Latest News on HealthTech and TechBio
“Wherever the art of medicine is loved, there is also a love of humanity.” By Hippocrates
Hi 👋 everyone and welcome back to another edition of MetaphysicalCells.
This week was like Dante's journey through Hell in Corfu, Greece (where I am located), with temperatures 🥵constantly above 40°C (104F), major wildfires 🔥 and a silent hybrid warfare with gazakia (incendiary explosive devices), reminding everyone “War and Capitalism” is still very trendy in a region where “West meets East”, while of course international bankers 🦈 are making money 💱.
Anyway, today’s newsletter is all about HealthTech and TechBio News 📰 .
Quote:
“How will you add value in the era of generative AI?
If you aren’t attempting to answer this question now by choice, it will ultimately be answered for you by force.”
By Hendrith Vanlon Smith Jr, CEO of Mayflower-Plymouth, Business Essentials
📌 According to a report published by dealroom.com, “HealthTech in the MENA region” (the MENA region refers to a group of countries situated in and around the Middle East and North Africa, or MENAT when explicitly includes Turkey) is now worth over $1.7B 💰, an 8x increase since 2017.
In particular, HealthTech startups in the region are taking off, with HealthTech investment reaching $106M in 2022, and founders having plenty of opportunities to attract funding, both from local and foreign investors.
HealthTech dedicated funds in the region are (from the blog “The State of Healthtech in the MENA region” written by Laura Rodriguez Bernate):
Blue Apple New Frontier Health Tech Fund based in Abu Dhabi, that invests in the medical and healthcare sector with a focus on hospitals, medical infrastructure, medical systems, healthtech and health promotion ($300M),
iGan Arabia based in Riyadh, that invests in emerging companies with strong IP that can benefit the MENA healthcare systems and populations ($250M),
TVM Capital Healthcare based in Dubai and Singapore, that invests in innovative – mostly tech - enabled and highly integrated – healthcare delivery companies, especially in the sector of single-specialty clinics ($250M),
Global Ventures Fund II located in Dubai, United Arab Emirates, that invests in information technology, healthcare, digital health, agtech, edtech and fintech sectors ($50M).
Oryx Fund in UK, investing in game-changing early-stage technology companies from Fintech, Healthtech, Enterprise tech, Logistics tech, and Edtech ($50M), and
Foundation Holdings Fund based in Dubai, a Healthcare, education and consumer investment firm ($30M).
Some of the HealthTech companies in the region that have raised big rounds include:
Vezeeta (A patient-centric digital healthcare platform that is re-imagining the future of the out-patient globally),
Altibbi (A digital health platform for the Arab World, providing access to health advice and information through website and apps),
MS Pharma (Promoting access to affordable generic pharmaceuticals and healthcare products across the META region) and
New Bridge Pharmaceuticals (Having core therapeutic areas of focus oncology, cardiology, neurology and rare diseases).
Overall, there is an increasing demand for telemedicine in the region, along with the adoption of remote monitoring solutions, that are game changers for prevention and proactive care. Since 2020, telemedicine service providers in medical centres and hospitals in the United Arab Emirates have started to offer a 360-degree solution using the help of a regulation-compliant health information system, supporting patients living in remotely built areas.
For more about MENA’s emerging HealthTech ecosystem download 📩 the report, (chapters: MENAʼs Tech ecosystem, Global HealthTech and HealthTech in MENA).
📌 NVIDIA Healthcare News
Nvidia is powering generative AI through a comprehensive suite of cloud services, pre-trained foundation models, cutting-edge frameworks, optimised inference engines and APIs to bring intelligence to your healtcare enterprise applications. During this On Demand Webinar “Implementing Large Language Models” you'll learn about:
Transformative power of LLMs in business operations and customer interactions. Examples of LLMs used in TechBio are:
“NYU, NVIDIA Collaborate on Large Language Model to Predict Patient Readmission” (that predicts a patient’s risk of 30-day readmission 🏥, as well as other clinical outcomes).
“Accelerating Gene Variant Detection With Deep Learning” (that is using an hybrid CPU/GPU workflow called HAT, to detect 🕵️ de novo variants from whole-exome and whole-genome sequencing datasets).
“Quicker Cures: How Insilico Medicine Uses Generative AI to Accelerate Drug Discovery” (to learn how 🔝 Insilico Medicine, by using generative AI, managed to reach the first phase of clinical trials in just two and a half years after the beginning of a project (instead of the six years of the old standard) and for one-tenth of the cost of the $400 million old traditional method.
“Building The World’s Largest Healthcare Data Platform” (learn how the UK National Health Service, NHS, aims to implement what is known as the NHS Federated Data Platform, to bring all of its information together into a single source where it can be used to improve decision-making and patient outcomes).
Pros and cons of training your own model vs using a service (build or buy).
Operational capabilities and infrastructure required to train LLMs.
How you can customise an LLM specifically for your use case through various tuning techniques. And
Safety and security for LLM-based conversational applications.
