A lot of AI firms choose to make grand claims, but Formation’s product is more regular than would be expected. Formation intends to collaborate with biotech companies to enhance the automation of their workflows. This could involve the generation of individualized patient recruiting content for particular groups of a clinical trial or the rapid production of adverse event reports generated by artificial intelligence.

“In the long term, the company’s goal is to build and train AI models that can better predict toxicity, tolerability, and one day, efficacy,” Formation said in a press release.

The firm also mentioned that it aims to give drug-producing groups the support they seek in terms of research and development through a so-called ‘AI R&D Scientist’ that it hopes to create in the near future.

Sanofi, which has additional AI-focused partnerships with partners such as Owkin and Exscientia, was a prominent participant in the series D, which was headed by a16z.

Formation, previously known as TrialSpark, has said that it intends to use this funds to continue investing in the acquisition and licensing of clinical-stage assets from its partners in the pharmaceutical and biotechnology industries.

There are currently three candidates in the firm’s clinical pipeline, one of which is a SYK/JAK inhibitor called gusacitinib, currently in phase 3 testing for the treatment of chronic hand eczema. A sodium channel blocker known as ASN008 is also being tested in phase 2 trials for the treatment of notalgia paresthetica and itching linked to atopic dermatitis. Additionally, an FGF18 medication known as sprifermin is also being tested in phase 2 for the treatment of knee osteoarthritis.

Gusacitinib and ASN008 were both acquired from Asana BioSciences in 2022, whereas sprifermin was obtained through a licensing agreement with Merck KGaA.

The company stated in the release that collaborating with Formation Bio allows smaller biotech firms to progress beyond their primary asset, paving the way for other drugs in their pipeline. For large pharmaceutical organizations, Formation Bio offers an off-balance-sheet method for development, helping them pursue more opportunities in a financially efficient manner.

A month ago, Sanofi announced that it would be forming a relationship with both Formation and OpenAI to develop new medications. This morning’s press announcement included a message from CEO Paul Hudson, in which he reaffirmed that the French pharmaceutical company is fully committed to artificial intelligence.

Hudson expressed pride in partnering with and investing in Formation Bio, emphasizing that their AI-driven approach to drug development will be extremely beneficial to the industry as a whole. He highlighted the shared goal of accelerating and enhancing the process of delivering new medicines to patients.

This combination is hoped to provide insight into whether there is a correlation between these factors and migraines.

People can be encouraged to complete the surveys through the proximal rewards in the form of points and in turn, more information, such as pharmacy claims and electronic health records can be acquired for further understanding of normal migraine treatments.

An initial pilot study of the MigraineSmart program, involving 3,492 participants, revealed the following findings:

– In terms of treatment, participants were asked what medications they took for their symptoms and 43% of them reported to have used prescription drugs. Triptans topped the list of medications used.
– From the migraines days to the other days, the participants were found to be moving an average of 50% less per day.
– More than half of the participants admitted that they have shared or would share their monthly MigraineSmart report with their physicians.

MigraineSmart is a multi-component platform that includes survey information as well as ePRO, wearables, and evidence-based content in the Evidation app.

In another survey of a considerably higher population of 21,707 people, more than 40% reported having not sought professional healthcare for the ailment in the previous year.

The pilot research also supported the hypothesis and the participants provided data that they are less physically active and took only half of the usual steps when they had a migraine. Migraines can be a chronic condition, and it is estimated that over 40 million people in the United States have been diagnosed with it.

Migraine is a recurrent and painful type of headache, which more than 40 million Americans experience- women are more vulnerable to this condition as compared to men. These headaches can, therefore, cause a good deal of discomfort, affecting physical and mental health, work productivity, and social interactions. As far as the factors concerning migraines, they are believed to be caused by genetic, environmental, and other factors.

According to a Reuters report last year, the company had intended to recruit ten patients when it submitted its application to establish clinical trials in the country. The goal of Neuralink’s implant is to provide patients who are paralyzed with the capacity to utilize digital devices solely through their thoughts. This could be beneficial to individuals who have sustained spinal cord injuries.

