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Biopharma of tomorrow: Transforming through digital and data

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The explosion of digital diagnostics and therapeutics, increasing patient-centeredness, interoperable data, and increased regulatory collaboration are all signs of the tremendous changes that digital technologies are driving in the health care industry. How can biopharma prepare for this new reality? How should biopharma companies transform their operating and business models to compete? In research and development (R&D), digital transformation has the potential to drastically improve productivity by applying artificial intelligence (AI) and computational biology to drug discovery and development and by making clinical trials much more efficient. In commercial, more targeted patient engagement and the application of behavioral science principles could lead to better patient outcomes. Persona-based marketing to health care providers could lead to greater market awareness and action. In supply chain, digital supply networks can result in much greater product visibility, traceability, and inventory control. How can companies leverage the promise of digital?


The rise of artificial intelligence in biopharma

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The pace and scale of medical and scientific innovation is transforming the biopharma industry. The need for better patient engagement and experience is spurring new business models. Data generated, captured, analysed and used in real time by innovative medical devices is biopharma's new currency. A key differentiator for companies is the extent to which they are able to generate insights and evidence from multiple data sources. Consequently, digital transformation is a strategic imperative. This report outlines how artificial intelligence-enabled technologies will impact the biopharma value chain and accelerate biopharma's digital transformation. Although there is a high level of innovation in the industry, biopharma companies are facing a complex and challenging environment due to increased competition and R&D cycle times, shorter time in market, expiring patents, declining peak sales, pressure around reimbursement and mounting regulatory scrutiny. As we have shown in our series of reports on'Measuring the return from pharmaceutical innovation', these factors are contributing to an alarming decline in the projected return on investment that large biopharma companies might expect to achieve from their late-stage pipelines, threatening their long-term futures.1 Digital transformation could provide a lifeline to biopharma research and development (R&D) and help reverse this trend. Digital transformation will also impact beyond R&D, as companies look to improve their operational performance, productivity, efficiency and cost-effectiveness across the entire biopharma value chain (see figure 1). Digital transformation will also impact business models, the development of new products and services, and how companies engage with health care professionals, patients and other customers. Ultimately, digital transformation is the next step in the evolution of biopharma companies.


Deloitte Survey: Scaling Artificial Intelligence (AI) Across the Life Sciences Value Chain

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Key quote "The life sciences industry has only begun to scratch the surface of AI's potential but the good news is biopharma and life sciences leaders see the potential and are willing to make the investments necessary to realize what's possible. They should be cautious, though, and carefully plan and strategize so those investments are used wisely and result in the desired outcomes. By spending time on a solid strategy, putting the building blocks in place for success and leveraging relationships with relevant partners, AI can help transform the life sciences industry as we know it and get the necessary products to market more quickly." Why this matters From R&D to manufacturing, supply chain to commercial functions, AI is beginning to have an impact on increasing efficiencies across the biopharma value chain, especially as a result of the COVID-19 pandemic. In addition, increased remote work environments helped life sciences leaders realize how effective digital solutions can be in helping their businesses run smoothly, transforming mindsets and enabling executives to lean into a future grounded in digitization, data and AI.


Digital R&D

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Digital technologies can transform how companies approach clinical development by incorporating valuable insights from multiple sources of data, radically improving the patient experience, enhancing clinical trial productivity, and increasing the amount and quality of data collected in trials. But where is the industry in adopting these transformative technologies? We interviewed 43 leaders across the clinical development ecosystem to understand the current level of adoption of digital technologies and how it can be accelerated. We found that the industry has been slow to digitize its clinical development processes, and that digital adoption varies widely. Even the most advanced organizations are simply piloting several technologies in different areas of clinical development, focusing on piecemeal solutions or new tools to support the existing process. Our research and client experience suggest that digital transformation is a complex, resource-intensive, and lengthy undertaking. But the rewards can be significant: Early adopters can benefit from better access to and engagement with patients, deeper insights, and faster cycle times for products in development. Many in our study expressed a desire to be fast followers, but given the complexity of operationalizing a digital strategy, the reality is that undue delay could put organizations at a competitive disadvantage. At the same time, our research also indicates that biopharma companies and contract research organizations (CROs) will need to overcome several challenges to realize the potential of digital in clinical development: immature data infrastructure and analytics, regulatory considerations, and internal organizational and cultural barriers. Biopharma companies should consider building updated data infrastructure and governance, engaging early with regulators to discuss new technologies, and developing a measured approach to evaluating and implementing technologies within their organizations. CROs can enable this change by advancing interoperable digital platforms and vetting promising technology applications. Cross-industry consortia could help advance the industry as a whole by offering a forum to share early successes and supporting the development of standards. The time to act is now.


Artificial Intelligence in Clinical Trials

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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.