Pharma: Striding Towards the Digital-only Reality with AI

Pharma companies have long been playing a critical role in our lives, albeit being in the background. However, COVID-19 has pushed the pharma industry firmly to the foreground. Governments and societies are pinning their hopes on the industry to help prevent the next epidemic while they continue to find cures to known health conditions and diseases.

Navigating a Complex & Fast-changing Landscape

In the wake of the pandemic, pharma companies face a growing number of challenges, especially in relation to Research and Development (R&D). Costs have spiraled over the last decade. In 2020, R&D spending in the pharma industry totaled about $200 billion globally. The journey from pre-clinical research to marketing can take anywhere between 12 to 18 years, pushing any kind of Return on Investment (ROI) into the distant future and increasing the risk to company finances and, in particular, liquidity. On the contrary, COVID-19 has set a precedent and expectation for the speedy development of essential vaccines.

The growth of generics is eating away at market share and regulations have become more complex. Pharma companies that fall foul of the rules can expect not just fines, but greater reputational damage than ever. Developing drugs to combat new and sometimes little understood conditions is particularly time consuming and costly. On the other hand, developing treatments that offer really significant improvements over existing products is also challenging. Meanwhile, the US Food and Drug Administration (FDA) has signaled its desire to bring more competition to the pharma market in an attempt to reduce the cost of medicines.

To remain competitive, drug companies need to invest in their R&D systems to speed up the discovery process. As industries across the board move towards a digital-only world, pharma companies must account for the different technologies that are transforming the R&D process. Digital adoption is the future of the pharma industry, and it is essential that pharma companies understand which technologies can deliver faster, more effective R&D.

Tapping into the Immense Potential of AI

Artificial Intelligence (AI) offers forward-thinking pharma companies the opportunity to revolutionize their drug discovery and development processes. Insilico Medicine, based in Hong Kong, for instance, has used AI and deep learning to design, synthesize and validate a novel drug candidate in 46 days – 15 times faster than what was previously thought possible.

As the Insilico Medicine case study demonstrates, it is already clear that AI, guided by humans, has the potential to manage the vast number of compound permutations needed for drug design. This fast-evolving technology enables the aggregation, harmonization and analysis of multiple data sources needed for discovery, design and clinical trials, thereby shortening the drug development process.

AI can increase novel drug discovery, minimize potential drug interactions, enhance the understanding of disease mechanisms, speed drug design, identify biomarkers, run pre-clinical experiments, design clinical trials, and provide deep insights more efficiently. It can be leveraged to predict the medicines that will ultimately work and the ones that will not, thus helping to reduce the investment in candidates unlikely to make it to market.

By becoming fully digital, pharma companies can gain real-time competitor insights and be vigilant to change in drug regulations across regions. With the use of AI and data analytics, they can streamline knowledge collation via available public and commercial data sources. This instantly provides R&D teams with insights needed on their competition as well as intelligence on local regulations, emerging conditions in a particular regime and the release of new drugs into the market.

AI and data analytics can process vast amounts of data, some of which is unstructured, in a fraction of the time taken by human beings. AI can “learn” what trends and developments to look out for in the data and only alert human beings to what is relevant.

Advancing CI with AI & Cloud at the Core

Cloud-based platforms allow pharma companies to become more agile, as they provide an immediate, interactive, and customizable way of storing Competitor Intelligence (CI) data. With a cloud-based platform, users can access insights across devices such as tablets, smartphones or desktops – through highly secure and seamless authentication. This is particularly important for sales representatives or attendees of conferences and meetings, as vital information can be shared quickly among all relevant parties within the organization.

Pharma groups can integrate their cloud solutions across company systems in a way that combines both Business Intelligence (BI) tools and commercial data. These platforms are customizable so that they can display in-house and external news, competitor profiles, and sales and pricing information via easily accessible dashboards and layouts. Combining valuable data analytics insights, AI and cloud-based platforms makes retrieving market insights streamlined for R&D teams.

Insights from CI have always enabled pharma companies to identify their core strengths in relation to their competitors. As well as transforming drug development, AI can enhance CI to cope with rapidly evolving markets and greater disruption. The more detailed, accurate and timely CI can help companies target unmet needs to transform not just their offerings, but entire business models. The analysis of the competitive environment can also guide their own R&D focus. This helps benchmark performance, identify strengths, and highlight priority areas. Superimposing results from business and CI helps pharma companies identify new avenues to differentiate their brand and fulfill market needs as they emerge.

Cloud computing can be used to complement AI-powered tools as they harvest data from millions of website pages to draw out valuable insights by tracking a competitor’s entire digital footprint, both on and off their site. Together, technologies can measure everything – from changes in the price of competitors’ products to new appointments. Natural Language Processing (NLP) can analyze the sentiment that competitors’ brands are experiencing on social media, and read customer reviews on a wide variety of platforms and convert it into usable data and actionable information.

Cloud computing enables companies to effortlessly increase or reduce their storage requirements for CI data. The insights that they develop can also be easily shared across all departments, from R&D to finance, on a variety of devices using intuitive interfaces such as dashboards.

Factoring in the Obvious Challenges

As small and large pharma companies rush to adopt these new technologies, they are bound to run into obstacles. Currently, for instance, patent laws largely do not acknowledge AI as an ‘Inventor.’ Hence, AI inventions cannot enjoy the same protection as traditional innovations. With the variety and scale of data used increasing, including genetic code, security risks are evolving too, necessitating new governance measures. AI will be limited by processing speeds, requiring pharma companies to adopt new technologies in their endeavor of maximizing AI impact.

As competitors possibly compress years of product research, development and launch efforts into mere months, companies will need to ensure that their CI can keep pace.

Even the largest pharma companies need to look for trusted technology partners to help them benefit fully from AI. As they focus on their core competencies, they can work alongside these partners, with their specialist knowledge and capabilities, to draw up a road map for the adoption of AI. Their partner organization should be able to identify the right solutions and then enable to scale them up rapidly in a controlled environment.

CI platforms such as PRECIZON from WNS have built-in readiness for the rapid acceleration of R&D enabled by AI. The pace of the market will increasingly demand Machine Learning (ML) intelligence for real-time user recommendations and content classification. Delivered on the cloud, such technologies can be integrated into other core systems with personalized content available through all devices. As AI increases pipeline pace, such scalable enterprise tools are becoming vital to ensure insights from robust automated systems that enable collaboration.

Ranking is a ML technique for recommendation systems such as intelligent CI. In what are known as “clustered approaches,” information about user behavior can be utilized to recommend items. These recommendations are generated with user-user or item-item similarity. Based on these similarity measures, the resulting suggestions are provided to the user. By predicting these user preferences, the portal delivers effortless user engagement.

The Time for Accelerated Innovation is Now

Interestingly, these new, innovative technologies feed off each other. As the growth of AI and automation drives various facets of pharma operations to becoming digital-only, enterprises will increasingly find themselves storing data on cloud-based platforms. They need to start now by integrating these new technologies into their everyday workflows to stay relevant and ahead of the competition.

Innovation has always been at the heart of the pharma industry. However, the urgency for new drugs is greater than ever, even as risks and uncertainties continue to mount. Implemented correctly, AI and other technologies will enable agile, forward-thinking companies to manage these risks, exploit new opportunities, and deliver for their employees, shareholders and patients around the world.

Akhilesh Ayer is the Head of Research & Analytics Practice and Mark Halford is the SVP- Client Services, Life Sciences and Healthcare Practice at WNS, a leading provider of global Business Process Management (BPM) services.