Positive Preclinical Proof-of-Concept Results For Liver Cancer Candidate, TXR-311

In September 2016, we announced a collaboration with the Asian Liver Center at Stanford University School of Medicine (the Asian Liver Center). The goal of this collaboration was to identify new drug candidates targeting hepatocellular carcinoma (HCC, the major form of adult liver cancer). Today, we announced a lead candidate, TXR-311, that has shown positive results in cell-based assays. I wanted to share a bit more background on liver cancer and details on why these results are exciting.

HCC is a primary cancer of the liver that tends to occur in patients with… 

READ THE FULL POST AT MEDIUM.COM

Seeing the power of AI in drug development

Today we announced our collaboration with Santen, a world leader in the development of innovative ophthalmology treatments. Scientists at twoXAR will use our proprietary computational drug discovery platform to discover, screen and prioritize novel drug candidates with potential application in glaucoma. Santen will then develop and commercialize drug candidates arising from the collaboration. This collaboration is an exciting example of how artificial intelligence-driven approaches can move beyond supporting existing hypotheses and lead the discovery of new drugs. Combining twoXAR’s unique capabilities with Santen’s experience in ophthalmic product development and commercialization… 

READ THE FULL POST AT MEDIUM.COM

Augmenting Drug Discovery with Computer Science

The short-list for the annual Arthur C. Clarke Award was recently announced and it reminded me of a post we did last fall on augmentation vs. automation. Clarke is a British science fiction writer who is famous for being the co-screenplay writer (with Stanley Kubrick) of the 1968 film 2001: A Space Odyssey. He is also known for the so-called Clarke’s Laws, which are three ideas intended to guide consideration of future scientific developments.

  1. When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong.
  2. The only way of discovering the limits of the possible is to venture a little way past them into the impossible.
  3. Any sufficiently advanced technology is indistinguishable from magic.

These laws resonate here at twoXAR where every week we meet with biopharma research executives who tell us — usually right after we say something like, “using our platform you can evaluate tens of thousands of drug candidates and identify their possible MOAs, evaluate chemical similarity, and screen for clinical evidence in minutes” — that’s “impossible” or “magic”!

READ THE FULL POST AT MEDIUM.COM

Mission Possible: Software-driven Drug Discovery

Originally published at Life Science Leader Online.

In the 25-plus years since the modern Internet was launched we have seen virtually every industry evolve by leveraging the connected, global computing infrastructure we can now tap into any time, from anywhere. Today, advanced software programming tools like machine learning, massive data sets and cloud-based compute are making it easier than ever to rapidly launch and globally scale software-driven services without the capital expense that was once required.

The debate about whether or not software will eat drug discovery is not a new one and remains a topic that can raise voices. As a formally educated computer scientist and cofounder of a company focused on software-driven drug discovery, I come to the discussion with my own biases.

There is no shortage of software in today’s biopharma R&D organization. Cloud-based electronic data capture (EDC), laboratory information management systems (LIMS), process automation, and chemical informatics are just a few of the well-established tools that support R&D and have a meaningful impact. While software has become a …

Read the full piece at Life Science Leader Online.

Riding the Wave of Data Science and Biomed Convergence

In 1993, I decided that I wanted to launch a biotech company.* Since then, my friends and I have brainstormed biotech-driven solutions in toothpaste, paint, energy, and medicine.

22 years later I have realized this dream. The path was by no means direct.  But despite my sometimes seemingly random education and career path, I have found myself at the nucleolus of bioinnovation: the intersection of data science and biomedicine. Fortunately, my timing was impeccable.  Technology is rapidly changing the rules for the life sciences and, like the many industries that it has already touched, software is finally enabling business models in the life sciences to scale to new heights.

I have posted previously about how biopharmas are shifting the burden of innovation to venture investors and startups and how, even at the POTUS-level, that data science is being recognized as the future of life science and healthcare.

More and more, we are seeing exciting examples of how biopharma companies are embracing big data, or at least confirming to the world that they need it, and how investors and startups are diving in to fill the gap.

Big pharma buys into big data

We were recently invited to speak at an internal Sanofi symposium called Convergence of Science, Technology, & Data Sciences – Impact on Pharma. We presented alongside leaders in the space including: Isaac Kohane (Co-Director of Harvard Medical School’s Biomedical Informatics Department), Vikram Bajaj (Chief Scientist, Google Life Sciences), and Avi Ma’ayan (Professor, Pharmacology and Systems Therapeutics at Mount Sinai).

