News 🆕 and Trends on TechBio
"Health care is becoming more digitized and consumer oriented. It’s not an overnight change, but more like how summer turns into fall – gradual yet very perceptible." Greg Scott, Deloitte Consulting
Hi 👋 everyone and welcome back to another edition of MetaphysicalCells on TechBio News.
News 🆕 and Trends
➡️ Last week Atomwise announced the nomination of its first AI-driven development candidate into clinical trials, via the platform AtomNet®, focused on TYK2 inhibition with an orally bioavailable and allosteric TYK2 inhibitor. TYK2 is a key mediator in cytokine signalling pathways linked to a broad range of immune-mediated inflammatory conditions and the modulation of the TYK2 pathway has the potential to treat a wide range of autoimmune and autoinflammatory diseases, including inflammatory bowel disease, systemic lupus erythematosus, psoriasis, and psoriatic arthritis. For that reason, Atomwise appointed Neely Mozaffarian, MD, PhD as Chief Medical Officer that brings over 25 years of immunology and clinical research experience.
Atomwise, that has a strategic multi-target research collaboration with Sanofi (with an upfront payment of $20 M and the potential for $1 billion in milestone-based payments plus tiered royalties) and has Gavin Hirst, PhD as Chief Scientific Officer (known for the discovery and development of lorpucitinib and mivatonib and co-inventor of vaborbactam which was approved by FDA in combination with meropenem), has raised over $174M so far.
➡️ Iambic Therapeutics (formerly known as Entos) just announced the closing of an oversubscribed $100M Series B financing (for a total of $153M) co-led by Ascenta Capital and Abingworth, and also including new investors NVIDIA, Illumina Ventures, Gradiant Corporation, Shanda and independent board member Bill Rastetter.
Iambic has combined physics and AI to create a differentiated drug discovery platform that achieves a step-change in the speed and success rate for delivering best-in-class and first-in-class development candidates with:
NeuralPlexer: a protein-ligand structure prediction 3D physics-based equivariant generative diffusion tool,
OrbNet: an AI-accelerated quantum chemistry Graph neural network architecture based on quantum features,
PropANE: a multi-endpoint property prediction tool, and
Magnet: a generative molecular design tool.
Iambic has discovered so far two candidates to advance into the clinic:
IAM-H1, a highly potent and irreversible tyrosine kinase inhibitor (TKI) that selectively targets HER2 and HER2 mutants, while sparing EGFR. And
IAM-C1, a potential first-in-class selective dual cell-cycle kinase CDK2/4 inhibitor to address unmet needs in terms of therapeutic window and treatment resistance in cell-cycle-driven cancers.
➡️ Novo Nordisk, the Danish pharmaceutical giant maker of Ozempic and Wegovy, announced a partnership with Valo Health that is working to harness high-quality real-world patient data and powerful AI-driven technology through Valo’s Opal Computational Platform and Biowire the human tissue modelling platform designed to speed up the discovery and development process.
Nordisk has also licensed three preclinical drug discovery programs in cardiovascular diseases discovered and developed by Valo using the Opal Computational Platform, while Valo will receive an upfront payment and a potential near-term milestone payment totalling $60M, and is eligible to receive milestone payments for up to 11 programs, totalling up to $2.7 billion, plus R&D funding and potential royalty payments. In total Valo has raised $460M.
➡️ Medeloop.ai, a healthtech startup for conducting early stage research and clinical trials, has just raised $8M in seed funding to launch an end-to-end solution for early stage clinical research.
In particular, by employing sophisticated AI techniques Medeloop’s platform streamlines research by simplifying grant applications, data collection, harmonisation, biomarker and trend identification and manuscript submissions. For example, you can easily send an email to your research participants to download the Medeloop participant data collection app, in order to:
Retrieve electronic medical records (EMR) from 16,500+ hospitals and medical clinics
Respond to surveys sent by researchers
Execute assessment tasks
Connect to over 220 wearable devices (e.g., Apple Watch, Fitbit, CGMs)
Scan and log food intake using computer vision
Communicate with the research team
Visualise their data (from thousands of disparate sources onto one platform: EMR, electronic health record EHR, wearables, multi-omics and environmental)
Sign 21 CFR part 11 compliant documents (consent forms, HIPAA waivers, etc.)
