In Europe, R&D investment is considerably lower than in the United States, with R&D investment growing 4.5 times between 1990 and 2017 and multiplying by more than 8 times in the US, comparatively.
Plans to accelerate pandemic recovery from the European Union through a €94.4bn into research over 7 years (nearly €11bn more than originally planned for the program, called Horizon Europe), are testament to the spotlight on R&D and the competitiveness involved.
Additionally, factors such as digitalisation and societal shifts are impacting transformation in life sciences and pushing reform in R&D activities to make them more cost and time effective.
Prioritising productivity in R&D often comes down to the need to work against the common barriers of lengthy patent processes, a confined pricing environment, and potential pricing reforms – it’s clear that pharma and biotech need to get the most out of their R&D projects. But how can they achieve this?
In a survey of 250 pharmaceutical executives, it was found that 72% believe that digital transformation is critical to achieving R&D imperatives.
Though there is the belief that digital transformation is critical, most companies aren’t approaching digital transformation strategically.
The main contributor to this was the tendency of pharma companies to work in silos, rather than implementing an at-scale approach across groups within the organisation.
That’s not to say that organisations can’t see the value in digital transformation as a means to improve R&D. Respondents cited the following as strategic imperatives that could be impacted by digitalisation:
Be more focused on patient outcomes
Revitalise the pipeline by enabling breakthrough science
But how exactly are pharmaceutical organisations looking to apply digitalisation to improve their R&D processes?
Cost and resource sharing is one example of changing funding models in R&D, with the ability to share among multiple healthcare stakeholders to lower R&D expenses.
Swiss life sciences clusters in particular are prime examples of this form of collaboration - especially their close links to academic institutions.
The main aim of collaboration as it relates to R&D is to share risk and relieve pressure on costs, which are a common barrier to productivity in clinical trials and R&D projects.
Around 9,000 new biopharma R&D partnerships were formed between 2005 and 2014 at an annual growth rate of 4% during that 10-year period.
More of these partnerships are beginning to form in the earlier stages of the R&D process (e.g. prior to a potential new therapy entering clinical trials), with the average number of early-stage partnerships – discovery, basic research, and preclinical – more than doubling between 2005 and 2014, according to Deloitte.
Another significant shift in the pharma and biotech space is the move away from a ‘one-size-fits-all’ product goal (e.g. blockbuster drugs) for medicine, and instead, a focus on more personalised approaches to medicine.
For example, the recent advances in proteomics, genomics, and metabolomics have allowed us to understand disease on a molecular level in terms of both diagnosis and treatment.
Biomarkers, another growing element of the medical process, can provide predictive value for diagnosis, disease progression, and cure/remission.
Individual needs are becoming a greater focus of the R&D process due to these substantial changes and innovative approaches, leading to the ability to target patient subgroups more effectively and deliver customised treatments.
Much of this change comes from the digitalisation in healthcare, with electronic health records, mobile apps and wearables playing a key role in the rise of personalised medicine.
These shifts could change the familiar randomised controlled trials due to the more precise nature of treatment and medicines, streamlining R&D in the sense that virtual trial models can be enabled, and more patients can be recruited across wider geographies, all with more accurate endpoints.
Data confidentiality and security are rising in prominence as a talking point for R&D, as digitalisation takes hold and personalised medicine is adapted and utilised more.
For personalised medicine to be effective, the R&D process requires data from a specific patient to ensure the effectiveness of treatments and medicines.
In order for the personalised medicine approach to streamline R&D, there needs to be a considerable focus on the security, confidentiality, and reliability of data.
One major change to the R&D to account for lengthy processes is machine learning (ML) techniques that are implemented to reduce the time to evaluate molecules, which rely on algorithms and security to function optimally.
For biotech and pharma businesses looking to remain agile and innovative with the technology changes impacting the broader life sciences sector, data security will be a huge point of consideration, as without it, these changes won’t be as effective at streamlining R&D.
R&D in pharma and biotech has long been subject to scrutiny for drawn-out regulatory processes, time-consuming trials, and other inefficiencies that impede the ability to be streamlined and productive.
New technological developments offer the chance to make R&D more precise, cost- and time-effective, and collaborative. However, many of the changes that could benefit R&D require strong security infrastructure for these processes to pay off.
Get in touch with the Panda team for expert advice on securing top life sciences talent for your company.