Frankly, I was pleasantly surprised to experience how open to innovation large pharma companies like Sanofi are; I was also excited by how enthusiastically they embraced the opportunity to explore how data science-driven approaches can augment the drug development process from discovery to clinical trials.

As #databio nerds, we were stoked by the interesting approaches others in the computational biology space were taking; from the business side, we were equally pleased to hear a very clear message best summarized by a quote from the former NIH Director and Sanofi President of R&D, Elias Zerhouni: “Big Data and the way we approach it is going to be determinant for the long-term success as an R&D Organization.

And this sentiment is being echoed throughout the industry:

“We have to build a data-analytics capability that we don’t have today. We’re also going to have to create partnerships and think about different types of people that we need to bring into our company so that we can take full advantage of that part of healthcare.”Joe Jimenez, CEO, Novartis

“Data will also help more efficiently develop medicines and better define which patients will most benefit.”  – Geno Germano, Group President, Global Innovative Pharma Business, Pfizer

“The [combined] role of health care and technology is going to be critical”Alex Gorsky, CEO, Johnson & Johnson

 An example of pharma action in this space recently is the doubling down on genomics. A number of large biopharma companies have publically announced their efforts to utilize big genomic data in both companion diagnostic and new drug development.

“Dramatic breakthroughs in understanding how the human genome functions are still in their infancy in terms of how they can be applied to drug discovery, but we can see their potential to transform the process. This is not an incremental change. We are aiming for transformative outcomes that could improve our ability to bring innovative and more effective new medicines to patients.”Lon Cardon, Senior Vice President of Alternative Discovery and Development at GSK

“The acquisition of Bina is a significant step for Roche to enable the promise of personalized healthcare by delivering the highest quality genomic data possible.”Dan Zabrowski, Head of Roche Sequencing

 Where there’s scale, there is VC…

a16z’s recent podcast, When Bio Meets Computer Science, discusses “how everything changes when software eats biology” and captures why life science businesses can now scale.

“These new startups have potential to have the kind of economics profile and the kind of financing needs of a software startup as compared to a pharma startup?

These new startups remind me a lot of software startups in 2005 when we see cloud computing start to realize. That’s sort of what we are starting to see now and because they have software at their heart, either literally or in terms of how they think about things, that they are organizing themselves in a cloud-like biology way, this would be very much on the Moore’s Law curve of things. And in a sense you could use this differentiate traditional biotech from this new crop of companies. That traditional biotech is governed by Eroom’s Law and these are governed much more by Moore’s Law”

Life science investors have questioned the value of platform companies in the recent past but the venture community is starting to warm up to them. VC’s recognize that the life science investment model of betting the farm on a potential billion dollar drug (unicorn drugs, anyone?) with a binary outcome is dying. Computation-driven platform companies coupled with the established CRO industry enable life science companies to look and act much more like software companies yielding stepwise, milestone-driven returns and valuations, with much smaller investments and shorter runways.

In a recent post on YC’s move in to the biotech realm, Atlas’ Bruce Booth commented:

“Although the math may be different, virtual biotechs doing drug discovery today are leveraging a similar trend: remove the heavy fixed costs of building out your own laboratory, purchasing expensive lab equipment, and then having to “feed” the system, and move to a lower cost virtual model of renting lab capabilities via a global network of CROs and collaborators. Others have already commented about the decreasing cost of DNA sequencing… but same holds for other aspects of drug discovery, like computer-aided drug design and structural biology. It’s easier to start a scientifically credible biotech today than ever before, and entrepreneurs can make real progress in validating a thesis on seed capital.”

Some examples of investments in the data science-driven drug development space today include Data Collective’s investments in Mousera and Atomwise and Atlas Venture Life Science’s investments in Nimbus Therapeutics and Numerate.

Software has enabled these dreams to become reality

I wish I was prescient enough in 1993 to have predicted the role of technology in transforming biopharma. Honestly, I was really just a kid eager to learn about biology and technology and explore the potential futuristic and off-the-wall applications. Applications that at the time seemed wacky, but today are becoming a reality.

At twoXAR, we use data science to accelerate the identification and validation of drug candidates for complex diseases. I hadn’t thought that this was possible until Andrew introduced me to the DUMA platform and demonstrated how, in minutes, we can identify new treatments for a disease. Since then we’ve translated our in silico results to the physical world and continue to do so through a growing list of exciting collaborations with commercial and academic discovery organizations.

