They are strings of amino acids. When combined in groups they build proteins. But on their own, they have a significant potential medical upside. Today peptides can be found in medications treating diabetes and multiple sclerosis. But they also can be used in the fight to contain COVID-19 in all its variants.
It is a peptide within the spike protein that enables the virus to enter cells and replicate using the latter’s genetic information. The binding capability of the peptide facilitates the virus. Building a counterattack peptide, therefore, sounds like a reasonable strategy.
Peptides can be designed by playing with amino acids. They can be built to bind to a specific molecular target with far greater facility than proteins. Philip Kim, Professor of Molecular Genetics and Computer Science, at the University’s Faculty of Medicine, is the lead author, sees peptides as having distinct advantages over protein-based therapeutics. He describes them as having long-term stability, being inexpensive to produce (at least 100 times cheaper), and prophylactic to prevent infection using a nasal spray to apply.
The caveat regarding peptides is that in the body they rapidly degrade in the presence of proteins associated with our immune response. The answer to this challenge is designer peptides.
Naturally occurring amino acids are left-handed. That’s the rotational direction of their single helix structure. All-natural peptides, therefore, are also left-handed and called L-peptides.
But using computers, Kim and his laboratory partners have designed right-handed peptides, mirror images of naturally occurring ones and designated D-peptides with the D standing for Dextra, the Latin word for right.
Laboratory partner, Pedro Valiente, has designed a number of D-peptides that ape naturally occurring ones that can be found in proximity to human cells in the body. The D-peptides bind to the surface of cells and the ones Valiente has created block the natural paths of entry for the COVID-19 spike or S-protein from entry.
In lab-grown cultures, the D-peptides have demonstrated that they can block COVID-19 from infecting human cells. In addition, because COVID-19 is airborne and often makes its first point of entry the nasal cavities, a prophylactic spray containing a mix of D-peptides would appear to be an effective preventive.
From the time of their first discovery of D-peptides’ ability to stop COVID-19, there has been a proliferation of mRNA vaccines and adenovirus vaccines and new antiviral medications that are in production or already distributed. As a result, Kim and his laboratory have looked beyond COVID-19 prevention, and are using D-peptides to target all coronaviruses with a goal to create a multivalent preventive treatment to stop future pandemics at a much earlier stage. Commercialization of the work is being done in partnership with Boston-Massachusetts-based Decoy Therapeutics.
A South Korean-based laboratory has done confirming tests on cultured human cells using the D-peptide treatment to deal with a range of COVID-19 variants. Kim has expressed confidence that his invention will work against the latest variant sweeping the planet, Omicron. Kim believes D-peptides can be used in highly targeted ways to deal with diseases such as cancer and Alzheimer’s. What are the timelines for seeing D-peptide treatments becoming available? Kim estimates two years.
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HPLC is a chromatographic methodology for separating compounds into their constituent components in order to identify, quantify, and purify them. The technique was first conceived of in the 1960s it has become ubiquitous around the world in laboratories doing analytical chemistry, biochemistry, and industrial research. Originally the H and P stood for high pressure because it involved liquids under higher pressure to distinguish it from another form of chromatography that uses gases. One of its more common medical uses is for the detection of beta-thalassemia, a blood disorder that affects hemoglobin production in blood.
As we humans become more sensitive to animal rights, HPLC is proving to be an effective alternative to the use of animals in laboratory testing. Scientists can gain the same information from using it as they do with animals, and the results are often more accurate. Animal testing, in fact, may in the near future be limited to pre-human clinical trial drug testing as HPLC replaces the need.
In diabetes testing, HPLC is reducing the number of animals used. It is widely known but animal rights organizations estimate that laboratory experiments cause at least 100 million animals to die annually. As a technique to replace the use of laboratory animals, HPLC is far superior in detecting toxins in substances. This allows experiments to advance to the use of human volunteers with no animal intermediaries. One example is the use by chemists of ICP-MS, a type of mass spectrometry that tracks and analyzes contaminants in substances we may be putting into our bodies and the environment.