Upload symptoms using voice or text
Connect with other patients from the same cohort, and
View environmental data sets (CDC Environmental Justice Index).
Then once you ensure that all your data is properly linked, cleaned, harmonised, and compliant with the highest industry interoperability, privacy and security standards (including HIPAA and SOC 2), you can properly tag your data based on Observational Medical Outcomes Partnership (OMOP), Systemized Nomenclature of Medicine – Clinical Terms (SNOMED CT) and taxonomy codes (such as International Classification of Diseases, ICD, and CPT) and subsequently use generative AI and natural language processing to query and analyse vast data sets. Finally, the automate paper writing tool allows you to write manuscripts quickly and efficiently, by using their text editor that leverages advanced large language models (LLMS).
➡️ Exscientia, one of the first AI drug discovery companies, announced last month a collaboration with Merck KGaA focused on the discovery of novel small molecule drug candidates across oncology, neuroinflammation and immunology, utilising Exscientia’s AI-driven precision drug design and discovery capabilities while leveraging Merck’s disease expertise in oncology and neuroinflammation, clinical development capabilities and global footprint. Under the terms of the agreement, Exscientia will receive an upfront cash payment of $20M from Merck and will be eligible for discovery, development, regulatory and sales-based milestone payments of up to $674M in aggregate, if all milestones for all three initial programmes are achieved.
Exscientia that had several programs so far in late discovery-early clinical development has provided this month an update on its pipeline prioritisation strategy designed to further strengthen the company’s focus, investment and infrastructure on programmes of greatest potential for differentiation and value creation. Accordingly, Exscientia intends to prioritise its internal development efforts and focus its resources on the most differentiated, highest value oncology targets within its portfolio, such as
LSD1, demethylates histone, that plays a critical role in regulating the expression of genes which suppress differentiation and drive the proliferation and survival of several tumour types.
EXS74539 or ‘539 is the LSD1 inhibitor (wholly owned by Exscientia), a potent selective, reversible and brain penetrant inhibitor with potential in both haematology and solid tumours progressing through IND/CTA-enabling studies. Exscientia intends to initiate a phase 1 healthy volunteer trial in the first half of 2024 that could support more efficient development of ‘539 in multiple indications and in combination with other therapies.
CDK7, cyclin-dependent kinase 7 or cell division protein kinase 7, is a member of the cyclin-dependent protein kinase (CDK) family known to be important regulators of cell cycle progression.
GTAEXS617 or ‘617 is the CDK7 inhibitor (co-owned with GT Apeiron Therapeutics redefining medical discovery, using AI to streamline the drug development process—from target selection to clinical trials), is in phase 1/2 trial to evaluate ‘617 for the treatment of solid tumours. So, the patient enrolment continues in the ELUCIDATE phase 1/2 adaptive trial in patients with advanced solid tumours including head & neck cancer, HR+/HER2- breast cancer, non-small cell lung cancer, pancreatic cancer, ovarian cancer and colorectal cancer.
In parallel to ELUCIDATE, GT Apeiron and Exscientia are undertaking a comprehensive translational initiative to study the potential enrichment for patients most likely to respond to GTAEXS617. This precision medicine-based approach involves the integration of data from clinical endpoints, peripheral and tumour multi-omics data and correlation of those data with previously collected ex vivo results to potentially predict GTAEXS617 treatment response, thus increasing the probability of treatment success.
Moreover, additional ongoing clinical, IND-enabling and discovery programmes are:
Exscientia’s MALT1 (MALT1 Paracaspase) inhibitor is progressing through IND/CTA enabling studies and the company expects to be able to provide further updates in the first half of 2024. The inhibitor, EXS73565, is highly differentiated due to reduced UGT1A1 inhibition (a key enzyme in detoxification of endogenous harmful compounds such as bilirubin) combined with potency and selectivity.
