Monday, August 1, 2016

Fibrosis Plays A Role In Ageing

Fibrosis, which mostly affects the lungs and hepatic tissues, accounts for up to 45 per cent of deaths in the developed world yet till date no effective therapeutic treatment has been developed, says a recent study.
Fibrosis, one of the age-related pathologies that disrupt organ functionality dramatically, is a progressive accumulation of extra-cellular matrix which occurs in a wide range of organs and potentially distort their structure and function.
Researchers at the US-based Insilico Medicine Inc utilised a new software tool to identify robust bio-markers of fibrotic disease and develop effective targeted therapy.
"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 bio-marker," said Eugene Makarev, Vice-President, Insilico Medicine Inc at Baltimore, in the study published in the journal of Cell Cycle.
"Despite many efforts, fibrosis is often mis-diagnosed. Our system is supposed to help with proper and timely diagnostic," added Makarev.
"Regeneration Intelligence" is a 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.
With broad screening across multiple fibrotic organs, the system identified pathogenic pathways that served as potential targets for the anti-fibrotic therapy.
Saturday, July 16, 2016

Insilico Medicine develops a new approach to concomitant cancer immunotherapy

Artificial intelligence to search for molecules boosting response rates in cancer immunotherapy.

Thursday, July 14, 2016, Baltimore, MD
Recent advances in cancer immunotherapy demonstrated complete remission in multiple tumor types including melanoma and lung cancers. Almost every major pharmaceutical company operating in oncology space started multiple programs in immuno-oncology with thousands of clinical trials underway. Immuno-oncology is now a very broad field ranging from treatment of a patient with an engineered antibody to genome editing of patient's immune cells.
Cancer immunotherapy is currently focused on targeting immune inhibitory checkpoints that control T cell activation, such as CTLA-4 and PD-1. Monoclonal antibodies that block these immune checkpoints (commonly referred to as immune checkpoint inhibitors) can unleash antitumor immunity and produce durable clinical responses in a subset of patients with advanced cancers, such as melanoma and non-small-cell lung cancer.
Immunoresistance often ensues as a result of the concomitant activation of multiple, often overlapping signaling pathways. Therefore, inhibition of multiple, cross-talking pathways involved in survival control with combination therapy is usually more effective in decreasing the likelihood that cancer cells will develop therapeutic resistance than with single agent therapy. While research efforts are now focused on identifying new inhibitory mechanisms that could be targeted to achieve responses in patients with refractory cancers and provide durable and adaptable cancer control, there are outstanding challenges in determining what combination of immunotherapies and conventional therapies will prove beneficial against each tumor type.
"Immunotherapy is the most promising area in oncology resulting in cures, but we need to identify effective combinations of both established methods and new drugs developed specifically to boost response rates. At Insilico Medicine we developed a new method for screening, scoring and personalizing small molecules that may boost response rates to PD-1, PD-L1, CTLA4 and other checkpoint inhibitors. We can identify effective combinations of both established methods and new drugs developed specifically to boost response rates to immunotherapy", said Artem Artemov, director of computational drug repurposing at Insilico Medicine.
Insilico Medicine, Inc. is one of the leaders in transcriptome-based pathway perturbation analysis. It is also a pioneer in applying cutting edge artificial intelligence techniques to biological and medical data analysis, particularly focused on in silico screening for new compounds against cancer and known drugs which can be repurposed against different cancers.
Recently, scientists at Insilico Medicine performed a large in silico screening of compounds that can be administered in combination with anti-PD1 immunotherapy to increase response rates. The researchers collected transcriptomic data from tumors of patients who either responded or failed to respond to standard immunotherapy, using both publically available and internally generated data. Next, they used differential pathway activation analysis and deep learning based approaches to identify transcriptomic signatures predicting the success of immunotherapy in a particular tumor type.
Finally, they analyzed drug-induced transcriptomic effects to screen for the drugs that can robustly drive transcriptomes of tumor cells from non-responsive state to the state responsive to immunotherapy. In other words, researchers developed approach that can predict whether drug of interest would induce a transcriptional signature that characterizes those patients that respond to cancer immunotherapy in non-respondents. This method allows personalizing these drugs to individual patients and specific checkpoint inhibitors. Among the top-scoring drugs, they found several compounds known to increase response rates in combination with cancer immunotherapy. One of the top-scoring compounds included a naturally-occurring substance marketed as a natural product.
A panel of leads for concomitant immunotherapy is part of a large number of leads developed using DeepPharma™, artificially-intelligent drug discovery engine, which includes a large number of molecules predicted to be effective antineoplastic agents, metabolic regulators, CVD and CNS lead, senolytics and ED drugs. Recently Insilico Medicine published several seminal papers demonstrating proof of concept of utilizing deep learning techniques to predict pharmacological properties of small molecules using transcriptional response data utilizing deep neural networks for biomarker development. "Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data," a paper published in Molecular Pharmaceuticals received the American Chemical Society Editors' Choice Award. Another recent collaboration with Biotime, Inc resulted in a launch of Embryonic.AI, deep learned predictor of differentiation state of the sample.