As labs shift to HPLC and use fewer animals, their experiments run more efficiently. They no longer need to source animals for testing including the costs of care and feeding. And HPLC is proving to be the best in vitro method to replace mice, eliminating the need for them altogether.
Unique advances in science constantly impact our lives. You are likely benefiting from scientific technology right now without even thinking about it. Now that you know how HPLC is being used you can assess your own purchases of pharmaceuticals and cosmetics and find out if the companies you buy from are no longer putting animals at risk for laboratory testing.
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March 13, 2020 – It seems appropriate that this week of disruption should end on a Friday, the 13th. It was only yesterday that I realized the calendar had aligned itself this way because of the extra day in February. Not that I believe in numerology or omens, it’s just an interesting coincidence.
In my morning inbox I was reading about how artificial intelligence (AI) saw the coming COVID-19 pandemic. BlueDot, which describes itself as a company that helps governments, citizens, and businesses to protect themselves from public health threats, has developed a dashboard application that combines public health and medical expertise with AI algorithms to assess risk and provide notification of infectious disease outbreaks.
In the case of COVID-19 it picked up on a sudden surge in pneumonia cases in Wuhan, China in December, nine days before the World Health Organization (WHO) began to track the rising incident of illness there.
COVID-19 isn’t the only infectious disease that BlueDot has been tracking. It has been following Dengue fever in Thailand, measles in the United States, Lassa fever in Nigeria, and mumps in Ireland. In each case the machine learning algorithms make predictions on the likelihood of these diseases spreading beyond containment to other countries.
BlueDot’s origins are here in my home city of Toronto. The outbreak of another coronavirus, SARS, in 2003 led to 44 deaths here. Under the leadership of Dr. Kamran Khan at downtown St. Michael’s Hospital, a scientific research program was launched called BioDiaspora. It began by studying the effects of air travel on the spread of infectious diseases, notable in the case of SARS. In 2009 the program predicted accurately a global influenza pandemic, the first of the 21st century. And in 2014 with private investment backing the research, renamed the venture to become BlueDot, what it calls “a metaphor for our small world,” and a reference to Carl Sagan’s description of Earth seen from Saturn.
In 2014 BlueDot using its risk models predicted the spread of Ebola throughout West Africa. In 2016 after a Zika outbreak in Brazil, predicted six months ahead that it would emerge in Florida. And in December it flagged COVID-19.
How did BlueDot know?
The AI contains a natural language processor capable of reviewing foreign-language news reports, government official proclamations, and animal and human disease online network reporting. Khan notes that BlueDot avoids social media which he describes as too messy. BlueDot even looks at global airline ticketing data.
Why? Because when combined with an area where there is an outbreak of an infectious disease, it can indicate its jump potential to other countries.
The machine language component of the AI can sift through all of this range of unstructured data. Why unstructured? Because this is not in a defined-field database. Khan, in an interview with Wired, states that through the AI technology, “we can pick up news of possible outbreaks, little murmurs or forums or blogs of indications of some kind of unusual events going on.” In the case of COVID-19, BlueDot tracked the online trail of local news and official pronouncements in Wuhan and elsewhere within China. And through the airline ticketing data could predict the virus’ jumps to Bangkok, Seoul, Taipei, and Tokyo within a few days.
The reports BlueDot produces are not totally the work of the AI. On-staff epidemiologists review its conclusions and then write the reports that get shared with governments, businesses, and the public.
The circumstances around COVID-19’s initial appearance in Wuhan, and Hubei Province, involved just a trickle of local reporting. The treating physician who first noted the symptoms of COVID-19 was seen as a whistleblower by the local authorities in the Chinese Communist Party. An attempt to suppress the news could be discerned in an online trail of shared information. The attempt to control the story turned out to be fatal for that treating physician who became infected as well as the tens of thousands in Wuhan and surroundings.
