Nanotechnologies - Trusting Disruption by Deltec

What Are Neural Implants?

Posted on June 23, 2023June 23, 2023 by Jean Chalopin

Neural implants, also known as brain implants, have been the subject of extensive research in recent years, with the potential to revolutionise healthcare. These devices are designed to interact directly with the brain, allowing for the transmission of signals that can be used to control various functions of the body.

While the technology is still in its early stages, there is growing interest in its potential applications, including treating neurological disorders, enhancing cognitive abilities, and even creating brain-machine interfaces.

According to Pharmi Web, the brain implants market is expected to grow at a CAGR of 12.3% between 2022 and 2032, reaching a valuation of US$18 billion by 2032.

During the forecast period, the market for brain implants is expected to experience significant growth, primarily due to the increasing prevalence of neurological disorders worldwide and the expanding elderly population. As the number of individuals in the ageing demographic continues to rise, so does the likelihood of developing conditions such as Parkinson’s disease, resulting in a surge in demand for brain implants.

This article will explore the technology behind neural implants and the benefits and considerations associated with their use.

Understanding Neural Implants

Neural implants are electronic devices surgically implanted into the brain to provide therapeutic or prosthetic functions. They are designed to interact with the brain’s neural activity by receiving input from the brain or sending output to it. These devices typically consist of a set of electrodes attached to specific brain regions, and a control unit, which processes the signals received from the electrodes.

The electrodes in neural implants can be used to either stimulate or record neural activity. Stimulating electrodes send electrical impulses to the brain, which can be used to treat conditions such as Parkinson’s disease or epilepsy. Recording electrodes are used to detect and record neural activity, which can be used for research purposes or to control prosthetic devices.

To function correctly, neural implants require a control unit responsible for processing and interpreting the signals received from the electrodes. The control unit typically consists of a small computer implanted under the skin and a transmitter that sends signals wirelessly to an external device. The external device can adjust the implant’s settings, monitor its performance, or analyse the data collected by the electrodes.

Neural implants can treat neurological disorders, including Parkinson’s disease, epilepsy, and chronic pain. They can also help individuals who have suffered a spinal cord injury or amputation to control prosthetic devices, such as robotic arms or legs.

The Benefits of Neural Implants

Neural implants have the potential to provide a wide range of benefits for individuals suffering from neurological disorders. These benefits include:

Improved quality of life. Neural implants can significantly improve the quality of life for individuals suffering from neurological disorders such as Parkinson’s disease, epilepsy, or chronic pain. By controlling or alleviating the symptoms of these conditions, individuals can experience greater independence, mobility, and overall well-being.

Enhanced cognitive abilities. Neural implants also have the potential to enhance cognitive abilities, such as memory and attention. By stimulating specific regions of the brain, neural implants can help to improve cognitive function, particularly in individuals suffering from conditions such as Alzheimer’s disease or traumatic brain injury.

Prosthetic control. Neural implants can also be used to control prosthetic devices, such as robotic arms or legs. By directly interfacing with the brain, these devices can be controlled with greater precision and accuracy, providing greater functionality and independence for individuals with amputations or spinal cord injuries.

Research. Neural implants can also be used for research purposes, providing insights into the workings of the brain and the underlying mechanisms of neurological disorders. By recording neural activity, researchers can gain a better understanding of how the brain functions and develop new treatments and therapies for a wide range of neurological conditions.

While there are significant benefits associated with neural implants, many challenges and considerations must be considered.

The Challenges

There are several challenges to consider regarding the use of neural implants.

Invasive nature. Neural implants require surgery to be implanted in the brain, which carries inherent risks such as infection, bleeding, and damage to brain tissue. Additionally, the presence of a foreign object in the brain can cause inflammation and scarring, which may affect the long-term efficacy of the implant.

Technical limitations. Neural implants require advanced technical expertise to develop and maintain. Many technical challenges still need to be overcome to make these devices practical and effective. For example, developing algorithms that can accurately interpret the signals produced by the brain is a highly complex task that requires significant computational resources.

