Fractal Therapeutics is a model-based drug discovery and development company, focused on the design and creation of novel assets in Oncology, Infectious Disease, Rare Diseases and other therapeutic areas.
Our team consists of seasoned pharma R&D executives who have developed and honed a unique approach to drug hunting. This approach- honed over the years in live project settings in big pharma- integrates informatics, mathematical modeling and simulation platforms tightly with in vivo and clinical pharmacology.
Fractal’s unique interlocking platforms are focused on providing robust model-based solutions to critical-path questions on the road to new asset creation and development. We are applying our platforms to efficiently build a pipeline of novel investigational agents, which we will seek to partner for early clinical development.
Arijit is a business and scientific leader focused on building scalable high-energy teams to execute contrarian strategies in drug discovery and development. He has a broad cross-disciplinary background, having spent 12 years in Takeda Pharmaceuticals in roles of increasing responsibility, spanning a diverse range of departments within the R&D organization, including computational biology, in vivo pharmacology, translational research, drug metabolism & pharmacokinetics and modeling & simulation. Over his career as a scientist, Arijit contributed to over 50 drug development programs, presented at over 100 conferences and seminars, and co-authored over 120 papers, book chapters and posters. At this point, Arijit plays a business and generalist role, focused on developing the next generation of scientific experts and thought leaders, and helping strong, independent scientists find their leadership voice. His attention in Perceptx is focused on turning the company's vision to business value, and using management and process frameworks to generate superior results.
Peter started his professional career as a software developer, writing enterprise Java applications in a startup setting. Since then, he’s held positions at Merck, Vertex Pharmaceuticals, Deloitte CRG, and Takeda Pharmaceuticals. During his time in pharma, Peter developed a deep familiarity with forecasting problems, and his engineering and software background led him to look for more robust solutions that led to the founding of Simplex Clinical Supply. Prior to founding Simplex, Peter built and ran the Decision Science group at Takeda Pharmaceuticals, which was responsible for providing practical modeling and simulation support for several different business groups at Takeda. One of the group’s projects involved the development of Simplex’s proprietary software platform, which Peter and his co-founders out-licensed from Takeda. Peter has a BS in Applied Physics from Columbia University, an MS in Biomedical Engineering from the University of California, San Diego, and an MBA from Carnegie Mellon.
Doug brings a broad, multidisciplinary skillset to bear on problems at the intersection of computation and biology. During his time at Takeda Pharmaceuticals, Doug ran a number of teams focused on custom multidisciplinary engineering approaches in various problem domains. These experiences have ingrained in Doug a passion for finding simple, effective solutions to complex problems. Doug has an undergraduate degree in Bioengineering from the University of Washington, and did his Ph.D in Biomedical Engineering at Georgia Tech.
Katie is a computational biologist with experience in using informatics and machine learning techniques to tackle drug discovery problems. Recognized on the 2016 Forbes 30 under 30 in healthcare list, she has worked as a bioinformatics lead on a number of cross-disciplinary academic-pharma collaborations. Katie has a B.A. in mathematics from SUNY Geneseo and a PhD in computational biology from Weill Cornell Medicine.
Christine applies engineering principles to find practical, efficient solutions toward problems in the pharmaceutical industry. She has coded and managed preclinical and clinical pharmacokinetic and pharmacodynamic modeling projects. Christine has a B.S. in chemical engineering from North Carolina State University and a PhD in chemical engineering from the University of Pittsburgh.
Follow the data
We believe that it is more important to get it right, than to be right. Our approach is agnostic to theory, and committed to experimental (especially clinical) data.
When the facts change, we change our minds.
We emphasize the use of mathematical modeling in every step of the drug discovery and development process.
Focused models aimed at answering specific scientific questions provide a faster (and more rigorous) path through drug discovery and development.
We believe that the best way to succeed in business is to question our competitor’s wisdom, not endorse it.
Popularly held beliefs in science can often be wrong.
Questioning conventional wisdom stimulates innovation, while at the same time providing opportunities for differentiation.
Agnostic and results-driven
Doing more with less is essential to quickly building sustainable, profitable businesses.
We take a lean approach, prioritizing the essentials and avoiding costly inefficiencies. We get the most out of every dollar by focusing on the critical path for execution, building lightweight but effective processes, and creating a disciplined, results-oriented culture within our teams.
