The Dialogue: Using Computational Platforms For Drug Discovery

Ewan Dunbar our consultant managing the role
Posting date: 12/10/2020 10:00 AM
We were recently LIVE with Jonathan Ward, CEO and Co-Founder of Genome Biologics to discuss how AI, computational platforms and Data and Analytics have changed and accelerated the research and development of drugs in Biotech and Pharmaceuticals.

Jonathan is a specialist in cardiovascular disease, one of the biggest killers globally. Annually, Cardiovascular Disease accounts for 17 million deaths, one-third of all deaths worldwide. His main line of work is in exploring and implementing how an integrated computational and biological approach to research and development can accelerate the creation of successful cardiovascular drugs, in a more financially and time efficient way. 

Genome Biologics was founded to spur the innovation that is often missing in the field of cardiovascular drugs. This unmet demand stems from the combination of expensive trials and high failure rates. However, Genome Biologics came up with a smart system, using both computational and biological systems, which allow the team to better predict failure before it happens. This amazing technology not only help increase success rates but makes the research more financially viable and a lot more attractive to potential investors. 

How does it all work?

The team starts with the computational side. Here they screen large amounts of data which comes from a diverse range of sources, including public repositories, and the main aim of this process is to identify matches between gene expressions in heart disease or heart failure and a drug, or a mixture of drugs, that can change those gene expressions. 

The result of the gene therapy derives mostly from the integrated biological platform, where the team can validate the drugs used. Using real hearts, the team test the drugs screened by the computational research and see which ones improve the heart function. These drugs then go through the lengthy process of clinical trial which can take anywhere between seven to 15 years to complete.

Using the technology for COVID-19

Jonathan and his team were approached early in the pandemic to see if their technology could help find a vaccine for COVID-19. Initially, the team said no due to the understanding that COVID-19 was a respiratory virus only. But as research continued and the virus was found to also affect the heart and other vital organs, Jonathan and his team looked at how they could apply their technology to identify any therapeutics. 

The team applied the computational system to filter out all the compounds within the coronavirus, looking at how these affected or changed the heart. The team were able to filter 5000+ compounds down to around 500 that were potentials for a repurposing approach to COVID-19. The idea was that they would be then be able to identify a drug, or a combination of drugs, that could boost the immune system and heart function to fight off the disease. 

In our event, Jonathan noted that, for all the vaccines being trialled and released to market over the past nine months, the computational approach has been favourable across the board from small labs to Pharma and Biotech giants, such as Pfizer. And, as we’ve seen in only the last few days, this approach has been incredibly successful as 90-year-old Margaret Keenan was one of the first people in the UK to vaccinated against COVID-19. 

Challenges for the industry post-pandemic

COVID-19 has forced the Data and Life Science industry to evolve and adapt quickly and efficiently in order to work under pressure and find viable treatments in a much shorter timeframe than usual. The past nine months has most certainly provided learnings that the industry can take with them into any future work but, of course, the field constantly evolves, and this ever-changing nature will undoubtedly create hurdles to overcome. 

Genome Biologics explained that handling and managing data will be the biggest challenge for Biotech companies moving forward, simply because of the sheer scale and amount being produced. The volume of data, while important, is overshadowed by the need for high-quality data. As Jonathan points out, “If you put in junk, you get junk out” and so, the focus will very much be on getting hold of and using good data from reputable sources to ensure research and drug creation can be done efficiently and effectively. 

The future of data

As we move forward, especially post COVID-19, there will be a lot more computational data analysis being used in the interpretation of medical information and data, which is both very advanced and exciting for the industry. However, this does mean that certain roles within the industry may become redundant, most likely pathologists. 

Pathologists are a large cost for many companies, as well as the hardest employees to source, making this element of research a very arduous and expensive problem. With the large uptake of computational systems, companies will be looking at creating a much cheaper and highly automated system. Not only will this reduce costs and time, but it will also greatly reduce the risks of human error in misinterpreting data. The further implementation of computational data in drug testing, means that there is a good chance human error could be eradicated from the industry in the next decade which would make a huge impact on the healthcare industry. 

Of course, there will always be limitations to consider. Here, no matter how good your computational platform is, you will always need the biological platform as the biological element is just as, important, if not more so, as the computational side. And so, humans will need to work together with advancements in technology to create an efficient and integrated system. Pharma, Biotech and drug discovery will never be a robot-only industry. 

Where should I start if I want to work in drug discovery?

With decades of experience in the industry, Jonathan explains how to break into drug research, explaining that you need to have a clear understanding of the vision, mission and types of projects that are undertaken. Jonathan outlined, “Ultimately, it all depends on the kind of companies you want work for. What I can say however is that smaller Biotech companies are perfect for those who want hands-on work. These firms are more dynamic, you are likely to be involved in more projects and thrown into the deep end and ultimately learn a lot more. In big pharma, your experiences may be a little more limited across daily tasks, projects and progression.”

There are a few key questions to look at as a professional already in the discipline:

  • What can you contribute to the role that someone else can’t?
  • What do you want to get out of the role? 

Working in drug discovery, research and a role that challenges a range of core issues in Life Science can be incredibly rewarding, but as Jonathan notes, “Make sure this job change is really what you want before you jump in. Like any role, do you research and get to know the role and company inside out.”

Over the past nine months, Harnham has seen an increase in the number of in-demand positions across the Data Analytics and Life Science industry. With our unique understanding of this arena and excellent relationships with some of the best organisations around, whether you are a client or a candidate, we can offer bespoke solutions to suit your needs. 

If you’re hoping to change career or are looking for the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

To watch this LIVE event in full, please visit: 

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