Cost. Neural implants can be costly and are often not covered by insurance. This can limit access to this technology for individuals who cannot afford the cost of the implant and associated medical care.

Ethical considerations. Using neural implants raises several ethical considerations, particularly concerning informed consent, privacy, and the potential for unintended consequences. For example, there may be concerns about using neural implants for enhancement or otherwise incorrectly.

Long-term durability. Neural implants must be able to function effectively for extended periods, which can be challenging given the harsh environment of the brain. The long-term durability of these devices is an area of active research and development, with ongoing efforts to develop materials and designs that can withstand the stresses of the brain.

While the challenges associated with neural implants are significant, ongoing research and development in this field are helping to overcome many of these obstacles. As these devices become more reliable, accessible, and affordable, they have the potential to significantly improve the lives of individuals suffering from a wide range of neurological conditions.

Companies Operating in the Neural Implant Space

Several companies are developing neural implants for various applications, including medical treatment, research, and prosthetics.

Neuralink, founded by Elon Musk, is focused on developing neural implants that can help to treat a range of neurological conditions, including Parkinson’s disease, epilepsy, and paralysis. The company’s initial focus is developing a ‘brain-machine interface’ that enables individuals to control computers and other devices using their thoughts.

Blackrock Microsystems develops various implantable devices for neuroscience research and clinical applications. The company’s products include brain implants that can be used to record and stimulate neural activity and devices for deep brain stimulation and other therapeutic applications.

Medtronic is a medical device company that produces a wide range of products, including implantable devices for treating neurological conditions such as Parkinson’s, chronic pain, and epilepsy. The company’s deep brain stimulation devices are the most widely used for treating movement disorders and other neurological conditions.

Synchron is developing an implantable brain-computer interface device that can enable individuals with paralysis to control computers and other devices using their thoughts. The company’s technology is currently being tested in clinical trials to eventually make this technology available to individuals with spinal cord injuries and other forms of paralysis.

Kernel focuses on developing neural implants for various applications, including medical treatment, research, and cognitive enhancement. The company’s initial focus is developing a ‘neuroprosthesis’ that can help treat conditions such as depression and anxiety by directly stimulating the brain.

Closing Thoughts

The next decade for neural implants will likely see significant technological advancements. One central area of development is improving the precision and accuracy of implant placement, which can enhance the efficacy and reduce the risks of these devices. Another area of focus is on developing wireless and non-invasive implant technologies that can communicate with the brain without requiring surgery.

Machine learning and artificial intelligence advancements are also expected to impact neural implants significantly. These technologies can enable the development of more sophisticated and intelligent implants that can adapt to the user’s needs and provide more effective treatment. Additionally, integrating neural implants with other technologies, such as virtual and augmented reality, could lead to exciting new possibilities for treating and enhancing human cognitive function.

The next decade for neural implants will likely see significant progress in the technology and its applications in treating a wide range of neurological and cognitive conditions. However, ethical and regulatory considerations must also be carefully considered as the field advances.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Why Life Expectancy Is Increasing

Posted on June 9, 2023June 9, 2023 by Jean Chalopin

Our average life expectancy has increased from 45 years in the 1850s to nearly 80 years today as a result of medical science. Researchers believe that our life spans will continue to grow, but there is an eventual hard limit.

Advances in medicine that are driving this lengthening life span range across a vast spectrum, including diagnostic developments, medical devices, prescription drugs, and procedures. These medical interventions are joined with healthier lifestyles, a more holistic approach to medicine, and more accurate and earlier diagnoses.

We will take a look at how medical science and technological advances have contributed to our lengthening lifespans.

Healthy Lifestyles and Life Expectancy

We are increasingly more conscious of the need to maintain a healthy lifestyle. Such a lifestyle comes in the form of improved diet and nutrition, exercising regularly, maintaining our mental and emotional health, and regularly assessing our health.

The healthy lifestyle trends that started with the 1970s running craze and subsequent 80s aerobics craze have more recently grown into fitness and healthcare wearables that allow consumers to monitor their personal health–keeping track of steps, activity, sleep, heart rate, stress, and other vital signs.

