top of page

The Rise of AI in the Biopharma Industry

Medical and scientific innovation are rapidly transforming the biopharma industry. The biopharmaceutical industry's growth is too prevalent to ignore, and it is easy to see why: they offer high efficacy and low side effects. However, there are technical and operational challenges that need overcoming. The technology and manufacturing sophistication required to reproduce large molecules reliably at scale barely exists. This is why AI is rising across biopharma.

Over 148 start-up companies and over 33 large biopharma companies are currently applying AI to drug research and discovery.

In an era where big data is the new oil, the precious data that is generated, captured, analyzed, and used in real-time by innovative medical devices is biopharma's new currency. However, what truly sets companies apart is their ability to generate insights and evidence from multiple data sources. This is why the need for digital transformation is a strategic imperative for those who wish to create a future-fit organization.

Key Takeaways

  • Digital transformation optimizes business efficiencies as well as R&D.

  • AI's growth is fueled by the recent abundance of data, faster computing at a more affordable price.

  • Cloud computing makes AI technology feasible and accessible.

  • AI helps keep biopharma compliant.

  • AI supports biopharma throughout the entire value chain.

Digital transformation and pharma

Digital transformation, where innovative technologies are used to reimagine an organisation, aid collaboration, and drive change management, could not only help with the dire need for rapid, accurate research and development (R&D) but could also help to improve operational performance, productivity, efficiency and cost-effectiveness across the entire biopharma value chain.

It is the next phase in biopharma companies’ evolution. In short, digital transformation will empower biopharma companies to innovate new products and services, engage more effectively with their customers, and execute processes more efficiently.

However, despite the clear benefits of digital transformation, lack of vision, leadership, and funding in the pharma industry are holding them back, according to a recent survey by Deloitte and MIT Sloan Management Review. However, pharma's commitment to digital transformation is rising, with 58% of survey respondents citing digital transformation as a top management priority and nearly 80% expect to gain value from digital initiatives within the next five years.

How AI technologies enable digital transformation in biopharma

While the fuel of digital transformation is collaboration, support, and embracing change, the natural driving forces behind digital transformation are the exciting technologies that give organizations a competitive advantage through data analytics. These include technologies such as artificial intelligence (AI), blockchain, cloud and virtual reality.

These technologies' primary function is to improve data quality, information flow, and the insights and predictions distilled from the data. Adjacent to that, pharmaceutical companies are experiencing how AI improves revenue and streamlines business operations.

AI is exceptional at extracting concepts and relationships from data, spotting patterns in data, and learning independently from these patterns. AI technologies include machine learning (ML), deep learning (DL), supervised learning, unsupervised learning, natural language processing (NLP), computer vision, speech and robotics.

Many biopharma organizations lean on the guidance of their chief information officers (CIOs) or chief digital officers (CDOs), or chief digital information officers (CDIOs) to drive this digital transformation.

No matter the job title, biopharma leaders' urgency to utilize the full potential of data to transform their businesses and operating models in the race against their competitors see AI holding the most promise.

Until recently, only a few companies had the expertise to maximize the full potential of AI technologies. Today, this situation is changing rapidly as cloud-based AI solutions like SEDGE create customized solutions that can be adopted at scale and process large data sets quickly. SEDGE also provides a team of data and analytics experts, which removes the need for extensive capital investment in IT infrastructure, employee upskilling, and talent procurement.

AI solutions like SEDGE give pharma the ability to process large data sets accurately and provides the computing and analytics resources they need to capitalize on AI.

Why AI, and Why now?

From a technical point of view, there are three primary reasons why AI growth in the biopharma sector is driving value: the availability of more data, improved computing power and decreasing computing costs.

There is more data

The amount of healthcare data being produced and made available on a daily basis is growing exponentially. Take, for example, the amount of genomics data generated in recent decades. It has grown from approximately 10MB per year in the mid-1980s to over 20 petabytes from 2015–19, an increase of over nine orders of magnitude in five years.

In the same way, biopharma companies are creating and harvesting ever-growing amounts of data from multiple sources throughout the biopharma value chain. This comes predominantly in the form of real-world data (RWD) from a continually growing variety of sources: electronic health records, medical imaging, insurance records, wearables and apps, social media, clinical trials, and genomic sequencing.

However, RWD is useless unless it is processed and analyzed to create real-world evidence (RWE). It is done by using AI and ML, which is fast becoming the favorite technology amongst biopharma. In fact, a recent survey showed 60% of biopharma currently uses ML tech to analyze RWD and 95% expect to use it in the immediate future.

Computers are more powerful

Computing processing capabilities have grown exponentially. AI’s ability to clean, process, and analyze structured and unstructured data requires algorithms to analyze data sources across the biopharma value chain. All of this depends on the availability of significant computing power, which is more readily available.

Computing costs less

The correlation between the pace of innovation increasing and the cost of computer power decreasing is marked in pharma. For example, the cost to sequence the first whole human genome has dropped significantly. In 2001 it would have cost close to US$100 million to do, where two decades later it costs barely US$1000. In the same way the cost of AI-specific chipsets have come down, with a leading providing experiencing a drop in price per unit of close to 50 percent between 2016 and 2017, according to Markets and Markets.

These developments all remove the financial barrier to entry for biopharmaceuticals looking to digitize their operations and R&D.