At Fractal, we believe in the value of software both as a scientific and as a business tool.
On the scientific side, modeling and simulation serve as a valuable prototyping tool while informatics and statistics serve as critical instruments to more deeply understand results. On the business side, we rely on software-driven processes to keep simple things simple, and make difficult things possible. A software-driven operations game is just tighter.
Applying consistent decision criteria in advancing drug molecules is easier said than done.
Limited pipeline sizes and timeline pressures can lead to tunnel vision, and project teams (and companies) are often cornered into trying to force the ‘right’ answers in the pursuit of superficial performance metrics. Pursuing a large and diversified portfolio lets us stay agnostic to the outcome for any given project.
Thinking like engineers
Best practices can be a powerful tool in the right context, but too often they are used to reinforce outdated legacy approaches.
Sometimes, the best way to design an efficient process is to do what makes sense, and avoid the ‘best practices’ trap.
We design our processes around the tools and techniques available in the 21st century, and ignore the way ‘things have always been done’.
For every question, we focus on the simplest effective answer.
Our belief is that it’s not difficult to find a complicated answer to a simple question. Doing the opposite- finding a simple answer to a complex question- represents insight.
We believe that good ideas are cheap, so killing them should be cheap too.
We use modeling and simulation as a design and strategy tool to pressure test ideas.
Approaching drug discovery and development from a mindset of abundance lets us take a clinically detached approach to our projects.
Focus on the stuff that matters
Sadly, even in the modern era, the potential of many cancer (and infectious disease) drugs is limited by toxicity. Companies have historically wasted substantial capital investing in molecules that lack a therapeutic window (doses that are both therapeutic and acceptably nontoxic).
We are able to efficiently assess the therapeutic window, and rely on it substantially in our go/no-go decisions.
Every program has a core of irreducible scientific risk.
By pursuing a critical-path approach, we are able to focus our investments on those questions that address this risk, thus enabling rapid go or no-go decisions and efficient use of capital.
We believe that the best defense against being wrong is to be wrong cheaply. Because no amount of preclinical biology can predict clinical success, we focus on reducing the expense and time needed to arrive at the pivotal clinical experiments.
Given this mindset, it follows logically that a broad pipeline, and rigorous decision criteria are the best insurance against scientific risk.
One of the most challenging aspects of preclinical development is pulling together an Investigational New Drug (IND) application for a first-in-human trial. At this stage, companies must provide scientific justification for the starting and projected efficacious dose, as well as justification for design choices in the trial protocol around dosing schedule, patient population, indication, and biomarker collection.
Fractal’s drug development experts will work with you to design the right experiments, manage their execution, and provide the right analyses for the IND application. By answering the correct questions in a rigorous manner, Fractal can help provide your asset a faster path to IND, without cutting corners. Our in-house experts will also work with you to assemble the IND and manage regulatory interactions.
Our modeling packages are provided on a target-exclusive basis with our partners.
Pharmacokinetic/pharmacodynamic (PK/PD) modeling links drug concentration in the body to drug effect and disease outcome. In the past decade or more, PK/PD modeling has played an increasingly critical role in drug development by shortening the path to the clinic and providing rigorous science that can withstand regulatory scrutiny.
Fractal specializes in leveraging PK/PD modeling to accelerate the steps in preclinical and early clinical development. While we typically engage proactively with partners in the creation of an integrated package, we can also work with you to provide a bespoke modeling package aimed at specific questions in development. These include:
Our modeling packages are provided on a target-exclusive basis with our partners
We engage with partners in the co-development of novel preclinical assets designed, created or sourced by us. Sponsored research agreements are our primary business model, focused around a pipeline of candidate assets, and an efficient screening and preclinical development path aimed at yielding one or more Investigational New Dug applications (INDs) in a short period of time.
Our approach begins with high-throughput asset sourcing and creation, coupled with focused critical-path experiments and modeling analyses. This allows us to focus on the projected therapeutic index of candidate drugs, which defines their potential. At every step along the way, our platforms allow us to precisely and quickly answer critical-path questions on the road to approval.
Our lean, fit-for-purpose approach to integrating modeling and informatics with pharmacology enables us to create value for our partners in an extremely resource-efficient manner.