The rest of the world is now paying for the Chinese Communist Party’s mistake. An AI like BlueDot in these circumstances, if seen as authoritative, could have been the difference between a local outbreak and the current global pandemic. Unfortunately, what BlueDot knew wasn’t picked up by the WHO immediately. That nine-day lag has turned out to come with a heavy price tag in terms of lives lost and the shaking of the world economy.
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States Julie Parsonnet, School of Medicine, Stanford University, “Our temperature’s not what people think it is. What everybody grew up learning, which is that our normal temperature is 98.6 [Fahrenheit], is wrong.” The study sampled 200,000 using data from a number of U.S. sources.
A recent British study of 25,000 concluded that our average body temperature today is 36.6 Celsius (97.9 Fahrenheit). The American research noted that the temperature of males born in the 21st century averaged 0.58 Celsius (1.06 Fahrenheit) lower than men born at the beginning of the 19th century. The difference for women was less at 0.32 Celsius (0.58 Fahrenheit).
A German physician, Carl Reinhold August Wunderlich, was the first to document normal body temperature ranging from 36.2 to 37.5 Celsius (97.2 to 99.5 Fahrenheit). The median was calculated to be 37 Celsius (98.6 Fahrenheit) and that’s what we learned in school. But in fact, some of us run hotter and some run colder.
When you consider normal body temperature has a variance of 1.3 Celsius (2.3 Fahrenheit) it would suggest that the data collected fits easily within that range.
The Stanford study is an interesting piece of work. The analysis was done on 677,423 human body temperature measurements covering populations spanning 157 years. Could the differences in the 19th century be a reflection of less precise thermometers and methods in obtaining body temperatures, or are these findings real?
The proof in the pudding is the measuring of modern populations because control of the calibration can be readily verified as accurate although biases could exist based on the type of thermometer used and from where the measurement was taken (oral versus other means).
Having taken potential variables into consideration for the more recently collected data, the researchers at Stanford concluded “the observed drop in temperature reflects physiologic differences rather than measurement bias.”
One of the reasons posited is Americans over the past two centuries have increased in size and weight. They are bigger and that might affect body temperature. The other possibility is that metabolic rates have declined since the beginning of the 20th century based on other experimental data.
Besides a resting metabolic rate, decreasing levels of inflammation in the general population because of improved living standards, better heating and air conditioning in homes, fewer chronic infections, less dental disease, and antibiotics could all play a factor. It is particularly interesting to link how much time Americans spend in heated homes in the winter, and air-conditioned homes in the summer in the latter part of the 20th and now the 21st century, a phenomenon described as thermoneutral, and its impact on metabolic response and subsequently body temperature.
The Stanford study summary states, “normal body temperature is assumed by many, including a great preponderance of physicians, to be 37°C …. Our investigation indicates that humans in high-income countries have changed physiologically over the last 200 birth years with a mean body temperature 1.6% lower than in the pre-industrial era. The role that this physiologic evolution plays in human anthropometrics and longevity is unknown.”
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We are not there yet, but already artificial intelligence (AI) is helping doctors to help patients. It’s only a question of time before we can see the middleman moved out of the equation.
Today AI in health care is being applied to improve operational efficiency and to decrease the case burden on physicians. This is where machine learning provides distinct advantages whether it be in documenting, charting, billing, or in the case of helping with diagnoses, recognizing patterns that indicate illness much faster and in greater volume than any human can. Pattern recognition means AI tools can be applied to all types of medical practice from nursing to social work, and to community health. Pattern recognition technology can observe a patient’s behaviours better than most health practitioners. It can always be on closing the care gaps that occur when patients are not being observed by humans. It can help patients to stay in homes rather than occupy hospital beds as it provides around the clock data to medical teams without human presence necessary. It even, in the form of robot assistants, provide basic support services and companionship.
Let’s look at some of the most recent reported advances in AI applied to health care.