The IDC predicts that the total wearable market will grow at a rate of 13.4% in the next five years, with an expected 219.4 million units being sold in 2022.

The wearables of today span multiple medical and health functions, from fitness trackers to smart health watches, including wearable ECG devices, and blood pressure monitors, biosensors, and more. These devices can collect physical and medical data with various levels of usefulness. They can monitor, analyse, and even predict health and mental well-being when paired with mobile and desktop applications.

The covid pandemic accelerated a growing trend toward telehealth and remote monitoring. This trend can be leveraged to move us in the direction of preventative healthcare for conditions such as heart disease and stroke.

There are now several wearable makers in the healthcare space, including Interplex, that has been a supplier to many manufacturers and disruptive wearable companies. They have a diabetes monitoring system that can help keep patients’ blood sugar levels more standard.

Wearables and Health Diagnoses

Wearables are no longer new to the market, and their usefulness and quality have consistently improved. They collect multiple data points related to one’s health, and when applying professional analysis to the collected data, they are now able to make early detection possible, which helps with disease prevention and in proposing better treatments. Currently, medical laboratories are providing up to 70% of lab testing to physicians in order to provide accurate diagnoses and treatment plans.

Clinical lab testing results for diagnostic decision-making are an essential part of clinical medicine. The selection of laboratory tests available to doctors has grown exponentially since they first surfaced in the 1920s. Now a wide array of tests can diagnose, screen for, and research disease, while others can monitor treatments and therapies to ensure effectiveness. It is now possible to design tests and equipment that fit the exact specifications needed for medical diagnosis, and this has moved into the area of genetic diseases.

Medical Treatment

Advances seen in medical equipment and treatment protocols have contributed to improvements in patient outcomes. A particular area of advancement is in surgical treatment. This advancement is mainly in a movement toward more precise surgical operations as well as minimally invasive procedures.

Equipment now being used can make cuts with lasers with high precision enabling delicate surgeries to be performed on the brain or eye, or they can even focus radio or other waveforms that ultimately produce surgical-like outcomes below the skin, without the surgeon having to make a single incision.

With these advances, minimally invasive but advanced laparoscopic surgery (keyhole), hysteroscopic surgery, and myomectomies are just a few of the procedures that have resulted from advancements in medical technology. Other medical fields have benefitted from these advances, including neurology, interventional equipment, and cardiology.

Beyond surgical precision and minimal invasiveness, mobile medical technologies are advancing, bringing medical technology and equipment into more hospitals, doctors’ offices, emergency rooms, and even homes, making a significant contribution to medical treatment and health outcomes.

Telemedicine

Healthcare professionals are increasingly using mobile medical equipment and devices from medical workstations to specialised equipment for telehealth, to deliver medical care to their patients wherever they may be (both patients and medical professionals).

Through the increase in transportable and telehealth solutions, mobile medicine is expanding the reach of healthcare far beyond the traditional hospital and clinical setting. The fields of teleradiology, telenephrology, and telepsychiatry are just a few examples of mobile medicine that have now become more commonplace and will likely continue to grow over the next decade. With advancing technology, more of these “tele” medical fields will be available and contribute to a significant change in the medical industry.

In the future, doctors with specialties will be able to practise much of their medicine from anywhere in the world, not needing to see their patients in person directly. This will be aided by virtual reality, augmented reality, and machines capable of testing, diagnosis, and even surgery from afar. The possibilities are endless in this space, and with 5G and soon-to-be 6G, much of this advancement we will likely see over the next two decades.

IoT Devices

Advancements in low-cost sensor tech, dependability, increased data storage and transmission capabilities, and low power consumption has meant that new devices will be possible that can change how we view medicine. With the increasing number of IoT devices coming to market that are connecting our homes, businesses, supply chains, and vehicles, we will also see similar devices for ourselves.

These devices will initially monitor specific health issues, allowing us to identify when a specific problem is occurring and potentially deal with it automatically. This is already happening with Implantable Cardioverter Defibrillators (ICDs).

