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

Let’s Augment, Not Automate

“Any sufficiently advanced technology is indistinguishable from magic.”

Science writer and futurist Arthur C. Clarke’s poignant “third law” only becomes more relevant as technological innovation accelerates and disciplines like computer science, data science and life science converge.

As we have been out in the field demonstrating the power of our technology platform to our collaborators, it has been interesting to hear their reactions when we tell them how it can
evaluate tens of thousands of drug candidates and identify their possible MOAs, evaluate chemical similarity, and screen for clinical evidence in minutes. These responses cover the gamut from, “Wow, this is going to revolutionize drug discovery!” to “this is magic, I don’t believe computers can do this…”

However, whether we are talking to the converted or the skeptical, as we get deeper into conversations about how our technology works, we come into agreement that using advanced data science techniques to analyze data about drug candidates is not magic. In fact, we’re doing what scientific researchers have always done – analyze data that arises from experiments. What’s different is that advances in statistical methods, our proprietary algorithms, and secure cloud computing enable us to do this orders of magnitude faster than by hand or with the naked eye.

The speed of our technology combined with the massive quantities of data that it processes, is simply enhancing the work that our collaborators have been doing in the lab for years. We believe that the most interesting and powerful new discoveries will arise at the intersection of open-minded life scientists combining their deep expertise with unbiased software.

Technologies like ours are meant to augment* the work of life scientists and help them accelerate drug discovery and fill clinical pipelines while leading society to a more robust and streamlined scientific process. Although DUMA might sound futuristic, today it is enabling therapeutic researchers to better leverage the value of their data and do it more rapidly than ever before.

Don’t believe the magic? Contact me and we’ll get a trial started to show you the science.

 

*Sidenote: I have been particularly interested in this interaction between humans and machines, which led me to a class at MIT called The [Technological] Singularity and Related Topics. One of those major topics was whether or not machines (including software) will replace aspects of society. One of my professors Erik Brynjolfsson, author of The Second Machine Age: stated that “We are racing with machines – let’s augment, not automate.”And we definitely share that view here at twoXAR.

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!

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.

Commercial Successes in Translational Bioinformatics

As we discussed in our previous post, our work at twoXAR is best described as translational informatics: using big data methods to bring novel, experimental research closer to the clinic. While this exciting field is still relatively young, significant advances have been made in the last fifteen years. As the life sciences’ capacity to generate large, comprehensive, and unbiased biological data sets (“-omics”) has grown, so has the need for powerful data science to scale their digital mountains of results. In this post, I’d like to highlight how small, innovative data science companies have already begun to contribute to this massive project, and how twoXAR is poised to meet current needs in the field.

In the late 1990s, the progress of the human genome project and the advent of molecular profiling technologies such as DNA microarrays set the stage for the “Cambrian explosion” of big data in biology. Some of the first innovations in the generation and decoding of large molecular profiling data sets came from Rosetta InPharmatics, which was acquired by Merck in 2001 for $620M. Rosetta’s early work established computational pattern recognition techniques that allowed researchers to detect cellular gene expression changes induced by treatment with pharmacological compounds.

This pioneering work paved the way for other translational informatics companies, which are tackling current big data challenges in various different ways. For example, Ingenuity Systems, a Redwood City-based bioinformatics company, has developed tools that provide researchers with curated literature summaries and rapid statistical analyses. Their software is currently a well-cited resource, with subscriptions purchased by many academic labs. In 2013, Ingenuity Systems was acquired by the research technology company Qiagen for $105M. Other companies, such as the San Jose and Bangalore-based Cellworks, generate computational simulations of disease states to screen drug candidates for clinical development. Their ordinary differential equation (ODE)-based models have led to successful collaborations with academic scientists, and the advancement of drug candidates to the validation stage. Their efforts have been supported by grants from the Wellcome Trust, and by investments from Artiman Ventures and Sequoia Capital.

These companies, and others like them, demonstrate how diverse data science approaches from small groups of computational innovators can address the challenges of translational informatics in impactful ways. The unique machine learning and data mining techniques we have developed at twoXAR are thus joining a young but powerful arsenal of modern tools for modern biology.

A Dose of Weekend Hacking: Hosting Stanford Medical School Hackathon

Last weekend, as part of MIT Hacking Medicine, I helped host the CareInnovations Patient Engagement Hackfest at Stanford Medical School. The Hackathon is an innovative weekend program that aims to create “more effective and reliable connections between patients, clinicians, and the information that can improve the quality and cost efficiency of healthcare” (Find out more here).  You may have seen my tweets about it all weekend. The event was sponsored by CareInnovations, (an Intel and GE joint venture) and went from Friday through Sunday, with about 100 participants attending the event. It was a really exciting crew of people with extremely varied backgrounds – it was just my type of crowd.

A large percentage of participants were clinicians, business & medical school students, software developers, and designers with industry experience. When you collect brilliant doctors, organization leaders, and computer scientists in the same room to discuss solutions in the healthcare space, you hear a lot of creative and fresh ideas about how to revolutionize medical care.

This weekend’s focus was on patient engagement, so we concentrated on questions like how to make it easy for patients to take their medication, how to collect accurate patient data in a useable format for physicians, and how to create medical tools that allow the patient to directly engage with their own health. It started with 30- and 60-second pitches on problems and solutions in the space and over the course of the weekend, teams self-assembled based on the specific problems they wanted to solve. In the end, 9 teams hacked and presented some pretty innovative hardware, software, and service-based solutions.

The most valuable element of this experience was seeing that these diverse teams could create incredible and unexpected solutions to a given problem and it reminds me that a diverse team is what makes for a successful business venture. I’ve worked on teams made up completely of engineers and teams composed of MBAs, and inevitably teams of people with the same ways of thinking will miss something important. The best way to create a sustainable solution to a problem is to have a team that can ideate from as many angles as possible.

