Experiments with animal models already resulted in significant breakthroughs resulting in up to 1,000 percent increases in lifespans. But extrapolating these advances to humans or other mammals proved to be extremely challenging. Human live orders of magnitude longer than the short-lived model organisms and there is no comprehensive set of aging biomarkers, allowing to track the effects of the many drugs that may extend lifespan. We are also different from other animals and the many drugs that work on mice do not work on humans.
To address these challenges the international team comprised of biogerontologists, geneticists, computer scientists and biomathematicians proposed using a computer simulation and laboratory validation approach using human cells and model organisms to predict what drugs may help fight aging in humans.
The Human Genome Project and the following revolution in sequencing and laboratory diagnostics resulted in the vast data on genetic and epigenetic profiles of cells and tissues from people of various ages. The proposed method uses this data to construct the cloud of molecular signalling pathways involved in aging and longevity and evaluates the effects of the very large number of drugs and drug combinations to simulate the young state of the cells and tissues. Scientists hope that this method may be used to find new drugs with aging-suppressive properties and predict the activity of the drugs that are already on the market. Also, people respond to the drugs differently and this method may be able to personalize the geroprotective therapy to the individual patients and help the drug companies conduct better clinical trials.
"There are thousands of compounds with known molecular targets and some are already used in the clinic. Due to high cost and the time it takes to complete the experimental work, it may not be possible to test all of the effects of these drugs even in mice. And the fact that the drug works in mice does not guarantee the same effect in humans. There needs to be a better way to predict the efficacy of the drug in humans. We proposed a method for doing that in silico using the multiple sources of data and we hope to validate this method in the very near future.", said Alex Zhavoronkov, PhD, the director of the Biogerontology Research Foundation in the UK. "Also, people are different, age at different rates and respond to drugs differently. The proposed method may be used to predict the personalized geroprotector regiments.", he added.
Many pharmaceutical companies already expressed their interest in bringing aging research into clinical practice, but the absence of the business models, accurate validation methods, and the inability to classify aging as the curable disease are major impediments to mainstream development of geroprotective drugs. In silico drug discovery may help accelerate this process. The group plans to present the results of their experimental work using this method at the Practical Applications of Aging Research Symposium at MipTek 2014 in Basel, Switzerland attended by over 3,000 delegates from the pharmaceutical industry.
“The decreases in cost and increased availability of genetic and epigenetic research as well as the breakthroughs in computer technologies are already helping make better decisions in biomedicine. The proposed method may take the in silico approach to drug discovery to the next level. If the can validate it in the laboratory, and we are working on that as we speak, this may revolutionize aging research”, said Anton Buzdin, the director of the First Oncology Research and Advisory Center.
The paper describing the new approach to screening and ranking of geroprotective drugs was published in the reputable scientific journal Frontiers in Genetics.
Citation: Zhavoronkov A, Buzdin AA, Garazha AV, Borissoff N and Moskalev AA (2014). Signaling pathway cloud regulation for in silico screening and ranking of the potential geroprotective drugs. Front. Genet. 5:49. doi: 10.3389/fgene.2014.00049
The study is supported by the UMA Foundation.