And a majority of the future pipeline will be advanced through high-value partnerships like:
EXS4318 (PKC-theta), DSP-0038 (5-HT1A agonist/5-HT2A antagonist) and DSP-2342 (dual 5-HT2A/5-HT7 antagonist) all continuing in phase 1 studies by partners Bristol Myers Squibb and Sumitomo Pharma.
First milestone achieved in the Sanofi collaboration.
The new collaboration with Merck KGaA initiated with 3 programmes.
Exscientia has raised a total funding of $429M over 8 rounds.
➡️ Hoth Therapeutics, a catalyst in early-stage pharmaceutical research and development, elevating promising drugs from the bench to pre-clinical and clinical testing. By utilising a patient-centric approach, they collaborate and partner with a team of scientists, clinicians, and key opinion leaders to seek out and investigate medications that hold immense potential to create breakthroughs and diversify treatment options.
Just this month, Hoth Therapeutics announced its initiative to utilise AI to both screen its current pipeline as well as utilise AI for licensing opportunities in acquiring or partnering novel therapeutics for rare diseases. For this reason, Hoth will form and operate a new wholly owned subsidiary named Merveille.ai, which will focus on screening for collaborative opportunities utilising AI in the field of drug discovery.
➡️ In an article published by Sifted last week and titled 11 drug discovery startups to watch, according four investors from H Tree Capital, General Catalyst, Norrsken VC and AlbionVC these are the (non-portfolio) eleven companies worth watching:
Vanela Bushi, cofounding general partner at H Tree Capital
Molecule One — Poland with M1 RetroScore powered by CAS, a synthetic accessibility scoring tool created to save your time by using DL models trained on CAS chemical reaction content to predict the likelihood of synthesis for novel small molecules.
In 2023, CAS, a division of the American Chemical Society specialising in scientific information solutions, and Molecule.one have established a strategic collaboration focused on the joint development of computer-aided synthesis design technologies.
CellVoyant — UK using AI-first live cell imaging to predict and optimise stem cell differentiation, to controllably manufacture any cell and tissue in the body at scale.
CHARM Therapeutics — UK applying revolutionary insights in 3D protein-ligand co-folding to target previously undruggable disease targets.
This year CHARM secured investment from NVIDIA’s venture investment arm, NVentures, for DL-enabled drug discovery research.
Leigh Brody, life science investor at AlbionVC
Isomorphic Labs — UK developing cutting-edge computational techniques in fields like deep learning, reinforcement learning, active learning, representation learning and more to solve some of the toughest challenges in drug discovery, and some of the most stubborn scientific problems in biology, chemistry and medical research today.
Ignota Labs — UK preventing safety failures in drug discovery with ATLAS (Automated Toxicity Labelling, Analysis and Screening) and Explainable AI.
Turing Biosystems — France their interpretable hybrid AI platform applies prior biological knowledge to patients and clinical data to select, optimise and design therapies. Turing Biosystems is capable of leveraging multiple layers of data such as multiomics data, microbiomes, diets and clinical data to optimise therapies
Alex Momeni, investor at General Catalyst
Cradle — Netherlands helps biologists design improved proteins in record time using powerful prediction algorithms and AI design suggestions.
Aqemia — Paris is a next-generation pharmatech company generating one of the world's fastest-growing drug discovery pipeline, with the mission to scale the drug discovery process, by leveraging a unique-in-the world technology combining quantum-inspired physics and ML.
Chemify — UK building the infrastructure to digitise chemistry and enable the execution of chemical code for drug discovery, chemical synthesis and materials discovery.
During this summer Chemify has raised £33.6M.
Christian zu Jeddeloh, associate at Norrsken VC
Multiomic Health — UK is a next-generation precision medicine business, applying computational systems biology to develop and commercialise data assets for metabolic syndrome, a cluster of closely-related diseases (atherosclerotic cardiovascular disease, type 2 diabetes, chronic kidney disease, non-alcoholic fatty liver), with many common risk factors and costing the healthcare system $2 trillion (forecast >$5.5 trillion by 2040). It is a 1.6x larger market than oncology, yet attracts only 40% of the VC dollars.
