̽��ֱ�� of Cambridge - pharmaceutical

Anti-inflammatory drug could reduce future heart attack risk

Anonymous — Mon, 02 Sep 2024 10:52:31 +0000

A cancer drug that unlocks the anti-inflammatory power of the immune system could help to reduce the risk of future heart attacks, according to research part-funded by the British Heart Foundation. By repurposing an existing drug, researchers hope it could soon become part of routine treatment for patients after a heart attack.

̽��ֱ��findings will be presented at the European Society of Cardiology Congress in London by Dr Rouchelle Sriranjan, NIHR Clinical Lecturer in Cardiology at the ̽��ֱ�� of Cambridge.

High levels of inflammation in blood vessels are linked to an increased risk of heart disease and heart attacks. After a heart attack, the body’s immune response can aggravate existing inflammation, causing more harm and increasing risk even further. However, NICE guidelines don’t currently recommend the use of any anti-inflammatory drugs to reduce future risk.

Now, a team of researchers, led by Dr Joseph Cheriyan from Cambridge ̽��ֱ�� Hospitals NHS Foundation Trust, have found that low doses of an anti-inflammatory drug called aldesleukin, injected under the skin of patients after a heart attack, significantly reduces inflammation in arteries.

̽��ֱ��researchers are currently following up patients to investigate the longer-term impact of this fall in inflammation. To date, in the two and a half years after their treatment, there have been no major adverse cardiac events in the group that received aldesleukin, compared to seven in the group that received the placebo.

Professor Ziad Mallat, BHF Professor of Cardiovascular Medicine at the ̽��ֱ�� of Cambridge who developed the trial, said: “We associate inflammation with healing – an inbuilt response that protects us from infection and injury. But it’s now clear that inflammation is a culprit in many cardiovascular conditions.

“Early signs from our ongoing trial suggest that people treated with aldesleukin may have better long-term outcomes, including fewer heart attacks. If these findings are repeated in a larger trial, we’re hopeful that aldesleukin could become part of routine care after a heart attack within five to 10 years.”

Aldesleukin is already used to treat kidney cancer, as high doses stimulate the immune system to attack cancer cells. ̽��ֱ��Cambridge team previously found that doses one thousand times lower than those used in cancer treatment increased the number of regulatory T cells – a type of anti-inflammatory white blood cell – in patients’ blood compared to a placebo.

In the current trial at Addenbrooke's and Royal Papworth hospitals in Cambridge, 60 patients admitted to hospital with a heart attack or unstable angina received either low dose aldesleukin or placebo. Patients received an injection once a day for the first five days, then once per week over the next seven weeks. Neither the participants nor their doctors knew whether they had received the drug or placebo.

At the end of treatment, Positron Emission Tomography (PET) scans showed that inflammation in the artery involved in patients’ heart attack or angina was significantly lower in the group treated with aldesleukin, compared to those who received the placebo.

̽��ֱ��anti-inflammatory effect of aldesleukin appeared even more striking in the most inflamed arteries, leading to a larger reduction in inflammation levels in these vessels and a bigger difference between the two groups by the end of the study.

Dr Sonya Babu-Narayan, Associate Medical Director at the British Heart Foundation and consultant cardiologist said: “Thanks to research, we have an array of effective treatments to help people avoid heart attacks and strokes and save lives. But, even after successful heart attack treatment, unwanted inflammation in the coronary arteries can remain, which can lead to life-threatening complications.

“A treatment to reduce inflammation after a heart attack could be a game-changer. It would help doctors to interrupt the dangerous feedback loop that exacerbates inflammation and drives up risk. This research is an important step towards that treatment becoming a reality.”

̽��ֱ��study was predominantly funded by the Medical Research Council, with significant support from the BHF and National Institute for Health and Care Research Cambridge Biomedical Research Centre (NIHR-BRC).

Originally published by the British Heart Foundation.

Repurposed cancer drug helps to calm inflammation in arteries.

Sebastian Kaulitzki/Science Photo Library via Getty Images

Illustration of human heart

̽��ֱ��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

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

How new model boosts supply and lowers prices for generic drugs

Anonymous — Thu, 21 Sep 2023 15:31:28 +0000

Civica Rx, a not-for-profit drug manufacturer founded by seven US health systems and three philanthropic organisations, increased supply security and lowered cost on aggregate for 20 drug products, according to the first empirical evidence of Civica’s impact published in the journal NEJM Catalyst.

“Results show that Civica was able to improve generic drug access above the wholesaler model,” says the article in NEJM Catalyst, a publication that is part of the New England Journal of Medicine family. “Chronic drug shortages have been an extremely challenging problem and elusive to sustainable improvement in the past. This makes these early results highly promising.”