Over the last 12 months, I have heard a variety of reactions to the speed and scalability that computational biology enables (and to the fact that both twoXAR founders are named Andrew Radin).  But, it’s clear that as software continues to penetrate all industries it will also keep altering the landscape of drug discovery and the life sciences. And, it’s refreshing to see executives and investors acknowledge the power of computation-based approaches and how they speed up discovery and validation of therapies and enable a new era of software-like scalability in the biopharmaceutical industry.

 

*feel free to ask me why!

Validating DUMA Independently

When independent scientific validation happens with new technologies it is an exciting time for both researcher and validator.

Some time ago we used our DUMA drug discovery platform to find new potential drug treatments for Parkinson’s disease. After processing over 25,000 drugs with our system, we identified a handful of promising candidates for further study. We noticed one of our highest ranked predictions was currently under study at an NIH Udall Center of Excellence in Parkinson’s Disease Research at Michigan State University.

We decided to be good citizens to the research community and provide our findings to the research team at Michigan State University. We prepared a 5-page PDF that summarized our computational prediction. When DUMA highly ranks a drug for efficacy it also provides the supporting evidence it used to make that prediction. This can include:

  • Calculated proteins of significant interest in the disease state,
  • How the drug interacts with those proteins or their binding neighbors,
  • Drugs with similar molecular substructures that have similar effects, and
  • Protective evidence found in clinical medical records.

We emailed our report to Dr. Tim Collier and figured that was the end of it. Much to our surprise we found ourselves on a phone call the next day with Tim and his colleague Dr. Katrina Paumier. Tim told us that we had independently validated work that had been going on for years.

As part of the review of the report, Tim and Katrina asked a number of questions on how we came up with the prediction we presented. We explained a bit about DUMA and how quickly it can be used to screen large databases of drugs and make predictions within a few minutes. They told us they had another promising drug under study and asked us to run it through DUMA. We returned the results on this new drug right away. It turned out this second candidate was highly predicted by DUMA to be effective in treating Parkinson’s disease. Once again our evidence matched their data, independently validating that they were on the right track with their second candidate.

Finally, Tim asked us to run one more drug through our system. He didn’t tell us much about this particular molecule, and we let DUMA process the data we collected on it. The prediction ranked this candidate relatively lower. We informed Tim that our system gave a low to moderate indication of efficacy, and supplied the evidence that DUMA had made to assign this ranking. This once again matched his own data about the compound.

Our work with Michigan State University continues today. We are working with Tim on providing new, novel compounds for further study. We have collaborated on combining the power of the DUMA drug discovery system with the expertise in Parkinson’s research labs.

Star Trek Medicine: Data Science in Life Science and Healthcare

From the White House to medical education data science is being recognized as the future of life science and healthcare.

President Obama recently appointed Dr. DJ Patil (fellow USCD Alum!) as U.S. Chief Data Scientist.  In his memo: Unleashing the Power of Data to Serve the American People Dr. Patil states, “The vast majority of existing data has been generated in the past few years, and today’s explosive pace of data growth is set to continue. In this setting, data science — the ability to extract knowledge and insights from large and complex data sets — is fundamentally important.” One of Dr. Patil’s priority areas is the Precision Medicine Initiative President Obama announced in January, which is great to see that medical data is recognized as a strong national interest.  But a focus on data science isn’t just seen at a national policy level, it continues to permeate in startups, medical school, and biopharma.

Last Friday Andrew and I attended the MIT Sloan Bioinnovations Conference – He spoke on the Big Data, Policy, and Personalized Medicine panel with several other companies noted here and naturally, the conversation focused on the power of computation in this space and whether or not our vision of “Star Trek Medicine” (as one audience member put it) was soon to come. During the rest of the conference, topics ranged from Policy to Biomedical Research to Financial Engineering to Education and I was excited that a common theme that ran through each of the sessions was data science and how it’s changing the medical landscape.

One example includes Jaime Heywood’s ALS Therapy Development Institute/PatientsLikeMe who used mathematical algorithms to determine that ½ of the animal studies they were attempting to reproduce (n=50) of an ALS drug could not even possibly have been statistically significant prompting more rigorous studies. When describing how they initially approached this, Jaime stated very matter-of-factly, “This can be done with math.” The power of data science in the life science and healthcare space is also being recognized in medical education. Dr. Jeffrey Flier, dean of Harvard Medical School, states in a recent WSJ piece: “There is palpable excitement at the interface of biology, psychology, engineering, sensor technology, computation and therapeutics… …The opportunities are immense and consequential.”

