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Statistical Approaches in the Development of Digital Therapeutics
Published in Oleksandr Sverdlov, Joris van Dam, Digital Therapeutics, 2023
Oleksandr Sverdlov, Yevgen Ryeznik, Sergei Leonov, Valerii Fedorov
Developing a new drug/treatment involves drug discovery (screening multiple candidate compounds and identifying several lead ones to progress further in development), pre-clinical development (chemistry, manufacturing and controls, and animal studies), followed by clinical development. The latter part is split into four phases. Phase I includes studies in humans to establish the compound's safety, tolerability, and pharmacokinetics and to identify doses/dose ranges suitable for testing in subsequent studies. Phase II includes studies in the target patient population to establish proof-of-concept (does the drug work as intended?), determine the therapeutic dose range, and select doses/regimens to be tested in large confirmatory trials. Phase III includes randomized controlled trials (RCTs) to test/confirm the pre-specified clinical research hypotheses. Evidence from phase III RCTs forms the basis for regulatory submission and drug approval. Phase IV includes post-marketing studies of long-term safety and optimization of drug use in subpopulations.
The Relationship-Centered Care Research Network
Published in Thomas S. Inui, Richard M. Frankel, Enhancing the Professional Culture of Academic Health Science Centers, 2022
Richard M. Frankel, Penelope R. Williamson, Dana Gelb Safran, Debra Roter, Mary Catherine Beach, Howard Beckman, Lisa A. Cooper, Judith A. Hall, Paul Haidet, Thomas S. Inui, William L. Miller, David L. Mossbarger, Howard F. Stein
In summary, members of the RCCRN expressed the view that having funding for the initiative in hand lent itself to the creativity and productivity of the group, and at least indirectly raised the question of whether the current mechanisms for doing science and discovery are limiting because individual researchers are forced to remain in a straight, narrow box to obtain funding. Being able to reach consensus on topics of interest to group members plus being able to explore new ideas and approaches without having to worry about competing against one another for scarce resources gave the group space and time to develop their ideas and methods to pursue them. As one participant, E, put it, “In the real world of scientific clinical investigators, the Relationship-centered Research Network was our Camelot.”
Artificial Intelligence (AI) vs. Natural Human Intelligence (NHI)
Published in Tom Lawry, Hacking Healthcare, 2022
To illustrate this point, researchers from the Lawrence Berkeley National Laboratory used AI to reveal new scientific knowledge hidden in old research papers. Using just the language in millions of old scientific papers, a deep learning algorithm was able to make new scientific discoveries by sifting through scientific papers for connections humans had missed. And while the experiment was focused on new discoveries in material science, the process could just as easily be applied to other disciplines such as medical research and drug discovery.5
Where lies the future of Ayurveda-inspired drug discovery?
Published in Expert Opinion on Drug Discovery, 2023
The current drug research is in search of newer approaches to improve the precision and efficiency of the discovery process. Artificial intelligence is being explored to improve target identification, drug design, and clinical trial optimization to accelerate the discovery process [1]. We need out-of-the-box thinking to bring disruptive, pathbreaking innovations. Despite advances in biomedical sciences and technology, the rate of new drugs discovered is not increasing. On the contrary, regulators are recalling many drugs for safety reasons. New trends in drug discovery include precision medicine personalized to individual genetic profiles and other factors that can improve safety and efficacy. The COVID-19 pandemic has further stimulated research on natural products and drug repurposing to reduce time and costs [2].
Galaxy for open-source computational drug discovery solutions
Published in Expert Opinion on Drug Discovery, 2023
Anamika Singh Gaur, Selvaraman Nagamani, Lipsa Priyadarsinee, Hridoy J. Mahanta, Ramakrishnan Parthasarathi, G. Narahari Sastry
Drug discovery in academia and industry poses quite contrasting challenges where understanding the mechanism of action is key for the former, success in clinical development appears to be of prime importance in the later. In the 21st century, open-source software packages are available for a wide range of bioinformatics and chemoinformatics applications that include downloading, manipulating, and processing of small and macromolecules along with their properties [1–5]. In general, drug discovery software and tools are commonly developed for all the diseases, although different types of diseases demand contrasting requirements. In recent times, with the advancement in computational fields, it is warranted to develop customized computational tools for a given disease. Galaxy is an ideally suited web-based open-source computational workbench to analyze large datasets. The Galaxy can be customized to integrate analysis and visualization tools in a single framework. Galaxy is an open-source web-based platform that helps researchers to overcome accessibility, reproducibility, and transparency challenges in data-driven biomedical research [6–9]. The users required a minimal level of programming skills and the ability to use the command line and handle unique file formats. Some tools have complicated source code and it is very difficult for the computationally inexperienced scientists to compile them and reproduce the analysis in a different environment.
Principles to guide spinal cord injury research partnerships: a Delphi consensus study
Published in Disability and Rehabilitation, 2022
Heather L. Gainforth, Rhyann C. McKay, Femke Hoekstra, Jocelyn Maffin, Kathryn M. Sibley, Mary E. Jung
To address the gap between discovery and use of research, people with lived experience of SCI, SCI organizations, and funding agencies are increasingly encouraging, and in some cases, requiring researchers to adopt partnered approaches to research. Specifically, partnership approaches in which researchers partner with relevant research users (e.g., individuals with SCI, families, friends, organizations, and clinicians) throughout the research process [7–9]. These partnered approaches aim to ensure research user engagement, are considered a promising means of producing research that is relevant, useful, used, and align with calls by people with lived experience of disabilities for there to be “nothing about us, without us” [6,10–15]. By engaging research users throughout the research process, these approaches support efforts across disciplinary domains to enhance democratic involvement in science, amplify disenfranchised voices, and engage in real-world problem solving to improve research relevance and impact [16–21].