Connect with us

Healthcare

AI Used To Create Drug Molecule That Could Fight Fibrosis

mm

Published

 on

AI Used To Create Drug Molecule That Could Fight Fibrosis

Creating new medical drugs is a complex process that can take years of research and billions of dollars. Yet it’s also an important investment to make for people’s health. Artificial intelligence could potentially make the discovery of new drugs easier and substantially quicker if the recent work of the startup Insilico Medicine continues to make progress. As reported by SingularityHub, the AI startup has recently utilized AI to design a molecule that could combat fibrosis.

Given how complex and time-consuming the process of discovering new molecules for a drug is, scientists and engineers are constantly looking for ways to expedite it. The idea of using computers to help discover new drugs is nothing new, as the concept has existed for decades. However, progress on this front has been slow, with engineers struggling to find the right algorithms for drug creation.

Deep learning has started to make AI-driven drug discovery more viable, with pharmaceutical companies investing heavily in AI startups over the past few years. One company has managed to use AI to design a molecule that could combat fibrosis, taking only 46 days to do dream up a molecule resembling therapeutic drugs. Insilco Medicine combined two different deep learning techniques to achieve this result: reinforcement learning and generative adversarial networks (GANs).

Reinforcement learning is a machine learning method that encourages the machine learning model to make certain decisions by providing the network with feedback that elicits certain responses. The model can be punished for making undesirable choices or rewarded for making desirable choices. By using a combination of both negative and positive reinforcement the model is guided toward making desired decisions, and it will trend towards making decisions that minimize punishment and maximize reward.

Meanwhile, generative adversarial networks are “adversarial” because they consist of two different neural networks pitted against one another. The two networks are given examples of objects to train on, frequently images. The job of one network is to create a counterfeit object, something sufficiently similar to the real object that it can be confused for the genuine article. The job of the second network is to detect counterfeit objects. The two networks try to outperform the other network, and as they are both increasing their performance to overcome the other network, this virtual arms race leads to the counterfeit model generating objects that are nearly indistinguishable from the real thing.

By combining both GANS and reinforcement learning algorithms, the researchers were able to have their models produce new drug molecules extremely similar to already existing therapeutic drugs.

The results of Insilico Medicine’s experiments with AI drug discovery were recently published in the journal Nature Biotechnology. In the paper, the researchers discuss how the deep learning models were trained. The researchers took representations of molecules already used in drugs to handle proteins involved in idiopathic pulmonary fibrosis or IPF. These molecules were used as the basis for training and the combined models were able to generate around 30,000 possible drug molecules.

The researchers then sorted through the 30000 candidate molecules and selected the six most promising molecules for lab testing. These six finalists were synthesized in the lab and used in a series of tests that tracked their ability to target the IPF protein. One molecule, in particular, seemed promising, as it delivered the kind of results that are desired in a medical drug.

It’s important to note that the fibrosis drug targeted in the experiment has already been extensively researched, with multiple effective drugs already existing for it. The researchers could reference these drugs, and this gave the research team a leg up as they had a substantial amount of data to train their models on. This doesn’t hold true for many other diseases, and as a result, there is a larger gap to close on these treatments.

Another important fact is that the company’s current drug development model only deals with the initial discovery process,and that the molecules generated by their model will still require many tweaks and optimizations before the molecules could potentially be used for clinical trials.

According to Wired, Insilico Medicine’s CEO Alex Zharvornokov acknowledges that their AI-driven drug isn’t ready for field use, with the current study just being a proof of concept. The goal of this experiment was to see how quickly a drug could be designed with the assistance of AI systems. However, Zhavornokov notes that the researchers were able to design a potentially useful molecule much faster than they could have if they had used regular drug discovery methods.

Despite the caveats, Insilico Medicine’s research still represents a notable advancement in the usage of AI to create new drugs. The refinement of the techniques used in the study could substantially shorten the amount of time required to develop a new drug. This could prove especially useful in an era where antibiotic-resistant bacteria are proliferating and many previously effective drugs losing their potency.

Spread the love

Healthcare

Team Develops Blood-Sampling Robot 

Published

on

Team Develops Blood-Sampling Robot 

A blood-sampling robot, able to perform as well or better than humans, has been developed by a team at Rutgers University. It was tested during the first human clinical trial of an automated blood drawing and testing device. 

Because the device can deliver quicker results, healthcare professionals would not have to spend so much time sampling blood. It would allow them to focus more on the treatment of patients within hospitals and other settings. 

The results were published in the journal Technology, and they were comparable to or exceeded clinical standards. The overall success rate for the 31 participants who had their blood drawn was 87%. 25 people had veins that were easier to access, and that success rate was 97%. 

