Pathway signatures in lung and liver fibrosis and glaucoma may play a role in aging

Insilico Medicine, Inc in collaboration with scientists from Atlas Regeneration, Inc, Vision Genomics, Inc and Howard University published two research papers on fibrosis in the lung and liver and fibrotic signatures in glaucoma. Scientists utilized the new software tool referred to as "Regeneration Intelligence" to evaluate the perturbation status of many signaling pathways. This new system aimed to identify robust biomarkers of fibrotic disease and develop effective targeted therapy.


Fibrosis, a progressive accumulation of extracellular matrix, can occur in a wide range of organs and potentially distort their structure and function; most commonly it affects lung and hepatic tissues, causing idiopathic pulmonary fibrosis (IPF) and liver fibrosis respectively. 

Fibrosis accounts for up to 45% of deaths in the developed world, yet to date no effective therapeutic treatment has been developed.

The two papers titled "Common pathway signature in lung and liver fibrosis" and "Pro-fibrotic pathway activation in trabecular meshwork and lamina cribrosa is the main driving force of glaucoma" were published in the journal of Cell Cycle.

"Fibrosis is one of the age-related pathologies that disrupt organ functionality dramatically. Currently, there are no approved anti-fibrotic remedy and no reliable fibrotic biomarker. "Regeneration Intelligence" is an intelligent system that can detect hidden fibrotic molecular signatures based on a pathway network analysis. The system can identify specific fibrogenic molecular changes regardless of detecting platform and tissue of origin. Despite many efforts, fibrosis if often misdiagnosed. Our system is supposed to help with proper and timely diagnostic." said Eugene Makarev, PhD, VP of pathway analytics at Insilico Medicine.

With broad screening across multiple fibrotic organs, a platform developed by Insilico Medicine identified pathogenic pathways that served as potential targets for the anti-fibrotic therapy. This approach led to a selection of the list of small molecules and natural compounds by their ability to minimize the signaling pathway difference between a fibrotic and a healthy state of the tissue. Further work with 'Regeneration Intelligence' provides promising opportunities to identify conserved biological pathways that play a critical role in fibrosis development.

"We are very excited to continue our collaboration with Insilico Medicine. Using the innovative "Regeneration Intelligence" software, we have discovered previously-undetected pro-fibrotic signatures in glaucoma, based on pathway analysis. This new knowledge will allow Vision Genomics, Insilico Medicine and Howard University to cooperatively select and develop anti-fibrotic small molecule interventions to minimize or reverse this fibrotic state, and restore the tissue to normal function." said Antonei Csoka, PhD, CEO of Vision Genomics, Inc.

The first AI system for human embryonic state analysis is available for testing

BioTime and Insilico Medicine collaborate on embryonic artificial intelligence using deep learning to study embryonic development

At Mensa Annual Gathering 2016, the annual event of the largest and oldest high IQ society transpiring in San Diego from June 29th to July 3rd, Dr. Michael West, CEO of BioTime, Inc announced the launch of a beta version of Embryonic.AI, an artificially intelligent system for analyzing the embryonic state of human cell samples using gene expression data. The first implementation of Embryonic.AI was launched by LifeMap Discovery, Inc, a subsidiary of BioTime, Inc and is freely available for beta testing. Scientists and companies from all over the world are welcome to test their stem cell and adult cell samples using Embryonic.AI and discuss collaboration alternatives. A brief video provides a general introduction to the Embryonic.AI system is available at https://www.youtube.com/watch?v=MbgZSDqPe78 and BioTime's press release is available at http://finance.yahoo.com/news/BioTime-presents-online-applying-artificial-120000254.html
 
"BioTime harnesses the largest collection of highest-quality gene expression data coming from scrupulously designed and controlled cell differentiation experiments we have seen to date. It was large enough to train a complex architecture of deep neural networks to work as a classifier and a predictor of the embryonic state. We recently tested Embryonic.AI using mouse data and noticed surprising results showing the capabilities of this system in cross-species analysis. Research projects using Embryonic.AI may transform our understanding of cancer and other diseases and possible developments in reinforcement learning may help navigate and control cellular differentiation states", said Alex Zhavoronkov, Ph.D., CEO of Insilico Medicine, Inc.

The system utilizes a sophisticated architecture of multi-class deep neural networks (DNNs) and DNN ensembles trained on thousands of samples of carefully selected cells of multiple classes: embryonic stem cells, induced pluripotent stem cells, progenitor stem cells, adult stem cells and adult cells to recognize the class and embryonic state of the sample, achieving high accuracy in simulations. The unique samples were generated using standardized protocols by BioTime, Inc. and profiled on a single microarray platform. The sample sets were augmented with carefully selected and manually curated data from public repositories coming from multiple experiments and generated on different platforms.

To design and implement the DNN architectures, BioTime partnered with the Pharmaceutical Artificial Intelligence (Pharma.AI) division of Insilico Medicine, Inc., a Baltimore-based bioinformatics company specializing in biomarker and drug development for aging and age-related diseases. There are many potential applications of this system in multiple areas ranging from cancer research and quality control to in vivo regeneration. Embryonic.AI may have future applications in comparing multiple biopsies of patients' tumor to search for cancer stem cells. One of the major challenges in organ engineering for drug testing is quality control of the engineered human tissues to ensure that it closely resembles the expected results. Embryonic.AI may have future applications in analyzing the embryonic state of these tissues and evaluating the effectiveness of many drugs in a high-throughput manner.

Recently Insilico Medicine published several key papers on applying deep learning techniques to biomedical applications in influential peer-reviewed journals including "Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data" in "Molecular Pharmaceutics" and ACS publication. The paper received the Editors' Choice Award from the American Chemical Society. "Applications of Deep Learning in Biomedicine" in also in Molecular Pharmaceutics and "Deep biomarkers of human aging: Application of deep neural networks to biomarker development" in Aging, one of the highest-impact journals in aging research. These studies were presented at the Machine Intelligence Summit Berlin on June 30th.

About Insilico Medicine 
 
Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore with R&D resources in Belgium, Russia, and Poland hiring talent through hackathons and competitions. The company aims to transform drug discovery and development by rapidly generating and validating thousands of new leads for multiple diseases and developing novel biomarkers using a technique called deep learning. The company pursues internal drug discovery programs in cancer, Parkinson's, Alzheimer's, sarcopenia and geroprotector discovery. Through its Pharma.AI division, the company provides advanced machine learning services to biotechnology, pharmaceutical and skin care companies. Since 2014 company scientists published over 40 research papers in peer-reviewed journals and collaborated with over 150 academics, biotechnology and pharmaceutical companies worldwide. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8