There’s a trick that most cardiologists wouldn’t claim to be able to do. A good cardiologist might detect heart failure by a quick look at a patient. Does the patient’s colour look right? Are extremities and fingernails looking bluish? Are there signs of puffiness and swelling in the feet and ankles? But before conclusively diagnosing congestive heart failure (CHF) the doctor would do an electrocardiogram (ECG), a chest x-ray, and have the patient wear a 24-hour halter monitor to provide a 5-minute-segmented record of heart function. Imagine, however, if an AI could draw a conclusion of CHF without all of the above and do it from a single heartbeat. That could not only save time but also help in early intervention when CHF is detected, a condition that 5 million Americans and 26 million around the world live with every day.
That’s what researchers at the University of Surrey have developed using a neural network that accurately detects CHF through the analysis of one ECG heartbeat. Dr. Sebastiano Massaro, Associate Professor of Organisational Neuroscience, at Surrey, is one of the researchers responsible for creating the AI. In a university news release, he states, “We trained and tested the CNN [Convolutional Neural Network] model on large publicly available ECG datasets featuring subjects with CHF as well as healthy, non-arrhythmic hearts. Our model delivered 100% accuracy: by checking just one heartbeat we are able to detect whether or not a person has heart failure. Our model is also one of the first known to be able to identify the ECG s morphological features specifically associated with the severity of the condition.”
The benefit of such an efficient diagnosis without tying up lots of medical resources cannot be underestimated.
Breast cancer is the most common cancer suffered by women globally. It is also the second leading cause of death. My wife goes through regular screenings for breast cancer, as do most women in North America and Europe. The tests include mammograms, ultrasound and physical examination. Women are taught to self-examine for early detection. The problem, however, is that screening results are 20% of the time wrong with 50% of women receiving annual mammograms experiencing at least one false cancer positive result within a 10-year period.
In the study reported in Nature, published online on January 1, 2020, Google’s DeepMind AI outperformed six radiologists in the U.S. study in interpreting mammogram results, reducing false cancer positive error rates by better than 350%. At the same time, it helped reduce workloads for those assigned in the United Kingdom to do second readings of mammogram results by 88%, a common practice there.
For the purpose of the study, the AI system provided a comprehensive report, not individual results. But it could be automated to give immediate feedback during screening which would alert radiologists to detected cancers.
A neural network developed at Stanford is being used to detect and diagnose skin cancers using imaging analysis. The researchers trained the convolutional neural network using a dataset of almost 130,000 clinical images and over 2,000 diseases and tested its performance against 21 board-certified dermatologists. The neural network achieved a performance equal to the dermatologists.
The goal of Stanford is to turn the AI detection tool into an application that can be put on a smartphone, capable of taking an image of a suspicious skin appearance and competently detecting carcinomas and malignant melanomas.
Early detection of Alzheimer’s is critical to treatment to stem the progression of the disease. Diagnosing it when clinical symptoms start manifesting themselves often comes when too many of the brain’s neurons have already died making it irreversible.
But an AI machine-learning algorithm trained to analyze a massive dataset of PET scans of patients eventually diagnosed with Alzheimer’s, mild cognitive impairment, and no disorders at all, successfully identified between 92 and 98% of patients who eventually developed the disease. Diagnosis occurred more than six years before doctors confirmed the disease.
The study was first published in Radiology in 2018. Sixteen studies since then have looked at a number of AI deep learning and machine learning algorithms aimed at early diagnosis of Alzheimer’s with accuracies over 98%.
For Alzheimer’s research AI represents a diagnostic breakthrough that may lead to much better treatment and even remission of the disease because of early detection.
Around 2012, a computer scientist-turned-biophysicist, Alex Zhavoronkov, noticed that AI was getting better at image, voice, and text recognition. He knew that all three tasks shared a critical commonality, massive datasets available to use to train the AI. By 2014, Zhavoronkov was using the technology for discovering new drugs.
