Applied BioMath is a biotechnology company harnessing advanced mathematical analysis tools to revolutionize pharmaceutical research and development.

We don’t make the drugs — we help make the drugs better, employing state-of-the-art mathematical algorithms to provide researchers with invaluable insight they can use to build better drugs faster and more affordably than ever before.

As anyone involved in the pharmaceutical development space can tell you, developing a new drug for the market is no small undertaking.

Behind each and every drug you see on pharmacy shelves, there are years or even decades of man-hours, and hundreds of millions or even billions of development dollars that went into developing it.

Advanced computer-aided drug design can have a game-changing impact when it comes to the efficiency and cost-effectiveness of drug development. But in spite of this fact, and the countless technological advances that have been made in recent years, drug discovery techniques and methodologies have remained largely unchanged for more than three decades, and there has yet to emerge software programs to aid pharmaceutical scientists in their development work.

At Applied BioMath, we decided it was time to change that, and equip innovative drug researchers everywhere with the tools they need to create the best drug possible, as quickly, efficiently and affordably as possible.

Headed up by a team of highly experienced, MIT and HMS -educated mathematicians and bio-technicians, Applied BioMath has developed an revolutionary tool-set that will transform the way pharmaceuticals are developed.

A lot of the math behind Applied BioMath is pretty advanced, but at its heart, it's actually pretty simple:

Using state-of-the-art mathematical algorithms, ABM is able to provide robust mathematical analyses that give pharmaceutical scientists a deeper understanding of the biology and mechanism behind their drug — in other words: what their drug does and how it does it.

Using the analyses provided by ABM, pharmaceutical scientists can:

Target dose feasibility and risk assessment

  Identify knowledge gaps

Prioritize experiments and resources

Define best-in-class drug properties

Drive critical go / no-go decisions preclinically and in clinical trials

All of which are invaluable factors that can dramatically improve efficiency and effectiveness of the drug development process, drive down costs, and facilitate best-in-class drug development.

ABM’s software platforms are built on cutting-edge open-source software, called Kronecker Bio, which one of our founders developed while he was completing his PhD at MIT. Using Kronecker Bio, ABM is able to generate accurate analysis and predictive models for biophysical and chemical reactions — a dramatic improvement over existing approaches which are built on approximations and inefficient algorithms which can lead to inaccurate results.

For the time being, ABM is making our algorithms available to pharmaceutical companies primarily on a service basis.

But we also think the time is right to take things one step further, and develop a software solution that will make our innovations more widely available and put our powerful algorithms directly in the hands of the researchers they have been designed to help!

When you're dealing with advanced mathematical models and complex algorithms, it's easy to get bogged down in the heavy-duty details. But let's take a moment to zoom out and look at the big picture: the tangible, real-world impact that more accurate, efficient, cost-effective research and development methods can have:

Identifying fast failures leads to less time spent spinning wheels and chasing after loose ends translates to faster, more efficient drug development, meaning new drugs get through development, onto pharmacy shelves and into the hands of the people they’ve been designed to help — faster.

More efficient drug development is more affordable drug development, which carries through the process to more affordable drug price points. In a nation plagued by sky-high healthcare costs, the value of innovations that keep prescriptions accessible and affordable cannot be overstated.

With less pressure to protect their bottom lines by focusing exclusively on conventionally “profitable” drugs—high-margin drugs with huge target audiences (e.g. high blood pressure medications)—drug researchers will be free to set their sights on treatments for diseases that, while they affect smaller numbers of people, have far more devastating effects.

Applied BioMath is already building up some incredible momentum in our mission to provide pharmaceutical scientists with more complete, comprehensive R&D analysis options.

We have already delivered the project for our first client, Jounce Therapeutics, to rave reviews, and our team is currently in talks with Jounce about the possibility of a multi-year, multi-project arrangement.

The pharmaceuticals industry is catching on to ABM and the revolutionary potential of our software: we have received 3 unsolicited inquiries regarding licensing opportunities with the software we currently use, as well as 2 inquiries for co-development / joint marketing, from MathWorks and Cray Supercomputing.

Up next: development of ABM’s software product, which will empower pharmaceutical companies everywhere to take control of their drug development processes with unprecedented levels of analysis and insight. For more information about the future of ABM and how you can be a part of it, please request access to the business plan page of this profile!

The team behind Applied BioMath has over half a century’s combined experience, spanning everything from mathematical biology and systems pharmacology to administration and finance, and including PhDs and advanced studies at MIT and Harvard and positions at one of the top pharmaceutical companies in the world.

John Burke has 17 years of experience in mathematical biology, systems biology and systems pharmacology. He received his PhD from Arizona State University in Applied Mathematics and went on to MIT as a Sr. Postdoctoral Fellow in Biological Engineering and then Harvard Medical School as Co-Scientific Director for the Cell Decision Process Center in the Systems Biology Department. Most recently, John was the global head of systems biology and pharmacology at Boehringer Ingelheim, one of the foremost pharmaceutical companies in the world.

Joshua Apgar has 15 years of experience in biotech and high performance computing. He received his PhD in Biological Engineering from MIT. Most recently, Josh was a Principal Scientist at global pharmaceutical leader Boehringer Ingelheim in the systems pharmacology group.

Andrew Sutherland has over 19 years of industry experience spanning both large MNCs and small startups. Most recently he was head of finance and administration at Litle & Co., an online payments processor. He was part of the executive team that grew the company from $8 million in revenue and 35 people, in 2006, to $60 million in revenue with 225 employees at the time of the sale ($361m at a 25x EBITDA multiple) in Nov 2012.

Douglas is the Chair of the Biological Engineering department at MIT

Dr. Yaffe is an attending surgeon at Beth Israel Deaconness Medical Center, Director of Clinical Outreach at the Koch Institute for Integrative Cancer Research, and a Professor of the Departments of Biology and Biological Engineering and HST at the Broad Institute at MIT.

Professor of Biological Engineering and EECS at MIT

Professor of Systems Biology and Pharmacology Harvard Medical School. Department of Biological Engineering, MIT