Evidence map›Paper›PMID 27118048›Full record

ReviewJournal of translational medicine2016

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

Alexander Schuhmacher, Oliver Gassmann, Markus Hinder

Open access · goldAbstract readReview
In one paragraph

Review in Journal of translational medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.

0numbers the graph read from it
0cells of the map it votes in
74citing papers in PubMed
129.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

74 citing papers in PubMed, 289 citations in OpenAlex.

  1. Article
  2. Observational
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  6. Review
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  11. International journal of molecular sciences · 2024
    Review
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  19. NTD Health: an electronic medical record system for neglected tropical diseasesBiomedica : revista del Instituto Nacional de Salud · 2022
    Article
  20. Review

14 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 2 countries.

Alexander SchuhmacherSchool of Applied Chemistry, Reutlingen University, Alteburgstrasse 150, 72762, Reutlingen, Germany. alexander.schuhmacher@reutlingen-university.de.
Oliver GassmannInstitute of Technology Management, University of St. Gallen, Dufourstrasse 40a, 9000, St. Gallen, Switzerland.
Markus HinderNovartis Institutes for BioMedical Research, Postfach, Forum 1, 4002, Basel, Switzerland.
Novartis Institutes for BioMedical ResearchReutlingen University · DEUniversity of St. Gallen · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Drug IndustryModels, TheoreticalResearchCooperative BehaviorRisk FactorsTime Factors

Identifiers

PMID27118048
PMCPMC4847363
OpenAlexW2344978273

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.