Partnership of LongeVC and Insilico Medicine to screen for projects maximizing longevity dividend

• At the "Ageing Societies 2016," The Economist conference in London, LongeVC and Insilico Medicine entered into a research collaboration
• LongeVC is a new venture fund investing in companies developing drugs and other interventions to cure or prevent age-related diseases with a mission to extend healthy human longevity
• Over 90% of small molecules that pass pre-clinical validation fail in clinical trials
• Insilico Medicine is one of the leaders in applying artificial intelligence and advanced pathway activation analysis techniques to predict the efficacy and safety of small molecules and the outcomes of clinical trials
• LongeVC will utilize Insilico expertise to identify projects with high probability of passing the clinical trials and maximizing the longevity dividend

 Insilico Medicine today announced a research collaboration with LongeVC, a venture fund dedicated to investing in companies targeting aging and age-related diseases. In the scope of this collaboration, Insilico Medicine's signaling pathway-based analytical methods and deep learned predictors will be used to evaluate the efficacy, adverse effects, and population-specificity of small molecules, antibodies, gene therapy, and other interventions.

The parties entered into a research collaboration at the "Ageing Societies 2016" conference organized by The Economist, one of the most reputable journals in the field (for more information, see: http://www.economist.com/events-conferences/emea/ageing-societies). 

"The percentage of the elderly population in developed countries is rapidly increasing and presents a major growth opportunity for businesses focused on increasing productive longevity, as well as treating and preventing age-related diseases. But identifying the winning technologies requires complex screening methods, so we partnered with one of the leaders in biomarker development and aging research to perform quality control and to assess the effectiveness of anti-aging interventions. We are aiming to identify projects that make the most impact and have a reasonably high probability of success. At the end of the day, everything we own, we rent for the length of our lifetime, and investing in longevity is one of the ways to maximize the value of our assets," said Garri Zmudze, managing partner at LongeVC. 

Insilico Medicine developed a portfolio of deep learned and parametric biomarkers of human aging utilizing simple blood biochemistry tests (for more information, see: http://www.Aging.AI), blood-derived and tissue-specific gene expression data, and imaging data. These biomarkers are essential for assessing the relevance of experiments performed on animals.

"When evaluating interventions related to aging and age-related diseases, it is important to have biologically-relevant biomarkers of aging that work in humans. The ability to backtest some of the interventions on retrospective data is very appealing. LongeVC is the first fund focused on longevity research, and we welcome this collaboration with Insilico Medicine, one of the most established players in bioinformatics and longevity science," said Fred Khodorov, CFP®, senior pharmaceutical and healthcare consultant, and advisor to LongeVC. 

Insilico Medicine's in silico Pathway Activation Network Decomposition Analysis ("iPANDA") technique, recently published in Nature Communications, doi:10.1038/ncomms13427 (for more information, see: http://www.nature.com/articles/ncomms13427), will be applied to projects where transcriptomic or proteomic data is available to analyze pathway-level effects of interventions. DeepPharma™ clinical trials scoring engine will be used to predict the outcomes of the Phase I (safety) and Phase II (efficacy) clinical trials. The scoring engine has already demonstrated proof of concept of using Deep Neural Networks ("DNNs") to predict the pharmacological properties of many drugs. 

Insilico Medicine's HedgePharma system, which predicts the outcomes of Phase II clinical trials, is a by-product of a massive backtesting project, where scientists working on predictive modeling analyzed almost all clinical trials and linked them to stock prices of biotechnology and pharmaceutical companies to understand if some of the clinical trials failed or succeeded when outcome measures were unclear. This system is now used by some venture funds and hedge funds to make investment decisions in biomedicine. 

"We are very happy to collaborate with LongeVC, an emerging venture fund intended to address one of the most pressing challenges of our time - aging, by investing in companies developing therapies that may extend human productive longevity. It is a pleasure to see that more and more smart investors are moving into the field of longevity science. I hope that other venture funds, hedge funds, and portfolio managers will utilize our expertise in omics-informed clinical trials risk estimates and our experience in analyzing the longevity dividend in terms of the productivity-adjusted life years ("PALY") of potential investment projects," said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc. 

###

About LongeVC

LongeVC is a next generation venture fund focusing on high-impact investments in biotechnology and healthcare companies developing therapeutic interventions or enabling technologies that have a chance to result in complete cures of major age-related diseases and producing maximum longevity dividends. The company is founded by a team of experts in information technology, pharmaceutical industry, telecommunications, and finance. 

