AI News, 40 Pharma Companies Using Artificial Intelligence in Drug Discovery artificial intelligence
Artificial intelligence in healthcare
Artificial intelligence (AI) in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data.
What distinguishes AI technology from traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user.
AI algorithms behave differently from humans in two ways: (1) algorithms are literal: if you set a goal, the algorithm can't adjust itself and only understand what it has been told explicitly, (2) and algorithms are black boxes;
AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care.
to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs.
that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.
During this time, there was a recognition by researchers and developers that AI systems in healthcare must be designed to accommodate the absence of perfect data and build on the expertise of physicians.
The ability to interpret imaging results with radiology may aid clinicians in detecting a minute change in an image that a clinician might accidentally miss.
A study at Stanford created an algorithm that could detect pneumonia at that specific site, in those patients involved, with a better average F1 metric (a statistical metric based on accuracy and recall), than the radiologists involved in that trial.
The emergence of AI technology in radiology is perceived as a threat by some specialists, as the technology can achieve improvements in certain statistical metrics in isolated cases, as opposed to specialists.
Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of cleft palate therapy in regard to facial attractiveness or age appearance.
In 2018, a paper published in the journal Annals of Oncology mentioned that skin cancer could be detected more accurately by an artificial intelligence system (which used a deep learning convolutional neural network) than by dermatologists.
One study conducted by the Centerstone research institute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response at baseline.
To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature.
Efforts were consolidated in 2013 in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms.
Other algorithms identify drug-drug interactions from patterns in user-generated content, especially electronic health records and/or adverse event reports.
DSP-1181, a molecule of the drug for OCD (obsessive-compulsive disorder) treatment, was invented by artificial intelligence through joint efforts of Exscientia (British start-up) and Sumitomo Dainippon Pharma (Japanese pharmaceutical firm).
The drug development took a single year, while pharmaceutical companies usually spend about five years on similar projects.
The subsequent motive of large based health companies merging with other health companies, allow for greater health data accessibility.
A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.
Intel's venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.
team associated with the University of Arizona and backed by BPU Holdings began collaborating on a practical tool to monitor anxiety and delirium in hospital patients, particularly those with Dementia.
The AI utilized in the new technology – Senior's Virtual Assistant – goes a step beyond and is programmed to simulate and understand human emotions (artificial emotional intelligence).
Doctors working on the project have suggested that in addition to judging emotional states, the application can be used to provide companionship to patients in the form of small talk, soothing music, and even lighting adjustments to control anxiety.
Virtual nursing assistants are predicted to become more common and these will use AI to answer patient's questions and help reduce unnecessary hospital visits.
Overall, as Quan-Haase (2018) says, technology “extends to the accomplishment of societal goals, including higher levels of security, better means of communication over time and space, improved health care, and increased autonomy” (p. 43).
Whil research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may nonetheless introduce several new types of risk to patients and healthcare providers, such as algorithmic bias, Do not resuscitate implications, and other machine morality issues.
We already have some scientists who know artificial intelligence and machine learning, but we want complementary people who can look forward and see how this technology will evolve.”
As of November 2018, eight use cases are being benchmarked, including assessing breast cancer risk from histopathological imagery, guiding anti-venom selection from snake images, and diagnosing skin lesions.
Why big pharma sees a remedy in data and AI
When Mac Holmes noticed a lump in the middle of his chest it took him a year to mention it to his physician.
Yet eight years since his diagnosis he also stands as a symbol of an idea fast gaining traction in the pharmaceutical industry, that data itself can be the drug that unlocks faster cures, bigger markets and higher profits.
Customarily securing permission to prescribe an existing drug to a wider group of patients would have required full-blown clinical trials that might have delayed the change for several years.
In a world where big pharma and big tech collide, investors are pouring billions of dollars into companies that offer access to the clinical insights contained in vast troves of anonymised patient records.
Chris Boshoff, chief development officer for Pfizer’s oncology division, says that by taking the conventional randomised control trials they had performed in women, and supplementing them with data generated by male users —
“To conduct a study in men, because we didn’t include them in [the initial studies], would have taken us another three to five years and here we could do all the work within 12 months and get the expansion,”
For executives preoccupied with improving pharma’s poor record on productivity, the power of data to accelerate drug discovery and reduce research costs represents fresh hope for corporate profits as well as patients.
Zach Weinberg, co-founder of Flatiron Health, which aggregated and analysed part of the data underpinning Ibrance’s label expansion to men, believes two major shifts in the landscape are driving pharma’s fascination with data.
Mr Weinberg, whose company operates from offices in New York’s SoHo district, says: “If you wanted to launch a drug in breast cancer 20 years ago, we would have thought of it as breast cancer.
Calculations for the Financial Times by Rock Health, a US venture fund focusing on digital health, suggest that in the five years to the third quarter of 2019 a total of $1.5bn across 69 deals has been invested in companies that use AI or machine learning, and that “either sell to biopharma or have biopharma as an end user”.
Other recent deals include Merck’s $40m investment last year in TriNetX, a clinical data and analytics company, while Roivant Sciences in 2018 committed $40.5m to Datavant, which connects fragmented health data sets to aid researchers.
Even five or six years ago, only about half the 20 biggest companies working on cancer drugs were deploying real world data, says Mr Weinberg.
He cites Rozlytrek, a treatment for metastatic non-small cell lung cancer approved by the FDA in August, which targets genetic mutations seen in only about 1 per cent of all patients with the condition.
Such data may also help reassure payers about the value of a new drug, no small advantage at a time when governments and insurers are exercising ever-tighter control of health budgets.
While identifying molecules that could turn into money-spinning drugs is the traditional business of big pharma, the key insight, on which he has sought to reposition the company since becoming chief executive in 2018, is that the data generated around those molecules is also a core asset.
Rather than writing off hundreds of millions of dollars in investment, Novartis took the data of the 10,000 patients involved in these initial studies and “discovered that this drug has a significant impact on lung cancer”, he says.
It has teamed up with several large pharma companies, including AstraZeneca and Sanofi, in work that Mr Narain believes can eventually lead to new drugs by yielding fresh insights into how they produce their effects.
Investors may soon become impatient for tangible results, in the form of stronger R&D pipelines, swifter approvals or drugs repositioned for new categories of patients —
8 Artificial Intelligence in Drug Discovery Trends and Statistics
For years now, I've documented AI in drug discovery startups, pharma's use of AI in drug discovery, and drugs in the AI in drug discovery pipeline.
My goal here is to highlight key trends and statistics related to AI in drug discovery.
I quantified this by looking at publications in PubMed mentioning AI in drug discovery or development (using this query).
I compared this to papers in drug discovery and development in general (using this query).
Not just an increase, but an increasing rate relative to drug discovery papers in general.
And as this chart shows, based on the data, the majority of startups focus on new and repurposed molecular entities.
This includes generating novel drug candidates, repurposing existing drugs, designing drugs, and validating and optimizing drug candidates.
Using data I've gathered on drugs in the AI in drug discovery pipeline, I wanted to look at where companies are focusing their efforts.
Similar to biopharma overall, oncology is well in the lead, followed by neurology and infectious disease.
compiled the data above using my own datasets, such as on startups using AI in drug discovery, pharma companies using AI in drug discovery, and drugs in the AI in drug discovery pipeline.
- On 2. marts 2021
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