Zhavoronkov had heard about a new AI technique known as generative adversarial networks (GANs). How does it work? By pitting two neural networks against each other, with minimal instructions the technology could produce novel outcomes. Before this researchers were using GANs to do things like design new objects or create one-of-a-kind, fake human faces. It was Zhavoronkov who saw pharmacological possibilities. He figured GANs would allow researchers to verbally describe drug attributes in terms such as “The compound should inhibit protein X at concentration Y with minimal side effects in humans,” and then let the AI construct an appropriate molecule from scratch.
Zhavoronkov set up Insilico Medicine on the campus of Johns Hopkins University in Baltimore, Maryland, and over three years using GANs was able to develop a drug discovery engine that sifts through millions of data samples and determines the biological signature and characteristics of specific diseases. It can then identify the most promising treatment targets and generate molecules perfectly suited for them. States Zhavoronkov, “The result is an explosion in potential drug targets and a much more efficient testing process … AI allows us to do with fifty people what a typical drug company does with five thousand.”
Right now, the GANS is being used to find new drugs for cancer, aging, fibrosis, Parkinson’s, Alzheimer’s, ALS, diabetes, and many others. The first drug coming to market is a treatment for hair loss slated to start Phase I trials in 2020.
Insilico is also in the early stages of using its tools to predict the outcomes of clinical trials in advance. If successful, the technique will enable researchers to shorten the time and money spent in the traditional testing process.
For those of you unfamiliar with the role proteins play in our bodies I refer you to an article posted on this site back in 2013 and a game called FoldIt, created by researchers at the University of Washington, which uses the collective effort of crowdsourcing to develop new protein folds. And you may not be aware that CRISPR/Cas9, the gene-editing tool taking genetic engineering mainstream, is in fact, a tool that uses a protein to excise bad genes and replace them with healthy ones. So proteins are a hot subject in the world of pharmacology today and scientists are using AI to identify new drug targets that bind with them.
Proteins consist of amino acids that fold in different ways to take on different functions. A protein containing a hundred amino acids, rather small by body standards, produces a googol-cubed-worth of potential shapes. (That’s a one followed by three hundred zeroes.)
Back in 1994, a biannual protein-folding competition was created with little success until 2018 when the creators of DeepMind turned neural networks loose on the problem. DeepMind took on the task of determining the most likely distance between a protein’s base pairs and the angles of their chemical bonds. They called their end-product, AlphaFold. In its first entry into the competition, it solved 25 of 43 protein-folding problems compared to the second-place finisher with only three.
How does AlphaFold work? It uses two neural networks, one focused on predicting distances between pairs of specific amino acids, and a second evaluating the possible angles of the chemical bonds between them. The two return a score estimating likely structures that match a compilation of known protein fragments, and the generation of completely new protein structures.
The potential implications of AI-accelerated protein folding for new pharma cannot be underestimated and what it could mean for treating patients with Huntington’s, ALS, myotonic dystrophy, and other diseases.
Another theatre of war for improved drugs is in the realm of drug delivery. Converging exponential technologies are paving the way for massive implications in both human health and industry shifts. The aforementioned CRISPR stands to revolutionize synthetic biology and the treatment of genetically-linked diseases. Researchers are now demonstrating how this gene-editing tool can be applied to create materials that shape-shift on command. Think of the potential of materials that can dissolve instantaneously when stimulated, releasing a specified drug in a highly targeted location.
Then there are nanobots, micro-machines capable of providing targeted drug delivery. In a recent review of medical micro- and nanorobotics, the lead authors from the University of Texas at Austin and the University of California, San Diego found numerous successful tests of in vivo operations using medical micro- and nanorobots.
Inefficient, slow-to-innovate, and risk-averse industries will be disrupted in the years ahead and Big Pharma is an area worth watching right now. Two areas, in particular, are getting headlines, advances in addressing longevity, and in disease prevention. The aforementioned Insilico is one of the companies leading the way.
The convergence of massive datasets, growing computational power, the rise of quantum computing, and innovations in AI, are bringing us closer to a world in which personalized drugs delivered directly to specified targets will become the standard of care. Rejuvenation biotechnology will be commercially available sooner than you think.
