Ancestry.com rejected a police warrant to access user DNA records on a technicality

DNA profiling company Ancestry.com has narrowly avoided complying with a search warrant in Pennsylvania after a search warrant was rejected on technical grounds, a move that is likely to help law enforcement refine their efforts to obtain user information despite the company’s efforts to keep the data private.

Little is known about the demands of the search warrant, only that a court in Pennsylvania approved law enforcement to “seek access” to Utah-based Ancestry.com’s database of more than 15 million DNA profiles.

TechCrunch was not able to identify the search warrant or its associated court case, which was first reported by BuzzFeed News on Monday. But it’s not uncommon for criminal cases still in the early stages of gathering evidence to remain under seal and hidden from public records until a suspect is apprehended.

DNA profiling companies like Ancestry.com are increasingly popular with customers hoping to build up family trees by discovering new family members and better understanding their cultural and ethnic backgrounds. But these companies are also ripe for picking by law enforcement, which want access to genetic databases to try to solve crimes from DNA left at crime scenes.

In an email to TechCrunch, the company confirmed that the warrant was “improperly served” on the company and was flatly rejected.

“We did not provide any access or customer data in response,” said spokesperson Gina Spatafore. “Ancestry has not received any follow-up from law enforcement on this matter.”

Ancestry.com, the largest of the DNA profiling companies, would not go into specifics, but the company’s transparency report said it rejected the warrant on “jurisdictional grounds.”

“I would guess it was just an out of state warrant that has no legal effect on Ancestry.com in its home state,” said Orin S. Kerr, law professor at the University of California, Berkeley, in an email to TechCrunch. “Warrants normally are only binding within the state in which they are issued, so a warrant for Ancestry.com issued in a different state has no legal effect,” he added.

But the rejection is likely to only stir tensions between police and the DNA profiling services over access to the data they store.

Ancestry.com’s Spatafore said it would “always advocate for our customers’ privacy and seek to narrow the scope of any compelled disclosure, or even eliminate it entirely.” It’s a sentiment shared by 23andMe, another DNA profiling company, which last year said that it had “successfully challenged” all of its seven legal demands, and as a result has “never turned over any customer data to law enforcement.”

The statements were in response to criticism that rival GEDmatch had controversially allowed law enforcement to search its database of more than a million records. The decision to allow in law enforcement was later revealed as crucial in helping to catch the notorious Golden Gate Killer, one of the most prolific murderers in U.S. history.

But the move was widely panned by privacy advocates for accepting a warrant to search its database without exhausting its legal options.

It’s not uncommon for companies to receive law enforcement demands for user data. Most tech giants, like Apple, Facebook, Google and Microsoft, publish transparency reports detailing the number of legal demands and orders they receive for user data each year or half-year.

Although both Ancestry.com and 23andMe provide transparency reports, detailing the amount of law enforcement demands for user data they receive, not all are as forthcoming. GEDmatch still does not publish its data demand figures, nor does MyHeritage, which said it “does not cooperate” with law enforcement. FamilyTreeDNA said it was “working” on publishing a transparency report.

But as police continue to demand data from DNA profiling and genealogy companies, they risk turning customers away — a lose-lose for both police and the companies.

Vera Eidelman, staff attorney with the ACLU’s Speech, Privacy, and Technology Project, said it would be “alarming” if law enforcement were able to get access to these databases containing millions of people’s information.

“Ancestry did the right thing in pushing back against the government request, and other companies should follow suit,” said Eidelman.

Paige raises $45M more to map the pathology of cancer using AI

One of the more notable startups using artificial intelligence to understand and fight cancer has raised $45 million more in funding to continue building out its operations and inch closer to commercialising its work.

Paige — which applies AI-based methods such as machine learning to better map the pathology of cancer, an essential component of understanding the origins and progress of a disease with seemingly infinite mutations (its name is an acronym of Pathology AI Guidance Engine) — says it will be use the funding to inch closer to FDA approvals for products it is developing in areas such as biomarkers and prognostic capabilities.

It also plans to use the funding to continue developing better ways of diagnosing and ultimately fighting the disease, as well as exploring further commercial opportunities for its work, specifically within the bio-pharmaceutical industry.

This round is being led by Healthcare Venture Partners, with previous investor Breyer Capital, Kenan Turnacioglu and other funds participating. The company is not disclosing its valuation, but PitchBook noted that a first close of this round (when it raised $33 million) put the valuation at $208 million. That would value Paige now at about $220 million with the $45 million close, more than three times its valuation in its previous round.

Paige first emerged from stealth back in 2018 — with a bang.

