•We reviewed financial, NME and bibliogr. data from 14 big pharma companies (1999–2018).•Most important sources of NMEs were own research (40%), M&A (41%) and licensing (19%).•We found a nearly ...linear correlation between R&D spending and R&D output.•Our investigations indicate economies of scale in pharma R&D.
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.
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.
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.
The upside of being a digital pharma player Schuhmacher, Alexander; Gatto, Alexander; Hinder, Markus ...
Drug discovery today,
September 2020, 2020-09-00, 20200901, Letnik:
25, Številka:
9
Journal Article
Recenzirano
•We have investigated the state of artificial intelligence in pharmaceutical R&D.•We have outlined here a risk and reward perspective regarding digital R&D.•The pharmaceutical industry is in an ...“early mature” phase of using AI in R&D.•It is worthwhile to invest to become 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’.
•We analyzed 21 leading research-based pharmaceutical companies by global sales 2019 regarding their inbound R&D activities between 2015 and 2019.•Over the last decade open innovation (OI) has found ...its way into and is a widely used R&D model of the research-based pharmaceutical industry.•The depth and breadth of implementation differs greatly across major industry players: While four pharmaceutical companies (Novartis, Otsuka, Gilead, and Allergan) rely more on traditional R&D concepts, the vast majority (15 out of 21 companies) also uses network-based OI models to supplement R&D. And two companies (Bayer and AstraZeneca) have opened their R&D into ecosystem-enabled.
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.
•FDA approved 176 new drugs for the 20 leading pharmaceutical companies (2014–2023).•An additional 33 new drugs were acquired through post-approval acquisition (2014–2023).•Big pharma launched 83 ...first-in-class drugs (2014–2023).•Most new drugs of the top 20 pharma companies were approved in oncology.•Big pharma’s share of the industry’s R&D output declined from 72% (2014) to 40% (2023).
This article addresses the research and development (R&D) productivity challenge of the pharmaceutical industry, focusing on United States Food and Drug Administration (FDA)-related new drug approvals of the top 20 pharmaceutical companies (2014–2023). We evaluated the degree of innovation in new drugs to determine the innovativeness of these leading companies. A key finding of our analysis is the decline in the number of new drugs approved by the FDA for these leading companies over the investigated time period. This trend suggests that some of the leading companies are losing ground in R&D innovation, raising concerns about their ability to sustain competitive advantage, ensure long-term market success, and maintain viable business models.
•To understand the AI technologies that are used in pharmaceutical research and development (R&D), we analyzed 271 AI-related publications from the top 20 pharmaceutical companies (published 2014 to ...Q3 of 2019).•To understand the AI activities of the Big Tech companies Alphabet, Amazon, Apple, IBM, and Microsoft in pharmaceutical R&D, we analyzed their scientific publications.•To understand the competencies of 398 AI startups in pharmaceutical R&D, we investigated their (1) determination, as illustrated by information from corporate communication; (2) knowledge, as proven by scientific publications and/or patent applications; and (3) ability to execute with respect to AI, as documented by collaborations with pharmaceutical companies and Big Techs.
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.
Is the blockbuster imperative broken? Schuhmacher, Alexander; Hinder, Markus; Boger, Nikolaj ...
Drug discovery today,
November 2023, 2023-Nov, 2023-11-00, 20231101, Letnik:
28, Številka:
11
Journal Article
Recenzirano
•Blockbuster drugs build the foundation of the high-risk and high-reward business model of the leading pharmaceutical companies.•They represent the only drug category that can positively impact ...operating results.•An exclusively blockbuster-driven business model also has its limitations, strategically and culturally.•New therapeutic modalities, e.g. gene and cell therapeutics, challenge the sustainability of the blockbuster model.
•Based on our in-depth experience on biopharmaceutical R&D, we have identified R&D-related risks by systematically analyzing scientific, peer-reviewed publications in terms of dedicated uncertainties ...described for drug discovery, preclinical development, clinical phases 1-3, post marketing activities, innovation management and intellectual property (IP) management using PubMed.•Thereby, we have identified 123 key R&D risks and grouped them into five R&D value chain segments and 27 respective process domains.•Next, we have applied the R&D risk map and identified 84 scientific publications describing 64 cases in which AI is addressing key R&D risks, thus, describing the case of AI 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.
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