Ontosight® – Biweekly NewsletterJune 17th – June 30th, 2024 –Read More
To unlock innovation, pharma needs to be honest about failure
Many successful leaders agree that a company’s willingness to embrace and learn from failure can unlock innovation – and it’s a lesson the pharmaceutical industry would do well to learn.
Take Jeff Bezos, for example. As the CEO of Amazon, he encourages designing bold experiments that may lead to failure. After Amazon acquired Whole Foods, he explained: “If you’re going to take bold bets, they’re going to be experiments. And if they’re experiments, you don’t know ahead of time if they’re going to work.”
Netflix provides another notable example of this mindset. As of the first quarter of 2018, Netflix had 125 million subscribers with a revenue of over $11 billion. Yet, when Netflix CEO Reed Hastings spoke at a technology conference, he said: “Our hit ratio is too high right now. We have to take more risk,” implying that the company needs to take more chances and, hence, fail more.
But in pharma companies, a fear of failure too often kills creativity and prevents researchers from taking chances. Instead of regarding failure as the worst possible outcome, researchers need to give themselves permission to fail and to share their failures with others – as long as they are able to extract valuable lessons to learn from the experience.
The problem pharma industry faces: under-reporting of failed trials
The pharmaceutical industry is built on innovation, yet few researchers in pharma are publicly transparent about their failures. A 2010 study showed that registered drug trials sponsored by pharmaceutical companies are less likely to be published within two years of study completion and are more likely to report positive outcomes than trials sponsored by other sources. The study provides evidence that failures in pharma studies are underpublicised.
Pharma companies need to understand that learning from mistakes is very important for bringing innovative drugs to market. Studies such as 2016’s Learning from Successes and Failures in Pharmaceutical R&D in the Journal of Evolutionary Economics have provided models to demonstrate that both successes and failures are important for making R&D investment decisions.
The problem is that there are currently no incentives for researchers to publicise their failures. This can result in unnecessary duplicative work by researchers at other pharma companies. According to Dr Jodi Black, deputy director of the Office of Extramural Research, NIH: “If we don’t report our results we will repeat the same mistakes.”
The solution for pharma industry: transparency at every turn
To solve the problem of underreported failures, pharma companies must incentivise and integrate transparency throughout their own organisation and in their relations with others in their field. There are two key steps to take on this journey.
1. Pharma companies must build a culture of transparency
Pharma companies need to acknowledge that transparency is a high value, and then implement it into their organisational culture. Researchers within the company should feel very comfortable sharing failure with their peers – and even with their supervisors. Transparency prevents companies from making the same mistakes over and over again and also prevents the duplication of effort across multiple research teams.
Similarly, companies must be willing to share failures and the insights gleaned from them with other companies in the space. As a company in a highly competitive field, it can be vulnerable to publicise failure and can seem counterintuitive to share insights. But in an environment of transparency, the benefit is reciprocal: each company will benefit from the transparency of their peers even as they share their own insights. The result will be more innovation and better health outcomes for patients.
2. Policies must be aligned across pharma to make transparency easier
In 2007, a data-transparency law was passed in the US that requires researchers to register and publish results from their clinical trials. However, loopholes in the law allow researchers to under-report negative results. In 2016, the law was strengthened by requiring that researchers disclose the design and results of all clinical trials within the first 21 days of enrolling their first patient. But full transparency remains an issue as policies for sharing data are often inconsistent.
According to the 2017 Pharmaceutical Companies’ Policies on Access to Trial Data, Results, and Methods audit study, published in the British Medical Journal (BMJ), policies for sharing clinical trial results commonly lacked timelines for disclosure. In addition, smaller pharma companies fell short of transparency commitments. More work needs to be done in aligning policies across the board to prevent loopholes.
Transparency alone is not enough for Pharma Companies
Merely being transparent about failure is not sufficient. Pharma companies must get good at learning from failure as well. When exploring new grounds in research, they must find ways to integrate data from failed research with data from successful research to reveal useful new insights.
To use all research data to its full potential, scientists need to make sense of disparate data. Here’s where data aggregation comes in. Pharma companies need to become experts at searching, gathering, and interpreting data from successful trials and unsuccessful ones. Data aggregation is critical for finding meaningful relationships in disparate data and for discovering novel insights.
Researchers should learn to use powerful semantic discovery engines such as iPlexus, which uses AI and machine learning to aggregate data from multiple data sources in real-time. Using such technology, researchers can learn from failures based on events happening right now, or a week prior, or even years before, all on one platform.
Ultimately, by being transparent about failure, pharma companies can cut down on duplicative work and better collaborate on discovering innovative drugs faster and less expensively.
Featured Blogs
Machine learning as an indispensable tool for Biopharma
The cost of developing a new drug roughly doubles every nine years (inflation-adjusted) aka Eroom’s law. As the volume of data…
Find biological associations between ‘never thought before to be linked’
There was a time when science depended on manual efforts by scientists and researchers. Then, came an avalanche of data…
Find key opinion leaders and influencers to drive your therapy’s
Collaboration with key opinion leaders and influencers becomes crucial at various stages of the drug development chain. When a pharmaceutical…
Impact of AI and Digitalization on R&D in Biopharmaceutical Industry
Data are not the new gold – but the ability to put them together in a relevant and analyzable way…
Why AI Is a Practical Solution for Pharma
Artificial intelligence, or AI, is gaining more attention in the pharma space these days. At one time evoking images from…
How can AI help in Transforming the Drug Development Cycle?
Artificial intelligence (AI) is transforming the pharmaceutical industry with extraordinary innovations that are automating processes at every stage of drug…
How Will AI Disrupt the Pharma Industry?
There is a lot of buzz these days about how artificial intelligence (AI) is going to disrupt the pharmaceutical industry….
Revolutionizing Drug Discovery with AI-Powered Solutions
Drug discovery plays a key role in the pharma and biotech industries. Discovering unmet needs, pinpointing the target, identifying the…
Leveraging the Role of AI for More Successful Clinical Trials
The pharmaceutical industry spends billions on R&D each year. Clinical trials require tremendous amounts of effort, from identifying sites and…
Understanding the Language of Life Sciences
Training algorithms to identify and extract Life Sciences-specific data The English dictionary is full of words and definitions that can be…
Understanding the Computer Vision Technology
The early 1970s introduced the world to the idea of computer vision, a promising technology automating tasks that would otherwise…
AI Is All Hype If We Don’t Have Access to
Summary: AI could potentially speed drug discovery and save time in rejecting treatments that are unlikely to yield worthwhile resultsAI has…