In 2023 Multiomic has closed a £5M ($6.2M) seed funding extension round to build its precision therapeutics discovery platform.
Iktos — France an innovative company specialising in the development of AI solutions applied to chemical research, more specifically medicinal chemistry and new drug design. The use of Iktos technology platform enables major productivity gains in upstream pharmaceutical R&D.
In 2023 Iktos secured €15.5M in Series A funding for its AI-powered drug discovery platform.
➡️ Evozyne, that combines evolution and DL technology to create highly functional novel proteins — called Natural Machines™ — by amplifying a protein’s function based on the rules of nature just announced the closing of an $81M Series B investment round that will fund the biotech’s generative AI-powered drug discovery platform and product development. Fidelity Management & Research Company and OrbiMed led the funding with participation from NVentures, NVIDIA’s venture capital arm. Previous investors Paragon Biosciences and Valor Equity Partners expanded their support in the round.
Evozyne also began a collaboration with NVIDIA in 2022 to develop a new DL model that can learn the rules of protein function and use these rules to design new proteins with improved functions. The model, known as the Protein Transformer Variational AutoEncoder (ProT-VAE) and presented this year, is built on NVIDIA BioNeMo, a framework for efficiently training and deploying large language models for biology. The resulting model represents a new DL technology that enables advances for data-driven design of synthetic proteins with engineered functions, that the pharmaceutical industry can now use to design therapeutic proteins.
➡️ Sanofi has just paid $10M upfront to BioMap, in a deal worth over $1 billion, to co-develop cutting-edge AI modules for drug discovery leveraging BioMap’s AI platform.
At BioMap they have a family of Pre-trained Large Language Models called xTrimo, short for Cross-Modal Transformer Representation of Interactome and Multi-Omics, trained on their curated and proprietary datasets, which include more than 6 billion proteins, 100 billion protein-protein interactions, and trillions of single-cell gene expression measurements from 100+ million cells. The model training is enabled by their leading super-computing centre and enhanced by their AI-centric, 100,000 sq ft, high-throughput wet labs. xTrimo is the first life science AI Foundation Model to hit 100+ billion parameters and, to date, the largest of its kind!
With offices at Silicon Valley, Beijing, Jiangsu and Singapore BioMap has launched the Life Science Leaderboard – the first tool that allows users to find and evaluate the performance of various task models for life science. BioMap has raised a total of $100M.
➡️ This week Sosei Group Corporation and PharmEnable Therapeutics have announced that they have expanded their collaboration agreement to include the discovery of a second neurological disease target.
PharmEnable Therapeutics is a drug discovery company focused on chemical novelty, diversity and complexity to tackle hard-to-drug conditions powered by an AI platform:
CHEMSAILOR: A hybrid human/AI-based intelligent cheminformatics system that identifies the most suitable regions of chemical space and proposes the best chemical synthetic options to deliver active molecules against a given biological target.
CHEMUNIVERSE: A proprietary and unique accessible chemical space, composed of novel virtual compounds with a high degree of complexity and diversity to ensure an efficient coverage of chemical space.
CHEMSEEK: A multicomponent and modular system of state-of-the-art AI methods and gold standard computational tools for efficiently mining chemUNIVERSE for a given biological target.
PharmEnable has raised a total of £9.3M in funding over 4 rounds
➡️ AI’s potential to accelerate drug discovery needs a reality check Nature Editorial
“Together, the discovery and preclinical stages take an average of six years. In February 2022, researchers at Boston Consulting Group (BCG) reported an examination of the research pipelines of 20 relatively new AI-intensive pharmaceutical companies between 2010 and 2021. Using publicly available data, the BCG group determined that about 15 drug candidates had reached clinical-trial stage. It then reconstructed the development timelines of eight of these. The consultants found that all eight had reached clinical trials within a decade. Five had done so in less than the historical average time.”
