Cambridge, Inc.

SEATTLE--( BUSINESS WIRE )-- CalmWave , the leader in eliminating non-actionable clinical alarms through data science and Transparent AI, today announced the appointment of healthcare and technology entrepreneur and executive Andrew Thompson to the company's Board of Advisors. Thompson joins the advisory board of world-class subject matter expert clinicians and renowned IT and Operations leaders, which includes: Howard Wilson, Chief Financial Officer, PagerDuty Dr. Richard Schaefer, Chief Health Informatics Officer, US Department of Veterans Affairs Dr. Heather Giannini, ICU Physician Dr. Jack Storey, ED Physician Dr. Nicolas Mark, ICU Physician “Andrew has spent his career at the nexus of healthcare and technology and has a deep appreciation for how to best address pressing problems facing our clinicians today, including alarm fatigue,” said Ophir Ronen, co-founder and CEO, CalmWave. “His expertise in hospital-focused healthcare aligns with our mission to revolutionize critical care environments. With Andrew on board, we are well positioned to accelerate our efforts in combating alarm fatigue through transparent AI, ultimately enhancing patient outcomes and staff retention.” Thompson is a Silicon Valley-based executive, entrepreneur, and investor with a 30-year track record of successful technology-based innovation. From 2003 to 2020, he was Co-Founder, Chief Executive, and Board Member of Proteus Digital Health, leading the company to unicorn status and a valuation of over $1.5B. The company’s digital medicine solutions dramatically improved clinical outcomes delivered by oral drugs. From 1994 to 1999, he was Co-Founder, Chief Executive, and Board Member of FemRx, a pioneer of innovative solutions for women’s health, completing a NASDAQ IPO and subsequent sale to J&J. From 1991 to 1993, he was Co-Founder and Chief Financial Officer of CardioRhythm, a leader in minimally invasive arrhythmia surgery, acquired by Medtronic. From 1992 to 1995, he was Chairman of the Board at Kistler Morse, leading a turn-around and sale of the company to Danaher. Thompson is a named inventor on over 50 patents. He received master’s degrees in Engineering from Cambridge and Education and Business from Stanford. “The widespread issue of alarm fatigue in ICUs poses a significant obstacle, undermining patient care and exacerbating the strain on nursing staff, who are grappling with escalating levels of burnout. Addressing this challenge is paramount,” said Thompson. “I am thrilled to join the team at CalmWave to improve the clinical working environment and optimize patient outcomes through the strategic integration of objective data and AI.” CalmWave is at the forefront of deploying state-of-the art digital technology into healthcare operations. CalmWave ingests high-frequency hospital data in real time and provides clinicians with a holistic view of the information with which to make the most informed decisions regarding alarm management and patient care. By leveraging Transparent AI to build knowledge from the vast amounts of data generated in healthcare settings, CalmWave enhances patient care, reduces the burden on staff and improves hospital operating performance. About CalmWave CalmWave's mission is to improve the lives of patients and providers, starting with eliminating non-actionable alarms for a more stable and quiet ICU. Alarm fatigue, endemic in the modern healthcare system, causes great stress for providers, patients, and their families, detrimentally impacting patient outcomes and staff attrition. The CalmWave platform remediates alarm fatigue, improves staff retention, and transforms Operations Health through data science and Transparent AI. CalmWave was recognized with the prestigious “Health Innovation of the Year” at the 2024 GeekWire Awards , following its 2023 win, “UX Design of the Year” . CalmWave is the fastest company to go from inception to funding at the AI2 Incubator , a startup incubator at the Allen Institute for AI . Headquartered in Seattle, CalmWave investors include Bonfire Ventures , Tau Ventures , AI2 Incubator , SeaChange Ventures , Hike Ventures , and the co-founders of PagerDuty . Visit CalmWave.ai for more information. Follow us on LinkedIn and X .

A Sanofi spokesperson said the pharma will try to find spots within the company for as many of the displaced staffers as possible. Sanofi will be closing the doors to a research facility in the biotech hub of Cambridge, U.K., a move that will impact around 90 people, a spokesperson for the French Pharma told Fierce Biotech. “Upon careful review of our R&D global footprint, we are proposing to end research operations in Cambridge, U.K., a site which joined our company through acquisition in 2021,” the Sanofi spokesperson told Fierce in an emailed statement March 13. The site previously belonged to antibody developer Kymab, a biotech that Sanofi acquired in 2021 for $1.1 billion upfront. The deal centered around an anti-OX40L monoclonal antibody designed to improve outcomes in patients with atopic dermatitis. That drug hit the main goals of a phase 2b trial last June, spurring talks of a phase 3 launch.   Despite that clinical success, it hasn't convinced Sanofi to maintain a footprint in Cambridge, a hub of biotech activity in the U.K. that is also the location of AstraZeneca's headquarters.   Around 90 people are employed at the Sanofi R&D site, all of whom have been informed of the closure, the spokesperson said. The Paris-based pharma will try to find a place within Sanofi for as many of the staffers as possible, the spokesperson added. The shuttering follows a strategy shift for Sanofi. The Big Pharma has decided to funnel cash into key programs rather than increase its operating profit margin, CEO Paul Hudson announced in October. In December, the company tipped three "pipeline-in-a-product” assets to generate 5 billion euros ($5.4 billion) in a year or more, one of several predictions intended to justify the decision to ditch its margin target. In better news for Cambridge's pharma credentials, AstraZeneca revealed last week that it will invest 200 million pounds sterling ($256 million) to expand the company's presence near its global headquarters. The plan includes a facility that will house around 1,000 employees, the drugmaker said.

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. 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. 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 which is computationally expensive and often inaccurate. Now, researchers from the University 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. "The 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." The 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. The 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. "The 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. The 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." The 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 pretrained 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. The 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. "The 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 -- resolve this fundamental low-data challenge and could unlock advances beyond late stage functionalisation." The research was supported in part by Pfizer and the Royal Society.