Pharma R&D Today
Ideas and Insight supporting all stages of Drug Discovery & Development
When research gives you lemons – turning R&D failures into opportunities and success
Posted on February 19th, 2019 by Veaceslav Mamaliga in Pharma R&D
As highlighted in my colleague, Nicki Catchpole’s last post on accidental drug discovery, 90 Years of Penicillin, the landscape of R&D is littered with ‘failures’ that turned out to be vital innovations. Today we take things like microwaves, pacemakers and x-rays for granted, but each came about purely through happy coincidence. Even post-it notes, for instance, were ‘discovered’ during a totally fruitless search for a stronger adhesive. Pharmaceutical R&D is no exception. With drug candidates going through multiple stages of testing, there can be failure at any point from concept to compound or from pre-clinical testing to post-launch.
Whether a compound failed to produce the desired result, there was a safety issue or it was simply not efficacious, the potential pitfalls for a new drug are many. Between 2006 and 2015, the overall likelihood of approval (LOA) from Phase I for all developmental candidates was 9.6 percent, and pharma companies’ pre-tax expenditure per drug approval was $1,395 million. However, one way to recoup these losses is to turn R&D ‘failures’ into new opportunities by exploring the following approaches, :
- Leverage the latest technology: Elsevier recently launched a new life sciences platform,
Entellectto help researchers connect and harmonize multiple disparate data sources into one data stream and prepare it for AI and machine learning. This can help researchers find actionable insights faster, saving valuable time and limiting the chance of downstream failure. The Entellectpress release contains more information about how the platform unlocks value with AI-ready life sciences data.
- Actively collaborate with other colleagues: Researchers are heavily invested in their own projects, and it can be difficult to take a step back and review alternative options, particularly after failure. In this instance, it can be useful to collaborate with colleagues from different areas of the business and see if the work completed so far could benefit another research area.
- Review data to find an alternative option: The sheer volume of health data is growing at an astronomical rate; an estimated 2,314 exabytes will be produced in 2020. The amount of data produced through the R&D process can provide a more comprehensive view
intothe development of the compound,and can unveil potential alternative pathways for drug development.
- Consider repurposing options: The pacemaker was developed after a ‘mistake’ was made during the building of a heart rhythm recording device. In a similar vein, many drugs we use today were initially created for alternative purposes – repurposing drugs can be one way for researchers to turn the ‘failure’ of drug discovery into success.
So next time you’re facing a supposed R&D failure, remember it’s not always necessary to go right back to the drawing board. Success could be one decision away.
Solutions Marketing Manager
Veaceslav Mamaliga joined Elsevier as Solutions Marketing Manager taking the lead on marketing and business development activities for Entellect, a new smart platform that empowers research and discovery, delivering AI-ready data to help pharmaceutical and life sciences companies improve the way they extract knowledge from big data. Veaceslav brings over fifteen years of professional experience in marketing, business development, project management and analytics. Prior to Elsevier Veaceslav has worked at IBM, Pierre Audoin Consultants and GE Healthcare.
R&D Solutions for Pharma & Life SciencesWe're happy to discuss your needs and show you how Elsevier's Solution can help.
Solutions Marketing Manager
- Elsevier uses machine learning to benefit pharmacovigilance
- Global Dominance in AI? China’s Got a Plan For That
- Healthcare Organizations, Including Major Pharmas, Band Together to Form AI-focused Group
- AI in Drug Discovery Has Great Potential – But Also Significant Barriers
- Three factors to unlock the value of AI for life sciences