A milestone worth celebrating

There have been a series of events that have defined the history of twoXAR. There was the lecture from Prof. Sirota on computational drug discovery that inspired me to invent. There was the encouragement from V. Paul Lee to patent my idea rather than publish an academic paper. There was the realization that Andrew M. Radin shared not only my name, but my passion for biotechnology and entrepreneurship.  There was that key moment while sailing the Charles River in the summer of 2014 when Andrew and I decided to launch this company.

More milestones followed. There was the day we received funds from our very first investor, Haya Al Ghanim. The email that told us we had been accepted to the StartX accelerator program at Stanford University. The phone call with Michigan State University’s Dr. Tim Collier where we learned our technology predicted in silico what he saw in the lab.

All along the way we’ve assembled an amazing team of scientists and engineers who continually push twoXAR’s technology forward, beyond my wildest imagination.

Today, I’m pleased to announce an exciting new milestone that will further shape our future. Andreessen Horowitz has led a $3.5M investment in twoXAR, along with the Stanford-StartX Fund and our visionary group of angel investors.

We will use these funds to grow our team, form new partnerships and further progress our drug candidates through preclinical studies. We are honored and pleased to have Andreessen Horowitz support our efforts. In addition to their investment, the team at Andreessen Horowitz, including our General Partner Vijay Pande, have signed up to help us with everything from recruiting to business development. It gives us great satisfaction to know we have a world-class organization standing behind us.

There are many more milestones, to be revealed on the road ahead. We look forward to sharing with you our success and discoveries along the way.

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.

Balancing Transparency and Protecting IP

The recent Theranos news has brought to mind the difficult balance between full technical transparency and protecting company IP. As co-founder of a company that has developed a technology that we believe will transform the drug discovery industry, I have struggled with how to best support our claims while protecting our trade secrets.

Early in our company history we thought about moving right from our computational drug predictions into animal studies without involving third parties. At the time, it seemed like the right idea. We could move quickly, reduce uncertainties and produce results in an efficient manner. We learned, however, that without anyone to validate our claims, it was difficult to develop the partnerships and raise the capital required to run these experiments.

Given these hurdles, we decided to  pursue a collaboration-based model. Today, we are working with scientists in academic and commercial biopharma labs who can validate our predictions independently without having any knowledge of our predictive algorithms. We do this by making computational predictions that are then compared to physical experiments made by our collaborators in the wet lab. As we continue to show that our computational predictions match the results they are generating independently in the wet lab, we build confidence in our technology while keeping our secret sauce under wraps.

While we feel that we’ve struck an appropriate balance for where we are today, we continue to think through the best ways to provide the right level of transparency for others to evaluate our claims. For us as scientists, it is important to have the feedback and input of the drug development community. We believe that it’s going to take more than just us to bring this new technology to market – we rely on our collaborators and partners to work with us in this regard.

We want to radically change the process of drug discovery, but, at the end of the day, we want to make sure we are able to do it without sacrificing our commitment to safety and efficacy through rigorous scientific validation.

Top 10 Things I Learned at StartX This Summer

Our first session at Stanford University’s StartX accelerator is coming to a close. It’s the perfect time to collect my thoughts on the top 10 things I learned at StartX these past months.

  1. Startups are hard
  2. Medical startups are even harder
  3. There’s torrential chaos behind every success story
  4. You know more than you think you know
  5. You know less than you think you know
  6. Evaluating the advice you receive is critical
  7. People will help you if you ask
  8. Grit is more valuable than intellect
  9. Grit without intellect is suicide
  10. There is always a way through

 

1. Startups are hard

A big part of the StartX experience is to share your stories with fellow founders. One of the key things you learn right away is that everyone, regardless of stage of funding and business, has big problems to solve. Funding. Customers. Traction. Recruiting. Competition. All of these things are amplified when you have limited resources and climbing uphill. Every day we are exposed to some of the brightest people on the planet, and I assure you, it’s hard for them too.

2. Medical startups are even harder

Having helped build three different consumer based internet startups, I was blissfully unaware of the extra burden that comes with a medical based startup. In consumer you build and deliver with no delay, and immediately get feedback on your work. Iterations are fast because you are directly connected to the end user. In the medical world you build and test, build and test, and then do some more building and testing. Despite shaving years of the drug discovery process, it will take quite some time before any of our work makes it into a human being. It’s why medical companies stay in StartX for two sessions, while other companies stay for one.

3. There’s torrential chaos behind every success story

The StartX community includes many serial entrepreneurs who share their stories. The outside world is exposed to the big exits, the signed deals and the brand-name VC investments. What the world doesn’t hear about is the twists and turns of deals gone bad, employees who need to be dismissed, or investors who push entrepreneurs in the wrong direction. The craziest story I’ve heard at StartX is from an entrepreneur who built a successful company only to have an unscrupulous contractor drain the bank account and walk away with all the cash.

