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Our current development work is focussed to oncology

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Taher Abbasi, COO and Co-Founder, Cellworks, shares how precision medicine is gaining momentum in healthcare and how simulation technology is able to identify right treatment for a patient and identify right patient for a clinical trial

From a background of semiconductor business in the automation engineering business to healthcare, how and why was the transformation?

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Taher Abbasi

Automation technologies and methodologies enables semiconductor business to shorten product development time, handle higher product complexity and achieve success rates of over 98 per cent. Computer simulation was one of the enabling technologies. In healthcare, the drug development cycle is 10 years, drug approval success rate is less than 8 per cent and response rate of approved drugs is below 30 per cent. The idea for Cellworks is to apply the best practices and successes from an orthogonal domain to healthcare.

Can you explain to us how do use the simulation technology to minimise or lower the deployment cost in the healthcare industry?

Simulation technology can identify right treatment for a patient and identify right patient for a clinical trial. Computer simulations can predict patient clinical responses to specific drug treatments thereby eliminating adverse events and costs of ineffective treatments. Simulations can identify novel treatments the patient is more likely to respond to.

Why precision medicine would be apt for cancer and oncology treatments?

Cancer is a disease where every patient’s tumour characteristics are different. Hence one-size-fits-all treatments have low response rate. Precision medicine is apt for cancer if one can predict prior to treatment if the patient would respond to a drug and alternatively what treatments would impact the disease.

Your company is now designing a range of drugs via computer modelling. How did you work or come out with a mathematical model which work out the chances of creating a successful drug or personal care product?

Our underlying product is a simulation model representing cancer physiology. This simulation model has been developed over 1000 man years of effort with continuous enhancements and validations. We do not create new chemicals. We build digital models of existing drug agents. These digital drug agents are simulated on computational patient models to match with the best therapeutic options for a patient.

What are the challenges you face as an information broker and trying to integrate data and biology?

The key Cellworks differentiator and opportunity is building a multi-disciplinary skillset team of sciences, mathematics and software. The platform development requires interpreting biology from research, modelling biology using mathematical equations and software coding, testing and validation.

How does Cellworks Group crunch a huge volume of biological data and conduct a predictive analysis of drugs and diseases? Elaborate.

There are two classes of biological data which are used for building a digital model of patient and precision medicine. One is the biological data of the underlying signalling and metabolic pathways to build the computational simulation model. The other class of biological data is the genomic measurements and clinical data for each patient. This is used for customisation per patient. The representation of biology using mathematical equations allow predictions to be made.

Can you update on your current work on testing virtual prototyping systems such as oncology, auto-immune disorders, infectious diseases, dermatology and metabolic syndrome, including type II diabetes?

Our current development work is focussed to oncology. Our product is a software engineering medical device which creates a digital model of a patient’s cancer using tumor genomic measurements. This digital model can be simulation-tested with targeted drug agents individually and in combination to predict the likelihood of cancer response and mechanistic insights into the scientific rationale. We continue to validate this product across clinical datasets.

Are big MNCs like Siemens, GE and Biocon guiding you and how?

We collaborate mainly with clinical partners at academic medical centres and co-author presentations and publications with them.

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