September 24th, Switzerland

Free Registration, Part of MipTec 2014, Congress Center Basel

FREE DRUG DISCOVERY CONFERENCE IN THE HEART OF EUROPE

MipTec is the largest drug discovery conference in Europe with over 3,000 delegates from pharmaceutical companies

ATTEND 10 OTHER SCIENTIFIC FORUMS AT MIPTEC

Stem Sells, Translational Medicine, Protein Therapeutics, Medicinal Chemistry and others

IN SILICO APPROACHES TO GEROPROTECTOR DISCOVERY AND DRUG REPURPOSING

New in silico approaches to evaluating the efficacy of and repurposing drugs for aging and age-related diseases

MEET YOUNG PROSPECTIVE SCIENTISTS AND RESEARCH MANAGERS

The forum will attract many young scientists in aging research providing the possibility to recruit scarse talent with unique skills and interests

AGING CONFERENCE FOCUSED ON REINVENTINT THE PHARMA INDUSTRY

Unlike other aging conferences, the forum is geared towards the pharmaceutical industry addressing the need for new markers and new drugs within the traditional regulatory frameworks

Tuesday, June 7, 2016

New Method Seeks To Diminish Risk, Maximize Investment In Cancer 'megafunds'

NEW YORK UNIVERSITY


Recognizing the high research and development costs for drugs to combat cancer, a team of researchers has devised a method to maximize investment into these undertakings by spotting which efforts are the most scientifically viable.
The work centers on "cancer megafunds," or Special Purpose Vehicles (SPVs), in which a collection of investors back a range of research projects, all designed to develop pharmaceuticals to battle cancer. By pooling resources and sponsoring multiple ventures, financial supporters aim to share the high costs of drug development, which include research and clinical trials that can run more than a decade.
However, SPVs mask risks to investors. Among these, as with many "portfolios," are "toxic assets" or "lemons" that threaten the fund by directing resources toward scientifically unsound initiatives.
The challenge, then, is to spot these lemons before too much money has been spent on them -- or too little directed toward more worthwhile studies. In other words, what's the optimal financial strategy to increase the likelihood that an investment is paying off scientifically?
This was the aim of the method, reported in the journal Oncotarget and developed by New York University's Bud Mishra, along with his colleagues and students: Xianjin Yang of Saudi Arabia's King Abdullah University of Science and Technology, Edouard Debonneuil of the University of Lyon, and Alex Zhavoronkov, CEO of InSilico Medicine.
The paper may be downloaded here: http://bit.ly/1PAjYyh.
The team analyzed their proposed financial model by mathematical analysis, followed by a series of simulations designed to replicate early-stage investment, which is the most risky portion of this process and when funding is scarce. It used one semester (approximately 15 weeks) as a unit of time and six years as the duration of the drug-development enterprise.
"Ultimately, such an unfortunate outcome could lead the financial markets to completely lose their appetite for megafunds," observes Mishra. "The principles studied here could be helpful: they will strongly improve the yields and risks associated with securitization, but also limit the possibility of hiding defects of the 'lemon' projects."
"The principles introduced in this paper go beyond cancer megafunds and may be applied more broadly, helping finance biomedical research to address a wide range of diseases, including rare diseases, as well as extend into aging and longevity and providing pension funds with new instruments to hedge longevity risk," notes Zhavoronkov.

Keywords: Cancer; SPV; Drug Development; Longevity

Sunday, May 29, 2016

Deep Learning Applied To Drug Discovery And Repurposing

Artificial Neural Nets predict pharmacological properties of small molecules based on transcriptional response signatures in cell lines