Within the device is an ultrasound image-guided robot that draws blood from veins. One of the possible developments is a fully integrated device that includes a module to handle samples and a centrifuge-based blood analyzer. This could be used in ambulances, emergency rooms, clinics, doctors’ offices, hospitals, and bedsides. 

The most common clinical procedure, numbered at more than 1.4 billion performed daily in the United States, is Venipuncture. This is a process that involves inserting a needle into a vein to get a blood sample or perform IV therapy. However, previous studies have shown that clinicians fail in 27% of patients without visible veins, 40% of patients without palpable veins and 60% of emaciated patients.

With the repeated failure to start an IV line boost comes an increased risk of phlebitis, thrombosis, and infections. It could also require the targeting of large veins in the body or arteries, and this is riskier and more costly. Because of this, venipuncture is one of the leading causes of injury to patients and clinicians. Other problems associated with difficulty accessing veins are that it can increase procedure time by up to an hour, it requires more staff and the estimated costs are more than $4 billion a year in the United States. 

Josh Leipheimer is a biomedical engineering doctoral student in the Yarmush lab in the biomedical engineering department in the School of Engineering at Rutgers University-New Brunswick.

“A device like ours could help clinicians get blood samples quickly, safely and reliably, preventing unnecessary complications and pain in patients from multiple needle insertion attempts,” Leipheimer said. 

The team hopes that the device can eventually be used in procedures such as IV catheterization, central venous access, dialysis and placing arterial lines. They will now work to refine the device and increase success rates in those patients with difficult veins to access. 

In order to improve its performance, data will be taken form this study and used to enhance artificial intelligence in the robot. 

The Rutgers co-authors include Max L. Balter and Alvin I. Chen, both graduates with doctorates; Enrique J. Pantin at Rutgers Robert Wood Johnson Medical School; Professor Kristen S. Labazzo; and principal investigator Martin L. Yarmush, the Paul and Mary Monroe Endowed Chair and Distinguished Professor in the Department of Biomedical Engineering. The study also had contributions from a researcher at Icahn School of Medicine at Mount Sinai Hospital. 

The newly developed device by the team at Rutgers is just another example of how robotics and artificial intelligence are overtaking the healthcare industry. These devices will greatly assist those working within healthcare, and they will make procedures and other forms of care much more successful.

 

Spread the love
Continue Reading

Healthcare

How AI Is Being Used In The Fight Against The Wuhan Coronavirus

mm

Published

on

How AI Is Being Used In The Fight Against The Wuhan Coronavirus

Artificial intelligence is being leveraged in the fight against the Wuhan Coronavirus. Artificial intelligence as being employed by researchers track the spread of the disease and to research potential treatments for the virus.

The Wuhan Coronavirus manifested in China in December, and in the two months since then it has spread across China and to other parts of the globe. It’s still unknown just how contagious the virus is and how quickly the virus could spread, although there are currently more than 40,000 confirmed cases within China. In order to get a better understanding of how the virus might spread and how fast the virus can spread, researchers are employing machine learning algorithms focused on data pulled from social media sites and other parts of the web.

Over the course of the past week,  the rate of infection seems to have decreased somewhat, but it’s unclear if the disease is falling under control or if new cases are becoming harder to detect.  While other countries around the world have only seen a few cases of coronavirus, in comparison to China, the world health community remains concerned about the virus’s ability to spread. Researchers are trying to get ahead of the viruses’ spread by using machine learning and big data collected from the internet.

As reported by Wired, an international team of researchers have extracted data from various parts of the internet, including posts from doctors and medical groups, public health channels, social media posts, and news reports, compiling a database of text that might relate to the coronavirus.  The researchers then analyze the data for signs that the virus could be spreading outside of China’s borders, making use of machine learning techniques in order to find relevant patterns in the data that could hint at how the virus is behaving.

The researchers sift through social media posts looking for potential symptoms of coronavirus, centering their search on regions where doctors think cases may manifest. The social media posts are processed using natural language processing techniques, techniques which can distinguish between posts where a person mentions their own symptoms versus someone saying symptom-related words in another context (such as discussing news about the coronavirus).

According to Alessandro Vespignani, as Wired reported, Northeastern University professor and expert contagion analyst, argues that even with advanced machine learning techniques it’s often difficult to track the spread of the virus because the characteristics of the virus are still somewhat unknown, and most social media posts are from media companies and currently about the outbreak in China. However, Vesignani believes that if the virus ever did take hold in the US it would become easier to monitor thanks to more posts concerning the virus.

Despite the challenge in gaining relevant information about the potential behavior of the coronavirus, the model created by the researchers does seem to be reasonably effective at finding clues within a large sea of social media posts. The model used by the researchers was able to find evidence of a viral outbreak on December 30th, although it took time to determine just how serious the situation would become. Crowdsourced information could improve the effectiveness of disease tracking models even further, as it enables the more efficient collection of relevant data regarding the virus. As an example, an analysis of data crowdsourced by Chinese physicians suggests that people younger than 15 years of age are more resilient to the virus.