Company website: http://www.longevc.com. Contact: hq@longevc.com. 

About Insilico Medicine, Inc.

Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore with Research and Development ("R&D") resources in Belgium, Russia, and Poland hiring talent through hackathons and competitions. The company utilizes advances in genomics, big data analysis, and deep learning for in silico drug discovery and drug repurposing for aging and age-related diseases. The company pursues internal drug discovery programs in cancer, Parkinson's Disease, Alzheimer's Disease, sarcopenia, and geroprotector discovery. Through its Pharma.AI division, the company provides advanced machine learning services to biotechnology, pharmaceutical, and skin care companies. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8.

iPANDA: A novel approach for precision medicine and drug discovery on gene expression data

Summary:

• Insilico Medicine collaborating with a large group of scientists from top-tier academic institutions, pharmaceutical and biotechnology companies developed and published a novel tool for deriving new insights from gene expression repositories;
• The method is called "in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development". The proof of concept was demonstrated the application of iPANDA as a biomarker developmental tool that can be used in personalized medicine applications;
• The consortium uses the iPANDA method for biologically-relevant dimensionality reduction when training the deep neural networks to predict the various pharmacological properties of molecules and developing biomarkers using highly-variable, sparse and highly-dimensional gene expression data;
• The paper is published in Nature Communications on the 16th of November, 2016, with aToday Insilico Medicine announced the publication of its in silico Pathway Activation Network Decomposition Analysis (iPANDA), a novel approach for analyzing signaling and metabolic pathway perturbation states using gene expression data, in Nature Communications. iPANDA is a scalable robust method for rapid biomarker development using gene expression data. In the present work Insilico Medicine team together with collaborators from the Johns Hopkins University, Albert Einstein College of Medicine, Boston University, Novartis, Nestle and BioTime demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures for breast cancer patients according to their sensitivity to neoadjuvant therapy.

"In this paper we describe one of our most sensitive and biologically-relevant approaches for integrating and analyzing gene expression data and present its application for stratifying responders and non-responders to targeted chemotherapy. However, the applications of this method stretch beyond precision medicine and we use it primarily for artificial intelligence and aging research. My team and many of our collaborators also use it to develop targetable tissue-specific signatures of senescence, cross-species gene expression analysis ", said Ivan Ozerov, PhD, head of senolytics group at Insilico Medicine and the lead author on the paper. 

"iPANDA is a universal tool, which allows us to perform in-depth analysis of the effects of external perturbations on the activation of signaling pathways and how it affects the downstream targets. A systematic use of such approach will contribute to obtain a better understanding of how genes involved in various cancers, disease and age-related diseases are dynamically controlled by sets of highly complex networks of signaling pathways. Information gained using in silico approaches are helpful to design more efficient therapies", said Ksenia Lezhnina, head of the "Nutriomi" personalized omics-informed nutrition group at Insilico Medicine and the co-author of the paper. 

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. While modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or disease condition, iPANDA produces highly consistent sets of biologically relevant biomarkers acquired on multiple transcriptomic data sets. 

"Deep learning is turning into a lego game, where the various techniques are combined as blocks of sophisticated architectures. However, ensuring biological relevance of the outputs and learned or generated features is difficult. When we started working on this method in 2014, we came up with the criteria for pathway activation scoring quality metrics that included the ability to find highly discriminative pathway markers, biological relevance, cumulative effect, batch effect minimization, consistent performance at the platform- , species-, tissue- and experiment-level, and most importantly, the ability to reduce the dimensionality of gene expression data for the deep neural networks. iPANDA was conceived with all these criteria in mind and we use it primarily for dimensionality reduction in applications utilizing deep learning", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine.

Signalling pathway activation scoring using iPANDA will likely help reduce the dimensionality of expression data without losing biological relevance and may be used as an input to rapidly developing deep learning methods especially for drug discovery applications. Using iPANDA values as an input data seems to be a particularly high-potential approach to obtaining reproducible results when analysing transcriptomic data from multiple sources. Therefore, while there is no single preferential approach for interpreting gene expression results, the iPANDA method of transcriptomic data analysis on the signaling pathway level may not only be useful for discrimination between various biological or clinical conditions, but may aid in identifying functional categories or pathways that may be relevant as possible therapeutic targets.

"Gene expression profiles generated on microarray equipment is one of the most abundant biological data types with hundreds of thousands of published experiments. A method which allows scientists to make better use of this data, perform quality control and integrate it with RNASeq and protein expression data is extremely important", said Charles Cantor, PhD, the co-founder and former CSO of Sequenom, former CSO of the Human Genome Project from the DOE and advisor to Insilico Medicine, a co-author of the publication. 

