09:45:06 +0000

̽��ֱ��spinout from the ̽��ֱ��’s Department of Chemical Engineering and Biotechnology has been awarded this funding by the European Innovation Council’s (EIC) ‘Transition Challenge’ investment programme which supports the development and commercialisation of innovative technologies.

This capital will allow Vector to develop its novel RNA delivery platform, increasing the safety, specificity and effectiveness of RNA therapies. ̽��ֱ��technology builds on more than 15 years of research in innovative materials and drug delivery by Professor David Fairen-Jimenez and his team.

Fairen-Jimenez, who is also Chief Executive Officer at Vector Bioscience, says: “RNA-therapies are, potentially, the most powerful cancer drugs. However, their targeted delivery remains a challenge. Our preliminary studies in vitro and in vivo have showcased the outstanding possibilities of our platform, leading to excellent efficacies with outstanding biocompatibility. Now, the EIC ‘Transition Challenge’ funds will help us take these discoveries to the clinic.”

Vector’s platform improves the targeted delivery of macromolecules – particularly RNA delivery. ̽��ֱ��technology is based on metal-organic frameworks (MOFs), nanoparticles that carry RNA molecules to their targets. MOFs have a number of advantages as a delivery mechanism: they offer controlled release of the RNA macromolecules, improving safety and selectivity. They also protect the RNA from degradation and increase their solubility and bioavailability.

Vector’s technology has shown promising results treating complicated cancers, including hard-to-treat tumours in the brain, lung and pancreas.

Established in 2021, Vector Bioscience has already been awarded £500k from Innovate UK. Now, with the additional investment from the EIC, it is in a position to design and develop its RNA delivery platform, with applications across different diseases.

Lluna Gallego-Segrelles, Chief Operating Officer at Vector Bioscience, adds: “Within just 18 months, we have attracted over £3 million in funding to commercialise our technology. This demonstrates there’s an immense interest around our drug delivery platform, which will bring the latest innovations in materials science to the pharmaceutical industry and the clinic. Now, our objective is to push our pioneer treatments into pre-clinical phases.”

Vector Bioscience has received a £2.2 million investment to help it take forward its drug delivery platform designed to make RNA cancer therapies more effective.

Scanning electron microscopy of highly crystalline metal-organic framework nanoparticles

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes