Tag Archive for: AI and pharma research

Well Known UK Biotech Company Leverages Open AI Technology for Drug Discovery

UK Biotech innovator e-Therapeutics says it intends to integrate Open AI’s GPT technology into its efforts to develop novel RNAi medicines.

The company has long been at the forefront of computational drug discovery; now, chief executive Ali Mortazavi has signaled e-Therapeutics’ intent to use Open AI’s GPT large language model (LLM) to further automate the quest to find new drug targets, notably in the area of gene silencing.

According to a company press release, specifically, he wants to transform and leverage its current technology HepNet using artificial intelligence.

“By placing LLMs at the core of our computation and harnessing GPT-4’s capabilities, we can now create specialized LLM ‘agents’ which will transform HepNet into a dynamic knowledge resource,” Mortazavi said in the release.

“GPT-4 and LLM integration will provide a unifying framework from which to drive every aspect of our pipeline and position e-Therapeutics as a global leader in hepatocyte biology and related diseases.

“Our long-term vision is to fully automate the preclinical drug discovery process, using GPT-4 and LLMs to access real-time information and interface with external applications, ultimately accelerating the development of life-saving treatments.

In the same press release, e-Therapeutics said it had a busy year, having made significant strides in its RNAi strategy, developing an expanding in-house pipeline of early candidates using the HepNet computational platform. The company said it is actively addressing high-need medical areas, with a focus on cardiometabolic diseases.

Citadel and AI for Drug Discovery

Similar to the way that e-Therapeutics is using Open AI’s GPT LLM to better determine RNAi drug targets, Citadel Discovery was launched in 2021 with the purpose of giving a kind of “open access” to the data and technology that will drive the future of pharma research.

Our platform provides an alternative framework for early drug discovery by leveraging our access to DNA-Encoded Libraries (DELs) to rapidly and cost-effectively generate readouts on tens of millions of small molecules that are used to train custom, project-specific AI models.

The costs of drug discovery continue to rise, with current estimates exceeding $2 Billion. Not to mention that bringing a drug successfully through all clinical trial phases takes, on average, 10-12 years in research and development. Artificial intelligence and machine learning in drug discovery hold the key to reducing these costs and timelines.

You can read much more about how AI is redefining drug discovery in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint, with a particular emphasis on drug discovery and Pharma research.

Rohit Mahajan is the President and Co-Founder of Citadel Discovery. He has particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

Citadel Discovery is dedicated to leveraging AI and MI for the purpose of democratizing access to the data and technology that will drive the future of biological exploration, drug discovery, and health technologies. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

Oracle and John Snow Collaborate With FDA to Enhance AI Tools for Drug Discovery

The FDA’s Sentinel Innovation Center has chosen Oracle’s Cerner Enviza and John Snow Labs to help develop innovative AI tools for drug safety and real-world evidence studies. The two AI innovators are now helping support the federal agency’s drug safety Sentinel Initiative. By developing AI tools aimed at extracting critical information from clinical notes within electronic health records (EHR), Oracle and John Snow will aid the FDA in better understanding the effects of medicines on large populations.

Specifically, while looking at the asthma drug montelukast and its possibility of mental health side effects, this two-year project will demonstrate how the use of AI and machine learning along with natural language processing (NLP) technology to analyze “unstructured data” such as handwritten notes can help fill gaps in knowledge.

Cerner Enviza leverages decades of life sciences expertise spanning commercial, real-world, clinical, and regulatory research. This includes working with a broad range of Oracle provider networks to help accelerate the discovery, development, and deployment of health insights and therapies. John Snow Labs is known for its AI and NLP in healthcare and is the developer of the Spark NLP library. Together, Cerner Enviza and John Snow Labs will develop a new methodology to enhance computerized queries, or phenotyping, of digital patient data and clinical notes to support pharmacoepidemiology.

Cerner Enviza, who will lead the team, was chosen by the Sentinel Innovation Center, which is headed by Mass General Brigham and Harvard Pilgrim Health Care Institute.

“Development and evaluation of tools that can enhance our ability to utilize unstructured EHR data is a key strategic priority for the Sentinel Innovation Center. We look forward to this new relationship and exciting initiative led by Cerner Enviza,” said Rishi Desai, Ph.D., Mass General Brigham executive leadership team member, Sentinel Innovation Center.

Traditional manual methods for analyzing clinician notes can often be a bottleneck for fully understanding the symptoms and outcomes that patients experience at the population level. However, advances in AI offer a scalable and transportable NLP processes.

“This is an incredible opportunity to work with these exceptional leaders to use Oracle’s de-identified EHR data to help transform unstructured clinical notes into validated and useable data for physicians and researchers,” said Mike Kelly, global head Cerner Enviza. “Connected technologies and unified data can accelerate innovation and, in turn, help providers realize better recommendations and outcomes for their patients.”

The truth is, particularly if you are researching something novel like new drug targets or emerging diseases like COVID-19, the vast majority of biomedical information out there is in unstructured formats that are in their raw form, such as doctor’s notes, hospital admission records, coroner’s reports, patent applications and so and so on. AI, when coupled with these kinds of NLP algorithms, offers a powerful solution to this problem. With NLP, AI algorithms are being written that can analyze any kind of document and other datasets and identify biologically relevant text elements such as the names of genes, proteins, drugs, clinical manifestations of a particular disease, and anything else relevant to a given drug discovery team’s target. NLP is one of the most powerful tools leveraged by AI for drug discovery, and this announcement by Sentinel Innovation Center shows that the FDA recognizes this.

This particular collaboration project, known as the Multi-source Observational Safety Study for Advanced Information Classification Using NLP (MOSAIC-NLP), is also supported by the participation of Children’s Hospital of Orange County, National Jewish Health, and Kaiser Permanente Washington Health Research Institute who, will provide clinical expertise and consulting.

Citadel and AI for Drug Discovery

Just as the FDA’s collaboration with Oracle and John Snow is targeted at leveraging AI to enhance Pharma research and improve drug discovery, Citadel Discovery was launched in 2021 with the purpose of giving a kind of “open access” to the data and technology that will drive the future of pharma research streamlining and lowering the costs of drug discovery and biological research.

The costs of drug discovery continue to rise, with current estimates exceeding $2 Billion. Not to mention that bringing a drug successfully through all clinical trial phases takes, on average, 10-12 years in research and development. Artificial intelligence and machine learning in drug discovery hold the key to reducing these costs and timelines.

You can read much more about how AI is redefining drug discovery in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint, with a particular emphasis on drug discovery and Pharma research.

Rohit Mahajan is the President and Co-Founder of Citadel Discovery. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

Citadel Discovery is dedicated to leveraging AI and MI for the purpose of democratizing access to the data and technology that will drive the future of biological exploration, drug discovery, and health technologies. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

AI Helps Researchers Find New Antibiotic to Attack “Superbug”

Scientists have used artificial intelligence to identify a new antibiotic that could prove to be quite useful in fighting a deadly drug-resistant bacteria commonly found in hospitals and medical offices.

Researchers report they used an AI algorithm to predict molecules that would neutralize the drug-resistant bacteria Acinetobacter baumannii. Researchers discovered a potential antibiotic, named abaucin, “can effectively suppress” the growth of the stubborn bacteria on the skin of mice, according to a study this week in the journal Nature Chemical Biology.

While the preliminary results on the potential new drug would need to be validated in larger studies, researchers believe the process used to winnow thousands of potential drugs to identify one that may work is an approach that can work in drug discovery.

“There’s a lot of trepidation around AI, and I genuinely understand it,” said Jonathan Stokes, lead author of the paper and an assistant professor of biomedicine and biochemistry at McMaster University in Ontario, Canada. “When I think about AI in general, I think of these models as things that are just going to help us do the thing we’re going to do better.”

Stokes teamed up with researchers from the Broad Institute of MIT and Harvard to screen for potential antibiotics to use on A. baumannii, a superbug that can cause infections in the blood, urinary tract, and lungs. This bacteria usually invade hospitals and healthcare settings, infecting vulnerable patients on breathing machines, in intensive care units, and undergoing operations.

The bug is one of many so-called “drug-resistant” bacteria. It had infected 8,500 in hospitals and killed 700 in 2017, according to the Centers for Disease Control and Prevention.

How Did the Researchers Use AI to Pinpoint A Particular Antibiotic?

In order to specifically target the deadly drug-resistant strain, the researchers evaluated 7,684 drugs and the active ingredients of drugs to find out which ones would be most effective against the bacteria which was grown in the lab.

Stokes said the lab team developed AI models to predict which ones would have the highest likelihood of antimicrobial activity, narrowing the field to 240 drugs or active ingredients. Researchers then narrowed the field again through testing before discovering a molecule RS102895, renamed abaucin, that appeared to be potent against the superbug.

Researchers said they could screen a much larger volume of potential drugs by using the predictive power of AI and machine-learning techniques. The study said while existing high-throughput screening can evaluate a few million drugs or chemical ingredients at once, algorithms developed from machine learning can assess “hundreds of millions to billions” of drug molecules.

Citadel and AI for Drug Discovery

Just as Stokes and his team used AI to find novel molecular formulas for the development of a new antibiotic specifically for combating a particular strain of deadly bacteria, Citadel Discovery was launched in 2021 with the purpose of giving a kind of “open access” to the data and technology that will drive the future of pharma research streamlining and lowering the costs of drug discovery and biological research.

The costs of drug discovery continue to rise, with current estimates exceeding $2 Billion. Not to mention that bringing a drug successfully through all clinical trial phases takes, on average, 10-12 years in research and development. Artificial intelligence and machine learning in drug discovery hold the key to reducing these costs and timelines.

You can read much more about how AI is redefining drug discovery in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint, with a particular emphasis on drug discovery and Pharma research.

Rohit Mahajan is the President and Co-Founder of Citadel Discovery. He has particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

Citadel Discovery is dedicated to leveraging AI and MI for the purpose of democratizing access to the data and technology that will drive the future of biological exploration, drug discovery, and health technologies. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

Digital Twins Are Reinventing Clinical Drug Trials

A cognitive digital twin is an AI-driven representation of a real-world system. The objects being twinned can be mechanical such as vehicles, ships, and airplanes, or biological, such as organs and biological processes. It is the latter that is radically altering pharmaceutical research and may very well change the nature of clinical drug trials forever.

Traditional drug discovery is a long and complex process that can take years and millions and millions of dollars. The long and intensive process of bringing a new drug through all phases of clinical trials and to market starts with recruiting the right candidates and then proceeds through many steps and phases of testing the drugs vs. placebos in the candidates that have been recruited for the trial.

Finding those patients is one of the most time-consuming aspects of the process. But that is all changing thanks to AI and, specifically, cognitive digital twin (CDT) technologies.

Cognitive digital twins behave virtually the same way, statistically, as their physical counterparts, which makes them ideal for the powerful ability of AI to assimilate massive amounts of data and make remarkably accurate predictions.

Digital twins have been used quite effectively for monitoring health and providing preventive maintenance for some very highly complex systems, such as high-performance sports cars to military aircraft.

Now, they are changing the very landscape of drug discovery by modeling perhaps the most complex system of all organs and even complete human beings. For example, digital twins of patients are now being used to find ideal candidates in that all-important recruitment phase of a drug trial. The twin is created using AI algorithms and machine learning to create a “virtual patient” by leveraging data from previous clinical trials and from individual patient records. The model predicts how the patient’s health would progress during the course of the trial.

This kind of CDT technology is also being used to create “virtual patients” who are “stand-ins” for the control group – the ones getting a placebo – in the typical double-blind drug trial protocol. The digital twin patient predicts how that individual patient would react if they were given a placebo, essentially creating a simulated control group for a particular patient. Think of it as splitting yourself into two distinct exact copies of yourself, one given the actual drug and the other given the placebo as a control. This makes for an even more accurate control group than just splitting all those in the trial into two groups as in typical trials, because the control group is now exactly the same as the group getting the drug. The digital twin virtually eliminates any variance between the drug group and the placebo group that could be based on genetic, physical, and lifestyle differences between the two groups.

Furthermore, replacing or augmenting control groups with digital twins could help patient volunteers as well as researchers. Most people who join a trial do so, hoping to get a new drug that might help them when already-approved drugs have failed. But there’s a 50/50 chance they’ll be put into the control group and won’t get the experimental treatment. Replacing control groups with digital twins could mean more people have access to experimental drugs.

In Silico Research

And finally, another area where CDT technology is making a tremendous difference in drug discovery is in the emerging area of “in silico” research, where digital twinning is used to create so-called “organs on a chip.” Digital twins of the human heart, lungs, and other organs are already being used to hyper-accelerate drug discovery.

One of the promises of CDT is to make complete in silico drug trials from start to finish a reality. Early successes occurring now are paving the way to a time in the not-so-distant future where no humans, nor animals, not even a single living cell will be required for drug discovery — and yet the impact of any given therapeutic or treatment option on a targeted organ, system or even an individual cell can be perfectly charted.

Citadel and AI for Drug Discovery

AI and machine learning are having a tremendous impact on healthcare in America, from streamlining hospital operations, to improved diagnostics and more intuitive telemedicine applications. However, AI’s greatest impact will likely be in the way digital twins and other AI solutions are revolutionizing pharmaceutical research.

To that end, Citadel Discovery was launched in 2021 with the purpose of giving a kind of “open access” to the data and technology that will drive the future of pharma research streamlining and lowering the costs of drug discovery and biological research.

The costs of drug discovery continue to rise, with current estimates exceeding $2 Billion. Not to mention that bringing a drug successfully through all clinical trial phases takes, on average, 10-12 years in research and development. Artificial intelligence and machine learning in drug discovery hold the key to reducing these costs and timelines.

Rohit Mahajan is the President and Co-Founder of Citadel Discovery. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

Citadel Discovery is dedicated to leveraging AI and MI for the purpose of democratizing access to the data and technology that will drive the future of biological exploration, drug discovery, and health technologies. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

Leading Cancer Center MD Anderson Taps AI for Drug Discovery

One of the country’s leading comprehensive cancer centers has just announced that it is tapping an artificial intelligence-powered drug discovery platform to aid its development of novel cancer therapeutics.

The center is working with AI developer Exscientia to aid its discovery of new cancer drugs. According to an MD Anderson press release, the collaboration will start with “jointly identified oncology targets and then employ Exscientia’s AI platform to design small-molecule drugs.” The resulting candidates will be examined by MD Anderson’s Therapeutics Discovery division and its Institute for Applied Cancer Science, and the most promising prospects will potentially advance into clinical proof-of-concept studies at the Houston cancer center.

MD Anderson’s drug discovery institute, known as IACS, and the cancer center’s other teams have to date helped graduate at least five small-molecule and antibody-based therapies into early-stage clinical testing, including through collaborations with Bristol Myers Squibb, Ionis, Astellas and more.

The financial terms of the joint venture were not disclosed; however, in their announcement, Exscientia and MD Anderson said they will “jointly contribute to and support each program” that is targeted for development.

Exscientia, has been a leader in AI-driven design of large-molecule drugs and antibody therapies. In addition to partnering with facilities such as MD Anderson and well-known pharmaceutical companies, earlier this year, Exscientia found itself with the rights to develop a drug of its own. After wrapping up an AI collaboration with Bayer to develop targets in cancer and cardiovascular disease, the two companies announced that Exscientia would retain the option to develop one of the two targets.

Citadel and AI for Drug Discovery

Similar to the partnership between Exscientia and MD Anderson, Citadel Discovery is sharing knowledge and expertise to better enable drug discovery by providing access to data, models, and results discounted for academics and by developing a sharing platform and an expanded list of drug targets.

Citadel was launched in 2021 with the purpose of giving a kind of “open access” to the data and technology that will drive the future of pharma research streamlining and lowering the costs of drug discovery and biological research.

The costs of drug discovery continue to rise, with current estimates exceeding $2 Billion. Not to mention that bringing a drug successfully through all clinical trial phases takes, on average, 10-12 years in research and development. Artificial intelligence and machine learning in drug discovery hold the key to reducing these costs and timelines.

Rohit Mahajan is a Managing Partner at BigRio and the President and Co-Founder of Citadel Discovery. He has a particular expertise in the development and design of innovative AI and machine learning solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

CarTwin has leveraged AI and Digital Twin technologies to create a digital, cloud-based clone of a physical vehicle designed to detect, prevent, predict, and optimize through AI and real-time analytics. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.