Paige.AI — as it was known at the time — was hatched inside the Memorial Sloan Kettering Cancer Center, one of the world’s foremost institutions both for working on cancer therapies and treating cancer patients, and along with a $25 million investment led by Jim Breyer, Paige had secured exclusive access to MSK’s 25 million pathology slides as well as its intellectual property related to the AI-based computational pathology that underpinned its work. These slides make up one of the biggest repositories of its kind in the world, and as all solutions and services built on machine learning are only as good as the data that’s fed into them, they were critical to the startup’s beginnings.

The startup also launched with some serious talent behind it.

Much of the computational pathology being used by Paige had been developed by Dr Thomas Fuchs, who is known as the “father of computational pathology” and is the director of Computational Pathology in The Warren Alpert Center for Digital and Computational Pathology at Memorial Sloan Kettering, as well as a professor of machine learning at the Weill Cornell Graduate School of Medical Sciences.

Fuchs co-founded Paige with Dr David Klimstra, chairman of the department of pathology at MSK, and Fuchs had originally started out as the CEO of Paige, but was replaced earlier this year by Leo Grady, who joined from another bio-startup, Heartflow (another company backed by Healthcare Venture Partners). Fuchs is still supporting the company, but no longer in an executive role.

In the nearly two years since it launched, there have been some milestones reached. The company, which has around 30 employees today, has been the first to get an FDA breakthrough designation (which helps expedite the long process of drug approvals in urgent areas where there are few or no other options for patients) for using AI in oncology pathology. It’s also the first to get a CE mark in the same category, which opens the door to working in Europe, too. Paige has so far ingested 1.2 million images into its slide database and is using them — in algorithms that also take in genomic data, drug response data and outcome data — to work on developing diagnostic solutions.

But as with all new medical products, progress is not measured in quarters as it might be with a more typical tech startup. Moving fast and breaking things is something to be avoided. So even with all of the above advances, there has yet to be any commercial products launched, nor is Grady giving any specific time frames for when they will. And when the company came out of stealth in 2018, it said it would be focusing on breast, prostate and other major cancers, although today it’s not as quick to specify what its targets will be when it does launch commercial products.

Similarly, it’s also expanding its remit from primarily clinical environments to pharmaceutical ones.

“The clinical side is still our focus, but this is an expansion and realisation that this has a broader impact, and that includes pharmaceutical customers,” Grady said. 

And the dropping of the .AI in its name was also intentional, in part a reaction it seems to how much AI gets thrown around today.

“There is a fundamental misconception, which is thinking of AI as a product and not a technology,” said Grady. “It’s a technology set that can allow you to do many things that could not have been done in the past, but you need to apply it in a meaningful way. Developing a good AI and putting that on the market will not cut it in terms of clinical adoption.”

The funding round, Grady said, saw a lot of interest from strategic investors, although the company intentionally has stayed away from these.

“We were approached by all of the scanner vendors and some of the biopharmaceutical companies,” he said. “But we made the decision to not take a strategic investment with this round because we wanted to be neutral with hardware vendors and not be too tied with any one.”

He also pointed to the challenges of talking to investors when you are working in a cutting-edge area (a challenge that has foxed many an investor also into backing the wrong horses, too, such as Theranos).

“We’re at the intersection of three areas: tech, medical devices and clinical medicine, and life sciences and biotech,” he said. “Many investors sit squarely in one and don’t feel comfortable in others. That makes the conversations challenging and short. But there has been an increasing blend between those three sectors.”

That’s where Healthcare Venture Partners fits into the mix. “Paige exemplifies the benefits of digital pathology and represents the bright future of AI-driven medical diagnosis,” said Jeff Lightcap of Healthcare Venture Partners, in a statement. “As hospitals embark on digital transformations, they will face challenges associated with these transitions. We believe Paige addresses many of these issues by enhancing the ability of clinical teams and pathologists to collaborate. We’re confident in Paige’s future and believe they will continue to develop cutting-edge technologies that enable pathology departments to transform their practices, which have changed little in the last century.”

“We applaud Paige’s commitment to building clinical AI products that will improve the diagnostic process and patient care,” added Jim Breyer of Breyer Capital, in a statement. “This is a critical time for Pathology, as pathologists are carrying a heavier workload than ever before. Paige understands their needs and the team has built cutting-edge technologies to address them. Paige represents the future of computational pathology and we look forward to their continued growth and success.”

Where top VCs are investing in digital health

The world of healthcare has notoriously been described as “broken” — plagued with high-friction workflows, sky-high costs and convoluted business models.

Over the past several years, a long list of innovative startups and salivating venture investors have pinned their focus on repairing the healthcare industry, but its digital transformation still appears to be in the very early innings. After a record-setting 2018, however, digital health investing continued to reach meteoric heights in 2019.

Mammoth pools of capital have flooded into various sub-verticals and business models, backing collections of new B2B and B2C companies focused on optimizing healthcare workflows, improving healthcare access and offering lower-cost distribution models. Over the past two years, digital health startups have raised well over $10 billion in funding across nearly 1,000 deals, according to data from Pitchbook and Crunchbase.

