Insilico Medicine, a big data analytics company applying deep learning techniques to drug discovery, biomarker development, and aging research has closed an investment from the billionaire biotechnology investor Jim Mellon. Proceeds will be used to perform pre-clinical validation of multiple lead molecules developed using Insilico Medicine’s drug discovery pipelines and to advance research in deep learned biomarkers of aging and disease.
Jim Mellon has spent a substantial amount of time familiarizing himself with recent developments in biogerontology. He does not just come in with the funding, but brings in expert knowledge and a network of biotechnology and pharmaceutical executives, who work very quickly and focus on the commercialization potential.
Jim Mellon announced his vision for longevity at the Master Investor show in London attended by over five thousand investors and entrepreneurs.
Juvenescence: Investing in the Age of Longevity is a book by Jim Mellon and Al Chalabi. It is a layman’s guide to longevity that will be released May 31, 2017. It investigates the new technologies and explains how to benefit from the life extending technologies both personally and professionally. It helps readers unravel the science, offers ideas on potential investment and reveals the views of the Key Opinion Leaders.
take a tour of many academic laboratories, biopharmaceutical companies, and Silicon Valley tech companies to research emerging trends and exciting discoveries. Juvenescence highlights promising technologies that are likely to generate substantial longevity dividends and create sustainable and profitable industries.
Through its focus on aging research and drug discovery, Insilico Medicine is bringing the knowledge gap between the consumer and pharmaceutical industries and collaborates with some of the largest pharmaceutical, cosmetics, and nutrition companies and academic institutions. In 2016, Insilico Medicine published several seminal proof-of-concept papers demonstrating the applications of deep learning to drug discovery, biomarker development, and aging research. A study published in Aging proposed a short list of molecules with likely geroprotective effects. In a recently published article at Nature Communications, Insilico Medicine describes a tool that it uses to study the minute changes in gene expression between young and old tissues and tissues afflicted by the disease. Another paper demonstrating the ability to predict the chronological age of the patient using a simple blood test, published in Aging, became the second most popular paper in the journal’s history.
Insilico Medicine was the first company to apply deep generative adversarial networks (GANs) to generating anti-cancer drugs with given parameters and published a seminal paper in Oncotarget. The paper published in Molecular Pharmaceutics, demonstrating the applications of deep neural networks for predicting the therapeutic class of the molecule using the transcriptional response data, received the American Chemical Society Editors’ Choice Award.
Abstract – In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state
Populations in developed nations throughout the world are rapidly aging, and the search for geroprotectors, or anti-aging interventions, has never been more important. Yet while hundreds of geroprotectors have extended lifespan in animal models, none have yet been approved for widespread use in humans. GeroScope is a computational tool that can aid prediction of novel geroprotectors from existing human gene expression data. GeroScope maps expression differences between samples from young and old subjects to aging-related signaling pathways, then profiles pathway activation strength (PAS) for each condition. Known substances are then screened and ranked for those most likely to target differential pathways and mimic the young signalome. Here we used GeroScope and shortlisted ten substances, all of which have lifespan-extending effects in animal models, and tested 6 of them for geroprotective effects in senescent human fibroblast cultures. PD-98059, a highly selective MEK1 inhibitor, showed both life-prolonging and rejuvenating effects. Natural compounds like N-acetyl-L-cysteine, Myricetin and Epigallocatechin gallate also improved several senescence-associated properties and were further investigated with pathway analysis. This work not only highlights several potential geroprotectors for further study, but also serves as a proof-of-concept for GeroScope, Oncofinder and other PAS-based methods in streamlining drug prediction, repurposing and personalized medicine.
The GeroScope algorithm preprocessed transcriptional data extracted from 57 bone-marrow derived human hematopoietic and mesenchymal stem cell samples. Pathway activation scores were calculated for “old” samples (donor over 60 years of age) compared to “young” (donor between 15 and 30 years of age). Then drug GeroScore ratings were calculated from a database of known geroprotectors and their targets
Abstract – In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development
Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.
In March 2017, the company launched its first geroprotector with its exclusive partner, Life Extension: http://www.geroprotector.com.
We believe that over the coming decade the life science sector will be leading one of the most meaningful periods of scientific discovery and advancement. This period of development has been underpinned by two seminal moments – the discovery of the structure of DNA and the sequencing of the human genome; the latter occurring nearly 50 years after the former. Subsequent breakthroughs stemming from the discovery of DNA will give new hope to those with certain diseases who relatively recently would have had none. These breakthroughs are coinciding with a period in which the world’s population is undergoing the most ubiquitous and rapid aging in its history. This, we believe, will lead to the life science sector gaining new prominence and that the biggest successes in the sector will ultimately dwarf the likes of Apple, Exxon and BHP that are the current colossi of the stock market.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.