Deep learning, frequently referred to as artificial intelligence, a branch of machine learning utilizing multiple layers of neurons to model high-level abstractions in data, has outperformed humans in tasks including image, text and voice recognition, autonomous driving and others, and is now being applied to drug discovery and biomarker development.
In a study published in Molecular Pharmaceutics, a prestigious journal published by the American Chemical Society, scientists from Insilico Medicine in collaboration with Datalytic Solutions and Mind Research Network trained deep neural networks to predict the therapeutic use of large number of multiple drugs using gene expression data obtained from high-throughput experiments on human cell lines.
Deep neural networks outperformed other machine learning techniques and did not result in significant drop in performance as the number of classes increased.
This is the first known application of deep learning to drug discovery using transcriptional response data.
May 26, 2016, Baltimore, MD
-In a recently accepted manuscript titled "Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data", scientists from Insilico Medicine, Inc located at the Emerging Technology Centers at Johns Hopkins University in collaboration with Datalytic Solutions and Mind Research Network presented a novel approach applying deep neural networks (DNNs) to predict pharmacologic properties of many drugs. In this study, scientists trained deep neural networks to predict the therapeutic use of a large number of drugs using gene expression data obtained from high-throughput experiments on human cell lines. Authors used a sophisticated approach of measuring the differential signaling pathway activation score for a large number of pathways to reduce the dimensionality of the data while retaining biological relevance and used these scores to train the deep neural networks.
"The world of artificial intelligence is rapidly evolving and affecting every aspect of our daily life. And soon this progress will be felt in the pharmaceutical industry. We set up the Pharma.AI division to help pharmaceutical companies significantly accelerate their R&D and increase the number of approved drugs, but in the process we came up with over 800 strong hypotheses in oncology, cardiovascular, metabolic and CNS space and started basic validation. We are cautious about making strong statements, but if this approach works, it will uberize the pharmaceutical industry and generate unprecedented number of QALY", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.
Despite the commercial orientation of the companies, the authors agreed not to file for intellectual property on these methods and to publish the proof of concept. Insilico Medicine is currently developing multimodal deep neural networks to predict a broad range of properties of drugs, small molecules and natural compounds for a range of applications including treating common and rare diseases, aging, regenerative medicine and increasing response rates in cancer immunotherapy.
"The field of machine learning have recently witnessed an impressive breakthrough in the area of pattern recognition and computer vision. Deep learning, technology to thank for this, continues to disrupt traditional approaches in many other subfields of machine learning. Originally in the 60s, inspired by how the brain works (at least how we understood it back then) deep learning has now developed into a mature engineering concept. The brain however, does not cease to puzzle researchers and, I am sure, contains more sources of inspiration for the future powerful methodologies.", said Sergey Plis, PhD, Director of Machine Learning at the Mind Research Network and CEO of Datalytic Solutions.
Earlier this month Insilico Medicine scientists published the first deep learned biomarker of human age aiming to predict the health status of the patient in a paper titled "Deep biomarkers of human aging: Application of deep neural networks to biomarker development"by Putin et al, in Aging and an overview of recent advances in deep learning in a paper titled"Applications of Deep Learning in Biomedicine" by Mamoshina et al, also in Molecular Pharmaceutics.
"This study is a proof of concept that DNNs can be used to annotate drugs using transcriptional response signatures, but we took this concept to the next level. We developed a pipeline for in silico drug discovery, which has the potential to substantially accelerate preclinical stage for almost any therapeutic and came up with a broad list of predictions with multiple in silico validation steps that, if validated in vitro and in vivo, can almost double the number of drugs in clinical practice", said Alex Aliper, president of research, Insilico Medicine, Inc and the lead author of the study.
Link to the advance online publication: http://pubs.acs.org/doi/abs/10.1021/acs.molpharmaceut.6b00248
Complete article can be read at:
http://www.eurekalert.org/pub_releases/2016-05/imi-dla052416.php

Saturday, May 21, 2016

Artificial Neural Networks Guess Patient's Age With Surprising Accuracy

INSILICO MEDICINE, INC.

