The Covid-19 pandemic is shaking up well-established business models and dispelling long-held assumptions, holding out an opportunity to reshape and democratise healthcare around the planet.
The future will call for a new approach to prevention, screening, diagnosis, therapy, monitoring and management of diseases. We are likely to see convergence of translational research in biomedicine with disciplines such as public health, epidemiology, infectious disease modelling and data science. The multidisciplinary approach will demand a reimagining of biomedical innovation.
The availability of genetic information, together with other phenotypic as well as medical information, is helping identify new drug targets by linking particular genes and their products to individual diseases. In addition to genomic data, other data have also moved into the spotlight. Proteomics and metabolomics, as well as epigenetics and an integrated view of all of these disciplines, are gaining traction. Also, the impact of lifestyle choices is starting to be factored in.
On the other end of the spectrum, electronic health records and other patient-related information in registries and hospital administration databases are helping establish real-world evidence for effectiveness of a particular medicine.
Researchers today are relying on multiplexed data sets to make drug discovery and development more innovative, cheaper and faster.
Data Science For Affordable Innovation
Drug discovery is capital intensive, research-intensive and intellectual property-intensive with inherently long time lines.
First, researchers must identify a potential therapeutic target. Then, a drug that acts on that target must be developed, purified and tested, both on cells in a petri dish and in living animals. In order to be approved, this new drug must meet rigorous safety specifications and pass through highly controlled phases of human testing.
It could take up to a decade for a new drug to complete the ‘lab to market’ journey, and cost over $2.5 billion.
With a ‘1 in 10’ success rate, the global pharmaceutical industry is looking at data science for enhancing the probability of success and shortening timelines.
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August 23, 2020