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.

New severity assessment in cystic fibrosis: signal intensity and lung volume compared to LCI and FEV1: preliminary results.

OBJECTIVES: Magnetic resonance imaging (MRI) aids diagnosis in cystic fibrosis (CF) but its use in quantitative severity assessment is under research. This study aims to assess changes in signal intensity (SI) and lung volumes (Vol) during functional MRI and their use as a severity assessment tool in CF patients. METHODS: The CF intra-hospital standard chest 1.5 T MRI protocol comprises of very short echo-time sequences in submaximal in- and expiration for functional information. Quantitative measurements (Vol/SI at in- and expiration, relative differences (Vol_delta/SI_delta), and cumulative histograms for normalized SI values across the expiratory lung volume) were assessed for correlation to pulmonary function: lung clearance index (LCI) and forced expiratory volume in 1 s (FEV1). RESULTS: In 49 patients (26 male, mean age 17 ± 7 years) significant correlation of Vol_delta and SI_delta (R = 0.86; p < 0.0001) during respiration was observed. Individual cumulated histograms enabled severity disease differentiation (mild, severe) to be visualized (defined by functional parameter: LCI > 10). The expiratory volume at a relative SI of 100% correlated significantly to LCI (R = 0.676 and 0.627; p < 0.0001) and FEV1 (R = - 0.847 and - 0.807; p < 0.0001). Clustering patients according to Vol_SI_100 showed that an amount of ≤ 4% was related to normal, while an amount of > 4% was associated with pathological pulmonary function values. CONCLUSION: Functional pulmonary MRI provides a radiation-free severity assessment tool and can contribute to early detection of lung impairment in CF. Lung volume with SI below 100% of the inspiratory volume represents overinflated tissue; an amount of 4% of the expiratory lung volume was a relevant turning point. KEY POINTS: • Signal intensity and lung volumes are used as potential metric parameters for lung impairment. • Quantification of trapped air impacts on therapy management. • Functional pulmonary MRI can contribute to early detection of lung impairment.