Nephrology


Amplifying intelligent drug design

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'The idea of understanding a disease from an evolutionary viewpoint to inform drug design still resonates today in how Exscientia is approaching the design of anticancer agents. 'I spent a season at the GlaxoWellcome labs in Stevenage making the compounds I'd designed, and vividly remember the excitement of discovering the first molecule we'd made was active.' These included topics such as the druggable genome, ligand efficiency and network pharmacology – all of which are familiar topics to drug discovery chemists today. An early success involved feeding historical data for the project that discovered erectile dysfunction drug tadalafil (Cialis) into the evolutionary drug design model.


Google could soon get access to genetic patient data

Daily Mail

By building a neural network, Google's algorithms can interpret huge amounts of genetic, health, and environmental data to predict a persons health status, such as their level of risk of heart attack (stock image) It was created after Google bought University College London spinout, DeepMind, for £400 million in 2014. By building a neural network, these algorithms can interpret huge amounts of genetic, health, and environmental data to predict a persons health status, such as their level of risk of heart attack. Google announced the first of its NHS collaborations in February 2016, saying it was building an app to help hospital staff monitor patients with kidney disease. ', With personalisation as their ultimate'goal', Google intend to use the machine learning algorithms which track our digital footprint and target users with personalised advertising based on their preferences.


DeepMind's kidney disease-fighting Streams app is coming to a new hospital

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Google's DeepMind, the British artificial intelligence firm behind the human-besting AlphaGo software, launched a healthcare platform in partnership with the U.K.'s Moorfields Eye Hospital and Royal Free London in 2015. Starting this month, doctors and nurses at Musgrove Park will get DeepMind's Streams app for iPhone, which helps spot early signs of acute kidney injury. "This is all about early detection of seriously unwell patients so that we can immediately escalate care, ensure a very rapid response, and make sure they are treated quickly by the right specialist doctor," Luke Gompels, a consultant in medicine at Musgrove Park Hospital, told the BBC. Last year, it acquired Hark, a task management app optimized for hospital environments that was co-developed by students from Imperial College London and the National Institute for Health Research.


Google DeepMind's NHS deal under scrutiny - BBC News

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A deal between Google's artificial intelligence firm DeepMind and the UK's NHS had serious "inadequacies", an academic paper has suggested. More than a million patient records were shared with DeepMind to build an app to alert doctors about patients at risk of acute kidney injury (AKI). In a statement, the ICO told the BBC: "Our investigation into the sharing of patient information between the Royal Free NHS Trust and Deep Mind is close to conclusion. It revealed that more than 26 doctors and nurses at the Royal Free are now using Streams and that each day it alerts them to 11 patients at risk of AKI.


Tokyo court clears ex-Novartis employee of Diovan drug ad exaggeration charge

The Japan Times

A former employee of the Japanese unit of Swiss pharmaceutical giant Novartis AG was found not guilty Thursday of exaggerating advertising claims for the blood pressure-lowering drug Diovan. Besides clearing 66-year-old Nobuo Shirahashi of a violation under a pharmaceutical affairs law that bans fraudulent and exaggerated advertising, the Tokyo District Court also found the Tokyo-based sales arm Novartis Pharma K.K. While acknowledging that clinical trial data for the drug were manipulated, presiding Judge Yasuo Tsujikawa determined that the drugmaker's published research paper based on the data was not an advertisement that falls under the purview of the pharmaceutical law. He supplied the research team with manipulated data concerning patients who were not administered the drug.


Smart care: how Google DeepMind is working with NHS hospitals

AITopics Original Links

Google DeepMind, the tech giant's London-based company most famous for its groundbreaking use of artificial intelligence, is developing a software in partnership with NHS hospitals to alert staff to patients at risk of deterioration and death through kidney failure. In early pilots at St Mary's Hospital, part of Imperial College Healthcare NHS Trust, where Darzi is a consultant surgeon, they found medical staff responded 37% faster when alerted by the Hark app than when they used pagers. Everybody has a smartphone ... but the people saving lives every day are hampered by using desktop computers Despite DeepMind's expertise in artificial intelligence (AI) and machine learning, the smartphone app being piloted does not use either technology. "But the people doing incredible work saving lives every day are hampered by using desktop computers and software designed a long time ago."


Hospital trust partners with AI firm for healthcare tech

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The Royal Free London NHS Foundation Trust has announced a five-year deal with DeepMind, which became established as one of the leaders in the field when it was acquired by Google in 2014. We want to lead the way in healthcare technology and this new clinical app will enable us to provide safer and faster care to patients – which will save lives. "Doctors and nurses currently spend far too much time on paperwork, and we believe this technology could substantially reduce this burden, enabling doctors and nurses to spend more time on what they do best - treating patients." Royal Free London and DeepMinds plan to further develop Streams, so it will provide instantaneous alerts to doctors and nurses, and be used for dealing with other patient conditions such as sepsis and organ failure.


Novel biomarkers increase power to predict therapeutic response in lupus

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The research team hypothesized that a targeted panel of urinary biomarkers reflecting initial resident and inflammatory cell activation (cytokines), signals for homing to the kidney (chemokines), activation of inflammatory cells (growth factors), and damage to resident cells, combined with artificial intelligence/machine learning modeling, might provide an early LN decision-support tool that could predict outcomes better than standard biomarkers alone. Outcome models using novel biomarkers plus traditional clinical markers demonstrated greater AUC and significance compared to models developed with traditional markers alone ([AUC 0.79; P 0.001] vs. [AUC 0.61; P 0.05], respectively). The combined models also demonstrated greater power to correctly predict LN therapy outcomes (responder versus non-responder) than models using only traditional markers (76% vs. 27%, respectively [P 0.002]). The team identified chemokines, cytokines, and markers of cellular damage as most predictive of LN therapy response.


Biomarker Signatures of Prostate Cancer

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The team built on previous work, in which they identified more than 130 proteins that differed between fluid samples collected from patients with prostate-confined tumors and those with tumors that had spread beyond the gland. The team analyzed urine samples from 50 patients with prostate cancer -- 37 with prostate-confined tumors and 13 with tumors that spread -- and 24 healthy controls. Twenty-four of these showed differences between patients with cancer and healthy controls, suggesting these markers could be useful for diagnosis. The team next analyzed urine samples collected from a second, independent group of 117 healthy controls and 90 patients with prostate cancer (61 with stage T2, prostate-confined cancer and 29 with stage T3, cancer that's spread to nearby tissues called seminal vesicles).


UK healthcare products regulator in talks with Google/DeepMind over its Streams app

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DeepMind and the Royal Free have also been criticized for not approaching the UK's medicines and healthcare devices regulator, the MHRA, prior to using the Streams app in hospitals. However earlier this month, New Scientist obtained a copy of the data-sharing agreement between DeepMind and the Royal Free -- which revealed that rather than only getting access to data from patients directly affected by AKI, the agreement in fact shared all hospital admissions data, extending back a full five years. It's this secondary usage scenario of the data-sharing agreement that has drawn specific criticism from patient data privacy groups, among others, given that the data in question is personally identifiable -- which normally, under NHS regulations, can only be shared with third parties with implied consent if it is to be used for direct patient care. The Royal Free spokesman declined to answer these specific questions, pointing to an online Q&A that was published on the same day the MHRA contacted Google to discuss the app.