May 19, 2016, Baltimore, MD - Insilico Medicine, Inc announced that a paper titled "Deep Biomarkers of Human Aging: Application of Deep Neural Networks to Biomarker Development" by Putin, et al, was accepted for publication by Aging, one of the highest-impact journals in aging research on 9th of May, 2016 and today became available online as advance publication at http://www.impactaging.com/papers/v8/n5/full/100968.html.
"It is exciting to see the power of deep learning applied to potential aging biomarkers. The availability of such markers is an essential prerequisite for any future clinical trials to try to ameliorate the effects of human aging", said Charles Cantor, PhD, CSO of Agena, Inc, former director of the Human Genome Project (DOE).
The availability of big data coupled with advances in highly-parallel high-performance computing led to a renaissance in artificial neural networks resulting in trained algorithms surpassing human performance in image and voice recognition, autonomous driving and many other tasks. However, the adoption of deep learning in biomedicine and especially in the pharmaceutical industry has been reasonably slow. In order to outperform more traditional machine learning methods, deep neural nets require large amounts of data and expertise with highly-parallel and high-performance graphics processing unit (GPU) computing.
Evgeny Putin, lead author on the paper commented: "While internally we are working on more sophisticated machine learning problems, Aging.AI is a good example, where DNNs outperform other machine learning methods and can be extended into multiple applications".
Insilico Medicine is working on over a dozen different applications of deep learning methods to regenerative medicine, embryonic development, cross-species comparison and drug discovery and repurposing providing contract research services and developing a range of molecules for cancer, metabolic and CNS pathologies.
To develop a data set of blood biochemistry and cell count samples Insilico Medicine collaborated with the largest independent laboratory test service provider in Eastern Europe, Invitro Laboratories. Scientists of both companies went through over a million samples to select a data set, with the optimal number of features from patients that came for routine blood checkups to build a data set of just over sixty thousand samples. Using this data set Insilco Medicine scientists then trained 40 different deep neural networks (DNNs) of different depth with a single neuron output predicting chronological age and optimized using different optimizers and started organizing these DNNs into an ensemble. Experimentally, 21 DNNs providing optimal performance were organized into an ensemble using a stacking model.
Using the best performing DNN in an ensemble scientists identified most important features contributing to the accuracy of predicting human chronological age: albumin, glucose, alkaline phosphatase, urea and erythrocytes. This finding may be relevant for further studies in biomarkers of aging.
"Inspired by Microsoft's How-old.net, which can recognize your age using a photograph, an approach we also employ in projects with skincare collaborators, we decided to train an ensemble of deep neural networks on a very large number of simple inexpensive historical blood tests linked to age and sex and built a predictor, which is scalable and can include many other data types to build more comprehensive biomarkers of aging. Aging.AI can in principle be extended as a biomarker of biological aging that can be used to assess the efficacy of various therapies", said Poly Mamoshina, research scientist at the Pharma.AI department of Insilico Medicine, Inc.
Read complete article at : http://www.eurekalert.org/pub_releases/2016-05/imi-ann051816.php

Sunday, May 15, 2016

More Funding For Alzheimer's Research Is Needed


At the White House, in Congress and even on the presidential campaign – three places where virtually nothing is agreed upon – there is a growing call to dramatically increase federal spending on Alzheimer’s research. 
Right now, Congress and the President are sending $350 million in additional funding to the National Institutes of Health (NIH) – a 60 percent hike, pushing the total for Alzheimer’s research spending over the $950 million mark for the first time, thanks in large part to the work of the bicameral, bipartisan Congressional Task Force on Alzheimer's disease founded by Senator Ed Markey (D-Massachusetts) and Representative Chris Smith (R-New Jersey).  
On the campaign trail, Democratic presidential candidate Hillary Clinton has aggressively pushed a plan to increase funding for Alzheimer’s research to $2 billion annually by 2025 while on the GOP side, Donald Trump has called for stepped up funding. 
Why? The humanitarian motive is certainly there. Probably no other disease will touch so many Americans in such a heartbreaking fashion, and in such ballooning numbers, over the next decade. The prevalence of the disease, increased life spans and the aging of Baby Boomers mean millions of families will know the terrible pain of a parent or sibling who begins to forget who they are.
Research into finding a way to stop the progression of the disease can save this country trillions of dollars over the coming decades. And curing, or controlling, this nightmare illness is no longer a dream – and certainly not a waste of our precious medical research funds.
More than that, for millions of American – for millions of people around the globe – the money we spend now will reap even bigger savings, saving in lives of the ones we love, for generations to come.
Those are dollars well spent.

Tuesday, May 3, 2016

Anti-Aging Vitamin Discovered: A Pill a Day Keeps Away Aging

An international team of researchers found that administering the vitamin nicotinamide riboside restores organs’ regenerative capacities and delays aging. The study published on April 28 in the journal Science explains that this restorative vitamin, which is close to vitamin B3, targets stem cells, paving the way for treatments targeting degenerative diseases like muscular dystrophy or myopathy.

Stem cells produce new specific cells to regenerate damaged organs. The stem cells can only do this if their mitochondria functions properly.  The researchers call the mitochondria as the powerhouse of the cell.
However, as people age, stem cells get fatigued, causing aging, poor cell regeneration, and even deterioration of some tissues and organs.

Since nicotinamide riboside is a precursor of NAD+, a molecule that regulates mitochondrial activity, the researchers administered it onto mice in the hopes of improving mitochondrial function. When they administered the vitamin to mice aged two years old, they observed that the animals’ muscular regeneration greatly improved. The mice also lived longer than those that did not receive the vitamin.

Researchers may have found the elixir of youth. Earlier studies have already shown that nicotinamide riboside improves one’s metabolism.
“This work could have very important implications in the field of regenerative medicine,” says Johan Auwerx, the head of École Polytechnique Fédérale de Lausanne’s Laboratory of Integrated Systems Physiology (LISP) in Switzerland. “We are not talking about introducing foreign substances into the body but rather restoring the body’s ability to repair itself with a product that can be taken with food.”

