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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.
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’.
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.
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.
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.
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.
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.
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.
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.
Der Begriff Value-Based-Selling kam erstmals in Europa zur Jahrtausendwende in Mode. Doch so neu ist das wertorientierte Verkaufen nun auch wieder nicht. So wird doch jeder gute Verkäufer dem Kunden stets die Kundenvorteile ausreichend transparent machen. Das war doch schon immer so, auch wenn das früher niemand mit Value Based-Selling bezeichnet hatte. Doch eine kundennutzenorientierte Formulierung im Verkaufsgespräch ist nur eine Seite der Medaille. Der Ansatz des Value-Based Selling geht weit darüber hinaus. Er hat mehr Substanz, als weitläufig bekannt ist.