ICDs are being implanted into patients. In the case of a cardiac event, they are informing medical professionals of a problem and providing a shock to the patient that will restart their hearts and save their lives.

*Courtesy of the* *American Heart Association*

These kinds of devices will expand with the IoT and become more common for many of our common ailments.

In the future, we will likely see devices with multiple functions, such as monitoring, aiding, and preventing devices all in one, able to identify many different ailments when they first become a problem and treat them before they grow in severity.

Life Expectancy

Much of the life expectancy gains we have seen over the past 150 years have been due to improvements in infant mortality and the advent of antibiotics and immunizations. Now that 1 in 5000 Americans is 100 or over, researchers are investigating the ageing process and how to slow it.

According to biologist Andrew Steele, the author of Ageless, we have been treating medicine in an unsystematic way. We have been focused on the endpoints of ageing, problems like heart disease and cancer, but not addressing the fundamental causes for these maladies.

But this is changing, and medicine is slowly shifting to a holistic approach where we first understand these hallmarks but then come up with treatments that intervene with them directly. This would mean a switch to preventative treatments, which can proceed earlier in life and stop people from getting age-related diseases in the first place.

For example, treatments for cellular senescence (chronic inflammation) already exist that target many redundant cells by killing them, preventing them, or removing them from the body, along with a toxic set of molecules that accompany them, contributing to cancer and heart disease.

These drugs have been shown to help extend the lives of mice, with fewer cancers, cataracts, and heart disease, even making them less frail as they age. Eventually, these same drugs may be given to humans.

Closing Thoughts

We have made several advances in medical science that have extended our lifespans and made us healthier. The technology we are now creating is directly impacting our health, being more connected with our doctors, and allowing us and them to receive information sooner–keeping us healthier.

In the coming decades, we will likely use genetic engineering to prevent genetic diseases from appearing at all. It is an exciting time for the medical field, and we, as patients, will most certainly be the beneficiaries.

Longevity and the Future

Posted on May 5, 2023May 5, 2023 by Jean Chalopin

With continuous advancements in medical technology, the science of longevity has seen incredible progress in the past few decades. According to the World Health Organization, the global average life expectancy increased from 64.2 years in 1990 to 72.6 years in 2019.

The same report states that, in high-income countries, life expectancy at birth can reach up to 80 years. With ongoing research and advancements, there is a high probability that the average life expectancy will continue to rise in the future. In this article, we will explore the advances in the science of longevity, including the latest discoveries, potential future developments, and ethical considerations.

The Science of Longevity

The primary goal of longevity research is to improve the quality of life by extending the number of healthy years an individual can enjoy.

Several research areas contribute to the science of longevity, including genetics, epigenetics, stem cell research, and nutrition. Recent studies show that our lifestyle habits and environment also significantly determine our life span.

Lifestyle Habits

Studies show that our lifestyle habits and environment can significantly impact our lifespan. For example, a study published in the American Journal of Clinical Nutrition found that eating a diet rich in fruits, vegetables, whole grains, nuts, and legumes reduces mortality risk from all causes, including cardiovascular disease and cancer.

Similarly, a study published in the British Medical Journal found that quitting smoking can add up to 10 years to a person’s life expectancy. The study also found that even those who quit smoking in their 60s can still add several years to their lifespan.

Other studies have looked at the impact of exercise on lifespan. A study published in the journal PLOS Medicine found that individuals who engaged in regular physical activity had a reduced risk of premature death from all causes, including cardiovascular disease and cancer.

Stress is also a factor that can impact lifespan. A study published in the journal ‘Science’ found that chronic stress can accelerate ageing at the cellular level by shortening telomeres. The study suggests that stress management techniques like mindfulness meditation and yoga may help slow ageing and extend lifespan.

These studies demonstrate that our lifestyle habits and environment can significantly impact our lifespan. Making healthy lifestyle choices, such as eating a nutritious diet, quitting smoking, engaging in regular physical activity, and managing stress, can help to extend our healthy years and improve our overall quality of life.

