Synergizing against breast cancer

I was about twelve when I found out my grandmother had breast cancer. My parents did a good job of shielding me from the worst of the details, but there is no way to avoid fear that comes from a loved one being diagnosed with cancer. As a kid, there wasn’t much I could do, but my grandmother loves to tell the story of me trying to comfort her by telling her I was going to do research to help cure her cancer. Little did I know at the time that treating cancer is not as simple as taking a pill once a day and that even identifying the right medicine is akin to finding a needle in a haystack.

Over the next seventeen years, as I pursued undergraduate and graduate studies in biology and genetics, I filled in those knowledge gaps, but felt no closer to changing the status quo of breast cancer…

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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… 

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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… 

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The AI 100 & Combining Artificial Intelligence with Human Intelligence in Drug Development

We at twoXAR were very honored to be included this week in The AI 100, CBInsight’s list of top private Artificial Intelligence companies. It’s given me a chance to reflect on how we employ AI relative to others in the industry.

 Our focus is on drug development — and being one of the few biopharma companies to be included in the list, we use AI in a unique way. Where others may be using AI as the sole ingredient…

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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.

The Power of “Lookup Biology”

Guest post by Marina Sirota, PhD, twoXAR Advisor and Assistant Professor, UCSF Institute for Computational Health Sciences

Earlier this month, Andrew A. Radin and I had the opportunity to attend acommunity outreach meeting at UC Irvine hosted by the NIH Libraries of Cellular Signatures (LINCS) consortium. It was a great and diverse community gathering of drug discovery researchers from academia, biopharma, startups, consulting companies and government funding agencies. For anyone interested in listening to the talks, some of them have been posted on YouTube.

The focus of day one was…

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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!

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.