̽��ֱ��NEJM Catalyst article (entitled “Vaccinating Health Care Supply Chains Against Market Failure: ̽��ֱ��Case of Civica Rx”) – is co-authored by the co-founders of the Healthcare Utility Initiative at Cambridge Judge Business School: Carter Dredge, Senior Vice President and Lead Futurist at SSM Health in St. Louis, Missouri (one of Civica’s founding health systems), who is a Business Doctorate candidate at Cambridge Judge, and by Stefan Scholtes, Dennis Gillings Professor of Health Management at Cambridge Judge.

Key breakthrough is structural rather than technological

“ ̽��ֱ��results of this study are very encouraging for patients and health systems,” says Carter Dredge. “ ̽��ֱ��innovation of Civica is not technological but rather structural: a new business model that injects a new type of supplier into a decades-old market for generic drugs in order to address a market failure.”

Civica is based on new business model called a health care utility (HCU) that prioritises access over profit. It was founded in 2018 to address generic drug shortages and high prices that have plagued health systems in the US and elsewhere, and now provides more than 75 critical medications at risk for shortages to US health systems.

Government intervention hasn’t solved problems in cost and supply

“Some problems in health care are so complex that traditional private-sector or governmental interventions alone have not been able to solve the problems,” the study says. “Although competition increases quality and reduces the cost of goods and services across a wide spectrum of industries, health care seems intractably resistant to standard forms of competition — particularly in its hyperspecialized supply chains.”

For example, the study says that the average price in 2022 for the uninsured for a box of five pen cartridges of insulin used to manage diabetes was more than $500, which results in 25% of Americans who rely on insulin being forced to ration their medications because of cost.

Study favourably compared Civica to 62 drug wholesalers

̽��ֱ��study focused on a cohort of 14 critical and shortage-prone hospital drugs that represented 20 distinct products (some medicines have multiple products due to different dose and vial size) between 2020 and 2022. Data comes from internal hospital pharmacy operations systems, supply chain purchasing databases, wholesaler product information, the American Society of Health System Pharmacists, and Civica.

̽��ֱ��authors estimated that Civica fulfilled its contractually guaranteed volume at 96%, whereas the wholesalers fulfilled their orders at 86%, with the difference being statistically significant (p=0.03). Further, Civica offered an additional product access benefit of 43% above the contractual minimum volume.

In addition, wholesaler prices at the order level were estimated to be on average 46% above the Civica price for the same product in the same year; however, through highly proactive health system purchasing efforts to buy more volume when prices were low from the 62 non-Civica manufacturers, this closed the actual achieved cost-savings gap between the wholesalers and Civica to 2.7% in aggregate, with Civica still being the lower-cost option.

( ̽��ֱ��14 medicines are: bivalirudin to prevent blood clotting, the antibiotic daptomycin, anti-inflammatory dexamethasone, narcotic pain medicine fentanyl, pre-surgery medicine katamine, labetalol for hypertension, local anesthetic lidocaine, seizure medication lorazepam, naloxone to treat opioid overdose, neostigmine for anesthesia reversal, ondansetron to prevent nausea, rocuronium bromide for general anesthesia, sodium bicarbonate for cardiac arrest, and the antibiotic vancomycin.)

New model sells drugs at same transparent price to all health systems

̽��ֱ��healthcare utility model is governed by stewards rather than owned, and pricing is uniform for all customers in a bid to maximise access rather than profits. Civica members purchase Civica medications at the same transparent price, as determined by the lowest appropriate cost necessary to sustainably provide the drugs over a 5-year period.

̽��ֱ��seven large US health systems that founded Civica are: Catholic Health Initiatives, now CommonSpirit Health; HCA Healthcare; Intermountain Healthcare; Mayo Clinic; Providence St. Joseph Health; SSM Health; and Trinity Health. ̽��ֱ��three founding philanthropies are the Gary and Mary West Foundation, the Laura and John Arnold Foundation, and the Peterson Center on Healthcare.

Civica now serves more than 50 US health systems

̽��ֱ��seven founding health systems have since been joined by more than 50 other health systems covering more than 1,500 hospitals and about 225,000 hospital beds. Through July 2023, more than 56 million cumulative patient doses of Civica medicines have been administered.

In conclusion, the authors say:

“ ̽��ֱ��problems we face in health care are daunting, but many of them are solvable with the right approach. In learning from Civica’s experience, some of the most fundamental answers may already be at our fingertips.

“This article provides the first empirical evidence that this approach is working.”

First empirical evidence for Civica Rx, a health care utility, finds increased supply security and reduced costs for health systems, says study in NEJM Catalyst authored by two Cambridge Judge Business School academics.

Eggy Sayoga via Getty Images

Pills and a capsule on pastel pink colored background. 3D rendered image.

̽��ֱ��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.