Zhavoronkov sees the timeline at “maybe 20 years … a reasonable horizon for tangible rejuvenational biotechnology.”
How might any of us use an extra 20 or more healthy years in our lives? What impact would that have on the world?
Another calculator says Maya ages 12 years in the first year and 6 in each subsequent year which would make her 90 in human years.
On Pedigree’s website, its dog age calculator goes by breed and in Maya’s case puts her age at 72 in human terms, a years older than me as of January.
It’s clear from the above that there hasn’t appeared to be an exact science on calculating the age of dogs compared to us, that is, until now.
New research studying chemical modifications to DNA over a lifetime, called epigenetics, has come up with the epigenetic clock.
What does the epigenetic clock tell us?
It tracks how sequences in our genetic makeup change from body wear and tear, lifestyle, and disease. The mechanism for the clock is DNA methylation, referring to the presence of methylcytosine found at different levels throughout the lifecycle of human DNA and that of other living things.
In a 2017 research paper entitled “DNA methylation dynamics in aging: how far are we from understanding the mechanisms?” the authors write “DNA methylation is currently the most promising molecular marker for monitoring aging and predicting life expectancy.”
Using the epigenetic clock, new research is attempting to determine a human-dog age comparison based on similar age-related methylation occurring in the two species’ genomes. Methylations can be seen in young and old dogs and humans, and the rates of change in both are similar. What might explain this is a common evolutionary trait in terms of DNA methylation going back to a common mammalian ancestry.
States Matt Kaeberlein, a biogerontologist from the University of Washington in Seattle, “We already knew that dogs get the same diseases and functional declines of aging that humans do, and this work provides evidence that similar molecular changes are also occurring during aging…a beautiful demonstration of the conserved features of the epigenetic age clocks shared by dogs and humans.”
Based on the comparison in methylation rates, a 7-week-old puppy is equal in age to a 9-month-old human baby. Then the clocks change as the one for dogs speeds up making 12 dog years equivalent to 70 for humans. DNA methylation in both species increases in aging. And it may vary in different dog breeds which has led some researchers to start the Dog Aging Project.
Getting back to Maya and figuring out an accurate age for her that isn’t based on the three methods described above, when the number 14 gets plug into the epigenetic dog clock in comes out to be 73.2 years in human terms. So the Pedigree calculator isn’t too far off. To read the original paper on dogs and epigenetic clocks, go to the link found here.
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The work at Scripps is not in isolation. In February of this year, researchers at the Foundation for Applied Molecular Evolution in Florida doubled down on DNA creating eight base pairings in a stable synthetic helical molecule. At the time they noted there was no reason to limit synthetic DNA to eight pairs. So why not up the ante to 10 or 12?
What makes these lines of research so interesting is they open our eyes to the possibility of new biological or synthetic life. Why would we want to do synthesize biology? Back in 2017, I wrote about the best way for humans to colonize Mars arguing that altering our DNA would make us adaptable to an environment alien to our species. It kind of makes sense that the only way to free humanity from living in a bubble as a spacefaring species would be to alter our genome so we could easily adapt to a place like Mars.
The International Space Station has already taught us what happens to our bodies and other biology in low-Earth orbit when away from the safety of our planet for a year. For humans, the results aren’t pretty. Our circulatory system is altered. Our muscles waste. Our bones thin. Our brains swell. Our autoimmune systems are compromised.
To survive, therefore, suggests we either transport environments to space and other celestial bodies that is Earthlike including our gravity, or make our DNA better at self-repair to deal with solar and cosmic radiation, low and micro-gravity environments, extremes of cold and heat, and more. An altered DNA would be able to create new versions of us with re-engineered muscles, bones, body organs, and circulatory systems. And having done that we would then do this for any other species that accompany us. The question then becomes, would a 6, 8, 10, 0r 12-base pair version of DNA make this transition possible? Is experimenting on humans to find out in the cards?