4. You know more than you think you know

I was surprised to find how helpful I could be to other StartX companies. While I’m yet to be a part of a billion-dollar exit, my role as CTO in my prior startups gave me more experience and wisdom that I had previously recognized. Every StartX founder has deep expertise or experience in a wide array of disciplines. Some of the best advice we’ve gotten to date, on pitching and selling, has come from other founders in the session with us.

5. You know less than you think you know

Every day the StartX community fills another gap in our knowledge. We speculate on the future and what we think it will take to conclude a successful scientific experiment, close a deal, or raise money. The community here offers their shared experience and allows us to think about things we had never considered before. What we’ve learned about working deals in the pharmaceutical space has been pure gold.

6. Evaluating the advice you receive is critical

StartX puts you in front of people who advise you all the time. Sometimes this advice is solicited by us, other times we receive it without asking. What we find is that often the experts disagree on what is our best path forward. We need to decide which mentors we think are best aligned with our world, and which are off the mark. The process of choosing which advice to follow is often more important that getting the advice itself. In retrospect, we realize we have sometimes received misaligned or mistimed advice from highly successful people. It’s sometimes tough to filter that advice given the high-profiles of those who advise us.

7. People will help you if you ask

There is a culture of mutual assistance at StartX. Part of being a founder in the community is actively helping others while being open about asking for help yourself. We quickly learned this applies outside of the walls of StartX as well. We’ve received help from world-renowned professors, CEOs of billion dollar companies, and scientific leaders from around the world – simply by asking – often with nothing expected in return.

8. Grit is more valuable than intellect

We are exposed to over fifty companies here at StartX. The ones we see making the most progress are those that take multiple hits on the chin and keep moving. Startups are about endless mini-failures that can wear down anyone without resolve. The mental attitude that backs up a culture of perseverance is a key factor behind those that make it and those that evaporate.

9. Grit without intellect is suicide

It’s a challenge to put a pencil in one of my ears and pull it out of the other. That doesn’t mean it’s a challenge worth pursuing. Part of accepting mentorship is being able to admit that your plans aren’t going to work. It’s sometimes tough to let go of what your heart says to do and listen to your head.

10. There is always a way through

We admire our peer companies that hit what seems like an impenetrable wall, only to wiggle and worm and find a way to get through. We’ve learned that every challenge has a solution as long as you are signed up to find it. Sometimes you need to grit your teeth and climb the mountain. Sometimes the fastest path is to hike around the mountain. In very special cases you can eliminate the mountain all together,

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.

Night of the Dead’s Living Data

We often speak of our trove of gene expression data:  RNA measurements from different human tissues, which allow us to identify genes that are expressed abnormally in disease patients compared to healthy people. By the time it gets to us, that RNA has been converted first into cDNA, then into a microarray or RNA-seq readout, then into a publication, and finally into an entry in a neat public database. But like babies and sausage, we must eventually pause to consider where this RNA comes from. The answer, especially for brain diseases, is often cadavers (otherwise known as dead people).

Realizing that so much scientific knowledge comes from the dearly departed initially gave me the heebie jeebies. I knew there were no other options, as brain biopsies are incredibly unpopular among the living. But weren’t readouts from dead tissues vastly different from live ones? My naïve intuition was that biological readouts would be like the electronic displays that report system diagnostics on my motorcycle: once the machine’s been turned off, the measurements become significantly less accurate reflections of the bike’s functioning state.

However, apparently one cannot extrapolate this logic from hogs to humans. It turns out that RNA, particularly in brain tissue, is quite stable post-mortem, and a reliable snapshot of brain function in life. Post-mortem protein measurements can be very robust as well; a recent study of more than 3,600 human cadaver brains has shifted the paradigm on which protein is the primary driver of Alzheimer’s Disease.

In a way, twoXAR’s work corroborates this principle. Our gene expression-based models of Parkinson’s Disease, schizophrenia, and Alzheimer’s Disease yield excellent predictions of known treatments and exciting, sensible repurposing candidates. Thus, I have come to acknowledge that like zombies, “undead data” can be surprisingly powerful.

elevenXAR, Inc.

April 1 is an exciting day for twoXAR, Inc. as we are now re-launching the company as elevenXAR, Inc. This comes after tense negotiations with all of our staff. But finally, we are pleased to announce that everyone on the team has agreed to legally change their names to Andrew Radin! We will of course continue the tradition of unique middle names. It’s our honor to welcome Andrew Carl Radin, Dr. Andrew Nikolay Radin, Andrew Aaron Radin, Andrew Tewei Radin, Dr. Andrea Karen Radin, Dr. Andrea Marina Radin, Dr. Andrew Isaac Radin, Andrew Dane Radin, and Andrew Michael Radin II as our new namefellows.

Go, Duma!