During the webinar a team of experts will stand by to answer your questions:
🦉 Pascal Chevereau, AI & Conversational AI Segment Sales:
Pascal has been with NVIDIA for more than 16 years in different roles. And for more than two years he’s been in EMEA segment sales, focusing on conversational AI, speech AI and natural language processing (NLP). And
🦉 Adam Czekalowski, Sr Developer Relations Manager:
Adam leads NVIDIA’s Developer Relations for generative AI and LLMs in EMEA, bringing more than 15 years of experience in the technology sector, with prior leadership at IBM and expertise in AI and big data.
Since we are talking about NVIDIA, you can have a limitless access to GPUs
for DL with Lambda Labs, that is one of the first cloud providers to make NVIDIA H100 Tensor Core GPUs available on-demand in a public cloud. Moreover, NVIDIA is close to a deal to take an equity stake ($300 million in new capital and may value Lambda Labs at more than $1 billion) in Lambda Labs, that competes with Amazon Web Services and other cloud providers in renting servers with Nvidia chips to companies.
Finally, AI Drug Discovery Firm Recursion Surges Following $50 Million Nvidia Investment.
A $50 million investment from the AI-linked chipmaker NVIDIA just sparked 🎇 a surge in Recursion, which uses ML to discover new medicines, since the new fund and collaboration will provide Recursion access to the most powerful AI computing company on earth. The goal of the collaboration is to commercialise and license AI models through BioNeMo, NVIDIA's cloud service for generative AI in drug development, by combining Recursion's vast dataset with NVIDIA's accelerated computing power.
Recursion is a clinical-stage TechBio company leading the space by decoding biology to industrialise drug discovery and has raised a total of $665.4M in funding over 18 rounds.
📌 Palantir is rapidly expanding it's presence in healthcare
Palantir, that specialises in big data analytics, announced this year a multiyear partnership with Cleveland Clinic to deliver an “operations virtual command center” to help the hospital system with data-driven decision making and resource allocation.
👉 “The right software is essential to modern healthcare, and we are thrilled to be working with Cleveland Clinic to bring the experience and expertise of Foundry to enable data-driven decision making and apply it to hospital operations…Not only will the Virtual Command Center improve efficiency and financial outcomes for Cleveland Clinic's facilities, but it will also allow for more patients to be seen and treated every day, and that's exactly the kind of mission-critical outcome Palantir was built on."
By Shyam Sankar, COO of Palantir
📌 Causaly, an AI platform for drug discovery and biomedical research, raises $60M
Causaly, a London startup, has raised $60 million, a Series B that will be going toward R&D and to continue building out its team. Yiannis Kiachopoulos, the CEO who co-founded the company with CTO Artur Saudabayev, said that Causaly has already worked with 12 of the world’s biggest pharmaceutical companies and some of the biggest names in medical research.
The company has now raised $93 million in total and is not disclosing valuation.
📌 Hippocratic A.I. is First Safety Focused LLM for Healthcare
Can Generative A.I. improve Healthcare in new ways?
For more
by Michael Spencer📌 Here’s how much pharma executives are paying for real-world data — and who they’re buying data from, from CB Insights, Digital Health.
Pharma companies are paying anywhere from $75K to $5M per year to acquire datasets that can speed up drug discovery, clinical trial design and regulatory reporting. These datasets that are being used to match patients to the right trials, design study protocols, create simulated control arms in clinical trials and identify promising drug targets, are know also as RWD — an umbrella term for patient data mined from health records, insurance claims, clinical registries, genomic databases and surveys of patient-reported outcomes.
Accordingly, pharmas are actively evaluating and investing in RWD vendors. For example:
Pfizer is working with Syapse on breast cancer data.
GSK partnered with Tempus to accelerate 🚄 with clinical trials and identify drug targets in oncology.
Sanofi has worked with Evidation, a company that collects patient-reported outcomes, across multiple therapeutic areas for over a decade.
To understand how pharma companies are making purchasing decisions, at CB Insights they mined Yardstiq transcripts — interviews that CB Insights conducts with software buyers — as well as Analyst Briefing surveys submitted to CB Insights by vendors. And here are the 5 key takeaways from this analysis:
The market is fragmented: Buyers find there is “no one-stop-shop” for data in all therapeutic areas. So, the market might consolidate as vendors try to gain a competitive edge by acquiring more granular and disease-specific datasets.
Integration with internal datasets: This is a key requirement for pharma buyers, along with transparency surrounding dataset limitations.
Demand for oncology data: Buyers have indicated that there’s an “unmet need for oncology real-world data,” including patient genomic datasets, and are turning to vendors like Tempus, Syapse and Flatiron.
AI features: From data curation to medical report generation to clinical trial applications, pharma executives are evaluating vendors based on their ML capabilities.
Non-standard pricing structures: Buyers have reported that “there wasn’t really a formal buying process” or that vendors “don’t really have a standard [pricing] model, but you can always agree on a collaborative model with them.”
For more download the report.
📌 Investors pour billions into generative AI startup Alphabet’s AlphaSense, that raised $100 million in April from Alphabet at a $2.5 Billion Valuation!