As we close out another strong year for innovation and venture investing in the sector, we asked nine leading VCs who work at firms spanning early to growth stages to share what’s exciting them most and where they see opportunity in the sector:

Participants discuss trends in digital therapeutics, telehealth, mental health and the latest in biotech and medical devices, while also diving into startups improving medical practitioner efficiency, evaluating the evolving regulatory environment and debating valuations and offering a ‘temp check’ on the market for digital health startups leveraging ML.

Annie Case, Kleiner Perkins

Although Kleiner Perkins has a long history of investing in iconic health companies, we believe it is still the early innings of digital health as a category today.

When I evaluate new opportunities in the space, I often start by thinking through how the company will move the needle on cost, quality, and access to care — the “iron triangle” of health care systems. Conventional wisdom has been that it’s impossible to improve all three dimensions simultaneously, but we are seeing companies leverage technology to shift this paradigm in meaningful ways.

It’s no longer just a promise. For example, Viz.ai is using artificial intelligence to detect and alert stroke teams to suspected large vessel occlusion strokes, enabling patients to get treatment faster. Their workflows improve access to life-saving care, deliver higher quality through reduced time to treatment (every minute counts as ‘time is brain’ in stroke care), and dramatically reduce the costs associated with long-term disability.

We are also seeing companies provide this type of tech-enabled care outside of the hospital setting. Modern Health is a mental health benefits platform that employers are making available to their employees. The platform triages individual employees to the right level of care, providing clinical care to those with diagnosable depression or anxiety, and making self-guided or preventative care available to everyone else. Their solution improves quality and access by offering mental health services to every employee and reduces the cost associated with untreated mental illness, lost productivity, or employee churn.

Heading into 2020, we’re eager to back digital health companies in new areas that leverage technology to impact cost, quality, and access. A few spaces that I’m excited about are behavioral health (mental health, substance abuse, addiction, etc), care navigation, digital therapeutics, and new models integrating telehealth, remote care and AI to better leverage medical professionals’ time.

Zavain Dar and Adam Goulburn, Lux Capital

Below are some thoughts and coming predictions on health tech broadly:

  1. Digital therapeutics continue to pick up steam — on the back of Pear and Akili, more companies push to FDA and enter the market. In addition, broader consumer platforms like Calm and Headspace look to broaden their offerings by investigating clinical approvals.
  2. At least one major pharma looks to expand its consumer surface area by acquiring one of the new digital, consumer-facing generics platform (ex Hims, Ro, NuRx).
  3. Venture funding for biotech continues to boom with at least three Series A’s of $100M or more in size.
  4. Drug discovery for neurodegeneration sees a renaissance. High-profile failings of Biogen and the beta-amyloid hypothesis sees a shift of innovation to early-stage biotech and venture creation.
  5. Big pharma has its DeepMind moment acquiring at least one machine-learning (AI) enabled drug discovery company.
  6. Clinical trial tech investments heat up; new companies and technologies emerge to make trials patients first and systems get smarter at finding the right patients at their point of care; large incumbents like IQVIA, LabCorp and PPD get acquisitive.
  7. At least three traditional Sand Hill Road tech venture firms open life science practices or raise dedicated funds.
  8. Machine learning targets chemistry driven by large advancements in transformer (NLP) models; has the time for computational chemistry finally come?
  9. HCIT sees a renaissance driven by increased CIO responsibility towards data interoperability. Companies either working on federated ML to allow systems to speak to each other or lightweight edge applications enabling rapid clinical deployment will see quick uptake and traction, until now impossible in HC.

Kristin Baker Spohn, Charles River Ventures (CRV)

In the last 10 years, digital health has exploded. Over $16B has been invested in the sector by VCs and we’ve seen IPOs from Livongo, Progyny and Health Catalyst, just in the last year alone. That said, there’s still a lot that mystifies people about the sector — there are spots that are overheated and models that will struggle to deliver venture scale outcomes. I’ve seen digital health evolve first hand as both an operator and investor, and I’m more excited than ever about the future of the space.

A few areas and trends that I’ve been following recently include:

The man behind Bezos’ next lunar guidance system talks future tech

Draper, the MIT spin-off engineering lab, is famed for developing the Apollo 11 Guidance Computer (not Draper Esprit, I hasten to add). Ken Gabriel, President and CEO, also recently made a major announcement. Blue Origin has now partnered with Lockheed Martin and Northrop Grumman to build elements of the company’s human-rated lunar lander, and Draper will lead the development of the lander’s avionics and guidance systems, with an aim to be ready to land a crew on the moon by 2024.