Neuralink was subjected to scrutiny from brain implant experts and former regulatory officials prior to the firm’s decision to disclose data from its trial this week. The brunt of the criticism was for Neuralink’s failure to engage in the industry-standard practice of releasing details regarding the study.

The U.S. Food and Drug Administration (FDA), which gave its approval to the clinical experiment, said that it is typically preferable for companies to share information about their research to increase public confidence and honor those who take part in the trials.

The FDA did not provide a statement on Neuralink, and officials from the firm did not reply to a request for comments.

The main completion date for the research being conducted by Neuralink is anticipated to be 2026, while the overall study is expected to be finished in 2031. Patients between the ages of 22 and 75 who have conditions such as quadriplegia will be recruited for the research.

Patients must have restricted mobility that has not improved for at least one year, and their life expectancy must be at least 12 months to meet the qualifying requirements displayed on the database pertaining to the program.

To be eligible, patients must have extremely limited or no mobility in their hands, arms, and wrists as a result of spinal cord injury or a neurological illness called amyotrophic lateral sclerosis (ALS).

According to the registration data provided, the “first-in-human early feasibility study” started in January. Noland Arbaugh, who suffered paralysis from the shoulders down in a diving mishap in 2016, became the first patient to have the device implanted in January by the company. Arbaugh has been using the device to play video games, surf the internet, and control his laptop cursor just through his thoughts, as per the company.

Posting trial materials to the U.S. National Institutes of Health’s ClinicalTrials.gov site is not required for early feasibility studies; however, registration on the database is generally required by prominent medical publications.

According to those within Neuralink, the company has been taking patient inquiries over the phone for a long time, even before it got the green light to start human trials last year.

Using a robot, a brain-computer interface (BCI) will be surgically implanted in an area of the brain associated with motor intention, Neuralink has stated.

Singh and Khan, who worked as an Adobe AI researcher and MIT biotech researcher respectively, tout their large language model technology, OncoLLM.

Only 20% of medical data exists and is stored in a structured, uniform manner, including information on patient demographics and laboratory values, and many software exists to quickly analyze this information. In contrast, generative AI that could analyze unstructured information did not exist prior to Triomics. The software achieves this by using institution-specific inputs that sift through the cumbersome details within patients’ medical records.

Additionally, the model can perform a wide range of tasks by utilizing specific use cases. There is gross inefficiency among cancer patients being assigned to clinical trials, as observed by statistics published in the Journal of Clinical Oncology. In its April issue, the journal shared that only about 20% of cancer patients are enrolled in a clinical trial. One important function that the model can perform is matching patients to relevant clinical trials. This helps manage new treatment options for cancer patients, test them out, as well as manage the side effects of existing treatments as identified by the National Cancer Institute.

In an example from the published study, it was observed that tasks that took medical practitioners hours of manual labor could be achieved within minutes through the use of the model. In the domain of matching patients to relevant clinical trials, healthcare staff has to devote hours upon hours to assign a single patient to a clinical trial because they have to be conscious of strict inclusion and exclusion criteria for trials. In comparison, the Triomics model took minutes to match 90% of the patients assigned to it to a clinical trial.

Executives at the company also pointed out the model’s competency when extracting structured data points from unstructured notes. Its accuracy in this regard was observed to be comparable to proprietary models like GPT-4 or Claude.

Despite being almost equally effective, there is a marked difference in the price points for both models. According to company representatives, Triomics is 40 times cheaper than these proprietary models. While the running costs for GPT-4 are over $6000 per hour, OncoLLM costs about $170 per hour.
An end-to-end large-scale empirical evaluation of the model’s abilities conducted in collaboration with the Medical College of Wisconsin led to Triomics setting up pilot systems across several cancer centers. Data from these centers regarding the model’s capabilities will be released by the end of this year.

The biobots, named Anthrobots, were crafted using unaltered human tracheal cells, resulting in self-assembling multicellular structures. Remarkably, these Anthrobots demonstrated the ability to stimulate the regrowth of live, injured human neurons, showcasing potential applications in regenerative medicine. The scientists aim to utilize Anthrobots for various purposes, including clearing arterial plaque, recognizing bacteria and cancer cells, and serving as tools in regenerative medicine.

