Animal extrapolation in preclinical studies: An analysis of the tragic case of TGN1412

The animal model of human disease as a core concept of medical research: Historical cases, failures, and some epistemological considerations

This article uses four historical case studies to address epistemological issues related to the animal model of human diseases and its use in medical research on human diseases. The knowledge derived from animal models is widely assumed to be highly valid and predictive of reactions by human organisms. In this contribution, I use three significant historical cases of failure (ca. 1890, 1960, 2006), and a closer look at the emergence of the concept around 1860/70, to elucidate core assumptions related to the specific practices of animal-human knowledge transfer, and to analyze the explanations provided by historical actors after each of the failures. Based on these examples, I argue that the epistemological status of the animal model changed from that of a helpful methodological tool for addressing specific questions, but with precarious validity, to an obligatory method for the production of strong knowledge on human diseases. As a result, there now exists a culture of biomedical research in human disease that, for more than a century, has taken the value of this methodological tool as self-evident, and more or less beyond question.

Scientific inertia in animal-based research in biomedicine

Despite aspirations to substitute animal experimentation with alternative methods and recent progress in the area of non-animal approaches, such as organoïds and organ(s)-on-a-chip technologies, there is no extensive replacement of animal-based research in biomedicine. In this paper, I will analyse this state of affairs with reference to key institutional and socio-epistemic barriers for the development and use of non-animal approaches in the context of biomedical research in Europe. I will argue that there exist several factors that inhibit change in this context. In particular, there is what I call "scientific inertia", i.e. a certain degree of conservatism in scientific practice regarding the development and use of non-animal approaches to replace animal experimentation. This type of inertia is facilitated by socio-epistemic characteristics of animal-based research in the life sciences and is a key factor in understanding the status quo in biomedical research. The underlying reasons for scientific inertia have not received sufficient attention in the literature to date because the phenomenon transcends traditional disciplinary boundaries in the study of animal experimentation. This paper addresses this issue and seeks to contribute to a better understanding of scientific inertia by using a methodology that looks at the interplay of institutional, epistemic, and regulatory aspects of animal-based research.

Quality Management Model for Phase I Clinical Drug Trials: A Structural Equation Model

This study aimed to construct a quality management model for phase I clinical drug trials. A cross-sectional survey was conducted and data were collected from 604 respondents at 69 institutions in China engaged in phase I clinical drug trials. Exploratory and confirmatory factor analyses were used to develop the survey tool. Structural equation modeling was used to construct a quality management model for phase I clinical drug trials. The results showed that the final survey tool had good reliability and validity (Cronbach's α=0.938, root mean square error of approximation=0.074, comparative fit index=0.962, and Tucker-Lewis index=0.955). The model included five dimensions: government regulation, industry management, medical institution management, research team management, and contract research organization (CRO) management. In total, 22 measurement items were obtained. The structural equation model indicated government regulation, industry management, medical institution management, and CRO management significantly affected the quality of phase I clinical drug trials (β=0.195, β=0.331, β=0.279, and β=-0.267, respectively; P<0.05). Research team management had no effect on the quality of trials (β=0.041, P=0.610). In conclusion, the model is valuable for identifying factors influencing phase I clinical drug trials and guiding quality management practices.