“While Blue Origin is the prime contractor, Lockheed Martin is building the ascent stage, Northrop Grumman is building the transfer element and Draper is doing the GNC (guidance, navigation and control),” Blue Origin CEO and founder Jeff Bezos said, announcing the move at the International Astronautical Congress in Washington. Blue Origin is competing for a NASA contract to develop a crewed lunar lander, or Human Landing System, for the Artemis program, which aims to return astronauts to the surface of the moon by the end of 2024.

TechCrunch sat down to chat with Gabriel, who previously he co-founded Google’s Advanced Technology and Projects (ATAP) group, to tlak about what he sees coming up in the future for the most advanced technologies. Prior to this, he was Deputy and Acting Director of the famed DARPA in the U.S. Department of Defense. During his tenure, DARPA advanced capabilities in hypersonics, offensive and defensive cyber, and big data analytics for intelligence and national security.

You’ve heard of CRISPR, now meet its newer, savvier cousin CRISPR Prime

CRISPR, the revolutionary ability to snip out and alter genes with scissor-like precision, has exploded in popularity over the last few years and is generally seen as the standalone wizard of modern gene-editing. However, it’s not a perfect system, sometimes cutting at the wrong place, not working as intended and leaving scientists scratching their heads. Well, now there’s a new, more exacting upgrade to CRISPR called Prime, with the ability to, in theory, snip out more than 90% of all genetic diseases.

Just what is this new method and how does it work? We turned to IEEE fellow, biomedical researcher and dean of graduate education at Tuft University’s school of engineering Karen Panetta for an explanation.

How does CRISPR Prime editing work?

CRISPR is a powerful genome editor. It utilizes an enzyme called Cas9 that uses an RNA molecule as a guide to navigate to its target DNA. It then edits or modifies the DNA, which can deactivate genes or insert a desired sequence to achieve a behavior. Currently, we are most familiar with the application of genetically modified crops that are resistant to disease.

However, its most promising application is to genetically modify cells to overcome genetic defects or its potential to conquer diseases like cancer.

Some applications of genome editing technology include:

  • Genetically modified mosquitos that can’t carry malaria.
  • In humans, “turning on” a gene that can create fetal type behaving cells that can overcome sickle-cell anemia.

Of course, as with every technology, CRISPR isn’t perfect. It works by cutting the double-stranded DNA at precise locations in the genome. When the cell’s natural repair process takes over, it can cause damage or, in the case where the modified DNA is inserted at the cut site, it can create unwanted off-target mutations.

Some genetic disorders are known to mutate specific DNA bases, so having the ability to edit these bases would be enormously beneficial in terms of overcoming many genetic disorders. However, CRISPR is not well suited for intentionally introducing specific DNA bases, the As, Cs, Ts and Gs that make up the double helix.

Prime editing was intended to overcome this disadvantage, as well as other limitations of CRISPR.

Prime editing can do multi-letter base-editing, which could tackle fatal genetic disorders such as Tay-Sachs, which is caused by a mutation of four DNA letters.

It’s also more precise. I view this as analogous to the precision lasers brought to surgery versus using a hand-held scalpel. It minimized damage, so the healing process was more efficient.

Prime editing can insert, modify or delete individual DNA letters; it also can insert a sequence of multiple letters into a genome with minimal damage to DNA strands.

How effective might Prime editing be?

Imagine being able to prevent cancer and/or hereditary diseases, like breast cancer, from ever occurring by editing out the genes that are makers for cancer. Cancer treatments are usually long, debilitating processes that physically and emotionally drain patients. It also devastates patients’ loved ones who must endure watching helpless on the sidelines as the patient battles to survive.

“Editing out” genetic disorders and/or hereditary diseases to prevent them from ever coming to fruition could also have an enormous impact on reducing the costs of healthcare, effectively helping redefine methods of medical treatment.

It could change lives so that long-term disability care for diseases like Alzheimer’s and special needs education costs could be significantly reduced or never needed.

How did the scientific community get to this point — where did CRISPR/prime editing “come from?”

Scientists recognized CRISPR’s ability to prevent bacteria from infecting more cells and the natural repair mechanism that it initiates after damage occurs, thus having the capacity to halt bacterial infections via genome editing. Essentially, it showed adaptive immunity capabilities.

When might we see CRISPR Prime editing “out in the wild?”

It’s already out there! It has been used for treating sickle-cell anemia and in human embryos to prevent HIV infections from being transmitted to offspring of HIV parents.

So, what’s next?

IEEE engineers, like myself, are always seeking to take the fundamental science and expand it beyond the petri dish to benefit humanity.

In the short term, I think that Prime editing will help generate the type of fetal like cells that are needed to help patients recover and heal as well as developing new vaccines against deadly diseases. It will also allow researchers new, lower cost alternatives and access to Alzheimer’s like cells without obtaining them post-mortem.

