Bruno Villoutreix ^1* Jean-Luc Poyet ² Katya Tsaioun ³

• ¹ Hôpital Robert Debré, Paris, France
• ² Institut National de la Santé et de la Recherche Médicale (INSERM), Paris, Île-de-France, France
• ³ Johns Hopkins Center for Health Equity, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States

Finding new medications is a complex and costly process. Yet, as we work towards creating better, safer, and faster treatments, it's essential to make this process more understandable and accessible to everyone. This Research Topic is dedicated to introducing the main concepts and methods in a way that is accessible to all. With contributions from experts across the field, this collection of articles aims to demystify the drug development pipeline and address some of the its most pressing challenges.The article by Singh et al. provides a comprehensive overview of the entire drug discovery process, explaining the key steps from basic research to the final stages of clinical trials and post-market surveillance (https://www.frontiersin.org/journals/drugdiscovery/articles/10.3389/fddsv.2023.1201419/full). By laying out the five main stages of drug discovery: pre-discovery, discovery, preclinical development, clinical trials, and approval, this article serves as a primer for readers unfamiliar with the field. The review also explains what are the main therapeutic agents (e.g., small molecules, peptides, biologics like antibodies…), the pros and cons of drug repurposing and highlights the high cost, long timelines and high attrition rates associated with drug discovery and development. The integration of artificial intelligence (AI) with traditional or novel experimental technologies, offers promising avenues to eventually streamline the process. Yet, many obstacles remain, including the lack of high quality data and the difficulty in understanding the disease state and human biology.Chavez-Hernandez et al. explore the critical role of chemical and biological data in drug discovery (https://www.frontiersin.org/journals/drug-discovery/articles/10.3389/ fddsv.2023.1222655/full). Their review underscores the importance of balancing the quantity and quality of data, especially as AI and machine learning methods become integral to the drug design process. The authors advocate for a better reporting of both active and inactive compounds to foster a more comprehensive understanding of bioactivity, emphasizing the need for balanced datasets that should drive more accurate predictions and hopefully lead to better treatments. Gadiya et al. explain the concept of FAIR (Findable, Accessible, Interoperable, Reusable) data management (https://www.frontiersin.org/journals/drug-discovery/ articles/10.3389/fddsv.2023.1226727/full). In an era where data is often siloed within institutions and companies, the lack of accessible datasets hinders progress. The authors first suggest that embracing FAIR principles across the drug discovery pipeline can enhance collaboration and thus improve the process. Then they further mention that the FAIR approach should help drug developers to learn from past efforts, thus reducing redundancy and accelerating the development of new therapies.Two articles discuss specifically the most commonly used types of therapeutics: small molecules. Southey and Brunavs explore small molecule drug discovery, outlining the various steps and the challenges in the field (https://www.frontiersin.org/ journals/drug-discovery/articles/10.3389/fddsv.2023.1314077/full). They note that despite well-established protocols and novel knowledge, the process still remains very complex. The authors then present emerging technologies aimed at overcoming current limitations, hopefully making the path to new drug approvals more efficient. In a related discussion, Giraud provides a mini-review of high-throughput screening and biophysical methods are used in the early stages of drug discovery (https:// www.frontiersin.org/journals/drug-discovery/articles/10.3389/fddsv.2024.1342866/ full). The article highlights the two main strategies used in the field: target-based and phenotypic-based discovery.Munster et al. present a compelling and timely review on the transformative potential of AI in the discovery of biologics, with a particular focus on antibodies, a major therapeutic area driving innovation in drug development (https://www.frontiersin.org/ journals/drug-discovery/articles/10.3389/fddsv.2024.1339697/full). Traditionally, antibody discovery relied heavily on animal models and lengthy experimental processes. The authors highlight the advancements in in silico approaches, which are now capable of accelerating antibody design while reducing reliance on animal testing. AI-driven approaches are presented to showcase the shift towards more efficient and de-risked antibody discovery processes, marking the beginning of an exciting new chapter in developing biologic treatments.Public engagement and participation are crucial components of advancing drug development. Wang et al. present the results of a survey on public awareness and willingness to participate in drug clinical trials (DCTs) in China (https:// www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1276536/full). Their findings reveal significant gaps in knowledge and highlight the demographic factors influencing participation rates. The authors call for improved public outreach and communication strategies to foster greater understanding and involvement in DCTs. This could have a significant impact on the success of treatments.The focus of several articles is about the growing concern of using animal models in drug discovery. Marshall and Conlee discuss the limitations of animal testing, noting the high failure rates of drug candidates that appear safe in animals but prove ineffective or toxic in humans (https://www.frontiersin.org/journals/drug-discovery/ articles/10.3389/fddsv.2024.1347246/full). The article suggests to move towards human biology-based testing methods, which not only promise to be more predictive of human responses but also align with ethical imperatives to reduce animal use. Krebs and Herrmann provide an overview of the international movement towards reducing animal testing in biomedical research (https://www.frontiersin.org/journals/ drug-discovery/articles/10.3389/fddsv.2024.1347798/full). They review new nonanimal research approaches that mimic human physiology. Despite of the emergence of these new approaches, the authors acknowledge the persistence of an animal methods bias. They call for a cultural shift in the scientific community, supported by changes in regulatory policies and funding incentives. Hartung offers a balanced perspective on the role of animal models in medical research (https:// www.frontiersin.org/journals/drug-discovery/articles/10.3389/fddsv.2024.1355044/ full). This author acknowledges their historical contributions but also highlight their limitations. The article argues for better, more humane alternatives that use novel methods, envisioning a future where drug development is both more effective and ethical.Together, these articles offer a comprehensive yet accessible overview of the drug discovery process. This Research Topic aims to help the public and patient communities better understand the world of drug discovery, empowering them to engage in the dialogue surrounding drug development. As the field continues to evolve, informed public involvement will be key to shaping a more transparent, efficient, and patient-centered approach to discovering new medicines.