Innovation in Drug Research and Development for Prevalent Chronic Diseases Proceedings of a Workshop (2021) / Chapter Skim
Currently Skimming:
3 New Technologies to Enable Research in Prevalent Chronic Disease
3 New Technologies to Enable Research in Prevalent Chronic Disease
Pages 15-28
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 15... ...
(Colón) • Integrating the use of digital health technologies and new modalities can improve the effectiveness of treatments for chronic diseases and give patients more control over their own health.
|
From page 16... ...
and machine learning, new modalities, throughout the drug R&D process offers opportunities to address critical barriers and streamline clinical trials for prevalent chronic diseases. Qi Liu, senior science advisor in the Office of Clinical Pharmacology and Translational Sciences at FDA, offered her perspective on how innovations -- new modalities, analytic tools, and new sources of data -- that have enabled drug R&D and led to new treatment options for cancer patients could be applied to other chronic disease areas.
|
From page 17... ...
She emphasized that "today's breakthroughs in oncology are the result of decades of investment in cancer research and drug development." In 1971, President Nixon signed the National Cancer Act,2 and in 2016, Congress passed the 21st Century Cures Act,3 which authorized $1.8 billion in funding for the Cancer Moonshot4 over 7 years. Industry has also invested heavily in oncology, which is now the largest pharmaceutical therapeutic area.
|
From page 18... ...
. New Analytical Tools Given the increased volume and types of data now available, Liu stated, "New analytical tools are needed to transform big data into smart decisions." She described a few examples of analytical tools that can play important roles in drug discovery and development: • Model-informed drug development (MIDD)
|
From page 19... ...
. • AI and machine learning offer numerous applications for better understanding diseases and drug targets, generating and evaluating drug candidates and combination therapy, improving clinical trial design, and advancing precision medicine by improving diagnosis and treatment.7 Liu shared a few examples of submissions with machine learning components received by FDA's Center for Drug Evaluation and Research, which included applications for predict ing drug response based on baseline factors, identifying predicated biomarkers for drug response, and identifying drug abuse–related problems in postmarket settings.
|
From page 20... ...
Colón pointed to the work of Avalyn Pharma, which is working on an inhaled version of pirfenidone.10 Early studies in animal models have shown promising results, suggesting that small inhaled doses of pirfenidone can deliver therapeutic levels of the drug in lung tissue. Although the results are preliminary, she said this example "highlights the innovative use of an existing drug with a new modality to change the paradigm and potentially help these patients significantly with a much lower systemic dose and even improved compliance." 8 The $50 billion figure is based on an estimate of $26 billion in 2004–2006, adjusted for inflation and an increase in prevalence.
|
From page 21... ...
To address this gap, ProterixBio -- of which Colón is the executive chair -- has developed a disease activity score that uses an algorithm that takes into account a variety of inflammatory and immune response biomarkers measured against known patient populations.11 Colón suggested that this approach has the potential to enable clinical trials for COPD and help physicians and health care systems better understand and monitor their patients over time. Colón envisioned a future in which partnerships among different companies could integrate technologies for measurement, treatment, patient coaching, and interventions and apply these approaches toward prevalent chronic conditions, such as respiratory disease.
|
From page 22... ...
Ngai suggested a few resources that could help support innovation in other fields, such as prevalent chronic diseases. For example, the BRAIN Initiative Cell Census Network13 aims to create comprehensive brain cell atlases that integrate molecular analyses, connectivity, physiology, and other data for the mouse and other mammalian species, including humans and non-human primates.
|
From page 23... ...
Just as AI might be used to make weather predictions based on temperature, pressure, humidity, and cloud cover, this technology can be applied to many other domains, including drug discovery. Similar to weather prediction, Radin said, the process of finding a drug to treat a specific condition involves a number of uncertainties: Will the molecule reach the desired target in the body?
|
From page 24... ...
SOURCE: Presented by Andrew Radin on February 22, 2021, at the Innovation in Drug Research and Development for Prevalent Chronic Diseases workshop.
|
From page 25... ...
The validation step checks that the technology works as intended in the field when used by real people. Speaking on the use of digital health technologies in clinical trials, Kunkoski listed a few potential applications.
|
From page 26... ...
enrolled in clinical trials," she said, "which means less time taking off of work, traveling less to research centers, which will enable more participation and greater retention in clinical trials." For researchers planning to use a digital health technology in a clinical trial, Kunkoski said, the technology does not necessarily need to be approved by FDA or cleared for marketing. Instead, the main focus is whether the technology can provide sufficient high-quality evidence for FDA to be able to draw conclusions about the safety and effectiveness of the therapeutic intervention being studied.
|
From page 27... ...
2018. Neuroethics guiding principles for the NIH brain initiative.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.