Also, AI and deep learning is modeled after human neural networks, so the process of genome editing could potentially help inform and influence new computer algorithms for self-diagnosis and repair, which will become an important aspect of future autonomous systems.

A chat about UK deep tech and spin-out success with Octopus Ventures

New research commissioned by UK VC firm Octopus Ventures has put a spotlight on which of the country’s higher education institutions are doing the most to support spin outs. The report compiles a ranking of universities, foregrounding those with a record of producing what partner Simon King dubs “quality spin outs”.

The research combines and weights five data points — looking at university spinouts’ relative total funding as a means of quantifying exit success, for example. The idea for the Enterpreneurial Impact Ranking, as it’s been called, is to identify not just those higher education institutions with a track record of encouraging academics to set up a business off the back of a piece of novel work but those best at identifying the most promising commercialization opportunities — ultimately leading to spinout success (such as an exit where the company was sold for more than it raised).

Hence the report looking at data over almost a ten year period (2009-2018) to track spin-outs as they progress from an idea in the lab through prototyping to getting a product to market.

The ranking looks at five factors in all: Total funding per university; total spinouts created per university; total disclosures per university; total patents per university; and total sales from spinouts per university.

Topping the ranking is Queen’s University Belfast which the report notes has had a number of notable successes via its commercialization arm, Qubis, name checking the likes of Kainos (digital services), Andor Technology (scientific imaging) and Fusion Antibodies (therapeutics & diagnostics), all of whom have been listed on the London Stock Exchange.

The index ranks the top 100 UK universities on this entrepreneurial impact benchmark — but the rest of the top ten are as follows:

2) University of Cambridge
3) Cardiff University
4) Queen Mary University of London
5) University of Leeds
6) University of Dundee
7) University of Nottingham
8) King’s College London
9) University of Oxford
10) Imperial College London

Octopus Ventures says the ranking will help it to get a better handle on which universities to spend more time with as it searches for its next deep tech investment.

It also wants to increase visibility into how the UK is doing when it comes to commercializing academic research to feed further growth of the ecosystem by sharing best practice, per King.

“We are looking at a number of data points which are all self-reported by the universities themselves to the Higher Education Statistics Agency. And then we combine those in the way that we think brings out at a higher level which universities are doing a good job of spinning out companies,” he says.

“It means that you take into consideration which university is producing quality spin-outs. So it’s not just spray and pray and get lots of stuff out there. But actually which universities are creating spin-outs that then go on to return value back to them.”

Microsoft uses AI to diagnose cervical cancer faster in India

More women in India die from cervical cancer than in any other country. This preventable disease kills around 67,000 women in India every year, more than 25% of the 260,000 deaths worldwide.

Effective screening and early detection can help reduce its incidence, but part of the challenge — and there are several parts — today is that the testing process to detect the onset of the disease is unbearably time-consuming.

This is because the existing methodology that cytopathologists use is time consuming to begin with, but also because there are very few of them in the nation. Could AI speed this up?

At SRL Diagnostics, the largest chain to offer diagnostic services in pathology and radiology in India, we are getting an early look of this. Last year, Microsoft partnered with SRL Diagnostics to co-create an AI Network for Pathology to ease the burden of cytopathologists and histopathologists.

SRL Diagnostics receives more than 100,000 Pap smear samples every year. About 98% of these samples are typically normal and only the remaining 2% samples require intervention. “We were looking for ways to ensure our cytopathologists were able to find those 2% abnormal samples faster,” explained Dr. Arnab Roy, Technical Lead for New Initiatives & Knowledge Management at SRL Diagnostics.

Cytopathologists at SRL Diagnostics studied digitally scanned versions of Whole Slide Imaging (WSI) slides, each comprising about 300-400 cells, manually and marked their observations, which were used as training data for Cervical Cancer Image Detection API.

A digitally scanned version of a Whole Slide Imaging (WSI) slide, which is used to train the AI model

Then there was the challenge of subjectivity. “Different cytopathologists examine different elements in a smear slide in a unique manner even if the overall diagnosis is the same. This is the subjectivity element in the whole process, which many a time is linked to the experience of the expert,” reveals Dr. Roy.

Manish Gupta, Principal Applied Researcher at Microsoft Azure Global Engineering, who worked closely with the team at SRL Diagnostics, said the idea was to create an AI algorithm that could identify areas that everybody was looking at and “create a consensus on the areas assessed.”

Cytopathologists across multiple labs and locations annotated thousands of tile images of cervical smear. They created discordant and concordant notes on each sample image.

“The images for which annotations were found to be discordant — that is if they were viewed differently by three team members — were sent to senior cytopathologists for final analysis,” Microsoft wrote in a blog post.

