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A new algorithm to predict drugs which may display population differences in response through the interrogation of the human genome and drug response pathways. The robustness of this strategy is evident from the accurate prediction of 10 of 11 drugs which were previously reported to show population differences in response. A high proportion of drugs with pharmacogenetics warning-labels are also predicted to show population differences in response.
This project is led by A/Prof Caroline Lee, Department of Biochemistry, NUS YLL School of Medicine.
The main problem that we are trying to address is drug response differences. Different individuals, due to genetic make up could response differently to a drug treatment. While one group of patients would response positively, another group could response more moderately, with side effects. Worse, another group could suffer adverse drug reactions, which have a significant health and socio-economic impact to patients.
This algorithm is a software that can predict adverse drug reaction based on ethnicity, drug or disease information. The patient reports will tell you the potential risk of adverse drug reaction based on their genetics makeup
Initially at the start of this LLP program, we have thought of a wide range of customer segments, whom we thought would benefit from the algorithm that we have developed in the lab. They range from clinicians and scientists, to pharmaceutical companies and health authorities.
But, while the majority of people who we talked to welcomed the idea, none of them are highly interested to implement the algorithm in their existing pipeline.
During these past weeks, we encountered multiple obstacles in finding the customer segment which could serve as our algorithm’s entry to the market. The clinicians thought that the algorithm needs to have clear actionable components that can help them make decision regarding drug prescription. Researchers are interested to use our genome-wide knowledge base if it is free of charge, whereas both the R&D division in the pharma industry and the health authority think that such information require clinical validation that involves huge number of individuals.
Despite the hurdles however, there is one segment that expresses a keen interest in adopting our algorithm into their existing system. When we talked to companies that are currently providing genetic testing or diagnostic kits to hospitals, they are quite excited with the potential. Based on our initial discussion, there is a potential area where the algorithm could fit into their existing system, whereby the algorithm could assist to better interpret genetic testing results. By complementing the existing set of genes or biomarkers panels in the system, the algorithm could provide deeper insights into the relevance of the test results towards different group of ethnicities, across the different drugs that are used to treat major diseases. Two of the potential entry points are in cardiovascular and oncology diseases.
Hence this is our new business model after some 10 weeks of customers discovery journey. To reach the end-users, who are the clinicians/genetic counselors, we will be talking to more genetic test kits providers, who could potentially license our product. They can also serve as an effective channel for delivering our value to the end-users. Our value proposition is to provide them with an accurate drug-response prediction tool, based on ethnicity-associated genomics information. To cater their needs, our product will also be highly customizable and updatable as new genomic data becomes available. Born at NUS, we will continue partnership with the university, while tailoring the business needs of our potential customers, using a licensing and profit-sharing revenue model.
Moving forward, we would like to continue interviewing more potential customers, focusing on the genetic testing kit providers, in addition to the end-users: the clinicians. We will first focus on clinicians treating cancers and cardiovascular diseases. In addition, we will also study the existing competitors, and establish proper benchmark, such as by exploring what is being done by Foundation Medicine and N-of-One. Hopefully, this could help us determine the minimum viable product features that are not only appropriate for our beac-head customers, but also allow us to sustain and grow the business.
Thank you. And we would like to acknowledge the mentors, especially Joseph, Dor Ngi and Susan, who have been highly supportive throughout this customers discovery journey at the NUS LLP program.