Study co-lead Michael Levin, expressed fascination at the unexpected capability of normal patient tracheal cells, devoid of DNA modifications, to autonomously move and encourage neuron growth in damaged areas. The researchers are now delving into understanding the underlying healing mechanisms and exploring additional functionalities of these constructs.

Human tracheal cells were chosen for Anthrobots due to their inherent anatomical characteristics, such as cilia-covered surfaces. These hair-like projections, when induced to grow outward, acted as “oars,” enabling the Anthrobots to move across tissue. The researchers observed the organic development of Anthrobots into various subtypes, including stationary “wigglers” and those moving linearly or in small circles, with a lifespan of 45 to 60 days in a petri dish.

This development builds upon previous work with Xenobots, another type of biobot created by Levin’s lab. Unlike Anthrobots, Xenobots were individually sculpted from frogs’ embryonic stem cells and focused on self-healing rather than healing other cells. The novel Anthrobots, grown organically from tracheal cells of adult donors, exhibit motility comparable to Xenobots but possess the added ability to stimulate the regeneration of adjacent neurons.

To assess the regenerative potential of Anthrobots, the researchers created a 2D layer of human neurons and induced a wound, mimicking injury. Employing the concept of “swarm intelligence,” Anthrobots were grouped into a “superbot,” strategically placed around the wound to facilitate repair. Remarkably, within 72 hours, the native neuronal tissue regrew to close the wound gap where superbots were present, indicating their role in the regenerative process.

The study sets the stage for further research to explore therapeutic applications of Anthrobots, potentially customizing them with different ciliated cell types or inducing ciliation in cells lacking natural cilia. Beyond regenerative medicine, these biobots hold promise for diverse applications, such as clearing arterial plaque. Similar biobots, including E. coli-based “microbots,” are already undergoing testing for potential use in cancer treatment by delivering drugs to tumor cells.

The collaborative endeavor seeks to leverage this advanced technology to enhance operations and promote sustainability at Amgen’s future assembly and final product packaging facility. This facility is scheduled to commence operations next year in the broader Columbus, Ohio, area.

The new Amgen facility is being meticulously designed to incorporate cutting-edge digital and automated technologies, featuring a connected platform on AWS utilizing Amazon SageMaker. SageMaker, a tool for constructing, training, and deploying machine learning models, will play a pivotal role in this initiative, as highlighted in an AWS press release.

The primary objective of this platform is to minimize the necessity for human intervention throughout the manufacturing process, among other objectives. Additionally, the system will facilitate the gathering and reporting of real-time performance metrics, seamlessly integrating them with machine learning models to foresee and avert equipment failures.

Over the last decade, Amgen and AWS have been actively engaged in the exploration of generative AI and machine learning technologies, particularly within the realms of the drugmaker’s research and development functions. Their collaboration extends to looking out for AI applications across various operational facets within Amgen, utilizing the capabilities of Amazon platforms.

Matt Wood, AWS VP of technology, remarked that customers in healthcare and life sciences are notably innovative. He pointed out that these industries had been actively engaging with AI for an extended period, well before the introduction of ChatGPT turned it into a popular trend.

In the biopharmaceutical industry, Amgen is not alone in its adoption of AI in response to the demands of an increasingly digital landscape. As a case in point, in June, Sanofi introduced a new application developed in collaboration with AI company Aily Labs, providing a comprehensive “360” view across all Sanofi activities. Concurrently, Sanofi committed to becoming “the first pharma company powered by artificial intelligence at scale.”

The introduction of the platform – named ‘plai’ – marked a significant milestone in Sanofi’s comprehensive digital transformation initiative across the entire company. Sanofi has integrated AI technologies into various teams, encompassing research, clinical operations, and product development, as part of its broader strategy.

In a parallel development, Boehringer Ingelheim, based in Germany, has joined the ranks of pharmaceutical companies leveraging machine learning tools for drug discovery. Recently, the company announced its collaboration with IBM, utilizing an artificial intelligence model to explore new antibody therapeutics.

Furthermore, Boehringer entered a target identification agreement with Phenomic AI, involving an initial payment of $9 million and the potential for up to $500 million in milestones. This strategic collaboration underscores the industry’s growing trend toward incorporating advanced AI technologies in various aspects of pharmaceutical research and development.