This week, the two revealed that their collaboration has started to show results. SRL Diagnostics has started an internal preview to use Cervical Cancer Image Detection API. The Cervical Cancer Image Detection API, which runs on Microsoft’s Azure, can quickly screen liquid-based cytology slide images for detection of cervical cancer in the early stages and return insights to pathologists in labs, the two said.

The AI model can now differentiate between normal and abnormal smear slides with accuracy and is currently under validation in labs for a period of three to six months. It can also classify smear slides based on the seven-subtypes of cervical cytopathological scale, the two wrote in a blog post.

During the internal preview period, the exercise will use more than half-a-million anonymized digital tile images. Following internal validation, the API will be previewed in external cervical cancer diagnostic workflows, including hospitals and other diagnostic centers.

“Cytopathologists now have to review fewer areas, 20 as of now, on a whole slide liquid-based cytology image and validate the positive cases thus bringing in greater efficiency and speeding up the initial screening process,” Microsoft wrote.

“The API has the potential of increasing the productivity of a cytopathology section by about four times. In a future scenario of automated slide preparation with assistance from AI, cytopathologists can do a job in two hours what would earlier take about eight hours!” Dr. Roy said.

SRL Diagnostics-Microsoft consortium said they are hopeful their APIs could find application in other fields of pathology such as diagnosis of kidney pathologies and in oral, pancreatic and liver cancers. The consortium also aims to expand its reach with tie-ups with private players and governments and expand the reach of the model even in remote geographies where the availability of histopathologists is a challenge.

The announcement this week is the latest example of Microsoft’s ongoing research work in India. The world’s second most populous nation has become a test bed for many American technology companies to build new products and services that solve local challenges as they look for their next billion users worldwide.

Last week, Microsoft announced its AI project was helping improve the way driving tests are conducted in India. The company has unveiled a score of tools for the Indian market in the last two years. Microsoft has previously developed tools to help farmers in India increase their crop yields and worked with hospitals to prevent avoidable blindness. Last year, the company partnered with Apollo Hospitals to create an AI-powered API customized to predict risk of heart diseases in India.

Also last year, the company also worked with cricket legend Anil Kumble to develop a tracking device that helps youngsters analyze their batting performance. Microsoft has also tied up with insurance firm ICICI Lombard to help it process customers’ repair claims and renew lapsed policies using an AI system.

Medopad raises $25M led by Bayer to develop biomarkers tracked via apps and wearables

Medopad, the UK startup that has been working with Tencent to develop AI-based methods for building and tracking “digital” biomarkers — measurable indicators of the progression of illnesses and diseases that are picked up not with blood samples or in-doctor visits but using apps and wearables, has announced another round of funding to expand the scope of its developments. It has picked up $25 million led by pharmaceuticals giant Bayer, which will be working together with Medopad to build digital biomarkers and therapeutics related to heart health. Medopad said it is also working on separate biomarkers related to Parkinson’s, Alzheimer’s and Diabetes.

The Series B is being made at a post-money valuation of between $200 million and $300 million. In addition to Bayer, Hong Kong firm NWS Holdings and Chicago VC Healthbox also participated. All three are previous investors, with NWS leading its $28 million Series A in 2018, bringing the total raised by Medopad to over $50 million. It also comes on the heels of the company signing a high-profile deals totalling some $140 million with a string of firms in China, including Tencent, Ping An, and the Chinese divisions of GSK, Johnson & Johnson and more.

The world where medicine mixes with tech in the name of doing things faster, better and with less expense had a big knock with the rise and calamitous fall of Theranos, the blood-testing startup that claimed to have developed technology to perform a multitude of tests tracking biomarkers using only a few drops of blood — tests that used to require significantly more blood (and expense) to run accurately. Great concept, if only it weren’t a scam.

While Medopad also tracks biomarkers, it’s taking a very different, non-invasive route to building its solutions. The company constructs its algorithms and tracking working with pharmaceutical and tech partners to build the solution end-to-end, leaning on advances in software and hardware to fulfil ideas that have been unattainable goals for a long time.

“For the past 25 years, we have been talking about this connected healthcare, but no one has doe it,” CEO Dan Vahdat, who co-founded the company with Rich Khatib, said in an interview. “The nature of the concept has just been too challenging. The approach is established but the computing and device technology wasn’t able to detect and read these things outside of hospital settings.”

In one example, a classic Parkinson’s test would have required a patient to go to into a doctor’s office for a 30-minute assessment to determine how a patient is walking. In recent times, however, with the advent of advanced computer vision and far better sensors on devices, a new category of digital biomarkers, as Vahdat calls them, are being created — for example, by tracking how a person is walking — her/his gait and other metrics — to provide similar guidance to a clinician on the patient’s progress. “These can be collected, for example, based on how you walk and talk, along with other vital signs,” he said.

