Industry Update - Jan 1

Due to Charismas and New Year's Eve, not a lot happened last week. So I decided to pick up a few stories from 2021 and discuss them.

1. The anti-aging race

This field have been active for a while and we have seen may companies raise funding in the last 5 years. However this year was different.

More than 3 dozen companies working exclusively in this space are listed here. As of December 2021, the top 20 companies alone have the combined evaluation more than 4 billion.

The Science: The advancement in the understanding of the science of aging has enabled us to dream big. Some recent developments listed below based on Sheekey science show's framework here. 

• Aging biology: 
• One key breakthrough was better understanding of Sirtuin family proteins and their relationship with aging process. For example over expression of one of the Sirtuin protein (SIRT6) in mice lead to reduced frailty and extended life span. 
• Also, last year had some advancements in understanding VEGF and aging clock through epigenetics. 
• Additionally some studies have pointed out the heritability of aging might have been overestimates previously, making this space even more commercially lucrative. 
• The fecal microbiota transplant from young mice to old ones provided some promising results improving biomarkers of aging.
• Aging is also correlated with misfolding of the proteins due to errors getting introduced with amino acid sequences. One approach focusses on slowing down the protein synthesis process increasing the fidelity of protein production. Experiments showed this increases life span of yeasts, worms and flies.
• Cellular Reprogramming:  It is a process of going from one cell type to the other (if we can do it for short duration, it is called interrupted reprogramming or transient reprogramming). Biological reprogramming, a technique discovered in 2006 by scientist Shinya Yamanaka revealed that after adding just four proteins, now known as Yamanaka factors, cells can be made pluripotent – that is, able to become any cell in the body. For example reprogramming skin cell to stem-like cell which then can become a heart cell. Many companies like Altos Lab (backed by Yuri Milner and Jeff Bezos) and New Limit (backed by Coinbase CEO) are using this regenerative therapy approach to treat aging.
• Senescence & Senolytics: Senescence cells accumulate with age and also drive some aspects of aging. So some studies in mice have been trying to remove Senescence cells using specific biomarkers like P16 or P21 which can help. Selective removal of Senescence cells is called Senolytics. Another strategy here is to activate immune cells to specifically remove Senescence cells. Last year witnessed some development in anti-body conjugate approach and vaccine approach to bind the receptors on the surface of Senescence cells and selectively remove them. An example of a company following this strategy is Unity Biotechnology, a startup that develops drugs which target senescent cells.
• Longevity Diet & supplements: We have seen many variants of the diet like Caloric restriction, fasting-mimicking diets, intermittent fasting, Protein restriction, Ketogenic diets etc. Here is a nice side by side comparison of those.

Challenges:

• Well funded companies like Unity biotechnology (backed by Peter Thiel, Jeff Bezos) have been working on senolytic approach since 2011 but some recent trial failures have pushed down the company stock from $16 to $1.46 since the IPO 3.5 years ago.
• Google-backed Calico Labs, founded in 2013, is a US-based R&D biotech that hopes to uncover the biological processes behind ageing and tackle age-related diseases. The company’s main aim is to develop and market new therapies for age-related conditions such as neurodegeneration and cancer, but it is also researching how biological reprogramming can reverse ageing in cells and tissues in the lab. Calico is in a long-running research collaboration with global biopharma AbbVie, but as yet it’s unclear how much progress the partnership has made in terms of discovering novel therapies for age-related disease.
• Other biotech companies hoping to prolong the human lifespan through cell reprogramming include billionaire-backed AgeX Therapeutics, UK-based Shift Bioscience, and US drugmaker Life Biosciences. So far, no ageing-focused companies have seen a reprogramming-based therapy advance into clinical trials on humans.

End note:

• We have early stage signals (through mouse models and proof of concepts), some well funded companies and lot of talented people working on this. There is long arduous path ahead of proving a specific approach works in humans trials and then real world as well.

2. Drug Discovery through AI: Drug Design, Target Discovery and outcome prediction

Last year it was hard to miss the flood of content around AI in Drug Discovery. Alphabet (Google's parent company) starting Isomorphic Labs made headlines. Although DeepMind's model AlphaFold2 was able to solve a 50 year old hard problem of protein folding prediction with a very high level of accuracy, some other companies have been working in this area for a while.

Credit: Stanford

The Problem:  Over 90 percent of drugs that make it to a clinical trial end up not working, as chemist and writer Derek Lowe pointed out in Science this summer. So the Pharma companies have to very picky about what molecule is being considered as candidates targeting what condition. This process requires among many things understanding of how proteins are folded, which can lead to better understanding of the drugable targets. That's just step one. After identifying drugable target the next step is picking up an appropriate molecule which can act as a drug candidate.

Here is a visual from Insillico Medicine's Pharm.ai solution which explains the steps visually.

In December, Odyssey entered into a SPAC agreement with BenevolentAI. The deal valued BenevolentAI at $1.7 billion is the biggest acquisition ever involving a European SPAC. BenevolentAI's platform has generated 20+ drug candidates using AI and machine learning including novel targets for treating ulcerative colitis and an atopic dermatitis. BenevolentAI also successfully identified Eli Lilly's Baricitinib as a treatment for COVID-19, which is now FDA emergency-use approved.