I came out of the event energized and ready to tackle big issues in healthcare. As an entrepreneur in the space, I was inspired by the teams at the hackathon. I got to see firsthand and with immediate results, how unconventional and diverse approaches to old problems are not only effective: they can change our world.

At twoXAR we embrace this mindset and strive to build a team with radically different backgrounds and ways of thinking who can effectively come together to improve lives through computation. In fact, post the hackathon, Stanford medical student Desiree Li joined the team to support us both on the science and business development side. I guess Silicon Valley is the place to find the crossover between doctor and entrepreneur.

We Filed Our Patent!

About five months ago, before twoXAR had a name or a team, Romy Celli, an intellectual property and biotech patent expert at Alston & Bird, had a close look at the drug discovery research Andrew had been working on at Stanford. Andrew’s first inclination was publish what he developed. It was only under advice from a friend—“You’re not going to publish this, you’re going to patent it”—that he found Romy. She had a feeling he’d discovered something exciting, and she took it to her colleague, Ishna Neamatullah, another biotech patent genius, to get a second opinion.

We’re grateful that these two people took a huge risk in helping twoXAR with this brainchild: they didn’t charge us a dime up front because they believe in what we’re doing. Not only that, but when Ishna took on the task of writing the patent (under Romy’s guidance), she turned a fifteen page academic paper into a comprehensive one-hundred-page document – and didn’t miss a thing. I could try to tell you how many people we’ve told about our technology and how many of them have said something along the lines of, “I have no idea what you’re talking about, but it sounds really interesting!” But I’ve lost count. Ishna, who is both wonderfully in synch with the wild workings of Andrew’s mind and incredibly humble, was on point in her rewriting the idea down to the smallest details. This is especially striking because the fifteen-pager really only described the function of the algorithm. It didn’t get into the thought-process behind it, or how we intended to apply the technology. Essentially, Ishna, through a few meetings, managed to get on Andrew’s wavelength and translate it all in legalese.

Which leads up to last week, when we filed our patent thanks to these generous and impressive lawyers, and twoXAR threw a cupcake thank you party for Romy and Ishna. Trader Joe’s gluten-free chocolate cupcakes are the best.

Defining Our Vision

I don’t want to get all motivational-speaker on you. Many of you probably are motivational speakers or could at least give a better speech than me if asked. But I want to talk about past failures and how they’ve made twoXAR one of the greatest and most exciting adventures I’ve ever taken. And that’s including the time I rode alone in a sleeping berth with 7 strangers on a twenty-nine-hour train through China and the time I separated my shoulder while mountain biking in the Blue Mountains in Australia.

When I founded the mobile scheduling company Thyme Labs last summer, I discovered that the number one factor to align a startup team and help focus and prioritize the infinite tasks and sub-goals it must accomplish is defining a vision. We were slow to do that at Thyme and in the course of managing the company, we changed our central goal, or our vision, a couple of times. There are several factors that led to Thyme’s wrap up including the fact that we tried to balance launching the company while in school and missing our lofty fundraising and development targets. But mostly it was that each team member was not on board with the vision which led to conflicting goals. If we had been on the same page, we could have overcome the other issues set before us. But I have learned a lot from my former team’s challenges. (Check out a few lessons from last summer)

At twoXAR we’ve nailed down our vision early, and we know how it will guide us in the long term.

Improving lives through computation.

These four simple words motivate us every day. What they mean is that we will keep striving to make drug discovery more intentional, more efficient, and more comprehensive and ultimately provide better care to the patients who need it. We believe the work we’re doing will enable the healthcare system to better address patients’ symptoms as a whole. By analyzing large biochemical and genomic data sets, we are determining new ways for drugs to alleviate symptoms better and faster with reduced negative side effects.  Computation is the key to breaking through constraints in the existing drug development process.

With a strong vision and the technology to back it, twoXAR is continually developing computational solutions to improve lives. So I’m making past failures into a gain not just for myself and twoXAR, but hopefully far beyond. And that is, like, really exciting.

twoXAR’s Co-Founder Moves West

I live and work in my co-founder’s house, and it’s not weird at all.

But it was a little at first. About six weeks ago when I moved to the Bay Area from Cambridge to start twoXAR, a lot of people thought I was crazy. I was turning down job offers at established organizations doing work that I would have probably found very fulfilling. Besides that, my parents’ worried voices rang in my ears, “Are you sure you should be doing this?” And then there were my own doubts about taking up space in this man’s home, not to mention the trouble it might be for his wife, Wendy, or their beagle, Xelda.

But the excitement far outweighed the concerns. If you know the startup world, then you know that it is all about taking that leap – taking risks – big, calculated risks. I had this in common with my friend, mentor, and new business partner: we were both more interested in cutting new paths than in going down the safe route. Our pasts overlap with stories about independent and wandering trips to China, sailing and various other adventures and – ya know – investing all we’ve got in an idea we believe in.

On the floor of our 10’x12’ office is a two-hundred-dollar computer equipped with an algorithm that we believe can change the way pharmaceuticals are discovered and developed. The office is about 12’ from the house where you might find Wendy and Xelda if you step outside the French doors. The pale yellow walls reflect the California sun all day as we jam away making twoXAR happen. Despite the hard work in this small room, we have a lot of laughs, as we share ideas, plans, and stories around many things we have in common, including our names (we can tell you about that and how we chose the name of the company next time).

The doubts I had about moving here are replaced with excitement. We’re a weird Bay Area family now – part scientist, part entrepreneur, part artist (that’s Wendy’s role), part hound. And we can’t wait to tell you about what we’re cooking up in our little yellow science lab/office/bonding chamber.