The researchers say that this could help age-related diseases, even the fatal ones such as myopathy. Myopathy is a muscle disease that causes muscle weakness, pain and muscle wasting.

The researchers did not observe any side effects even after giving nicotinamide riboside at high doses. Still, the researchers assert that more studies are still needed to gather more insight about this vitamin and consequences on aging.

Keywords: Anti aging, stem cells, regeneration.


Wednesday, April 6, 2016

Insilico Medicine to Present Deep Learned Biomarkers At The Deep Learning in Healthcare Summit

Insilico Medicine to present a range of deep learned biomarkers of ageing and deep learned predictors of biological age at the RE-WORK Deep learning in Healthcare Summit.


Baltimore, MD -
Alex Zhavoronkov, PhD, CEO of Insilico Medicine will present a range of deep learned biomarkers of ageing and deep learned predictors of biological age at the RE-WORK Deep Learning in Healthcare Summit in London, 7-8th of April. The first such predictor is already available online at http://www.Aging.AI trained on hundreds of thousands of human biochemistry and cell count samples linked to chronological age, gender and health status. Transcriptomic and signalomic ageing markers and predictors of chronological and biological age and cross-species comparison will be discussed. The official summit website is https://www.re-work.co/events/deep-learning-health-london-2016 .

"RE-WORK summits are clearly outperforming most industry conferences in agility, openness, diversity and focus on applications of deep learning in multiple areas and we are happy to be invited to present at their Deep Learning in Healthcare Summit in London. Artificial intelligence will transform biomarker development and drug discovery much sooner than most pharmaceutical companies and regulators expect and we are happy to be at the forefront of this emerging trend", said Alex Zhavoronkov, PhD, CEO or Insilico Medicine, Inc.

The Pharmaceutical Artificial Intelligence division of Insilico Medicine is providing a broad range of machine learning services in biomarker development, drug discovery and high-throughput screening optimization to some of the most innovative pharmaceutical and biotechnology companies. In 2015 scientists at Insilico Medicine published over 20 research papers in reputable academic journals.
"While our main project is related to applying artificial intelligence to identify and personalize Geroprotectors, interventions that slow down or reverse human biological aging, we have a solid pipeline of pharma and biotechnology research projects that help us fund our own research, gain unique expertise and establish reputation as a trusted partner. If you are reading this, please follow us on PubMed or ResearchGate and consider collaborating with our team and please attend our conferences in St. Petersburg and Basel", said Alex Aliper, president of Insilico Medicine, Inc.

Tuesday, April 5, 2016

Update on Disputed “Youthful” Protein

Studies reach conflicting conclusions on GDF11 as a rejuvenating factor.



 Growth differentiation factor 11 (GDF11) has been hailed an anti-aging protein, capable of spinning back the clock on aged mouse heart and muscle. But a few studies have found evidence to the contrary. Most recently, researchers reported in Aging Cell last month (March 28) that GDF11 did not improve muscle function of older animals as had been observed by others.
“We have been unable to confirm the reported activity of GDF11,” the team from GlaxoSmithKline and Five Prime Therapeutics wrote in its report. In particular, the researchers were unable to replicate the results of a 2014 study from Amy Wagers and Richard Lee of Harvard University and colleagues.

The GlaxoSmithKline group treated skeletal muscle satellite cells from older mice with GDF11, but did not see increased activity as Wagers and Lee had reported. Administering the protein to young mice resulted in a reduction of lean mass as well, the team noted.

These latest results fall more in line with a 2015 study from the Novartis Institutes for Biomedical Research than with those reported by the Harvard team. But Lee and collaborators later produced evidence that the discrepancy between their results and the Novartis findings was due to reagents. “I think a lot of this is, early on in the field the reagents aren’t perfect, the assays aren’t perfect, so people get results that look like they’re opposing each other,” Lee told The Scientist in October 2015. “We need better assays, we need more science, we really just need to do more work.”

Indeed, the debate over GDF11’s role in aging is far from settled. In a pair of commentaries in the April issue of Circulation Research, the Lee/Wagers group and a separate team, led by Steven Houser of Temple University, laid out the evidence for GDF11’s functions and attempt to explain the discrepancies between different labs’ results.
“The data from the Lee/Wagers groups, and the associated media coverage, have given hope to aged individuals with cardiac, skeletal muscle, and central nervous system dysfunction,” Houser and colleagues wrote. “However, there is now sufficient concern about these data and we hope that any proposed rGDF11 clinical trials will do no harm".

Keywords: Anti aging; Heart; gdf11; Regeneration