In December, Roche and Genentech committed up to $12 billion to Recursion to use their OS in a major AI Drug Discovery collaboration. The aim of the collaboration is to advance therapies in 40 programs that include key areas of neuroscience and oncology. They plan to develop new machine learning algorithms to generate highly granular Phenomaps of human cellular biology.

3. AI in Medical Imaging

AI has been used in medical imaging for a while now. Dozens of well funded companies operate in this space. However some interesting new patterns were observed in 2021. 

Let's start with PathAI, a company with mission is to improve patient outcomes with AI-powered pathology. Their platform was built to enable substantial improvements to the accuracy of diagnosis and the measurement of therapeutic efficacy for complex diseases, leveraging modern approaches in machine learning. The company raised $165 million is 2021 and also acquired Poplar Healthcare, a laboratory services company.

In September, in a historic milestone, the FDA approved the first ever AI-based pathology product by Paige.ai for clinical use. Paige Prostate is the first and only AI-based pathology product to receive FDA approval for in vitro diagnostic use in detecting cancer in prostate biopsies. Paige Prostate is a clinical-grade AI solution designed to identify foci that could indicate cancer, enabling fast, accurate in vitro diagnosis. Pathologists using the technology have been shown to significantly increase their capabilities to accurately and effectively find prostate cancer.

Although PaigeAI and PathAI are positioned to compete with each other, they seem focussed on generating their own USP. Paige is primarily after diagnostics, and although Path has companion diagnostics as part of their core value proposition they seem to be focussed on measuring efficacy of treatments through various strategic partnerships 

 
In December, Gleamer AI demonstrated that their algorithm can quickly detect and flag X-rays with positive fractures helping hospitals reduce missed fractures by 29%. Due to the large number of X-rays that have to be read by radiologists, patients often have to wait hours in the ER before they can be seen, evaluated, and receive treatment. Fracture interpretation errors represent 24% of harmful diagnostic errors in the ER.

Dental industry is also seeing increased interest in companion diagnostics. Overjet a startup spun off from Harvard's innovation lab went through series A and series B in 2021 and now values at 425 million. Started in 2018 Overjet has been hyperfocussed on execution. In May of last year, FDA has authorized one of the first artificial-intelligence-based technologies for use in dentistry, a software platform from Overjet. With the 510(k) clearance, Overjet will be able to market its Dental Assist software for clinical use, selling it directly to dental practices. The software is already employed by insurance companies to make claims processing more accurate and efficient.

Finally, my favorite example of Viz.ai. This company is a textbook case of how AI needs to be seamlessly incorporated into medical workflow. In 2020, Viz.ai came up with a workflow for stroke victims. Essentially it is AI-charged push notifications + a group chat. It’s deceptively simple…but that works. Average time to notify a specialist in standard of care was 58.72 minutes vs. 7.32 minutes with Viz.ai. In 2021 Viz.ai successfully replicated this workflow for pulmonary embolism. commercial launch of AI-driven solutions for acute pulmonary conditions. Diagnosis and care coordination of patients suffering from pulmonary embolism (PE) can be challenging, with the average arrival-to-treatment times lasting more than 8 hours. Viz.ai uses deep learning to identify suspected pulmonary embolism disease in under two minutes. The Viz Platform is now utilized in over 850 hospitals across the U.S. and Europe. Obviously the approval from CMS has helped tremendously where Viz.ai demonstrated how this tech creates new workflow and measurably better outcomes for patients.

4. Data Privacy and AI

Patient Data privacy is one of the core requirements in healthcare industry. It prevents misuse and abuse of patient data. However limits on accessing or sharing this valuable data also acts as a roadblock for continued innovations. The solution lies in technology. Through various novel approaches many companies are trying to solve this issue.

Synthetic data - There are a few different ways to produce synthetic data, but it often involves a generative adversarial network (GAN). Limited data access, privacy protections, lack of quality data and the time and financial burden of data annotation can all make synthetic data an attractive component when building models.

Syntegra, Unlearn AI

Federated Learning - In federated learning, an algorithm is trained across multiple decentralized servers, then aggregated into a more robust composite algorithm, all while keeping the original training data separate. (The privacy technique was pioneered by Google in 2016 as a way to allow mobile devices to collectively learn prediction models without moving data to a centralized server, but its implications extend well beyond the mobile market.)


Owkin, a New York- and Paris-headquartered company recently became a unicorn. The company — founded by a former hematology/oncology professor and an artificial intelligence researcher — has spearheaded federated-learning solutions for medical research that also integrate with NVIDIA’s prominent FL models.


Other privacy preserving solutions include Homomorphic Encryption, Tokenization, Multiparty Computation, Homomorphic Encryption etc.


Differential Privacy - Put simply, differential privacy involves intentionally injecting noise into a data set. That helps anonymize data, but in a manner that still allows for statistical analysis. It hinges on a metric called a privacy budget. But the better the budget, the more data required.

5. Early cancer detection

This is another hot area where multiple companies are developing tests using various approaches and also technology (read AI) to detect the cancer as early as possible.

6. Single cell multi-omics

7. Clinical Trials

There are a wide variety of areas here like offering novel solutions in recruitment, retention, medical adherence (WineHealth), Outcome prediction, success prediction (Lokavant, Pharm.ai), safety profiling, SAE detection, mHealth e.g. walk test