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10.1111/cbdd.13954

http://scihub22266oqcxt.onion/10.1111/cbdd.13954
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suck abstract from ncbi


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pmid34549885      Chem+Biol+Drug+Des 2022 ; 99 (1): 32-45
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  • Lack of efficacy of mono-mode of action therapeutics in COVID-19 therapy - How the lack of predictive power of preclinical cell and animal studies leads developments astray #MMPMID34549885
  • Haberland A; Muller J
  • Chem Biol Drug Des 2022[Jan]; 99 (1): 32-45 PMID34549885show ga
  • The diverse experiences regarding the failure of tested drugs in the fight against COVID-19 made it clear that one should at least question the requirement to apply classical preclinical development strategies that demand cell and animal efficacy models to be tested before going into clinical trials. Most animals are not susceptible to infection with SARS-CoV-2, and so this led to one-sided virus replication experiments in cells and the use of animal models that have little in common with the complex pathogenesis of COVID-19 in humans. Therefore, non-clinical development strategies were designed to meet regulatory requirements, but they did not truly reflect the situation in the clinic. This has led the search for effective agents astray in many cases. As proof of this statement, we now bring together the results of such required preclinical experiments and compare with the results in clinical trials. Two clear conclusions that can be drawn from the experience to date: The required preclinical models are unsuitable for the development of innovative treatments medical devices in the case of COVID-19 and mono-action strategies (e.g. direct antivirals) are of very little or no benefit to patients under randomized,blinded conditions. Our hypothesis is that the complex situation of COVID-19 may benefit from multi-mode drugs. Here, the molecular class of aptamers could be a solution.
  • |*COVID-19 Drug Treatment[MESH]
  • |*Drug Discovery/methods[MESH]
  • |*Drug Evaluation, Preclinical/methods[MESH]
  • |Animals[MESH]
  • |Antiviral Agents/pharmacology/*therapeutic use[MESH]
  • |Disease Models, Animal[MESH]
  • |Humans[MESH]


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