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Pre-clinical evaluation of advanced nerve guide conduits using a novel 3D in vitro testing model
(2018)
Autografts are the current gold standard for large peripheral nerve defects in clinics despite the frequently occurring side effects like donor site morbidity. Hollow nerve guidance conduits (NGC) are proposed alternatives to autografts, but failed to bridge gaps exceeding 3 cm in humans. Internal NGC guidance cues like microfibres are believed to enhance hollow NGCs by giving additional physical support for directed regeneration of Schwann cells and axons. In this study, we report a new 3D in vitro model that allows the evaluation of different intraluminal fibre scaffolds inside a complete NGC. The performance of electrospun polycaprolactone (PCL) microfibres inside 5 mm long polyethylene glycol (PEG) conduits were investigated in neuronal cell and dorsal root ganglion (DRG) cultures in vitro. Z-stack confocal microscopy revealed the aligned orientation of neuronal cells along the fibres throughout the whole NGC length and depth. The number of living cells in the centre of the scaffold was not significantly different to the tissue culture plastic (TCP) control. For ex vivo analysis, DRGs were placed on top of fibre-filled NGCs to simulate the proximal nerve stump. In 21 days of culture, Schwann cells and axons infiltrated the conduits along the microfibres with 2.2 ± 0.37 mm and 2.1 ± 0.33 mm, respectively. We conclude that this in vitro model can help define internal NGC scaffolds in the future by comparing different fibre materials, composites and dimensions in one setup prior to animal testing.
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.
In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.
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.
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.
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.
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.
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.
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.
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.