The startup is also working with teaching hospitals to build other clinical trials. For example it has a partnership with the Royal Wolverhampton to better track Aortic Stenosis, when heart valves narrow and restrict blood flow.

“This is a very exciting project and fits with our ethos of ‘proactive’ and ‘one to many care’ which, we think, will benefit patients and release valuable clinical time,” said Professor James Cotton at The Royal Wolverhampton NHS Trust, in a statement.

Longer term, it’s also working with Janssen (a division of Johnson & Johnson) a possible way of tracking early signs and progress of Alzheimers by way of cognitive tests that someone can take at home.

Medopad has a healthy approach to the work it is doing reminiscent of the kind of collaboration that is typical in the world of science, which perhaps is the aspect that sets it apart best from the vapourware of the world. “We won’t claim that we can do what others can’t, but we are using foundations that were built years ago, to discover and commercially deploy solutions via our channel.” He added that Babylon in the UK and Collective Health in the UK are two companies he admires for taking a similar approach in their respective fields of doctor/patient care and health insurance.

The fact that the company works so closely with Tencent and other Chinese companies is notable at a time when there is a lot of scrutiny of China and how its companies may be using or working with personal data in countries like the US and UK. Vahdat said that all patient data is only collected with consent, and if any data from Medopad is passed to its partners, it’s anonymised. A patient’s data, furthermore, does not leave the country in which it is collected.

The Tencent partnership, he added, was largely to help build the company’s AI engine, with China’s massive population providing a ripe background to train machine learning algorithms.

Medopad’s main asset, in any case, is not data, but the algorithms and methods it uses to collect and process digital biomarkers.

“We are a big believer in the fact that data is not our product,” he said. “That is something we are really proud of.”

Will unreliable research bury your healthcare startup?

For healthtech founders and funders, scientific claims and conclusions are more than policy — business models depend upon the lucid appraisal of clinical problems, evaluating inadequacies in current standards of care, a clear understanding of disease pathways, and designing superior interventions. 

At each step along this value chain, founders stand on the shoulders of the scientists that preceded them to obtain reliable evidence. When they promote their own innovations, credibility is a critical prerequisite. But where does credibility come from?

A 2012 study selecting 50 common cookbook ingredients found that 80% had publications linking their consumption to cancer risk; according to some reports, tomatoes, lemons, and celery all cause cancer. The to-and-fro of nutrition science is emblematic of a larger dynamic related to fickle research findings across disciplines. Because investigators seeking to build upon seminal studies struggle to reproduce the original findings, researchers have deemed the problem a reproducibility crisis

Simulations have found that up to 85% of published findings could not be replicated. In turn, tens of billions of dollars are wasted and countless patient lives are adversely impacted annually due to unreliable research. 

Historically, academic research and healthcare VC have had considerable overlap, but in recent years, this co-dependence has increased as researchers are looking more and more for financial support. Government research funding has seen a steady decline, with private sources now supporting almost 60% of the spend. Biomedical VC has been portrayed as a critical source of risk capital for early-stage research and a key engine for its translation at later stages.

UK biotech startup Mogrify injects $16M to get novel cell therapies to market soon

Cambridge, UK-based biotech startup Mogrify, which is working on systematizing the development of novel cell therapies in areas such as regenerative medicine, has closed an initial $16 million Series A.

The raise from investors Ahren Innovation CapitalParkwalk and 24Haymarket follows a $4M seed in February — taking its total raised to date to $20M.

Put simply, Mogrify’s approach entails analysis of vast amounts of genomic data in order to identify the specific energetic changes needed to flip an adult cell from one type to another without having to reset it to a stem cell state — with huge potential utility for a wide variety of therapeutic use-cases.

“What we’re trying to do with Mogrify is systematize that process where you can say here’s my source cell, here’s my target cell, here are the differences between the networks… and here are the most likely points of intervention that we’re going to have to make to drive the fate of an adult cell to another adult cell without going through a stem cell stage,” says CEO and investor Dr Darrin Disley.

So far he says it’s successfully converted 15 cells out of 15 tries.

“We’re now rapidly moving those on through our own programs and partnership programs,” he adds.

Mogrify’s business has three main components: Internal program development of cell therapies (current cell therapies it’s developing include enhancing augmented cartilage implantation; non-invasive treatment of ocular damage; and for blood disorders). It’s also developing a universal source of cells for use in immunotherapy — to act as “disease-eaters”, as Disley puts it.

Speculative IP development is another focus. “Because of the systematic nature of the technology we’re in a position very rapidly to identify areas of therapy that have particular cell conversions at their essence — and then drive that IP generation around those cells very quickly and create an IP footprint,” he says.

Partnering deals is the third piece. Mogrify is also working with others to co-develop and bring targeted cell therapies to market. Disley says it’s already closed some partnerships, though it’s not announcing any names yet.

