Aging, Dermatology, JAMA / 26.11.2019
Topical Organ Rejection Drug Reduced Skin Aging
MedicalResearch.com Interview with:
Christian Sell, PhD
Associate Professor of Biochemistry and Molecular Biology
Drexel University College of Medicine
MedicalResearch.com: What is the background for this study?
Response: In terms of background, the drug rapamycin targets a pathway that scientists know is critical for growth and development but is also a key regulator of lifespan in many model organisms such as worms, flies, and mice. This pathway is known as the mTOR pathway. Rapamycin is already in use clinically, it is given to people who have received organ transplants to prevent rejection and is also in trials to treat some forms of cancer, at very high doses.
Many studies in mice have shown that rapamycin delays aging and prevents age-related disorders such as the decline in heart function and cognitive function. Based on this work, there is a strong expectation that these results will translate into humans, but no studies have been done due to concerns regarding potential side effects of rapamycin when the drug is given orally to prevent rejection. Our previous studies have shown that a very low dose of rapamcyin can reduce the aging of human cells and improve cell growth, while the high does used for organ transplant patients actually block cell growth. We decided to test the impact of low dose rapamycin on aging in the skin because we could treat people safely. Previous studies have shown that the drug does not get into the blood stream when high doses were given topically to people with a rare genetic disorder, so we knew that the low doses used in our study would not get into the bloodstream and would be safe for the patients.
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Dr. Helen Marsden PhD
Skin Analytics Limited
London, United Kingdom
MedicalResearch.com: What is the background for this study?
Response: In this technology age, with the explosion of interest and applications using Artificial Intelligence, it is easy to accept the output of a technology-based test - such as a smartphone app designed to identify skin cancer - without thinking too much about it. In reality, technology is only as good as the way it has been developed, tested and validated. In particular, AI algorithms are prone to a lack of “generalisation” - i.e. their performance drops when presented with data it has not seen before. In the medical field, and particularly in areas where AI is being developed to direct a patient’s diagnosis or care, this is particularly problematic. Inappropriate diagnosis or advice to patients can lead to false reassurance, heightened concern and pressure on NHS services, or worse. It is concerning, therefore, that there are a large number of smartphone apps available that provide an assessment of skin lesions, including some that provide an estimate of the probability of malignancy, that have not been assessed for diagnostic accuracy.
Skin Analytics has developed an AI-based algorithm, named: Deep Ensemble for Recognition of Malignancy (DERM), for use as a decision support tool for healthcare providers. DERM determines the likelihood of skin cancer from dermoscopic images of skin lesions. It was developed using deep learning techniques that identify and assess features of these lesions which are associated with melanoma, using over 7,000 archived dermoscopic images. Using these images, it was shown to identify melanoma with similar accuracy to specialist physicians. However, to prove the algorithm could be used in a real life clinical setting, Skin Analytics set out to conduct a clinical validation study.