Precision Medicine Needs Global Genetic Data

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Axel Schumacher, co-founder and chief scientific officer of Shivom, a data-driven next generation precision-medicine company in the blockchain space, calls for Pharma to leverage big-data on a global level to ensure all patients have a therapy option. 

 

The key to success for pharma players is to sponsor the accumulation of whole-genome datasets.

 

The healthcare ecosystem is shifting dramatically. Recent reports demonstrated that large IT companies occupy a vast share of R&D spending in healthcare, with R&D budgets of the top five US IT companies, Amazon, Alphabet, Intel, Microsoft and Apple, all topping the leading Pharma R&D spender, Roche. If you add Facebook, Oracle, Cisco and the giant Asian IT giants such as Alibaba, Baidu, Samsung, Huawei, Tencent and others to the mix, it becomes clear that there is likely not a single pharma company anymore represented in the top 10 of global healthcare R&D spenders!

This situation will not be without consequences. Not only because those IT companies have access to a huge direct-to-consumer market, but many of those new healthcare players also have massive initiatives aimed at becoming firmly embedded in the global healthcare ecosystems. By collecting and using massive healthcare and socioeconomic datasets they can now complement and compete for consumer adoption in health and disease management – and this is precisely what they will do.

 

So what can Pharma do to keep up?

There are still ways to make the best out of a difficult situation. It is important to realise that investing the right way, successful treatment can happen for every patient and as such those investments can provide an excellent environment for pharma companies to bloom. 

Nevertheless, to move into the next gear, companies not only have to find better methods for diagnosing and treating complex diseases, but they also need to make sure all patients will find a therapy option – and for that, the industry needs to commit to leveraging more and better data in line with IT companies such as Amazon.

Pharma companies cannot and should not do this alone; they need external platform providers that provide the necessary expertise in genomics, data security (cryptography), and artificial intelligence (to make the data actionable) and who can help them building trust with people to share their genomic and healthcare data in a secure manner. With recent data breaches plaguing the IT and healthcare industry, healthcare providers and patients will be reluctant to share their highly sensitive genomic and healthcare data with players like Facebook or Google, not even to mention the Chinese IT companies. This is a business of trust.

 

The underrepresentation of nonwhite ethnic groups in scientific research and clinical trials is a worrying and ongoing trend. Human genomic databases are (so far) of low quality but to make it worse, they are also heavily skewed toward people of European descent. If left unaddressed, the inherent bias the databases contain will continue to contribute to the lack of diversity seen in drug R&D as well as to the uneven success rates in precision medicine.

 

Some pharma companies already recognized the importance of tapping into genetic data from direct-to-consumer (DTC) genetic sequencing companies. However, the old, established DTC companies are using already outdated single-nucleotide polymorphism (SNP) array data which is ignoring larger variation in genome sequences that are more difficult to assess. We know now that there are far more of these larger, so-called structural genetic variations that cause genetic disease and impact the way drugs are metabolized by individuals and ethnic populations. SNP-based technologies were not capable of detecting them accurately, but more advanced technologies are now allowing scientists to identify variations that in many cases have never been seen before.

The key to success for pharma players is to sponsor the accumulation of whole-genome datasets, combined with other omics and socioeconomic data to provide a win-win situation for all participants. Small startups from the genetics and AI field may not have the resources, but they certainly have the experience and technology that pharma lacks.

Data collection needs to be done on a global level, as such partners should be selected that have expertise in whole-genome sequencing and who have access to the Asian and African markets as well. To have access to those regions is essential, as the underrepresentation of nonwhite ethnic groups in scientific research and clinical trials is a worrying and ongoing trend. Human genomic databases are (so far) of low quality but to make it worse, they are also heavily skewed toward people of European descent. If left unaddressed, the inherent bias the databases contain will continue to contribute to the lack of diversity seen in drug R&D as well as to the uneven success rates in precision medicine.

Lastly, pharma stakeholders are advised to form pre-competitive consortia to speed up the data collection, curation- and sharing tasks to build clinico-genomic databases that integrate genomic information with, metabolomic, epigenetic, and proteomic data as well as patient outcomes to better monitor real-world data. Initiatives like these will help to ensure that the whole healthcare vertical can move forward in a valid and meaningful way.

It is well understood that there are reasons why researchers and other stakeholders are often reluctant to share genome sequences and other R&D data, but given the benefits to the healthcare ecosystem from data-sharing, pharma participants should do their part to sponsor and share. Technologies exist already to make anonymized study data available to the broad research community, which will allow scientists to analyze massive data sets and ask different questions that were not even anticipated in the original study planning.

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