AlphaSense has spent over a decade refining its proprietary AI and NLP technology, so users can easily surface and track insights from millions of documents across earnings, broker research, company documents, expert calls, and more instantly. Their platform, trusted by thousands of the world’s top financial institutions and corporations, is also the leading market intelligence platform for life sciences in order to stay up to date on clinical trial pipelines, evolving markets and disease areas.
📌 Artificial Muscles Flex for the First Time: Ferroelectric Polymer Innovation in Robotics
A new ferroelectric polymer, that converts electrical energy into mechanical strain, has been developed by Penn State researchers showing potential for use in medical devices and robotics, and overcoming traditional piezoelectric limitations. Researchers improved performance by creating a polymer nanocomposite, significantly reducing the necessary driving field strength, and expanding potential applications.
📌 “Potentially we can now have a type of soft robotics that we refer to as artificial muscle.”
“This would enable us to have soft matter that can carry a high load in addition to a large strain. So that material would then be more of a mimic of human muscle, one that is close to human muscle.”
“…this 🆕 material can be used for many applications that require a low driving field to be effective, such as medical devices, optical devices and soft robotics.”
By Qing Wang, Penn State professor of materials science and engineering and co-corresponding author of the study recently published in the journal Nature Materials.
📌 How AI Is Detecting Diabetic Retinopathy
Eyenuk, is a pioneering company in medical AI, that recently gained FDA approval for an innovative technology that analyses images of the back of your eye and immediately detects if you have some form of diabetic retinopathy.
The EyeArt system of Eyenuk autonomously analyses patient's retinal images acquired, using an integrated fundus camera, and robustly detects signs of disease while returns an easy-to-read report in under 60 seconds. The EyeArt system by incorporating a number of DL and image analysis algorithms automatically assesses the quality of the images, detects the presence and extent of lesions, and determines level of disease based on internationally recognised clinical scales.
But there is more regarding AI and eyes, RetiSpec a Toronto-based medical imaging company that uses AI and a retinal scan can now help detect early signs of Alzheimer’s!
After all, Paulo Coelho was right in the Manuscript Found in Accra
“The eyes are the mirror of the soul and reflect everything that seems to be hidden; and like a mirror, they also reflect the person looking into them.”
📌 The FDA recently announced that more than:
520 FDA-approved AI medical algorithms are now available in the US,
written by Dave Fornell.
AI algorithms in medicine are not new — the FDA approved the first AI algorithm in 1995, and medical imaging makes up the majority of the 500 FDA-cleared AI algorithms (Radiology: 396, Cardiology: 58, Hematology: 14, Neurology: 10, Clinical chemistry: 7, Ophthalmic: 7, Gastroenterology and urology: 5, General and plastic surgery: 5, Pathology: 4, Microbiology: 4, Anesthesiology: 4, General Hospital: 3, Orthopedic: 1 and Dental: 1).
The areas of AI in imaging include:
• Diagnostic aids that can automatically identify critical findings.
• Automation of time-consuming functions such as quantification, contouring and auto complete of text in reports.
• Workflow improvements and automation.
• Data mining applications.
• Clinical decision support for next steps in the patient's care or to ensure imaging exam meets the guidelines.
• Modality specific AI to iso-center patients, choosing imaging protocols, or speeding MRI exam time.
• AI to enhance image reconstructions to improve image quality resolution unidentify to fix imaging artefacts.
• Guidance AI to help imagers get the best possible images, even if they are novice users of the system or are unfamiliar with the anatomy.
• Automatic anatomical identification, labelling and contouring of organs or specific types of tissue.
For the complete list of FDA-cleared algorithms “Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices” and “FDA-approved A.I.-based algorithms”. For more: “Medical AI Evaluation” an aggregation of 141 FDA approved AI devices with reports on how each device was evaluated.
📌 AIDDISON™: HARNESSING GENERATIVE AI TO REVOLUTIONIZE DRUG DISCOVERIES
Finally, Merck’s new AIDDISON™ drug discovery software taps into the power of AI, ML and CADD methods – providing a one-stop shop for AI-generative methods, virtual screening of large chemical spaces, and tools for hit-to-lead discovery and optimisation.
AIDDISON allows researchers to explore unbounded chemical space and generate ideas for entirely new compounds, and can help to rapidly identify the best drug-like candidate molecules based on their predicted activity. The tool also includes a synthetic accessibility score from our SYNTHIA™ retrosynthesis software, which predicts whether it will be possible to make the compound through chemical synthesis.
Until next time 🥤☀️🍉,
PS: “How Close Are We To A Vaccine For Parkinson’s Disease?”
On July 17, 2023, Vaxxinity, Inc a U.S. company pioneering the development of a new class of medicines, announced new data from a Phase 1 clinical trial demonstrating that antibodies derived from its investigational immunotherapeutic for Parkinson’s disease, UB-312, slows seeding of alpha-synuclein in cerebrospinal fluid of patients with Parkinson’s. For more: “Continued progress for Vaxxinity against Parkinson’s”.