Genetic Research

Genetic research has made significant progress in identifying the genes contributing to ageing and age-related diseases. Studies have identified several genetic variants associated with an increased risk of Alzheimer’s, cancer, and heart disease.

Researchers are also exploring the potential of gene editing technologies, such as CRISPR, to modify genes associated with ageing and disease.

One study published in Nature Genetics found a genetic variant associated with an increased risk of Alzheimer’s disease that affects the immune system’s ability to clear beta-amyloid protein from the brain.

Beta-amyloid protein is a hallmark of Alzheimer’s disease. Another study published in the journal Nature Communications identified a genetic variant associated with an increased risk of heart disease that affects the metabolism of fats in the liver.

Epigenetics Research

Epigenetics is the study of changes in gene expression without altering the underlying DNA sequence. Recent research has shown that epigenetic changes can significantly impact ageing and age-related diseases.

For example, a study published in Aging Cell found that specific epigenetic changes in the brain are associated with cognitive decline in ageing adults. Another study published in Nature Communications found that DNA methylation changes in the blood are associated with ageing and age-related diseases, such as cancer and cardiovascular disease.

Stem Cell Research

Stem cell research focuses on developing therapies to regenerate damaged tissues and organs. Recent advancements in stem cell research have shown promising results in animal studies, including restoring damaged heart tissue and reversing age-related muscle loss.

A study published in the journal Cell Stem Cell found that injecting old mice with muscle stem cells from young mice improved muscle function and strength in the older mice. Another study published in the journal Nature found that transplanting neural stem cells into the brains of ageing mice improved cognitive function.

Nutrition Research

Nutrition research has shown that a healthy diet can significantly impact our lifespan. Studies have shown that diets high in fruits, vegetables, whole grains, and lean protein can reduce the risk of chronic diseases and improve overall health. Researchers are also exploring the potential of calorie restriction and intermittent fasting to extend lifespan.

Case Study in Okinawa

The Okinawan population in Japan is a fascinating case study in the science of longevity. Okinawa is known for having one of the highest percentages of centenarians in the world, with a significant number of individuals living beyond 100. Researchers have been studying the factors that contribute to the long lifespan of Okinawans for many years.

One of the critical factors that researchers have identified is the Okinawan diet, which is high in fruits, vegetables, and whole grains and low in calories and saturated fat. The traditional Okinawan diet consists of sweet potatoes, vegetables, tofu, seaweed, and fish. The diet is rich in antioxidants and anti-inflammatory compounds, which may help to reduce the risk of chronic diseases such as cardiovascular disease and cancer.

Regular physical activity is another factor that contributes to the longevity of Okinawans. Many Okinawans engage in physical activity, such as walking, gardening, and traditional martial arts practices. This physical activity may help to reduce the risk of age-related diseases and maintain physical function in old age.

Social connections are also a crucial factor in the longevity of Okinawans. Many Okinawans maintain strong social connections throughout their lives, which can provide emotional support and a sense of purpose. Studies have shown that social isolation is associated with increased mortality risk and poor health outcomes, emphasising the importance of social connections for overall health and longevity.

In addition to these lifestyle factors, genetic and environmental factors may also contribute to the longevity of Okinawans. Researchers have identified several genetic variations that may play a role in the long lifespan of Okinawans, including variations in genes related to insulin sensitivity and inflammation. Environmental factors, such as low pollution levels and high exposure to natural light, may also contribute to the longevity of Okinawans.

Potential Future Developments

The future of longevity research looks promising, with ongoing advancements in medical technology and genetic analysis. Here are some potential future developments in the field of longevity.

Anti-Aging Drugs

Several drugs that can delay ageing and age-related diseases are currently in development. These drugs work by targeting specific genes and proteins that are associated with ageing and age-related diseases.

Gene Editing

Gene editing technologies such as CRISPR can potentially modify genes associated with ageing and disease. Researchers are exploring the potential of these technologies to extend lifespan and reduce the risk of age-related diseases.

