Literature DB >> 28407174

Opinion versus evidence for the need to move away from animal testing.

Thomas Hartung1,2.   

Abstract

Science is based on facts and their discourse. Willingly or unwillingly, facts are mixed with opinion, i.e., views or judgments formed, not necessarily based on fact or knowledge. This is often necessary, where we have controversial facts or no definitive evidence yet, because we need to take decisions or have to prioritize. Evidence-based approaches aim at identifying the facts and their quality objectively and transparently; they are now increasingly embraced in toxicology, especially by employing systematic reviews, meta-analyses, quality scoring, risk-of-bias tools, etc. These are core to Evidence-based Toxicology. Such approaches aim at minimizing opinion, the "eminence-based" part of science. Animal experiments are the basis of a lot of our textbook knowledge in the life sciences, have helped to develop desperately needed therapies, and have made this world a safer place. However, they represent only one of the many possible approaches to accomplish all these things. Like all approaches, they come with shortcomings, and their true contribution is often overrated. This article aims to summarize their limitations and challenges beside the ethical and economical concerns (i.e., costs and duration as well as costs following wrong decisions in product development): they include reproducibility, inadequate reporting, statistical under-powering, lack of inter-species predictivity, lack of reflection of human diversity and of real-life exposure. Each and every one of these increasingly discussed aspects of animal experiments can be amended, but this would require enormous additional resources. Together, they prompt a need to engineer a new paradigm to ensure the safety of patients and consumers, new products and therapies.

Entities:  

Keywords:  alternative methods; animal models; limitations ; preclinical research; reproducibility

Mesh:

Year:  2017        PMID: 28407174     DOI: 10.14573/altex.1703291

Source DB:  PubMed          Journal:  ALTEX        ISSN: 1868-596X            Impact factor:   6.043


  6 in total

Review 1.  Big-data and machine learning to revamp computational toxicology and its use in risk assessment.

Authors:  Thomas Luechtefeld; Craig Rowlands; Thomas Hartung
Journal:  Toxicol Res (Camb)       Date:  2018-05-01       Impact factor: 3.524

2.  The 3Rs Principle in Animal Experimentation: A Legal Review of the State of the Art in Europe and the Case in Italy.

Authors:  Enrico Maestri
Journal:  BioTech (Basel)       Date:  2021-05-20

3.  3S - Systematic, systemic, and systems biology and toxicology.

Authors:  Lena Smirnova; Nicole Kleinstreuer; Raffaella Corvi; Andre Levchenko; Suzanne C Fitzpatrick; Thomas Hartung
Journal:  ALTEX       Date:  2018       Impact factor: 6.043

4.  Animal Safety, Toxicology, and Pharmacokinetic Studies According to the ICH S9 Guideline for a Novel Fusion Protein tTF-NGR Targeting Procoagulatory Activity into Tumor Vasculature: Are Results Predictive for Humans?

Authors:  Wolfgang E Berdel; Saliha Harrach; Caroline Brand; Kathrin Brömmel; Andrew F Berdel; Heike Hintelmann; Christoph Schliemann; Christian Schwöppe
Journal:  Cancers (Basel)       Date:  2020-11-26       Impact factor: 6.639

5.  The use of human induced pluripotent stem cells to screen for developmental toxicity potential indicates reduced potential for non-combusted products, when compared to cigarettes.

Authors:  Liam Simms; Kathryn Rudd; Jessica Palmer; Lukasz Czekala; Fan Yu; Fiona Chapman; Edgar Trelles Sticken; Roman Wieczorek; Lisa Maria Bode; Matthew Stevenson; Tanvir Walele
Journal:  Curr Res Toxicol       Date:  2020-11-15

6.  Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility.

Authors:  Thomas Luechtefeld; Dan Marsh; Craig Rowlands; Thomas Hartung
Journal:  Toxicol Sci       Date:  2018-09-01       Impact factor: 4.849

  6 in total

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