Yes

Training a new breed of clinical triallist

skbf2 — Thu, 03 Feb 2022 11:48:27 +0000

Cambridge's Experimental Medicine Initiative, working with AstraZeneca and GSK, is training specialists who can work out at an earlier stage of clinical trials if a treatment is likely to succeed.

AstraZeneca/GSK/ ̽��ֱ�� of Cambridge collaborate to support UK national effort to boost COVID-19 testing

plc32 — Tue, 07 Apr 2020 10:46:52 +0000

A new testing laboratory will be set up by AstraZeneca, GSK and Cambridge at the ̽��ֱ��’s Anne McLaren Building. This facility will be used for high throughput screening for COVID-19 testing and to explore the use of alternative chemical reagents for test kits in order to help overcome current supply shortages.

Alongside this new testing facility, AstraZeneca and GSK are working together to provide process optimisation support to the UK national testing centres in Milton Keynes, Alderley Park and Glasgow for COVID-19, providing expertise in automation and robotics to help the national testing system to continue to expand capacity over the coming weeks.

While diagnostic testing is not part of either company’s core business, we are moving as fast as we can to help where possible - with a focus on providing our world class scientific and technical expertise - working both with the Government’s screening programme and alongside the wider life sciences sector and specialist diagnostic companies.

Further updates on progress will be issued on this work in due course.

We continue to pay tribute to those working on the frontlines of this pandemic, in the UK and globally. Defeating COVID-19 requires a collective effort from everyone working in healthcare and we are committed to playing our part.

As part of the UK Government’s announcement of a new five pillar plan to boost testing for COVID-19, AstraZeneca, GSK and the ̽��ֱ�� of Cambridge have formed a joint collaboration to take action to support this national effort.

Everyone in this ̽��ֱ�� and private industry partnership is working hard to help our health service fight COVID-19.

Vice-Chancellor Stephen J Toope

̽��ֱ��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

Strategic partner: AstraZeneca

skbf2 — Mon, 11 Nov 2019 16:20:03 +0000

Scientists at AstraZeneca, a global biopharmaceutical company, have been working with Cambridge ̽��ֱ�� for more than two decades. What are the secrets of their success?

AI learns the language of chemistry to predict how to make medicines

sc604 — Mon, 02 Sep 2019 23:00:01 +0000

̽��ֱ�� of Cambridge researchers have shown that an algorithm can predict the outcomes of complex chemical reactions with over 90% accuracy, outperforming trained chemists. ̽��ֱ��algorithm also shows chemists how to make target compounds, providing the chemical ‘map’ to the desired destination. ̽��ֱ��results are reported in two studies in the journals ACS Central Science and Chemical Communications.

A central challenge in drug discovery and materials science is finding ways to make complicated organic molecules by chemically joining together simpler building blocks. ̽��ֱ��problem is that those building blocks often react in unexpected ways.

“Making molecules is often described as an art realised with trial-and-error experimentation because our understanding of chemical reactivity is far from complete,” said Dr Alpha Lee from Cambridge’s Cavendish Laboratory, who led the studies. “Machine learning algorithms can have a better understanding of chemistry because they distil patterns of reactivity from millions of published chemical reactions, something that a chemist cannot do.”

̽��ֱ��algorithm developed by Lee and his group uses tools in pattern recognition to recognise how chemical groups in molecules react, by training the model on millions of reactions published in patents.

̽��ֱ��researchers looked at chemical reaction prediction as a machine translation problem. ̽��ֱ��reacting molecules are considered as one ‘language,’ while the product is considered as a different language. ̽��ֱ��model then uses the patterns in the text to learn how to ‘translate’ between the two languages.

Using this approach, the model achieves 90% accuracy in predicting the correct product of unseen chemical reactions, whereas the accuracy of trained human chemists is around 80%. ̽��ֱ��researchers say that the model is accurate enough to detect errors in the data and correctly predict a plethora of difficult reactions.

̽��ֱ��model also knows what it doesn’t know. It produces an uncertainty score, which eliminates incorrect predictions with 89% accuracy. As experiments are time-consuming, accurate prediction is crucial to avoid pursuing expensive experimental pathways that eventually end in failure.

In the second study, Lee and his group, collaborating with the biopharmaceutical company Pfizer, demonstrated the practical potential of the method in drug discovery.

̽��ֱ��researchers showed that when trained on published chemistry research, the model can make accurate predictions of reactions based on lab notebooks, showing that the model has learned the rules of chemistry and can apply it to drug discovery settings.

̽��ֱ��team also showed that the model can predict sequences of reactions that would lead to a desired product. They applied this methodology to diverse drug-like molecules, showing that the steps that it predicts are chemically reasonable. This technology can significantly reduce the time of preclinical drug discovery because it provides medicinal chemists with a blueprint of where to begin.

