Four steps to building a culture of innovation in the pharmaceutical industry –

The quest for innovation is a major driver of pharma. From the discovery of therapeutic biologics in the 1920s, such as Salvarsan and insulin, to successful drugs born in the 1990s, such as Lipitor and Humira, pharma has always been on the hunt for revolutionary new drugs.

However, technological advancements are constantly transforming the way industries and opinion leaders work. While the desire for innovation is still at the heart of pharma, the industry still grapples with slower and less efficient traditional R&D models – and therefore lags behind in maximizing the benefits of newer models of R&D. ‘innovation. In order to remain competitive, pharmaceutical companies must learn to foster a culture of innovation at all levels of their organization and at every stage of drug development.

Here are four things pharmaceutical companies can do to create a vibrant culture of innovation.

1) be honest about failure

Most researchers want to learn from failure, but only a few are publicly transparent about failure. When a clinical trial does not achieve the expected results, more recent studies can benefit from the lessons learned from this failure. However, the researchers involved in the failed trial are generally not encouraged to share their mistakes or the lessons they learned in the process.

Model predictions and studies such as “Lessons to be learned from successes and failures in pharmaceutical R&D” from 2016 in the Journal of Evolutionary Economics have clearly demonstrated that both successes and failures can contribute to the investment decisions of the pharmaceutical R&D department. Therefore, there should be no shame in being honest in the face of failure. In fact, the faster pharmaceutical companies learn from their failures, the more successful they will be in bringing innovative drugs to market.

On average, it takes 12 years for drugs to pass preclinical testing to FDA approval, and it takes seven years to Medical equipement to follow the process. The road to approval is too long, and the increasing complexity of traditional clinical trials has contributed to the high cost of drug development failure. Too much time passes before researchers have a chance to learn from their failures, and the stakes are high.

In a Chorus article, the authors contrast the traditional model of drug development with an alternative model they call “quick win, quick fail”, which places more emphasis on getting proof of concept faster. Therefore, there is more chance of success, even when there are many failures along the way. In other words, failing quickly (and more often) is a great catalyst for success.

In addition to failing quickly, pharmaceutical companies must find more effective ways to create failure through experiments. According to Amy edmondson, a Novartis Professor of Leadership and Management at Harvard Business School, one skill business leaders should have is the ability to create failure through experience.

But how can the pharmaceutical industry as a whole learn from these failures? It starts with companies’ willingness to encourage open sharing of lessons learned from failure. When a drug fails in research or clinical trials, companies should encourage researchers to publicize the failure so that others can learn from it. Individual researchers within the same company should not be territorial about their research, but should share lessons learned from failure with each other. In an ideal world, this sharing of failures should also take place among different companies, although they should make a concerted effort to embrace this level of transparency.

2) Building data analysis as a core skill

In the past, the core competencies of pharmaceutical companies were research and marketing. Recently, data analytics has become a forerunner on the list of basic skills.

In order to be increasingly innovative in the discovery of new drugs, pharmaceutical companies must be empowered to elevate their data analysis capabilities. They need to exercise control over their own data analytics and not just rely on an outside organization to help them leverage their own data. That’s why we don’t just sell a product or service – we sell an ecosystem that allows internal analytics teams to work faster and smarter.

In-house data science teams with advanced analytics capabilities are increasingly common in the pharmaceutical industry. As with any new skill, learning quickly will support the effectiveness of these abilities. The pharmacy will need to learn to reduce the lag in data analysis, by mimicking other industries that work with real-time data and information.

Businesses need to become proficient in not only extracting the available data points and sets, but also explaining the relationships and connections between disparate data points and pointing out smart ways to use the data. It is not an easy task, but there is a recent emergence of tools, such as our iPlexus platform, which allow data analysis within a company to find these relevant relationships and information. .

Pharmaceutical companies must also become experts in network analysis. We are now in the age of algorithms, precision medicine and data-driven analysis. Pharmaceutical companies must take advantage of these new technologies. They must learn to analyze data in new ways. For example, the use of “low-code” (designing and developing new software by leveraging third-party libraries, APIs, and infrastructure) can help pharmaceutical companies respond to their needs faster. With low-code, they would be able to create something new without coding from scratch. A die advantages low-code can help drug companies make sure they get drugs to market faster.

Additionally, to catalyze innovation, pharmaceutical companies must use tools like AI and machine learning to make sense of complex data and inform decision-making. Clinical researchers know that precision, repeatability, and reproducibility are important in bringing a drug to market, but pharmaceutical companies still count a lot on human contribution to perform high volume tasks. The low code can be used with robotic process automation (RPA) and artificial intelligence (AI) to mine data and create innovative products.

3) Encourage cross-functional collaboration

Data silos and the lack of open data are big challenges in the pharmaceutical industry. Pharmaceutical companies should encourage researchers to share knowledge and collaborate on projects.

They need to engage more in new models of open innovation that encourage data sharing without risking losing intellectual property. Examples of open innovation models are open sourcing, crowdsourcing, public-private partnerships (PPP), collaborations with university centers and outsourcing to virtual R&D.

Let’s briefly identify the roles that each of these models plays in achieving innovation in the pharmaceutical sector:

  • Open source enables pharma to gain knowledge from the public, especially in the development of drugs for neglected diseases
  • Crowdsourcing is similar to open sourcing, but instead of drawing ideas from the general public, crowdsourcing calls on the skills of experts in the field.
  • PPPs allow companies to combine resources and share risk
  • Recruiting experts in academic centers can help solve complex problems
  • R&D virtualization helps businesses stay focused on core technologies while partnering with virtual businesses that help them pursue underutilized tasks.

Thanks to these open innovation models, traditional R&D models evolve from internal prototypes to external prototypes. While this transition has caused large pharmaceutical companies such as Merck, AstraZeneca and Pfizer to cut back on their in-house R&D departments, open innovation has produced measurable innovation benefits for pharma. In a recent Bain & Company survey, 91% of executives interviewed recognized that they needed to increase their company’s capacity for innovation because it is “essential to create future competitive advantage and generate profits”.

4) Automate lower level cognitive tasks

To focus on creativity and innovation, pharmaceutical researchers need to spend less time performing lower-level cognitive tasks, such as administrative work and collecting, cleaning, and organizing data. Performing these tasks limits thinking outside the box. Researchers need more brain bandwidth to achieve “Aha!” ” moments.

McKinsey and Company research shows that approximately 45% tasks people get paid for can be automated with the help of technology. Examples of automation technologies in the pharmaceutical and healthcare industries include IBM’s Watson and Innoplexus iPlexus Platform.

The use of automated systems allows researchers and pharmaceutical companies to focus on high-end creative tasks. This can result in more meaningful work and, in turn, can catalyze innovation.

By taking steps to encourage the sharing of failures, making data a key skill, encouraging cross-functional collaboration, and automating lower-level tasks, pharmaceutical companies can create the culture of innovation they need to stay competitive in this dynamic field.

About the Author:

Lawrence Ganti, Innoplexus

Lawrence Ganti is CEO | Americas of Innoplexus, an artificial intelligence-based data analytics company specializing in the life sciences. Prior to joining Innoplexus, Lawrence spent 15 years leading teams across functions and geographies of the pharmaceutical industry. He also worked as a research associate for McKinsey & Company and holds an MBA from IMD in Switzerland and a BA from Babson College in the United States.


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