The startup is drawing on around a decade’s worth of recent work genomics science. And specifically on a data-set generated by an international research effort, called Fantom 5, which its founders had early access to.

“We started with that massive Fantom data-set. That’s the baseline, the background if you like. Think of it like two cities in America: Chicago and New York. There’s your source cell, there’s your target cell. And because you have all the background data of every piece of the network — every building, every skyscraper — if you look at the two you can identify the difference in the gene expression, therefore you can identify which factors will regulate a wide array of those genes. So you can start identifying the differences between the two,” explains Disley.

“We’ve then added to that massive data sets in DNA-protein and protein-protein interactions… so you start to now overlay all of that data. And then we’ve added on top of that new next-gen sequencing data and epigenetic data. So you’ve now got this massive data-set. It’s like having a network map between all the different cell types. So you’re therefore then able to make predictions on how many interventions, what interventions are needed to drive that change of state — and it’s systematic. It doesn’t just recommend one set. There’s a ranking. It can go down to hundreds. And there is some overlap and redundancies, so for example if one — you’re preferred thing — doesn’t work the way you wanted it to you can go back and select another.

“Or if there’s an IP issue around that factor you can ignore that piece of the network and use an alternative route. And once you’ve got to your target cell, if it needs to some tweaking you can actually re-sequence it and take that back and that’s your starting cell again. And you can go through this optimization process. So what comes out at the other end… you’ve got a patent that it like a small molecule composition of matter patent; it’s the therapeutic. So you’re not coming out with the target, you’re actually coming out with here is the composition of matter on the cell.”

In terms of timeframe for getting novel cell therapies from concept to market Disley suggests a range of between four and seven years.

“Once you’ve identified the cell type that can be be the basis of your GMP manufacturable process and then you can tweak that to take it to the therapeutic indication you can develop a cell therapy and bring that to market in five years,” he says. “It’s not like the old days with small molecules where it can take ten, 15, 20 years to get a serious therapy on the market.

“When you’re treating patients… is because there are no other treatments for them, when you go into phase two and do your safety study [and] efficacy study you’re actually treating patients already in terms of their disease. And if you get it right you can get a fast track approval. Or a conditional approval… so that you may not even have to do a phase 3 [testing].”

“We’re not using any artificial intelligence here,” he also emphasizes, pointing to his experience investing in companies in the “big extreme data space” which he argues do best by using “unbiased approaches”.

“AI I think is still trying to find its way,” he continues. “Because in its essence it will be able to get to answers with smaller amounts of data but it’s only as good as the data you train it on. And the danger with AI… it just learns to recognize what you want it to recognize. It doesn’t know what it doesn’t know.

“In combination, once you continue to generate this massive cell network data etc you can start applying aspects of machine learning and AI. But you couldn’t do Mogrify with AI without the data. You have to do it that way. And the data is so complex and combinatorial — 2,000 transcription factors, in terms of regulation of those genes, they then interact in network to do the protein-protein interactions, you’ve got epigenetic aspects of that, you could even start adding cell microbiome effects to that later — so you’ve got a lot of factors that could influence the phenotype of the cell that’s coming out the other end.

“So I think with AI you have to be a little careful. I think it will be a more optimizing tool once you’ve got sufficient confidence in your system.”

The plan for the Series A funding is to ramp up Mogrify’s corporate operations and headcount — including bringing in senior executives and expertise from industry — as well as spending to fund its therapy development programs.

Disley notes its recent appointment of Dr Jane Osbourn as chair as one example.

“We’re bringing in more people with a lot of cell therapy experience from big pharma, around then more on the manufacturing and delivery of that — so really building so that we’re not just a tech company,” he says. “We’ve very strong already, we’re already 35 people on the tech and early stage drug discovery side — we’re going to add another 30 to that. But that’s going to be increasingly more people with big pharma, cell therapy development, manufacturing experience to get products on to market.”

Partner search is another focus for the Series A. “We’re trying to find the right strategy partners. We’re not doing services, we’re not doing products — so we want to find the right strategic partners in terms of doing multi-programs in a partnership,” he adds. “And then a series of more tactical deals where people have got a specific problem with a cell conversion. These more turnkey deals, if you like. We still get up-fronts, milestones and royalties but they’re smaller.”

Despite now having enough money for the next two to two and half years it’s also leaving the Series A open to continue expanding the round over the next 12 months — up to a maximum of another $16M.

“We have so many interested investors,” Disley tells us. “This round we didn’t actually open our round. We did it with internal investors and people we’re very close to who we’ve worked with before, and there were investors lining up… [so] we are leaving it open so that in these next 12 months we may choose to increase the amount we bring in.

“It would be a maximum of another $16M if it was an A round but we may decide just to go straight forward if we progress very fast to a much bigger B round.”