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We investigated the state of artificial intelligence (AI) in pharmaceutical research and development (R&D) and outline here a risk and reward perspective regarding digital R&D. Given the novelty of the research area, a combined qualitative and quantitative research method was chosen, including the analysis of annual company reports, investor relations information, patent applications, and scientific publications of 21 pharmaceutical companies for the years 2014 to 2019. As a result, we can confirm that the industry is in an ‘early mature’ phase of using AI in R&D. Furthermore, we can demonstrate that, despite the efforts that need to be managed, recent developments in the industry indicate that it is worthwhile to invest to become a ‘digital pharma player’.
In the period from the 1950s to 2013, the American Food and Drug Administration (FDA) approved 1346 new molecular entities (NMEs) or new biologics entities (NBEs). On average, the approval rate was 20 NMEs per year. In the past 40 years, the number of new drugs launched into the market increased slightly from 15 NMEs in the 1970s to 25–30 NMEs since the 1990s. The highest number of new drugs approved by FDA was in 1996 and 1997, which might be related to the enactment of the Prescription Drug User Fee Act (PDUFA) in 1993.
Artificial intelligence (AI) technologies, such as machine learning or deep learning, have been predicted to highly impact future organizations and radically change the way how projects are managed. The Project Management Institute (PMI), the network of around 1.1 million certified project managers, ranked AI as one of the top three disruptors of their profession. In an own study on the effect of AI, 37% of the project management processes can be executed by machine learning and other AI technologies. In addition, Gartner recently postulated that 80% of the work of today's project managers may be eliminated by AI in 2030.
This editorial aims to outline today's project and portfolio management in context of pharmaceutical research and development (R&D), followed by an AI-vision and a more tangible mission, and illustrate what the consequences of an AI-enabled project and portfolio management could be for pharmaceutical R&D.
Today, virtualizing pharma R&D is increasingly related with data analytics and artificial intelligence (AI), technologies that have been developed by software companies outside the healthcare sector. The process of virtualizing pharma R&D is closely related to the technological advancements that result in the generation of large data sets ranging from genomics, proteomics, metabolomics, medical imaging, IoT wearables and large clinical trials, making it necessary for pharma companies to find new ways to store and ultimately analyze information. As a consequence, pharma companies are experimenting with AI in R&D ranging from in-silico drug design to clinical trail participants identification or dosage error reduction.
Research and Development (R&D) is crucial for the growth and future success of research-based pharma companies. To maintain their R&D organisations efficient, pharmaceutical companies started to hedge the potential of open innovation to cut R&D costs and to access external knowledge. These new strategies could be divided into several categories: open source, innovation centres, crowd sourcing and virtual R&D.
Pharmaceutical companies are among the top investors into research and development (R&D) globally, as product innovation is still the main growth driver for the industry and because the related complexities necessitate enormous R&D investments. The market demand for new medicines to be more efficacious or to provide better safety than existing drugs and the regulatory need to prove superiority in clinical trials are reasons why drug R&D is increasingly expensive and pharmaceutical companies need to manage extraordinarily high costs per approved new compound.
Comparative analysis of the R&D efficiency of 14 leading pharmaceutical companies for the years 1999–2018 shows that there is a close positive correlation between R&D spending and the two investigated R&D output parameters, approved NMEs and the cumulative impact factor of their publications. In other words, higher R&D investments (input) were associated with higher R&D output. Second, our analyses indicate that there are "economies of scale" (size) in pharmaceutical R&D.
It is known that the costs related with drug research and development (R&D) and the timelines to develop a new drug increased over the past years. In parallel, the success rates of drug projects along the pharmaceutical R&D phases are still very low, and the outcome of all R&D efforts is stagnating. In consequence, the R&D efficiency defined as the financial investment per drug has been steadily decreasing. As innovation is the major growth driver of the pharmaceutical industry, reliable data on R&D efficiency and new concepts to overcome these challenges are of great interest for R&D managers and the sustainability of the pharmaceutical industry as a whole. This book chapter reviews publications on R&D performance indicators of the past years, such as the success rates and timelines per phase. Additionally, it illustrates the factors influencing the success rates, timelines, and costs of pharmaceutical R&D most and, thus, the denominators of the R&D efficiency.
Clinical development is historically the phase in which a potential new medicine is being tested in phase 2 and phase 3 patient trials to demonstrate the new molecules' efficacy and safety to support the regulatory approval of drugs by health authorities. This relatively focused approach has been considerably expanded by a number of forces from within the pharmaceutical industry and equally important by changes in the healthcare systems. The need to identify the optimal patient population, showstoppers leading to discontinuation of clinical programs, the silent but constant removal of surrogate endpoints for registration, and the increased demand for real-life data which are used to demonstrate the patients' benefit and which have an ever-increasing role for pricing and reimbursement negotiations are today an integral part of this phase.
This chapter will review both the nuts and bolts of clinical development but also recent developments in this area which shape the environment and how the different players have reacted and what options might need to be explored in the future.
The reduced research and development (R&D) efficiency, strong competition from generics, increased cost pressure from payers, and an increased biological complexity of new target indications have resulted in a rethinking and a change from a traditional and more closed R&D model in the pharmaceutical industry toward the new paradigm of open innovation. In the past years, pharmaceutical companies have broadened their external networks toward research collaborations with academic institutes, technology providers, or codevelopment partners. To fulfill the demand to reduce timelines and costs, research-based pharmaceutical companies started to outsource R&D activities. In addition, internal R&D processes were adjusted to the more open R&D model and new processes such as alliance management were established. The corporate frontier of pharmaceutical companies became permeable and more open. As a result, the focus of pharmaceutical R&D expanded from a purely internal toward a mixed internal and external model. Today, the U.S. pharmaceutical company Eli Lilly may have established the most open model toward external innovation, as it has integrated its innovation processes with its business model. Other companies are following this more open R&D model with newer concepts such as new frontier sciences, drug discovery alliances, private public partnerships, innovation incubators, virtual R&D, crowdsourcing, open source innovation, and innovation camps.
