You might not think you know about personalised medicine, but we’ve all experienced it with recent decisions around who should get which type of COVID-19 vaccine. Life sciences data supported many in the scientific community to help make these determinations during the pandemic.
The value of life sciences data is far-reaching and constantly evolving, with new companies entering the space across the value chain every year. The business outlook is enticing, with the global life science analytics market valued at $23 billion in 2020. The market is expected to grow at a CAGR of 15.4% to 2028.
According to Deloitte, one-third of pharma execs believe COVID-19 accelerated digital transformation in the pharma sector by five or more years. One key challenge going forward will be to manage the reams of life sciences data produced around the world and actually glean insights from it.
We’ve taken a look at the life sciences data market, unpacking it to understand the underlying value chain more fully. Here’s our short primer.
The opportunity of life sciences data
Why do we need data in life sciences today? Because the future of treatment pathways is personalised and precision medicine.
Traditional medicine is generalised and can lead to a benefit or not — but it also could end in an adverse event for the patient. Stratified medicine is slightly more targeted through grouping patients by various factors.
Precision medicine, however, groups patients by genomic data, leading to much more targeted treatment for patient groups and better medical outcomes in the long-term.
Data is critical to delivering this vision, including genomic data, treatment compounds, phenotypic data, care pathways, and more.
Players in the life sciences data space are coming up at an opportune time. Suppliers have seen sequencing costs decline significantly since the early 2000s, leading to an increase in testing and an abundance of data.
Meanwhile, biopharmaceutical companies have seen their return on investment in pharma R&D drop considerably since the 1990s. This means they have an acute need to improve that ROI in their clinical trials. Accessing and analysing more data is one way to accomplish this.
The life sciences data landscape in 2021
As the life sciences space has evolved, the challenge has centered around how to manage data. This is important because it allows scientists to reach insights faster and more effectively, thereby improving ROI on clinical trials and existing drugs (typically a long and painful process).
Plenty of players are sensing this opportunity, all tackling different elements of the value chain. Examples of growing European companies shaping this space include:
- Lifebit — Which empowers biomedical data custodians to make their data findable and usable for data consumers.
- Causaly — Fastest way to find evidence, explore hidden connections and make new predictions in biomedical science.
- Seqera — Creating better data pipelines on any infrastructure to reduce costs and improve time to results.
- Iqvia — Creating intelligent connections with an expansive portfolio of capabilities and technologies, unparalleled data, and global healthcare expertise.
- SeqOne Genomics — Focused on developing state-of-the-art genomic analysis tools for clinical applications in the fields of cancer and rare disease.
- Sano — Connects people with research in personalised medicine
Here’s our market map:
The life sciences market is heavily concentrated amongst the top 20 companies, making them critical buyers for this type of data.
As the life sciences space continues to progress, we’re watching how up-and-coming players in the market shape their offerings to help scientists reach insights faster and improve health outcomes more efficiently.
If you’re in the life sciences data space, do let us know. We’d love to speak with even more founders taking on this extraordinary and important task.