Analysis of pharma R&D productivity - a new perspective needed.

R&D productivity continues to be the industry's grand challenge. We analyzed the R&D input, output, and outcome of 16 leading research-based pharmaceutical companies over 20 years (2001-2020). Our analysis shows that pharma companies increased their R&D spending at a compound annual growth rate of 6% (2001-2020) to an average R&D expenditure per company of $6.7 billion (2020). The companies in our investigation launched 251 new drugs representing 46% of all CDER-related FDA approvals in the past 20 years. The average R&D efficiency of big pharma was $6.16 billion total R&D expenditures per new drug. Almost half of the leading companies needed to compensate for their negative R&D productivity through mergers and acquisitions.

The dominant logic of Big Tech in healthcare and pharma.

Digital health and digital pharma are considered supportive tools for patients and healthcare providers (HCPs), making the market highly attractive for industry players. Not surprisingly, Tech Giants have started to move into this area. We utilized established management models and publicly available information sources, such as annual company reports, and performed a thorough analysis to uncover the underlying business models of Alphabet, Amazon, Apple, IBM, and Microsoft in order to better understand their intention and course of entering the healthcare and pharma industries. Our results indicate that Big Tech or Tech Giants do address the needs of patients and physicians, while having built clear value propositions, value chains, and revenue models to sustainably revolutionize the healthcare and pharma industries.

Open innovation: A paradigm shift in pharma R&D?

Open innovation (OI) holds promise to accelerate, diversify, and innovate research and development (R&D) in the pharmaceutical industry. It remains to be assessed in which way and to what extent OI is leveraged in practice by current pharmaceutical R&D organizations. Therefore, here we comprehensively analyzed 21 research-based pharmaceutical companies and benchmarked their implementation of OI. Our data showed that OI is an integral part of R&D of all assessed pharmaceutical companies; models typically used are research collaborations, innovation incubators, academic centers of excellence, public-private partnerships (PPPs), mergers and acquisitions (M&A), licensing, or corporate venture capital (VC) funds. In addition, we conclude that the implementation of OI differs greatly across corporations and, consequently, that R&D organizations of research-based pharmaceutical companies can be classified based on their level of OI implementation into three distinct types: predominantly traditional R&D; network-based R&D; and R&D ecosystems.

Systematic risk identification and assessment using a new risk map in pharmaceutical R&D.

Delivering transformative therapies to patients while maintaining growth in the pharmaceutical industry requires an efficient use of research and development (R&D) resources and technologies to develop high-impact new molecular entities (NMEs). However, increasing global R&D competition in the pharmaceutical industry, growing impact of generics and biosimilars, more stringent regulatory requirements, as well as cost-constrained reimbursement frameworks challenge current business models of leading pharmaceutical companies. Big data-based analytics and artificial intelligence (AI) approaches have disrupted various industries and are having an increasing impact in the biopharmaceutical industry, with the promise to improve and accelerate biopharmaceutical R&D processes. Here, we systematically analyze, identify, assess, and categorize key risks across the drug discovery and development value chain using a new risk map approach, providing a comprehensive risk-reward analysis for pharmaceutical R&D.

Translational precision medicine: an industry perspective.

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.

R&D efficiency of leading pharmaceutical companies - A 20-year analysis.

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.

Big Techs and startups in pharmaceutical R&D - A 2020 perspective on artificial intelligence.

We investigated what kind of artificial intelligence (AI) technologies are utilized in pharmaceutical research and development (R&D) and which sources of AI-related competencies can be leveraged by pharmaceutical companies. First, we found that machine learning (ML) is the dominating AI technology currently used in pharmaceutical R&D. Second, both Big Techs and AI startups are competent knowledge bases for AI applications. Big Techs have long-lasting experience in the digital field and offer more general IT solutions to support pharmaceutical companies in cloud computing, health monitoring, diagnostics or clinical trial management, whereas startups can provide more specific AI services to address special issues in the drug-discovery space.

The upside of being a digital pharma player.

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'.

Open innovation and external sources of innovation. An opportunity to fuel the R&D pipeline and enhance decision making?

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.

Pre-clinical evaluation of advanced nerve guide conduits using a novel 3D in vitro testing model.

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.

Changing R&D models in research-based pharmaceutical companies.

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.

Models for open innovation in the pharmaceutical industry.

The nature of the pharmaceutical industry is such that the main driver for its growth is innovation. In view of the vast challenges that the industry has been facing for several years and, in particular, how to manage stagnating research and development (R&D) productivity, pharmaceutical companies have opened their R&D organizations to external innovation. Here, we identify and characterize four new types of open innovator, which we call 'knowledge creator', 'knowledge integrator', 'knowledge translator' and 'knowledge leverager', and which describe current open R&D models.

How to create innovation by building the translation bridge from basic research into medicinal drugs: an industrial perspective.

The global healthcare industry is undergoing substantial changes and adaptations to the constant decline of approved new medical entities. This decrease in internal research productivity is resulting in a major decline of patent-protected sales (patent cliff) of most of the pharmaceutical companies. Three major global adaptive trends as driving forces to cope with these challenges are evident: cut backs of internal research and development jobs in the western hemisphere (Europe and USA), following the market growth potential of Asia by building up internal or external research and development capabilities there and finally, 'early innovation hunting' with an increased focus on identifying and investing in very early innovation sources within academia and small start-up companies. Early innovation hunting can be done by different approaches: increased corporate funding, establishment of translational institutions to bridge innovation, increasing sponsored collaborations and formation of technology hunting groups for capturing very early scientific ideas and concepts. This emerging trend towards early innovation hunting demands special adaptations from both the pharmaceutical industry and basic researchers in academia to bridge the translation into new medicines which deliver innovative medicines that matters to the patient. This opinion article describes the different modalities of cross-fertilisation between basic university or publicly funded institutional research and the applied research and development activities within the pharmaceutical industry. Two key factors in this important translational bridge can be identified: preparation of both partnering organisations to open up for new and sometime disruptive ideas and creation of truly trust-based relationships between the different groups allowing long-term scientific collaborations while acknowledging that value-creating differences are an essential factor for successful collaboration building.

Identification and characterization of secreted effector proteins of Chlamydophila pneumoniae TW183.

We report the expression of several chlamydial effector proteins in Chlamydophila pneumoniae, as well as their time-dependent secretion into the inclusion membrane. Localization of the respective genes within type III secretion gene clusters as well as bioinformatic analysis suggest that the identified proteins are type III-secreted effector proteins. Immunocytochemistry with antisera raised against CpMip (C. pneumoniae macrophage infectivity potentiator, Cpn0661), Pkn5 (Cpn0703), Cpn0709, Cpn0712 and Cpn0827 showed secretion of the respective proteins into the inclusion membrane at 20 h postinfection (hpi). CpMip was detected within the inclusion membrane from 20 to 72 hpi, whereas Cpn0324 (CopN) was located in this compartment at 72 hpi only. This was confirmed by co-localization of the respective proteins with IncA, an inclusion membrane marker protein. These data illustrate the fact that different effectors are being expressed and secreted during different time intervals of the infection cycle. Proteins Cpn0706 and Cpn0808 were not secreted by C. pneumoniae. The immunophilin FK506, known to inhibit the activity of Legionella, C. trachomatis and C. psittaci Mip proteins, was shown to interfere with chlamydial infection. Here we report the putatively type III-dependent secretion of CpMip into the inclusion membrane as well as the effect of its inhibition on C. pneumoniae infection of HEp-2 cells.