Today, the life sciences industry is at a critical inflection point. Its public profile has elevated due to its success at quickly developing vaccines to combat the COVID-19 pandemic. It has also built up a lot of trust. Despite the persistent issue of vaccine hesitancy, health -- including life sciences -- rose up in the rankings to become the second most trusted sector after technology, according to the 2021 Edelman Trust Barometer.[1] While the life sciences industry rightly has the approval and trust of its stakeholders -- including heath companies, insurers, clinicians and patients -- such approbation gives rise to an important challenge going forward.

If there's a silver lining to the COVID-19 pandemic, it's how the world came together to find a solution as one. Throughout the pandemic, technology has enabled global collaboration, as more people shifted to working from home, while science allowed multiple COVID-19 vaccines to be developed and rolled out in record time, despite working remotely. The convergence of governments and industries, particularly biopharmaceuticals, to innovate and create a solution to the crisis was inspiring, but it raises questions around what's needed to see this pace of innovation again. Do we need a global crisis to innovate? Or can biopharma companies forge their own pathways to innovate, while making solutions more affordable and accessible to those who are most affected: patients?

As scientists, we are no stranger to skepticism, having been taught to look at everything critically. While doling out skepticism, we also come across it often when working with other healthcare leaders or fielding questions from audiences at conferences or pitching an AI approach to investors. Much of the healthcare industry is still stuck in the 20th century, and hence, it is not so surprising that new technologies such as an AI-driven approach to biopharma may be met with raised eyebrows and thought to be doomed to failure from the outset. On the one hand, we have people saying that AI could revolutionize biopharma and help us to discover new treatment options, with Deloitte predicting that the AI/biopharma industry will be worth $3.88 billion by 2025. On the other, we have Elon Musk warning that AI could spell the end of civilization as we know it.

A common thread emerging among new biopharma companies; they're hiring as many data scientists as wet lab scientists and their data server budgets are growing faster than that of their protein purification process. Here's what that means to you and your company. Bill Gates isn't the only tech industry insider turned life sciences "outsider" currently throwing his weight around the biosciences. There's a new breed of biopharma leader on the rise, an ilk identified not by their lab coats but by their pocket protectors, and not by their hair nets but by their propeller heads. The market worth of data scientists and digital transformation specialists is rapidly appreciating as biopharma firms seek to explore how harnessing data and appropriately processing it will help them work faster and smarter, from drug discovery to clinical trials and beyond.

The COVID-19 pandemic has increased the focus on the use of artificial intelligence (AI) across the life sciences organization, from R&D to manufacturing, supply chain, and commercial functions. During the pandemic, company leadership and management realized that they could run many aspects of their business remotely and with digital solutions. This experience has transformed mindsets; leaders are more likely to lean into a future that lies in digital investments, data, and AI because of this experience. At present, the life sciences industry has only begun to scratch the surface of AI's potential, primarily applying it to automate existing processes. By melding AI with rigorous medical and scientific knowledge, companies can do even more to leverage this technology to transform processes and achieve a competitive edge. AI has the potential to identify and validate genetic targets for drug development, design novel compounds, expedite drug development, make supply chains smarter and more responsive, and help launch and market products. We will highlight a number of these use cases in this report.

Biopharma companies are relying more and more on artificial intelligence and machine learning (AI/ML) to help them uncover the intricacies of disease mechanisms and open up strategies to develop novel medicines for treatment. As a result, the BioWorld Artificial Intelligence price-weighted index, which includes biopharmaceutical companies, medical devices and health care services companies, has climbed in value and is currently up almost 37% year-to-date. Fueling the index has been biopharmaceutical company Bioxcel Therapeutics Inc., of New Haven, Conn., which is utilizing artificial intelligence to identify improved therapies in neuroscience and immuno-oncology. Its shares (NASDAQ:BTAI) have been on a tear so far this year, gaining a whopping 229%, catalyzed by significant clinical progress in its product pipeline. In July, the company reported that it had initiated an expanded access program at Massachusetts General Hospital (MGH) to provide its alpha 2A adrenoceptor agonist, BXCL-501, a sublingual thin-film formulation of dexmedetomidine, to individuals diagnosed with COVID-19 who are critically ill in the intensive care unit and may require calming or arousable sedation.

Traditional'linear and sequential' clinical trials remain the accepted way to ensure the efficacy and safety of new medicines. However, the lengthy tried and tested process of discrete and fixed phases of randomised controlled trials (RCTs) was designed principally for testing mass-market drugs and has changed little in recent decades (figure 1).1 RCTs lack the analytical power, flexibility and speed required to develop complex new therapies that target smaller and often heterogeneous patient populations. In addition, suboptimal patient selection, recruitment and retention, together with difficulties managing and monitoring patients effectively, are contributing to high trial failure rates and raising the costs of research and development.2 Over the past few years, biopharma companies have been able to access increasing amounts of scientific and research data from a variety of sources, known collectively as real-world data (RWD). However, they have often lacked the skills and technologies to enable them to utilise this data effectively.

The biopharmaceutical sector is facing digital disruption from multiple sources, the chief among them being artificial intelligence (AI) and cloud. Deloitte predicts that in 2019/2020, all industries will accelerate their use of cloud-based AI software and services, including biopharma companies. Among companies that adopt AI technology, 70 percent will obtain AI capabilities through cloud-based enterprise software, and 65 percent will create AI applications using cloud-based development services. In order to keep up with the changing competitive landscape and customer expectations, biopharma companies will need to accelerate their use of AI-enabled services. Biopharma organizations have started using scaled versions of AI over the last 1-2 years in some areas.

Drug discovery is the process of identifying new medicines for treating or curing human diseases.1 Historically, the discovery of new medicines involved extracting ingredients from natural products and basic research to find potential treatments. Progress was generally slow, frustrating and labour-intensive. The majority of drugs discovered during the 20th century were chemically synthesised small molecules, which still make up 90 per cent of drugs on the market today.2 Their advantages include simple manufacturing and administration routes. They also have low specificity and a stable shelf life, meaning they are safe and effective for large groups of people.

This week, we have launched the first in a series of reports on artificial intelligence (AI) and its potential impact in driving the digital transformation of biopharma. This overview report, Intelligent biopharma: Forging the links across the value chain, explores the challenges and opportunities in AI adoption and the potential ways that AI might impact the different segments of the biopharma value chain (see Figure 1).1 The pace and scale of medical and scientific innovation, together with increasing competition, lengthening R&D cycle times, shorter time in market, expiring patents, declining peak sales, pressure around reimbursement and mounting regulatory scrutiny are challenging the existing biopharma business and operating models. These challenges have also had a massively negative impact on the expected return on investment that large biopharma companies expect to achieve from their late-stage pipelines. Consequently, companies are looking to digital transformation as a key differentiator and essential part of their change management strategy. AI technologies are some of the most anticipated of these digital technologies.