I’ve heard similar sentiment from senior executives at biopharmaceutical companies that I have spoken to – that the future of drug discovery resides in the data (whether biological, chemical, clinical or otherwise) and the surrounding analytics that can reveal hidden insights. However, industry professionals also express that it’s not yet apparent how the data sciences will transform the industry – that is where startups have room to show them how.

The shift in the recognition of the importance of data science is clear and being seen across the spectrum of public and private sector in the medical space. At twoXAR, we are excited to be a part of enabling society to reach Star Trek heights in medicine faster, cheaper, and ultimately more accurate.

The Ghosts of Biopharma Present: Biopharma’s Innovation Outsourcing

The holiday season has always been a time for reflection on the closing year and hope for the new one. In this spirit, today I’ll explore the Ghosts of Biopharma Present, and how startups in the Biopharma space may be the Tiny Tims that herald a brighter future.

As its cash cow patents expire, many biopharmaceutical companies are experiencing significant decreases in revenue [1]. Meanwhile, the cost of bringing a drug to market has soared in the past decade [2], creating a decreasing tolerance for risk, and thus for innovation. As a result, Big Pharma is increasingly slashing its R&D departments to appease investors and maintain their bottom line [3].

The last quarter alone has seen a wave of layoffs at many heavy hitter companies. This month, GlaxoSmithKline announced its plan to cut 900 R&D jobs at its Research Triangle Park in North Carolina, a decision prompted by the decline in sales of its star product Advair as cheaper inhalers gain market share [4]. In October, Southern California-based Amgen caved to pressures from activist hedge fund investors and announced that it would lay off up to 1,100 people [5]. While these cuts may buoy the next few quarters’ balance sheets, the LA Times wonders “whether such high-stakes face-offs result in short-term benefits to shareholders at the expense of a company’s ability to invest in its operations and thrive long term.” In other words, if companies continue to act like Scrooge, they, their investors, and their patients may face a bleak Ghost of Biopharma Future.

So how does Scrooge change course? He engages with the outside world. For biopharmaceutical companies, that means embracing the paradigm of external innovation. External innovation can take many forms: partnerships with academia, collaboration and risk sharing across two or more companies, and even crowd-sourcing problems through open competitions [6]. But some of the greatest potential for rapid innovation may lie in “Biopharma co-creation”: the funding and acquisition of Biopharma startups.

This approach combines the flexibility, efficiency, and innovation of small companies with the expertise and resources of Big Pharma, which gets to access new research while limiting and externalizing their risk.  A recent report from the Silicon Valley Bank on healthcare venture fundraising indicates that this approach is on the rise, and that large Biopharma companies are pointing their investment arms toward early series funding of preclinical and Phase I-stage research companies [7].

Different Biopharma giants have approached external innovation in different ways. Some have formed partnerships with venture capital firms to jointly fund new companies. For example, in 2013 GlaxoSmithKline created a $495 M fund with Avalon Ventures to launch 10 companies. While this investment is substantial, it is a fraction of the estimated cost of bringing a single drug to market [2], and fosters a diverse portfolio of innovation.

Other approaches include supporting therapeutic research companies in more physical ways. Johnson & Johnson, which has funded external innovation since 1973 [8], has more recently opened a network of bio incubators and innovation centers in California, Massachusetts, London, and Singapore [9]. Diego Miralles, head of the California Innovation Center, affirms, “At the end of the day, we’re going to live or die by the success of the biotech startups… Unless that space of entrepreneurs in biotech is robust, we are all in trouble, both as an industry and a society. Therefore our approach is to support the entrepreneur in any way possible.”

As startups are increasingly recognized as a major locus for bio-innovation, even Google is getting in on the action. In the last year, health and life sciences startups grew from 9% of Google Venture’s investments to 36% [10].  In particular, Google Ventures has focused on data-driven biopharma startups, such as Flatiron Health. Its CEO Nat Turner points out that “Google is trying to buy into technology that’s changing older industries and suits its big data expertise.”

Indeed, several Big Pharma companies are also investing in startups that develop “Target Generating Platforms” [7]. Thus, the synergy of data science, startup innovation, Biopharma expertise, and venture funding may create a viable alternative to traditional R&D that will “bless us, every one.”

 


[1] Levine D. (2014), Transforming health care. Burrill Media LLC.