The impact of external innovation on new drug approvals: A retrospective analysis
Pharmaceutical companies are relying more often on external sources of innovation to boost their discovery research productivity. However, more in-depth knowledge about how external innovation may translate to successful product launches is still required in order to better understand how to best leverage the innovation ecosystem. We analyzed the pre-approval publication histories for FDA-approved new molecular entities (NMEs) and new biologic entities (NBEs) launched by 13 top research pharma companies during the last decade (2006-2016). We found that academic institutions contributed the majority of pre-approval publications and that publication subject matter is closely aligned with the strengths of the respective innovator. We found this to also be true for candidate drugs terminated in Phase 3, but the volume of literature on these molecules is substantially less than for approved drugs. This may suggest that approved drugs are often associated with a more robust dataset provided by a large number of institutes. Collectively, the results of our analysis support the hypothesis that a collaborative research innovation environment spanning across academia, industry and government is highly conducive to successful drug approvals.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
United States FDA drug approvals are persistent and polycyclic: Insights into economic cycles, innovation dynamics, and national policy
It is challenging to elucidate the effects of changes in external influences (such as economic or policy) on the rate of US drug approvals. Here, a novel approach, termed the Chronological Hurst Exponent (CHE), is propos…
Time SeriesTime Series AnalysisSingular Secular Kuznets-like Period Realized Amid Industrial Transformation in US FDA Medical Devices: A Perspective on Innovation from 1976 to 2020
Introduction: Since inception, the United States (US) Food and Drug Administration (FDA) has kept a robust record of regulated medical devices (MDs). Based on these data, can we gain insight into the innovation dynamics …
MarketingDetecting drug-drug interactions using artificial neural networks and classic graph similarity measures
Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the dr…
Graph SimilarityLink PredictionPredictionAgentic AI in medicine: architectures, applications, evaluation, and challenges for clinical translation
Large language models and multimodal foundation models are enabling medical artificial intelligence (AI) systems to move beyond isolated prediction and undertake multistep clinical tasks that require planning, tool use, …
Question AnsweringData-centric challenges with the application and adoption of artificial intelligence for drug discovery
Introduction: Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges currently limiting the impact and sc…
Drug DiscoveryUncertainty Quantification