Up until now our species and every other one on Earth has been limited to DNA and RNA as building blocks of life. But researchers at the Tokyo Institute of Technology, the German Aerospace Center, and Emory University, have been experimenting to find out if there are other biomolecules out there. What they have concluded is the discovery of more than 1.16 million likely alternatives.
In a paper appearing in the Journal of Chemical Information and Modeling entitled, “One Among Millions: The Chemical Space of Nucleic Acid-Like Molecules,” the authors write “there are large spaces of unexplored chemistry relevant to pharmacology and biochemistry and efforts to understand the origins of life.” One of the researchers, Professor Jim Cleaves, at the Tokyo Institute, noted they started out on a path to find out if there were analogs to DNA and RNA, the two biological molecules we know and thought they would find a few more. Instead they found 1.16 million plus.
Why was this possible? Because the difference between DNA and RNA is a single atom substitution. So other single substitutions could conceivably create completely new biological molecules capable of replicating an alien version of what happened here on Earth.
Their findings also raise other questions. When we look for life on places like Mars, should our experiments be limited to attempting to find just DNA or RNA fragments? And if not, how would we devise experiments to recognize an alien biomolecule? The analog to alien biomolecules here on Earth is viruses. Remember how difficult it was until recently for science to detect and understand these very different biomolecular packages to the DNA that created cellular biology?
And could this research help us find new places to plant seeds beyond Earth? No doubt once we realize that the biomolecules that are in us are not unique to creating life, we have the means to create synthetic or hybrid alternatives to who we are now and that just might allow us to tolerate the conditions of almost any terra firma in The Universe. In the end, if we succeed, will we then no longer be us?
The concept of rejuvenating our bodies through the use of stem cells is no longer a foreign idea. It comes with a degree of controversy as clinics offering stem cell treatment are popping up across the world making promises to cure diseases, restore joints, ease arthritis, and cure cancer. That promise remains tantalizing because the idea to be able to regenerate any damaged tissue in you using cells from you seems fantastic.
Why is the promise of stem cells possible?
The human body is unbelievably smart. It can recognize foreign cells and organ rejection can occur even in cases when a donor is considered to be a near-perfect match to the patient. But grow an identical heart derived from the patient’s own cells, and the results may be different. So in a world where technology is evolving to accommodate anything’s possible, why not tissue regeneration.
When did we take the first regenerative medicine steps?
It first started in 1954 with the first kidney transplant. And today, according to the Alliance for Regenerative Medicine, there are 1,060 clinical trials and 900 companies globally focused on this field. Here’s an idea of what financing for the industry currently looks like:
Not quite. Regenerative is a subset of personalized medicine, an approach that attempts to tailor-fit treatment to an individual. It is a departure from the current practice by physicians and biomedical companies of “one-size-fits-all” medicine. It could mean tailoring or developing a unique drug treatment to match a person’s DNA. With regenerative medicine the treatment is designed to allow the body to repair itself through cell, tissue, and organ replacement. The treatments are hyper-customized using a patient’s own stem cells. As you can imagine, getting this type of treatment to scale is proving to be pretty expensive. But its promise has lured close to one thousand companies to work in the field as can be seen in the graphic displayed below.
The Alliance for Regenerative Medicine has compiled the clinical trials by medical category. The largest number are devoted to oncology followed by heart and circulatory diseases, diseases of the central nervous system, and musculoskeletal diseases. A visual breakdown of trials can be seen in the image below:
Treatments currently on the leading edge include:
As exciting as this burgeoning field is, the high cost involved and the cutting-edge nature of the technology is holding the field back. But the data shows that things are looking up. Research and Markets predicts a staggering 23.35% compound annual growth by 2022 compared to 2016 numbers. And experts forecast growth to $66 billion annually.
Why not? Lowered risks, minimally invasive procedures, and more efficient disease treatment and prevention should eventually guarantee lower costs and higher recovery and cure rates. Just what insurers, hospitals, and doctors want.