Regenerative Therapies

Regenerative therapies such as stem cell treatments have shown promising results in animal studies. Researchers are exploring the potential of these therapies to regenerate damaged tissues and organs in humans.

Artificial Intelligence

Artificial intelligence (AI) can potentially revolutionise the field of longevity research. AI can analyse large datasets and identify patterns to help researchers develop new therapies and treatments.

Ethical Considerations

The potential to extend lifespan raises several ethical considerations that must be addressed. One concern is the unequal distribution of life-extending therapies.

If these therapies are only available to the wealthy, it could widen the gap between the rich and the poor. Another concern is the potential for overpopulation and strain on resources if the population continues to age and live longer. Researchers and policymakers must consider these ethical implications as they develop new therapies and treatments.

Closing Thoughts

In conclusion, the science of longevity has made significant progress in recent years, thanks to advancements in medical technology and research. Genetic, epigenetics, stem cell, and nutrition research have contributed to our understanding of ageing and age-related diseases.

Future developments in anti-ageing drugs, gene editing, regenerative therapies, and artificial intelligence promise to extend a healthy lifespan. However, researchers must also consider the ethical implications of extending lifespan, including unequal distribution of therapies and strain on resources. With ongoing research and advancements, the future looks bright for the science of longevity.

What Is Messenger RNA?

Posted on April 18, 2023April 18, 2023 by Jean Chalopin

The flow of genetic information is an essential process in biology that involves the transfer of genetic material from DNA to RNA to proteins. At the heart of this process is a molecule called messenger RNA (mRNA).

Without mRNA, cells would not be able to create the proteins necessary for various cellular processes, and genetic information would not be able to flow from the nucleus to the cytoplasm.

This article discusses what mRNA is and its role in molecular biology.

Defining Messenger RNA

Messenger RNA is a ribonucleic acid (RNA) molecule that plays a central role in the flow of genetic information in cells. mRNA molecules are transcribed from DNA in the cell nucleus and carry the genetic information encoded in the DNA to the ribosomes, the cellular organelles responsible for protein synthesis.

The mRNA molecule is a single-stranded RNA molecule complementary to the DNA sequence from which it was transcribed. The sequence of the mRNA molecule is determined by the order of nucleotides in the DNA template strand, with each three nucleotides, called a codon, corresponding to a specific amino acid. The sequence of amino acids, in turn, determines the sequence of the protein that will be synthesised.

mRNA synthesis is initiated by binding an enzyme called RNA polymerase to a specific region of the DNA called the promoter. The RNA polymerase then unwinds the double helix structure of the DNA and begins transcribing the DNA sequence into a complementary mRNA molecule. As the mRNA molecule is synthesised, it is processed to remove non-coding regions called introns and join the coding regions, called exons, to create a mature mRNA molecule.

The mature mRNA molecule is then exported from the nucleus and travels to the cytoplasm, where it binds to ribosomes. The ribosome reads the sequence of nucleotides on the mRNA molecule in groups of three, each corresponding to a specific amino acid. As the ribosome moves along the mRNA molecule, it synthesises a protein by joining its amino acids in the order specified by the mRNA sequence.

mRNA in Therapeutic Intervention

Messenger RNA has become an essential tool in therapeutic intervention due to its ability to control protein expression and serve as a template for the production of specific proteins. mRNA-based therapies offer several advantages over traditional protein-based therapies and small molecule drugs.

First, mRNA-based therapies are highly specific, encoding the exact protein of interest. This specificity allows for targeted therapies that selectively block a disease-causing protein’s activity or replace a missing or defective protein.

Second, mRNA molecules are rapidly degraded in cells, allowing for precise control over the protein expression level. This feature makes mRNA-based therapies more flexible and adaptable than traditional protein-based therapies, where the protein dosage can be challenging to regulate.

Finally, mRNA molecules can be modified chemically to improve their stability, increase their efficiency, and target them to specific cells or tissues. These modifications allow for greater control over the delivery and distribution of the mRNA molecule and its encoded protein.

mRNA Vaccines

One of the most significant advances in messenger RNA-based therapies has been the development of mRNA vaccines, such as the Pfizer-BioNTech and Moderna COVID-19 vaccines. These vaccines use mRNA molecules encoding the spike protein of the SARS-CoV-2 virus to stimulate an immune response against the virus.

