Version 1
: Received: 24 May 2023 / Approved: 26 May 2023 / Online: 26 May 2023 (03:29:17 CEST)
How to cite:
Mochel, J.P.; Allenspach, K. Leveraging Data from Spontaneous Animal Disease Models to Streamline Pharmaceutical Research: A Tale of Two Species. Preprints2023, 2023051827. https://doi.org/10.20944/preprints202305.1827.v1
Mochel, J.P.; Allenspach, K. Leveraging Data from Spontaneous Animal Disease Models to Streamline Pharmaceutical Research: A Tale of Two Species. Preprints 2023, 2023051827. https://doi.org/10.20944/preprints202305.1827.v1
Mochel, J.P.; Allenspach, K. Leveraging Data from Spontaneous Animal Disease Models to Streamline Pharmaceutical Research: A Tale of Two Species. Preprints2023, 2023051827. https://doi.org/10.20944/preprints202305.1827.v1
APA Style
Mochel, J.P., & Allenspach, K. (2023). Leveraging Data from Spontaneous Animal Disease Models to Streamline Pharmaceutical Research: A Tale of Two Species. Preprints. https://doi.org/10.20944/preprints202305.1827.v1
Chicago/Turabian Style
Mochel, J.P. and Karin Allenspach. 2023 "Leveraging Data from Spontaneous Animal Disease Models to Streamline Pharmaceutical Research: A Tale of Two Species" Preprints. https://doi.org/10.20944/preprints202305.1827.v1
Abstract
According to data from the U.S FDA, the likelihood of a new drug progressing from Phase I clinical trials to market approval is only 8% (DiMasi et al., 2016). Furthermore, a significant attrition rate of 35% occurs during Phase II studies due to the failure to demonstrate clinical efficacy (Arrowsmith, 2011). These statistics underscore the urgent need to reassess the current research and development (R&D) paradigm. To address this challenge, a promising approach called reverse translational pharmacology (RTP) has emerged, aiming to enhance the predictive value of preclinical models used in biomedical research by leveraging animal models that spontaneously develop analogous diseases to humans (Schneider et al., 2018). The rationale behind RTP is that knowledge gained from clinical trials in canines can subsequently inform and guide human clinical trials, leading to improved outcomes for both species. Additionally, this approach capitalizes on the opportunity to stimulate veterinary drug development by leveraging pharmacokinetic (PK), efficacy, and safety data obtained from human clinical studies (Schneider et al., 2018). The application of reverse translational pharmacology holds immense potential for expediting drug development and advancing medical knowledge. By employing animal models that naturally develop diseases akin to those found in humans, researchers can enhance the predictive capacity of preclinical models, thereby mitigating the high attrition rates encountered in later stages of clinical trials. Moreover, the exchange of knowledge and data between human and veterinary medicine engenders a synergistic relationship, fostering progress in both domains (Schneider et al., 2018). As this innovative approach gains momentum, fostering collaborations between basic and clinician scientists, pharmaceutical companies, and veterinary professionals becomes increasingly imperative. By sharing resources, expertise, and findings, we can pave the way for groundbreaking discoveries and the development of safe and efficacious treatments for diverse diseases. Embracing reverse translational pharmacology offers an avenue to redefine the R&D paradigm, bringing us closer to a future where a higher proportion of drugs successfully traverse the arduous path from the laboratory to market approval, ultimately benefiting patients across species. In this brief commentary, we highlight four examples of diseases where reverse translational modeling has been used to support pharmaceutical research, including some applications developed in our research laboratory (SMART Pharmacology) at the Iowa State University College of Veterinary Medicine. In doing so, this report also seeks to provide a brief overview of the heuristic value of computational approaches in comparative research. To further illustrate the value of these in silico approaches, we deliberately chose to employ some features of ChatGPT to produce the present summary (OpenAI. (2023). GPT-3.5 [ChatGPT]. Retrieved from https://openai.com).
Keywords
One Health; Comparative Research; Reverse Translation
Subject
Medicine and Pharmacology, Veterinary Medicine
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
