Sponsored by:
Produced by:
Amir Emadzadeh
Director of software engineering, Genentech (Roche)
When used correctly, data analysis creates exciting opportunities to work more efficiently and cut costs.
Is the full power of data being harnessed within pharmaceuticals?
The power of data is now commonly accepted across the world. When used correctly, data analysis creates exciting opportunities to work more efficiently and cut costs. But even though this is commonly accepted, there are not many industries who take full advantage of the data available to them. For example, within pharmaceuticals there is currently a problem with feasibility analysis for clinical trials. When a clinical trial protocol is ready and handed over to clinical operations (clin-ops), the clin-ops team then needs to find the right sites and investigators to conduct the trial, alongside forecasting how long it will take to enroll the required number of patients.
But this process is very outdated, with clin-ops teams often relying on gut instinct when forecasting and sticking with the same sites and investigators they have previously used, regardless of their performance. Because they are not appointing the most relevant sites and investigators on a trial-by-trial basis, they open themselves up to significant delay risks. As a point of reference, oncology trials in the US cost between 5 and 10 million dollars per day to run. So just one day of delay can cost upwards of $5 million dollars! When you look at these numbers and consider how easily a proper data-science driven planning process would help to reduce them, the investment in data analytics and visibility becomes a no-brainer.
Having access and understanding of a wider range of data sources allows clin-ops teams to create more diverse trials and ultimately help to address the imbalance in treatment options.
Aside from financial benefits, what opportunities does data science present in pharmaceuticals?
Alongside the financial benefits we talked about before, using data analytics correctly is also vital to creating better patient outcomes. Traditionally, clinical trials have only been run on narrow groups of people and do not take account of how race or background may impact the trial results and therefore subsequent treatment outcomes. Within oncology, less than 5% of patients in trials have been of African-American descent, even though African-Americans represent nearly 15% of the total US population. This inequality in clinical trials leads to inequality in treatment options and patient outcomes. Utilizing the correct data sources when planning clinical trials is crucial to overcoming this problem. Having access and understanding of a wider range of data sources allows clin-ops teams to create more diverse trials and ultimately help to address the imbalance in treatment options.
Maximizing ROI from data science simply cannot be achieved overnight, but the effort is well worth it.
How can pharmaceutical businesses maximize their ROI from data science?
To maximize their ROI from data science, pharmaceutical businesses need to make sure that they are bringing in the right people with software and data science expertise. Whilst pharmaceutical companies have significant employee strength across chemistry and biological sciences, data science is such a cutting-edge technology that it requires a very specific skill set in order to be internalized. From here, pharmaceutical companies need to ensure they have the right structure in place for their data scientists to succeed. This will likely be a blend of developing internal capabilities, establishing embedded and/or centralized AI teams, as well as use of external support. Each business will have its own unique balance to be found and there is no ‘one size fits all’ solution. This means that maximizing ROI from data science simply cannot be achieved overnight, but the effort is well worth it.
Genentech is a biotechnology company dedicated to pursuing groundbreaking science to discover and develop medicines for people with serious and life-threatening diseases. Their transformational discoveries include the first targeted antibody for cancer and the first medicine for primary progressive multiple sclerosis.
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We are a global player in life-science R&D informatics, providing customers with cutting-edge multi-omics data management and integration software. We develop innovative off-the-shelf and custom solutions for clients in pharma, biotech, agriscience, consumer goods, and research centers. Omics Data Manager (ODM), our flagship product, helps organisations create a FAIR catalogue of multi-omics investigations (studies, samples, omics data), with powerful tools for curating rich and standardised metadata in bulk, as well as optimised RESTful APIs for scalable cross-study, cross-omics integrative search.
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