The mRNA is encapsulated in lipid nanoparticles and delivered to cells, where it is translated into the spike protein. The immune system recognizes the protein as foreign and produces antibodies against it, protecting against infection.

Messenger RNA-based therapies have also shown promise in treating various diseases, including cancer, where they can be used to induce the expression of tumour-suppressing proteins or target cancer cells for destruction by the immune system. In one recent study, mRNA was used to encode chimeric antigen receptor (CAR) T cells, genetically engineered immune cells that target and destroy cancer cells. The mRNA-encoded CAR T cells were highly effective in killing cancer cells in vitro and in mouse leukaemia models.

mRNA-based therapies offer a powerful new disease treatment and prevention approach, potentially revolutionising medicine.

mRNA Use Cases

There are several use cases for mRNA-based therapies.

Cancer Immunotherapy

In a study published in Nature, researchers used mRNA to program immune cells to attack cancer cells. The mRNA encoded a chimeric antigen receptor (CAR) that recognized and targeted cancer cells. When the mRNA was delivered to immune cells in vitro, the resulting CAR T cells were highly effective in killing cancer cells. The researchers suggest that this approach could be used to develop personalised cancer immunotherapies.

Genetic Disease Therapy

In a study published in the New England Journal of Medicine, researchers used mRNA to treat a patient with cystic fibrosis. The mRNA encoded a functional copy of the CFTR gene, which is mutated in patients with cystic fibrosis. The mRNA was delivered to the patient’s lungs via nebulisation. The treatment improved lung function and reduced respiratory symptoms, suggesting that mRNA-based therapies could effectively treat genetic diseases.

Vaccine Development

The development of mRNA-based vaccines has been one of the most exciting recent applications of mRNA technology. The Pfizer-BioNTech and Moderna COVID-19 vaccines, which use mRNA to encode the spike protein of the SARS-CoV-2 virus, have been highly effective in preventing COVID-19 infection.

A study published in the New England Journal of Medicine found that the Pfizer-BioNTech vaccine was 95% effective in preventing COVID-19 infection in clinical trial participants.

These case studies demonstrate the potential of messenger RNA-based therapies in various applications, including vaccines, cancer immunotherapy, and gene therapy.

Risks With mRNA

Despite the promise of messenger RNA (mRNA) as a powerful tool for therapeutic intervention, there are several risks and challenges associated with its use.

One of the primary concerns is the potential for off-target effects, where the mRNA molecule produces unintended proteins or triggers an immune response against normal cells. This risk can be minimised through careful selection and design of the mRNA molecule and its delivery system, but it remains a significant challenge in developing mRNA-based therapies.

Delivering mRNA molecules to their target cells presents another challenge. mRNA is a large, hydrophilic molecule that rapidly degrades in the bloodstream and other extracellular fluids. Delivering mRNA molecules to cells is essential for their effectiveness in protein translation. Researchers have developed several approaches to overcome this challenge, such as utilising lipid nanoparticles to protect mRNA molecules from degradation.

Another risk associated with mRNA-based therapies is the potential for immune system activation or adverse reactions. Though these are generally mild and short-lived, mRNA vaccines have been associated with side effects such as injection site pain, fever, and fatigue. More serious adverse events have been reported in rare cases, such as anaphylaxis.

Finally, there is a risk that the rapid degradation of mRNA molecules in cells could limit the effectiveness of mRNA-based therapies. mRNA molecules have a relatively short half-life in cells, which could limit the duration of protein expression and the therapeutic effect. However, this risk can be mitigated by using modified mRNA molecules that are more stable or by administering multiple doses of the mRNA over time.

Closing Thoughts

While mRNA-based therapies offer many exciting disease treatment and prevention possibilities, they also present significant challenges and risks. Careful consideration and planning will be required to maximise the potential benefits of mRNA-based therapies while minimising the risks.