Biopharma companies are ready to take advantage of these factors through AI adoption

Biopharma is on the cusp of exploiting these three aligned factors to transform their organizations digitally. However, there are still considerable barriers to overcome.

Migrating to the cloud

Traditionally, biopharma companies store and analyze their data ‘on premises’ (on local servers). However, recently the trend is moving towards more biopharma companies using cloud computing and data storage for analytics and predictive modeling.

Cloud has numerous benefits compared to storing and analyzing data locally, such as meeting data security, privacy and regulatory requirements. It also removes the need to update local IT architecture, hardware and software continuously and is often more affordable.

Navigating complex regulations

Organizations in the biopharma industry have to navigate an increasingly complex regulatory landscape. Additional to changes in regulatory compliance, the personal data integral to nearly all biopharma business operations falls under various data protection requirements, such as Europe’s General Data Protection Regulation (GDPR), which dictates what can be done with personal data, particularly by organizations in healthcare. Those who fail to adhere to these can face stringent penalties as well as devastating brand damage.

Those organizations that are compliant and collaborate with regulators to be set apart from the rest, as both parties embrace AI and other digital technologies to drive the economy, efficiency, and efficacy of regulatory operations.

AI helps regulators increase their efficiency by automating time-consuming, repetitive manual tasks, optimizing inspection and enforcement efforts, and analyzing volumes of data quickly. As for regulations concerning AI technology, regulators currently require manufacturers of AI algorithms to resubmit clearance applications when they make significant modifications to the software. Recently, the Food and Drug Administration (FDA) published an exploratory white paper with a guideline allowing companies to include plans for any modifications they anticipate would take place to their algorithms. Similarly, the European Medicines Agency (EMA) recommended that an AI test ‘laboratory’ be established to understand how AI can be fully utilized for decision-making across critical business processes.

For this reason, biopharma organizations would do well to proactively engage with the FDA, EMA and other regulators to ensure future guidance from regulators is fit for purpose.

AI powers the Future of Biopharma

Digital transformation will see biopharma companies using technologies to innovate faster, to optimize processes and remove barriers. AI technologies will lead this transformation and help improve efficiencies, fuel new products or services, and support new business models.

How AI impacts the entire biopharma value chain

AI impacts the entire biopharma value chain, first aggregating and synthesising information from data, then predicting and prescribing data-driven actions through the clinical development, manufacturing and supply chain, launch and commercial, and post-market surveillance and patient support phases.

Research and discovery

AI tools like SEDGE radically improve productivity when applied with computational biology to drug discovery and development. As a result, research and discovery are cheaper, faster and more effective. It reduces the number of failed drugs by quickly and accurately identifying patterns in large volumes of data. SEDGE AI also improves target identification and safety and efficacy assessments using predictive modeling and in silico testing

A primary role of AI algorithms is to predict interactions between molecules to understand the mechanisms of disease. These could help quantify new biomarkers, identify, design, validate and optimize novel drug candidates; and single out existing drugs that have the potential to be repurposed for other indications. There is also a move towards using AI technologies to design and run preclinical experiments in a bid to speed up the discovery, development, and launch of a new drug.

Clinical development

Clinical trials are typically slow, expensive, and dependent on a large number of manual processes. These challenges make them ripe for transformation. Biopharma companies are investigating how AI can optimize trial design and patient recruitment, analysis of both trial results and real-world evidence (RWE), and how automation can increase the speed and efficiency of results publication. This could lead to lower development costs, increased adherence and improved outcomes. Additionally, the rich data sets and AI-driven analytics available support the development of highly effective personalized treatments without the need for large-scale, expensive clinical trials

Manufacturing and supply chain

Both the biopharma manufacturing and supply chain sectors create vast amounts of real-time data that have been underutilized for years. AI technologies like SEDGE provide real-time data processing with actionable insights and predictions that make supply chains genuinely data-driven.

These AI-powered insights allow biopharma companies to improve their supply chain planning, inventory management, demand forecasting, logistics optimization, workforce planning and procurement.

Launch and commercial

AI technologies like SEDGE help biopharmaceutical companies coordinate product launches better by predicting the best time to launch and the ideal audience to target. AI tools like SEDGE also improve patient engagement, help with physician decision support and marketing operations, and predict market access and pricing decisions. AI stratifies health care practitioners (HCPs) into various 'personas' depending on their habits, preferences, and receptivity to marketing messages, which supports a personalized, tailored approach to service and marketing.

Post-market surveillance and patient support programs

After the product has launched, AI technologies gather, assess, learn and predict the market’s receptivity to the product, as well as peaks and troughs and trends in the market. This allows biopharma to know where there are opportunities in the market and when to move on. Additionally, AI-enabled patient support programs can transform customers’ relationship with biopharma, improve enrollment, adherence and retention, and deliver an improved overall patient outcome.

Ready for AI to Transform your Company?

SEDGE is a cloud platform that combines powerful AI algorithms with a highly user-friendly interface. SEDGE processes vast amounts of data with ease to produce meaningful, actionable insights that give you a competitive advantage. With SEDGE, you do not need a full team of data analysts to capitalize on AI's benefits in biopharma.

Incredibly smart, powerful, quick, and valuable, SEDGE is the tool to bring your business into the digital age.

The information or views expressed in this article is authentic to the best of our knowledge, and as such, it is prone to errors and the absence of some key information, for more information please review our Terms of Use.


bottom of page