Liver Injury is the number one cause of withdrawal of drugs post marketing. Such withdrawal results in enormous damages to pharmaceutical and biotechnological companies, in terms of finances as well as reputation. Traditional animal toxicity testing methods have proved to be insufficient when it comes to predicting toxicity observed in the clinic.

What the industry needs today is a robust predictive method that integrates data and insights from multiple in vitro methods. This alone will result in a deeper understanding of the impact of a drug on the liver. To this end, Strand's ‘Virtual Liver' is a unique systems approach based on mathematical modelling of the kinetics of essential biochemical pathways involved in liver homeostasis.

Strand's Virtual Liver allows researchers to understand the evolution of DILD, i.e. what are the pathways impacted and how this impact translates over the short and long term into biological changes. It also enables early identification of markers of a specific form of toxicity and to use the same platform to predict the impact of both small molecule and biological agents.

Scientifically Advanced Hepatotoxicity prediction

• 25 man-years of R&D have already been performed in developing the platform. The platform can predict toxicity of several known drugs and toxins well

• This includes prediction of hepatotoxicity due to necrosis, cholestasis and steatosis, which are the major forms of liver toxicity observed in the clinic

Combines in silico and wet-lab techniques

• A major challenge in hepatotoxicity prediction is the understanding of drug biotransformation and the impact of active metabolites on the liver. Strand's wet-lab approach deals efficiently with biotransformation issues

Support for Toxicogenomic Data

• Our platform allows researchers to input their toxicogenomic data and generate actionable hypotheses for further exploration along with suggested experiments to identify specific (potential) modes of toxicity.

• However, Virtual Liver is mechanistic and has not "been trained" on a class of molecules. It is not therefore limited by chemical class, an issue that pure toxicogenomic approaches face

Modelling Methodology Expertise

• Strand's multi-faceted approach gives Virtual Liver its edge over competing technologies. Firstly, we have the ability to automatically mine text, generate and manage databases of knowledge around a disease. We can combine various types of modeling approaches, correlative analyses such as clustering and classification, statistical inferencing and differential equation-based dynamical simulations, all within the same platform.

• This allows scientists to connect genomic, proteomic, in vivo, and in vitro data to clinical endpoints, integrate disease knowledge and predictive simulation capabilities, allowing systematic and rapid research.

 

Flexible Business Model

• Strand does not believe in a one-size-fits-all approach. Wherever needed, we proactively use our existing models and tailor them to address issues of specific interest to an organization, e.g. we have used our Steatosis module to address some key questions in cardiovascular disease for a pharma collaborator.