Sunday, July 3, 2016

NMN - The Latest Anti-Aging Drug to be Tried on Humans

A promising compound which has been shown to significantly slow the aging process in mice might be tried on humans as early as next month, scientists say. If the trials work out well it would be the first genuine anti-aging drug to appear on the markets.
According to Japan News, Researchers at Keio University and Washington University in St. Louis are set to begin first clinical trials of the compound known as nicotinamide mono nucleotide (NMN), previously tested on mice.

NMN is a substance produced within the bodies of living things which may be also found in a variety of food products, such as milk.

Scientists believe in NMN’s potential to extend human life as experiments on mice showed that the compound has the ability to counter the declines in metabolism, eyesight and glucose intolerance which come into play as we get older. It was also found that the compound activated proteins called sirtuins, whose production decreases due to the aging process.

Although scientists express great hope that the substance will work as an elixir of life, it is not exactly clear how the compound will behave when applied to humans.

“The age-retarding effect of NMN has been only detected in such animals as mice. It’s necessary to carefully inspect the effects [of the substance],” said Professor Daisuke Koya of Kanazawa Medical University, an expert in the aging process, Japan News reports.

According to Japan News, if proven safe and efficient in clinical trials, the drug is most likely to be distributed as a product similar to ‘food with functional claims.’

The study comes at a time when the Japanese population continues to age and birth rates are down.

Read the complete article at:
Tuesday, June 7, 2016

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


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:
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
Friday, June 3, 2016

This App Maps Your Face Wrinkles to Help You Deal With Aging

RYNKL is a mobile app that aims to make users aware of their facial wrinklescapes beyond mere vanity. In the long-run, the creators want to help you extend your lifespan.
For the moment, snap a selfie and the app will map the saggy areas around your mouth, eyes, and forehead. Using a mixture of deep learning and imaging techniques, RYNKL can work out each user’s “wrinkle index” by looking at the depth of sag and the breadth and number of creases. Users can also compare how their faces fare in relation to people in the same peer group.
If this is making you feel insecure already, chill. Believe it or not, Alex Zhavoronkov, one of RYNKL’s creators and co-founder of Youth Laboratories—a company applying machine learning techniques to the beauty industry—has an argument prepared for why he’s making you confront your decrepitude.
“Right now, if people were to realise that aging is a problem, but there is something they can do about it, they might be able to gain significant longevity,” Zhavoronkov told me, adding that the trick was to believe you had a long lifespan.
According to him, people can only take an active approach to preserving their looks when they are aware of the rate at which their face is deteriorating. Zhavoronkov said that while one’s knowledge of facial decline would have been “psychologically damaging” 25 to 30 years ago when technology was less advanced, now there are enough supplements and deeper insights from the healthcare industry to allow people to make choices that could slow the aging process.
“We want to look at pictures and see how age-related features on the face correlate with those in the blood and be able to predict people’s health status that way,” said Zhavoronkov. “The first step in the quest for healthy longevity is understanding that aging is bad. It leads to a very large number of diseases and is a pathological process in itself.”
Keywords: Aging; Wrinkle Analysis; AI; Insilico Medicine; RYNKL.
Sunday, May 29, 2016

