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.

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.

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.

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.

Use of population pharmacokinetic modeling and Monte Carlo simulation to describe the pharmacodynamic profile of cefditoren in plasma and epithelial lining fluid.

Cefditoren is a broad-spectrum, oral cephalosporin that is highly active against clinically relevant respiratory tract pathogens, including multidrug-resistant Streptococcus pneumoniae. This study described its pharmacodynamic profile in plasma and epithelial lining fluid (ELF). Plasma and ELF pharmacokinetic data were obtained from 24 patients under fasting conditions. Cefditoren and urea concentrations were determined in plasma and bronchoalveolar lavage fluid by liquid chromatography-tandem mass spectrometry. Concentration-time profiles in plasma and ELF were modeled using a model with three disposition compartments and first-order absorption, elimination, and transfer. Pharmacokinetic parameters were identified in a population pharmacokinetic analysis (big nonparametric adaptive grid with adaptive gamma). Monte Carlo simulation (9,999 subjects) was performed with the ADAPT II program to estimate the probability of target attainment at which the free-cefditoren plasma concentrations (88%) protein binding and total ELF concentrations exceeded the MIC for 33% of the dosing interval for 400 mg cefditoren given orally every 12 h. After the Bayesian step, the overall fits of the model to the data were good, and plots of predicted versus observed concentrations for plasma and ELF showed slopes and intercepts very close to the ideal values of 1.0 and 0.0, respectively. In the plasma probability of target attainment analysis, the probability of achieving a time for which free, or unbound, plasma concentration exceeds the MIC of the organism for 33% of the dosing interval was <80% for a MIC of >0.06 mg/liter. Similar to plasma, the probability of achieving a time above the MIC of 33% was <80% for MIC of >0.06 mg/liter in ELF. Cefditoren was found to have a low probability of achieving a bacteriostatic effect against MICs of >0.06 mg/liter, which includes most S. pneumoniae isolates with intermediate susceptibility to penicillin, when given in the fasting state in both plasma and ELF.