]]>Diamandis writes, my friend Neil Jacobstein is the “go-to expert” when it comes to AI. With his over 25 years of technical consulting experience in the field he holds the AI and Robotics Chair at Singularity University, and is a Distinguished Visiting Scholar at Stanford’s MediaX Program, a Henry Crown Fellow, an Aspen Institute moderator, and sits as a member of the National Academy of Sciences Earth and Life Studies Committee.
The predictions that follow cover the period from this year to 2024. Here are Jacobstein’s prognostications.
AlphaGo Zero, a machine learning computer program trained to play the complex game of Go, defeated the Go world champion in 2016 by 100 games to zero. But instead of learning from human play, AlphaGo Zero trained by playing against itself — a method known as reinforcement learning. Building its own knowledge from scratch, AlphaGo Zero demonstrates a novel form of creativity, free of human bias. Even more groundbreaking, this type of AI pattern recognition allows machines to accumulate thousands of years of knowledge in a matter of hours.
While these systems can’t answer the question, “What is orange juice?” or compete with the intelligence of a fifth grader, they are growing more strategically complex, merging with other forms of narrow AI. Within five years, who knows what successors of AlphaGo Zero will emerge, augmenting both your business functions and day-to-day life.
A group of Chinese and American researchers has recently created an AI system that diagnosis common childhood illnesses, ranging from influenza to meningitis. The system trained on electronic health records compiled 600,000 patients, and 1.3 million outpatient visits, produced diagnoses with unprecedented accuracy. While U.S. health systems do not tout the same level of accessible, universal health data as do Chinese systems, the country is making progress implementing AI to assist in medical diagnoses. Dr. Kang Zhang, Chief of Ophthalmic Genetics at the University of California, San Diego, has created a system to detect signs of diabetic blindness, relying on medical histories and images.
Jacobstein states “we will soon see an inflection point where doctors will feel it’s a risk to not use machine learning and AI in everyday practice because they don’t want to be called out for missing an important diagnostic signal.”
Researchers estimate that there are 1060 possible drug-like molecules which are more than the total number of atoms found in the Solar System. But today, chemists make drug predictions based on properties influenced by molecular structure and then synthesize variants to test their hypotheses. Using quantum computing could transform this time-consuming, high-cost process into an efficient, life-changing, drug discovery protocol. States Jacobstein, “Quantum computing is going to have a major industrial impact… not by breaking encryption, but by making inroads into design through massive parallel processing that can exploit superposition and quantum interference and entanglement, and that can wildly outperform classical computing.”
The incorporation of AI into almost every aspect of our lives leaves us vulnerable to “Deep Attacks” which use AI-generated content to avoid human and AI controls. We have seen examples of this in fake videos of former President Obama speaking fabricated sentences, and an adversarial AI fooling another algorithm into categorizing a stop sign as a 45 mph speed limit sign. Without appropriate cyber protections, AI systems can be manipulated to conduct any number of destructive acts whether it be ruining reputations or changing the performance of an autonomous vehicle.
Jacobstein states, “We all have security systems in our buildings, homes, around the healthcare system, air traffic control, financial organizations, the military, and intelligence communities. But we all know that these systems have been hacked periodically and we’re going to see that accelerate. So, there are major business opportunities … to get ahead of that curve before it bites you.”
Just as modern computing transformed our relationship with bits and information, AI will redefine and revolutionize our relationship with molecules and materials. Today AI is used to discover new materials for clean-tech energy innovations such as solar panels, batteries, and artificial photosynthesis. But using traditional computing hampers our ability to innovate in a world where we face the climate change issue and the need to accelerate a transition to a lower carbon future. For example, it takes about 15 to 20 years to create a single new material based on current design and development methods. But as AI design systems grow they will cut down the timeline needed for new materials discovery allowing us to accelerate the transition. With companies like Kebotix already on their way to streamlining the creation of chemistries and materials at the click of a button, we are getting to the point where atomically precise manufacturing will enable us to produce what was previously unimaginable.