GPU-based Deep Learning Enhances Drug Discovery Says Startup

Sifting the avalanche of life sciences (LS) data for insight is an interesting and important challenge. Many approaches are used with varying success. Recently, improved hardware – primarily GPU-based – and better neural networking schemes are bringing deep learning to the fore. Two recent papers report the use of deep neural networks is superior to typical machine learning (support vector machine model) in sieving LS data for drug discovery and personalized medicine purposes.
The two papers, admittedly driven by a commercial interest (Insilico Medicine), are nevertheless more evidence of deep neural network (DNN) progress in LS research where large datasets with high dimensionality have long been difficult to handle. Using DNN to train models and produce answers is proving quite effective; in these two studies both straightforward and more complicated neural network techniques were used. Snapshot:
Broadly, neural networks try to emulate the way biological neural networks operate. Artificial neural networks are generally presented as systems of interconnected “neurons” which exchange messages between each other. The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning. In essence they can be trained to understand and solve classes of problems.
The first study cited here relied on a standard multilayer perceptron (MLP), which is a feed forward artificial neural network model that maps sets of input data onto a set of appropriate outputs. In this instance, researchers worked with data from three cell lines (A549, MCF-7 and PC-3 cell lines from the LINCS project) that were treated with various compounds to elicit gene expression transcriptional profiles. Researchers began by classifying the compounds into therapeutic categories with DNN based solely on the transcriptional profiles. “After that we independently used both gene expression level data for “landmark genes” and pathway activation scores to train DNN classifier.” In total, the study analyzed 26,420 drug perturbation samples. Shown below is a representation of the DNN used in the drug study.
Study design: Gene expression data from LINCS Project was linked to 12 MeSH therapeutic use categories. DNN was trained separately on gene expression level data for “landmark genes” and pathway activation scores for significantly perturbed samples, forming an input layers of 977 and 271 neural nodes, respectively.
The details of the study are fascinating. Use of all the criteria was key to accuracy and the DNN effectiveness in coping with high dimensionality was a critical enabler.
In the second study, a more complicated ensemble approach proved most effective. Notably, this wasn’t a gene expression data analysis; rather it was based on blood-based markers. Data from roughly 60,000 blood samples from a single laboratory were analyzed. The five most predictive markers – albumin, glucose, alkaline phosphatase, urea, and erythrocytes – were identified. The best performing DNN achieved 81.5 percent accuracy, while the entire ensemble had 83.5 percent accuracy. The paper suggests the ensemble approach is likely most effective for integration of multimodal data and tracking of integrated biomarkers for aging.
Both studies required substantial compute power including the parallel processing capability of GPUs. NVIDIA assisted by providing early access to its DIGITS DevBox, which is a roughly 30Tflop deep learning machine featuring 4 Titan X GPU. “We also used a 2X Tesla K80 GPU system,” said Alex Zhavoronkov, an author on both papers and CEO of Insilico Medicine. “The original DNN in the molecular pharmaceutics [work] was trained on aDatalytics GPU cluster in New Mexico,” said Alex Zhavoronkov, CEO of Insilico Medicine and an author on both papers.
Insilico, founded in the 2014 timeframe, chose to focus on deep learning and signaling pathway activation analysis, which is an effective way to reduce dimensionality in gene expression data. “We are essentially a drug discovery engine now,” said Zhavoronkov, who has long been familiar with GPU technology having worked for several years at ATI Technologies. He’s also an ex-pat from Russia who has maintained close ties there; Insilico Medicine has grown to a staff of 39 including 22 in Moscow. Eleven are focused exclusively on deep learning.
Zhavoronkov divides the current deep learning community into three segments: one that is using off-the-shelf systems and tools; a second that is pushing the boundary and developing their own tools; and elite third components primarily focused on neural network R&D and developing new paradigms, citing Google DeepMindas one of the latter. “We fall into the middle category but also with domain expertise in drug discovery. There are few companies that have both.”
Perhaps predictably bullish, he said, “Both papers are first in class and demonstrate that deep learning can be very powerful in both drug discovery and biomarker development. In a short time we got over 800 strong hypotheses for both efficacy and toxicity of multiple drugs in many diseases.”
[i] Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data, Molecular Pharamaceutics, published by the American Chemical Society,; the manuscript is now posted on the “Just Accepted” service of the ACS. Authors listed: Alexander Aliper, Sergey Plis, Artem Artemov, Alvaro Ulloa, Polina Mamoshina, Alex Zhavoronkov
[ii] Deep biomarkers of human aging: Application of deep neural networks to biomarker development, published in the May issue of Aging (Vol 8, No5), Authors listed: Evgeny Putin, Polina Mamoshina, Alexander Aliper, Mikhail Korzinkin, Alexey Moskalev, Alexey Kolosov, Alexander Ostrovskiy, Charles Cantor, Jan Vijg, and Alex Zhavoronkov.

Complete article can be read at:
Tuesday, May 17, 2016

Anti Aging Vitamin. A Pill A Day Keeps Aging At Bay.

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.

However, with age, stem cells get fatigued, causing aging, poor cell regeneration, and even deterioration of some tissues and organs. So, the research team thought of targeting the cells through their mitochondria to improve the stem cell condition.

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.

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