The Cellworks Biosimulation Platform
BioPharma is challenged with high failure rates in clinical trials resulting in unacceptably low FDA approval rates and correspondingly high R&D costs. A big reason for this is the complexity of the disease, and the lack of a full understanding of the mechanism by which a drug works. Though clinical trials do attempt to select a homogeneous population, disease heterogeneity and drug complexity result in a high failure rate.
Most engineered products, be it a semiconductor chip, a fighter jet, or a drug-eluting stent are modeled and simulated, in software, during their design phase before they are sent into fabrication and manufacturing. This avoids costly trial-and-error. So, why not apply the same efficiency to medicinal drug development? The general answer to this question has been, ‘biology is too complex’, or ‘it is impossible.’
Cellworks Therapy Response Index (TRI)
The Cellworks platform was created precisely to minimize the trial-and-error, empirical approach used in drug development, via biosimulation technology.
In a nutshell, biosimulation is the ability to predict the phenotype response of a human cell to an external stimulus, such as a drug ligand or radiation. We utilize this technology, to determine a patient’s Therapy Response Index, or TRI. A treatment-naïve patient’s TRI determines whether the patient will respond to standard care treatment. For relapsed or refractory patients, TRI determines the combination of FDA approved drugs to which the patient will respond.
Primary Components of the Cellworks Platform
Five primary components of the Cellworks Platform are a bio simulator, pathway models, drug models, a therapy design methodology and a cloud-based infrastructure.
We use machine learning techniques at the level of statistically appropriate biochemical pathways to understand their behavior and represent them mathematically in the model. We combine these smaller pathways into larger macro interactions using a manual curation approach. The bio simulator is built to handle differential equations and other mathematical structures, at the rate of millions per second per biosimulation. Drug models include a quantitative model of drug targets, binding affinities, reaction rate constants coupled with pharmacokinetic and drug economic data. The therapy design engine is an intelligent combinatorial examination of drug combinations to understand phenotype response. The cloud-based infrastructure can run millions of prediction biosimulations in parallel, and has a demonstrated ability to handle thousands of patients per day. The Cellworks platform was conceived as a unified analytical representation of biological knowledge, collected from all relevant heterogeneous datasets. It is continuously enhanced as new biological knowledge is gained by researchers across the globe.
The Cellworks platform starts with the model of a healthy human cell whose physiology can be simulated at the level of biochemical pathways. Modulated by a patient’s genomic profile it creates a personalized model of the patient’s tumor cell. It then dynamically simulates the phenotype response of this tumor to various drug agents. For the desired phenotypes, by examining interactions across a huge network of signaling and metabolic pathways, the Cellworks platform reveals actionable therapies for an efficacious response.
Conventional ex vivo testing and machine learning technologies operate in a ‘black box’ mode, with little explanatory power. Cellworks is different. We describe with complete explanatory power and with full causality, the mechanism by which a therapy works for a given patient’s disease.