➡️ Just this week, Fujitsu Limited and the HPC- and AI-driven Drug Development Platform Division of the RIKEN Centre for Computational Science announced that they have developed an AI drug discovery technology that can predict structural changes of proteins from electron microscope images as a 3D density map in wide range by utilising generative AI.
This technology enables the accurate acquisition of protein conformations and changes based on experimental data in more than ten times less time than conventional procedures, thereby enabling innovation in the design process of drugs that bind to target proteins such as bacteria and viruses. Moving forward, Fujitsu and RIKEN will use the newly developed generative AI technology as one of the core technologies for realising next-generation IT drug discovery technology that can analyse the complex relationships between target proteins and antibodies, and predict global structural changes of molecules with high accuracy and speed.
➡️ Evvy, with an at-home vaginal microbiome test (the first and only CLIA-certified, mNGS vaginal health test) using AI to improve health care and study biomarkers in the female body, has just raised a $14M Series A. Priyanka Jain, the CEO and co-founder of Evvy, spoke with Caroline Hyde on Bloomberg Technology on how her company is using AI to improve health care and how they want to close the gender health gap by discovering and leveraging overlooked female biomarkers — starting with the vaginal microbiome.
Surprisingly, women and people with vaginas weren’t required to be included in US clinical trials until 1993, and also we’re more likely to be ignored in the ER and at the doctor’s office. Accordingly, it takes women 4 years longer than men to receive a diagnosis for the same disease. And this is something that at Evvy they want to change. So far they have helped over 15,000 patients take control of their vaginal health. In total Evvy has raised $19M.
➡️ Tendo, a software company committed to creating seamless healthcare experiences for patients, clinicians and caregivers, has acquired MDsave, a leading healthcare marketplace that democratises care access for all, to include MDsave’s marketplace in its platform, enabling patients to easily seek, schedule, pay for and manage healthcare with transparent pricing through one solution.
Tendo has two applications:
The Tendo Patient Care Journey, personalised for patients and optimised for clinicians, improving end-to-end engagement and outcomes for both primary and specialty care. That includes:
Search, schedule, referrals
Forms, surveys and check-in
Notifications, reminders
Labs, medications, visit summaries
Chat between patients and care team
Educational material
Telehealth, RPM integration
Payment capture, integration, and
Family and caregiver communication.
Tendo Insights, that provides actionable recommendations to clinical, quality, CDI and revenue teams. And improves clinical outcomes and patient experience while boosting revenue and elevating quality rankings. And includes:
Conclusive data from a robust national benchmarking dataset
What if scenarios across revenue and quality with opportunity algorithms
Multi-site and system-level comparisons
Optimal encounter-level documentation improvement
Accurate and complete CC (complication or comorbidity), MCC (major complication or comorbidity) and HCC (hierarchical condition category) capture
Facility, service line and physician performance insights
HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) and reputation benchmarking, insights, and improvement opportunities, and
Federal quality penalty reduction.
➡️ Finally, Canada just announced this month it’s investing $49M in Conscience (a Canadian non-profit focused on AI drug discovery and open science) which uses AI and open science to expedite medical discoveries. At Conscience instead of a drug development model driven by the promise of significant profit, they rely on:
Open science: bringing together companies, researchers and institutions to exchange data and discoveries, avoid duplication and inefficiencies, and reduce costs. And
Artificial Intelligence: The best use of AI requires collaboration among all companies and researchers, to ultimately create a rigorous data set that can be used to train AI tools to generate sound drug target predictions.
Their CACHE Challenge is an open competition that turns drug discovery into a team sport, where companies and researchers, using AI competitively, put in their best predictions to solve a rare disease problem. Their predictions are evaluated and benchmarked in a state-of-the-art laboratory. The first CACHE Challenge is well underway, with data scheduled to be released in December 2023, and results to be presented at a symposium in March 2024. It targets an under-researched area of the LRRK2 gene in Parkinson’s disease.
Until next time,