Artificial intelligence can also be combined with data collected from mobile devices to build models that can potentially predict the direction a virus is spreading as well as the rate of a spread. For instance, Researchers from University of Southampton used mobile data to determine the path that the virus may have taken as it moved out of Wuhan in the days following its manifestation. Other researchers analyzed data collected by Tencent, a Chinese mobile app developer, and found that the restrictions imposed by the Chinese government potentially reduce the virus’ spread, buying vital time to develop a plan of attack.

As Fortune reported, the startup Insilico Medicine has made use of artificial intelligence to identify molecules that could potentially treat the coronavirus. Insilico’s AI identified thousands of possible drug molecules over the course of four days. Insilico explained that the 100 most promising candidates will be synthesized and all of their research on molecular structures will be published for other researchers to take advantage of. Medical researchers and companies are fast-tracking the development and testing of treatments, with the US-based biotech company Gilead planning to start the immediate testing of a new antiviral drug within the Wuhan region.

After Insilico decided to begin researching treatments, it focused its research on an enzyme called 3C-like protease. The coronavirus relies on this enzyme to reproduce and spread. According to Insilico, it decided on this specific enzyme because it’s quite similar to other viral proteases whose structures have already been documented, and because Shanghai Tech University had developed a model of the 2019-nCoV 3C-like protease. In the span of four days Insilico was able to generate hundreds of thousands of candidate molecules and choose only the hundred or so that were most likely to be useful. The results of the research were recently published in the repository bioRxiv and on Insilico’s website.

Spread the love
Continue Reading

Healthcare

AI Being Used to Analyze Retinal Images

Published

on

AI Being Used to Analyze Retinal Images

In a newly developed approach, artificial intelligence (AI) is being used to analyze retinal images. The system could be used by doctors in order to select the best treatment for patients suffering from vision loss from diabetic macular edema, a diabetes complication. That problem often leads to vision loss among working-age adults.

One of the first types of therapy that is often used as a line of defense against diabetic macular edema is anti-vascular endothelial growth factor (VEGF). The problem with VEGF agents is that they do not work for everyone. Those who could benefit from the therapy need to be identified first since it requires multiple injections. Those injections cost a lot, and they are burdensome for both patients and physicians.

The leader of the research team is Sina Farsiu from Duke University.

“We developed an algorithm that can be used to automatically analyze optical coherence tomography (OCT) images of the retina to predict whether a patient is likely to respond to anti-VEGF treatments,” she said. “This research represents a step toward precision medicine, in which such predictions help clinicians better select first-line therapies for patients based on specific disease conditions.”

The work was published in The Optical Society (OSA) journal Biomedical Optics Express. In the journal, Farsiu and her team demonstrated how the new algorithm is capable of accurately predicting whether a patient is likely to respond to anti-VEGF therapy, after just one volumetric scan.

“Our approach could potentially be used in eye clinics to prevent unnecessary and costly trial-and-error treatments and thus alleviate a substantial treatment burden for patients,” Farsiu said. “The algorithm could also be adapted to predict therapy response for many other eye diseases, including neovascular age-related macular degeneration.”

The newly developed algorithm is based on a novel convolutional neural network (CNN) architecture. A CNN is a type of artificial intelligence, and it assigns importance to various aspects or objects in order to analyze images. The algorithm was used by the researchers to examine images acquired with OCT, which is a noninvasive technology. OCT produces high-resolution cross-sectional retinal images, and it is considered the standard of care for the assessment and treatment of various eye conditions.

“Unlike previously developed approaches, our algorithm requires OCT images from only a single pretreatment timepoint,” said Reza Rasti, first author of the paper and a postdoctoral scholar in Farsiu’s laboratory. “There’s no need for time-series OCT images, patient records or other metadata to predict therapy response.”

The new algorithm works by highlighting global structures in the OCT. At the same time, it also enhances local features from diseased regions. It searches for CNN-encoded features that can be correlated with anti-VEGF response. 

The algorithm was tested with OCT images from 127 patients who had undergone treatment for diabetic macular edema with three consecutive injections of anti-VEGF agents. The algorithm then analyzed OCT images that were taken prior to the anti-VEGF injections, and the algorithm’s predictions were compared to OCT images taken after anti-VEGF therapy. This told researchers whether or not the therapy resulted in an improvement of the condition. 

The algorithm was found to have an 87 percent accuracy rate for predicting those who would respond to treatment. It had an average precision and specificity of 85 percent and a sensitivity of 80 percent.

The researchers now want to confirm the findings and undertake a larger observational trial of patients who have yet to go through treatment.

 

Spread the love
Continue Reading