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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.
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.
The efficiency of pharmaceutical research and development (R&D) reflected by increasing costs of R&D, long timelines, and low probabilities of technical and regulatory success decreased continuously in the past years. Today, the costs for discovering and developing a new drug are enormously high with more than USD 2 billion per new molecular entity (NME), while the average overall success of a research project to provide an NME is in the single-digit percentage rate, and the total timelines of R&D easily exceeds 10 years questioning the return on investment (ROI) of pharmaceutical R&D. As a consequence and also caused by numerous patent expirations of blockbuster drugs that increased the pressure to return to an acceptable ROI, the pharmaceutical industry addressed this challenge and the related causes and identified several actions that need to be taken to increase the output/input ratio of R&D. This book chapter will review the pipeline sizes and the R&D investments of multinational pharmaceutical companies, will describe new processes that have been implemented to increase the reach and to reduce costs of pharmaceutical R&D, and it will illustrate new innovation models that were developed to increase the R&D efficiency.
New drugs serving unmet medical needs are one of the key value drivers of research-based pharmaceutical companies. The efficiency of research and development (R&D), defined as the successful approval and launch of new medicines (output) in the rate of the monetary investments required for R&D (input), has declined since decades. We aimed to identify, analyze and describe the factors that impact the R&D efficiency. Based on publicly available information, we reviewed the R&D models of major research-based pharmaceutical companies and analyzed the key challenges and success factors of a sustainable R&D output. We calculated that the R&D efficiencies of major research-based pharmaceutical companies were in the range of USD 3.2–32.3 billion (2006–2014). As these numbers challenge the model of an innovation-driven pharmaceutical industry, we analyzed the concepts that companies are following to increase their R&D efficiencies: (A) Activities to reduce portfolio and project risk, (B) activities to reduce R&D costs, and (C) activities to increase the innovation potential. While category A comprises measures such as portfolio management and licensing, measures grouped in category B are outsourcing and risk-sharing in late-stage development. Companies made diverse steps to increase their innovation potential and open innovation, exemplified by open source, innovation centers, or crowdsourcing, plays a key role in doing so. In conclusion, research-based pharmaceutical companies need to be aware of the key factors, which impact the rate of innovation, R&D cost and probability of success. Depending on their company strategy and their R&D set-up they can opt for one of the following open innovators: knowledge creator, knowledge integrator or knowledge leverager.
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.
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.
Historically, research and development (R&D) in the pharmaceutical sector has predominantly been an in-house activity. To enable investments for game changing late-stage assets and to enable better and less costly go/no-go decisions, most companies have employed a fail early paradigm through the implementation of clinical proof-of-concept organizations. To fuel their pipelines, some pioneers started to complement their internal R&D efforts through collaborations as early as the 1990s. In recent years, multiple extrinsic and intrinsic factors induced an opening for external sources of innovation and resulted in new models for open innovation, such as open sourcing, crowdsourcing, public–private partnerships, innovations centres, and the virtualization of R&D. Three factors seem to determine the breadth and depth regarding how companies approach external innovation: (1) the company’s legacy, (2) the company’s willingness and ability to take risks and (3) the company’s need to control IP and competitors. In addition, these factors often constitute the major hurdles to effectively leveraging external opportunities and assets. Conscious and differential choices of the R&D and business models for different companies and different divisions in the same company seem to best allow a company to fully exploit the potential of both internal and external innovations.
This book investigates and highlights the most critical challenges the pharmaceutical industry faces in an increasingly competitive environment of inflationary R&D investments and tightening cost control pressures. The authors present three sources of pharmaceutical innovation: new management methods in the drug development pipeline; new technologies as enablers for cutting-edge R&D; and new forms of cooperation and internationalization, such as open innovation in the early phases of R&D. New models and methods are illustrated with cases from Europe, the US, and Asia. This third fully revised edition was expanded to reflect the latest updates in open and collaborative innovation, the greater strategic importance of venture capital and early stage investments, and the new range of emerging technologies now being put to use in pharmaceutical innovation.