“Our platform is like a GPS for chemistry,” said Lee, who is also a Research Fellow at St Catharine’s College. “It informs chemists whether a reaction is a go or a no-go, and how to navigate reaction routes to make a new molecule.”

̽��ֱ��Cambridge researchers are currently using this reaction prediction technology to develop a complete platform that bridges the design-make-test cycle in drug discovery and materials discovery: predicting promising bioactive molecules, ways to make those complex organic molecules, and selecting the experiments that are the most informative. ̽��ֱ��researchers are now working on extracting chemical insights from the model, attempting to understand what it has learned that humans have not.

“We can potentially make a lot of progress in chemistry if we learn what kinds of patterns the model is looking at to make a prediction,” said Peter Bolgar, a PhD student in synthetic organic chemistry involved in both studies. “ ̽��ֱ��model and human chemists together would become extremely powerful in designing experiments, more than each would be without the other.”

̽��ֱ��research was supported by the Winton Programme for the Physics of Sustainability and the Herchel Smith Fund.

References:
Philippe Schwaller et al. ‘Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction.’ ACS Central Science (2019). DOI: 10.1021/acscentsci.9b00576

Alpha Lee et al. ‘Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space.’ Chemical Communications (2019). DOI: 10.1039/C9CC05122H

Researchers have designed a machine learning algorithm that predicts the outcome of chemical reactions with much higher accuracy than trained chemists and suggests ways to make complex molecules, removing a significant hurdle in drug discovery.

Our platform is like a GPS for chemistry

Alpha Lee

Денис Марчук from Pixabay

Background abstract line

Yes

Machine learning algorithm helps in the search for new drugs

sc604 — Mon, 11 Feb 2019 20:00:00 +0000

̽��ֱ��researchers, led by the ̽��ֱ�� of Cambridge, used their algorithm to identify four new molecules that activate a protein which is thought to be relevant for symptoms of Alzheimer’s disease and schizophrenia. ̽��ֱ��results are reported in the journal PNAS.

A key problem in drug discovery is predicting whether a molecule will activate a particular physiological process. It’s possible to build a statistical model by searching for chemical patterns shared among molecules known to activate that process, but the data to build these models is limited because experiments are costly and it is unclear which chemical patterns are statistically significant.

“Machine learning has made significant progress in areas such as computer vision where data is abundant,” said Dr Alpha Lee from Cambridge’s Cavendish Laboratory, and the study’s lead author. “ ̽��ֱ��next frontier is scientific applications such as drug discovery, where the amount of data is relatively limited but we do have physical insights about the problem, and the question becomes how to marry data with fundamental chemistry and physics.”

̽��ֱ��algorithm developed by Lee and his colleagues, in collaboration with biopharmaceutical company Pfizer, uses mathematics to separate pharmacologically relevant chemical patterns from irrelevant ones.

Importantly, the algorithm looks at both molecules known to be active and molecules known to be inactive and learns to recognise which parts of the molecules are important for drug action and which parts are not. A mathematical principle known as random matrix theory gives predictions about the statistical properties of a random and noisy dataset, which is then compared against the statistics of chemical features of active/inactive molecules to distil which chemical patterns are truly important for binding as opposed to arising simply by chance.

This methodology allows the researchers to fish out important chemical patterns not only from molecules that are active but also from molecules that are inactive – in other words, failed experiments can now be exploited with this technique.

̽��ֱ��researchers built a model starting with 222 active molecules and were able to computationally screen an additional six million molecules. From this, the researchers purchased and screened the 100 most relevant molecules. From these, they identified four new molecules that activate the CHRM1 receptor, a protein that may be relevant for Alzheimer’s disease and schizophrenia.

“ ̽��ֱ��ability to fish out four active molecules from six million is like finding a needle in a haystack,” said Lee. “A head-to-head comparison shows that our algorithm is twice as efficient as the industry standard.”

Making complex organic molecules is a significant challenge in chemistry, and potential drugs abound in the space of yet-unmakeable molecules. ̽��ֱ��Cambridge researchers are currently developing algorithms that predict ways to synthesise complex organic molecules, as well as extending the machine learning methodology to materials discovery.

̽��ֱ��research was supported by the Winton Programme for the Physics of Sustainability.

Reference:
Alpha A. Lee et al. ‘Ligand biological activity predicted by cleaning positive and negative chemical correlations.’ PNAS (2019). DOI: 10.1073/pnas.1810847116

Researchers have designed a machine learning algorithm for drug discovery which has been shown to be twice as efficient as the industry standard, which could accelerate the process of developing new treatments for disease.

̽��ֱ��ability to fish out four active molecules from six million is like finding a needle in a haystack

Alpha Lee

Yes