"The iPANDA algorithm is a very sensitive pathway perturbation analysis method, which allows for granular comparison of the differentiation states of human cell and tissues using highly sparse gene expression data. It also serves as a biologically-relevant dimensionality reduction and feature selection tool for training the deep neural networks", said Michael West, Ph.D., CEO of BioTime, Inc

###

About Insilico Medicine, Inc

Insilico Medicine, Inc. is a bioinformatics company headquartered at the Emerging Technology Centers (ETC) located at the Johns Hopkins University Eastern campus in Baltimore with R&D resources in Belgium, Russia and Poland. A pioneer in applying deep learning techniques to biomarker development and drug discovery, the company is constantly hiring highly-efficient and motivated talent through the bioinformatics and deep learning hackathons and competitions including DeepHack, GeneHack, SkinHack, AgeNet, Innovate Hamburg and others. The company is pursuing 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. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8

Insilico Medicine launches a deep learned biomarker of aging, Aging.AI 2.0 for testing

Today, Insilico Medicine, Inc., a company applying latest advances in deep learning to biomarker development, drug discovery and aging research, launched Aging.AI 2.0, the blood biochemistry predictor of human age. Capitalizing on the success of Aging.AI 1.0 platform, using just 41 blood biochemistry biomarkers launched in January 2016 and tested by thousands of people, the Aging.AI 2.0 allows users to use just 33 parameters from their recent blood test to guess their chronological age. The system is available for beta testing via http://www.Aging.AI .

The Aging.AI 2.0 has slightly higher mean absolute error than the previous version; however, it covers more population groups and works slightly better on the long tail of the older population. The research study behind the Aging.AI system was published in a leading peer-reviewed journal in the field of aging: Putin, et al, "Deep biomarkers of human aging: Application of deep neural networks to biomarker development." Aging 8, no. 5 (2016): 1-021 and recent studies demonstrated that these markers are population-specific. At the recent "3rd International Aging Research for Drug Discovery" conference in Basel, Switzerland, Dr. Mun Yew Wong, the CEO of Asia Genomics presented the first insights into a study demonstrating that certain population groups in Asia are guessed younger in older age by the deep neural networks trained on Eastern European population and the mean absolute error is higher. 

"Deep Learning with no doubt has a huge potential in healthcare, but unfortunately, very few groups are applying it to aging research. Aging is one of the most complex and multifactorial processes killing millions every year and causing more suffering than any other known disease. We are developing deep integrated biomarkers of aging that incorporate blood biochemistry, transcriptomics and even imaging data to be able to track the effectiveness of the various interventions we are developing", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine.

Insilico Medicine also sees the applications of deep learned biomarkers of aging in multiple applications including clinical trials enrollment, clinical practice and regular health checkups. 

"Old-school physicians were trained to guess the age of the patient the moment he or she walked into the office and if the patient looked significantly older than the chronological age, more extensive testing was advised. This may be the case with Aging.AI, since what we are really looking for when training the DNNs to guess the age of reasonably healthy people is the biomarker of health status. And even though the system may guess your age with significant error when you first use it, what we want to study is the differential changes for each individual patient so people could monitor their health and adjust their lifestyle", said Alex Aliper, president of European operations at Insilico Medicine. 

Aging.AI 2.0 was trained on more samples from North America, and Central Europe and may demonstrate lower error rates on across population groups than Aging.AI 1.0. Insilico Medicine is constantly looking for collaborators with large data sets to develop better biomarkers of aging and disease. Please contact Insilico Medicine for collaboration opportunities. 

"Our research team is primarily focused on developing transcriptomic blood-derived and tissue-specific deep learned biomarkers of aging and disease trained on a large number of gene expression datasets and blood biochemistry data is not our primary focus. However, encouraged by the success of the first version of Aging.AI, we decided to improve our algorithm and change the interface of the web-version to made it more user-friendly. The system is easy to use I hope that this would stimulate more people to participate in aging research and to pay more attention to their own health. We are working on integrating the blood biochemistry data with gene expression data in order to build a comprehensive, biologically-relevant biomarker of aging" - said Polina Mamoshina, a Research Scientist of Pharmaceutical Artificial Intelligence division of Insilico Medicine.

###

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. It utilizes advances in genomics, big data analysis and deep learning for in silico drug discovery and drug repurposing for aging and age-related diseases. 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. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8