Syntensor is developing new machine learning methods coupled with first-principles thinking in biology to help drug developers, clinicians and physicians answer the fundamental question: Will it work?
Systems simulators have accelerated progress in every modernized field of engineering… except biology
Dynamical systems simulation tools have driven rapid innovation and growth in the aerospace industry and computing powered by advances in complex circuit design (see Moore's Law). Without equivalent tools, most drugs in clinical trials don’t make it to patients, while those that do may be ineffective or, at worst, harmful.
We’re on a mission to transform how medicines are developed and prescribed using advanced ML coupled with first-principles thinking in biology
The complexity and scale of the human biological system means ML methods are generally applied to only one part of it at a time: single cell or molecule RNA-Seq, protein-protein interaction networks or metabolic flux modeling, for example. However, results have shown that inferring the efficacy (or systemic toxicity) of a drug in a human from narrow biological sub-domains doesn’t work for drug development.
- 90% failure rate for drugs in clinical trials
- $1.3 billion to get a new drug to market
- 30-60% prescribed medicines are ineffective
- 4th leading cause of death in the US is adverse drug reactions
Instead, we need to accurately model the dynamics of the system, in order to predict the properties of the components within that system when they are perturbed by disease or drugs. The status quo is not sustainable. The total indirect cost of patient harm is estimated as $1 to 2 trillion globally per year.
We’re solving the 'complexity problem' in biology
Advances in machine learning methods for dynamical systems modeling and high performance computing mean it is now possible to model biological processes dynamically at an appropriate, systemic scale. Syntensor is the leader in the field and the first to apply these new fundamental methods commercially at scale.
Building a platform for every drug and all patients
The methods we’re using mean our platform is generalizable across diseases, therapeutic modalities and patient genotype/phenotype. Scientists and doctors will be able to simulate the effects of a drug on a patient to evaluate whether it will work and whether there are ‘red flags’ indicating off-target toxicity that could result in harm.
We’re a multi-disciplinary team of ML Researchers, Drug Discoverers, Software Engineers and Product Designers building a platform to predict drug efficacy and toxicity: a transformative tool for drug development and precision prescriptions.
Cofounder and CEO/CTO
Syntensor is the result of Clayton’s life-long passion for algorithms and biology. After an undergraduate degree in genetics, Clayton’s PhD research was devoted to computational synthetic biology. He launched Syntensor out of Cambridge University’s entrepreneurial ecosystem
Cofounder and CPO
Rosie is a product leader who specializes in building multidisciplinary teams to bring emerging technologies to market. Previously, she has been VP at Novartis, Data42 and COO at BenevolentAI, and prior to her move into biotech, built and scaled global brands at London-based startups
Gabrielle Griffin, PhD
Founding Bioinformatics Scientist
Before joining Syntensor, Gabi was Director of Bioinformatics at Sumitovant Biopharma and Lead Bioinformatics and Drug Discovery Scientist at BenevolentAI where she was technical lead for their partnership with AstraZeneca.
Founding Product Engineer
Zdenek builds data-driven interfaces, specializing in the visualization and mapping of complex data. He was previously Head of Development at DATA4CHANGE and Lead at Signal Noise (part of The Economist Group).
After her MPhil (Management) at the University of Cambridge, Anni started her career as a Wealth Analyst in the city. At Syntensor, she applies her diverse ops toolkit with precision, while wearing many hats.
Spencer is a seasoned advisor who specializes in helping high growth, mission focused companies develop and execute growth strategies in new and emerging markets.
ML Ops + Engineering Contributor
Maksym is a PhD candidate in Yoshua Bengio’s lab at MILA where he is building large scale generative models. Prior to MILA, he attended MIT and was a Harvard Medical School Visiting Fulbright Scholar. He holds a Masters in Bioinformatics and studied Genetics as an undergraduate.
ML Research Contributor
Michael is a PhD candidate at Stanford working at the intersection of deep learning, generative models, numerical methods and control of dynamical systems. He cofounded DiffeqML with Stefano Massaroli and is a core-maintainer of the Torchdyn library for neural differential equations.
ML Research Contributor
Stefano is a post doc in Yoshua Bengio’s lab at MILA working in deep learning, dynamical systems, optimization & control. He cofounded DiffeqML with Michael Poli and is a core-maintainer of the Torchdyn library for neural differential equations.