However, recent case studies have demonstrated the potential of mRNA-based therapies in various applications, including vaccines, cancer immunotherapy, and gene therapy. The development of mRNA-based therapies represents an exciting frontier in biomedicine, with the potential to revolutionise how we treat and prevent disease.

Drug Discovery and AI

Posted on April 13, 2023April 13, 2023 by Jean Chalopin

There is very little that is efficient in drug discovery and development. Approximately 90% of medications that reach commercialisation fail. Each one costs more than $1 billion and takes ten years to develop.

However, technical developments in data collection are advancing artificial intelligence in drug discovery, which might open the door to discovering treatments for disorders that have eluded medical researchers for millennia.

How Does Drug Discovery Work?

Drug discovery procedures are often drawn-out and laborious. Academic or industrial scientists create molecules. They search for ‘targets’ (such as proteins), where the molecule can go in the body to deliver treatment.

Researchers must ensure that the molecule doesn’t mistake a healthy protein for a target. Otherwise, a drug floating about in the body may bind to and destroy a healthy cell, causing a poisonous effect. Once a target is obtained, it is removed from the body and tested against molecules in the laboratory to see what sticks.

However, when clinical trials move forward, many of these medications fail due to unanticipated toxicity in the body or the drug itself not performing as well in people as it did in the lab. This is why most investments fail.

Fortunately, artificial intelligence (AI) can improve the efficiency of drug discovery.

AI Improves Drug Discovery

Platforms for drug discovery can more accurately forecast the effects of drugs early on by utilising data. AI links molecules with targets and models how they will behave within the body, increasing the likelihood that they will survive clinical trials and reduce patient toxicity rates.

Although the impact of AI on conventional drug discovery is still in its infancy, when AI-enabled capabilities are added to a traditional process, they can significantly speed up or otherwise improve individual steps and lower the costs of conducting expensive experiments. AI algorithms can alter the majority of discovery jobs (such as the design and testing of molecules) so that physical trials are only necessary to confirm findings.

Pharma companies are partnering with AI drug discovery platforms, with Amgen and Generate Biomedicines announcing a deal worth up to £1.9 billion in 2022.

Pharma businesses need to prepare for a future in which AI is frequently utilised in drug research, given the revolutionary potential of AI. The applications are many, and pharma businesses must decide where and how AI can best contribute.

New players are ramping up quickly and providing considerable value. In practice, this entails taking the time necessary to comprehend the full impact that AI is having on R&D. This includes separating hype from real accomplishment and realising the distinction between standalone software solutions and end-to-end AI-enabled drug discovery.

The AI-First Approach

There are five elements to an AI-first approach in drug discovery.

Vision and Strategy

Companies must create an AI roadmap that outlines specific, high-value use cases compatible with certain discovery initiatives. Focus and prioritisation are crucial.

Businesses should choose a limited number of use cases that are dispersed throughout the various research stages. Otherwise, AI will be viewed as a sideline and not directly related to the company’s R&D strategy or financial objectives.

Technology and Data

Prior to creating a complete tool or platform, concentrate on developing a proof-of-concept algorithm: the bare minimum analysis that verifies your capacity to draw insightful conclusions from your data in a particular scientific environment. If the insights prove worthwhile, you can spend money industrialising the tool and improve the user interface.

AI Partnerships

To be the preferred partner for big AI players, pharma companies must adopt new behaviours and ways of working. Partnerships are a powerful strategy for accelerating AI discovery and building a true value proposition.

Companies should consider how they share data, what their culture looks like in the context of AI, and how quickly they can adapt their business model to new technology. Although those things may not seem critical to the business, they will be for potential AI partners.

Internal Resource Management

Data scientists and engineers are a unique breed. They do not always fit into companies and cultures that are primarily focused on medicine.

But pharmaceutical businesses need more than just expertise in data science and software. Senior decision-makers will probably need to be trained on how AI-generated recommendations are made if only to stop suggestions from being revalidated using conventional methods. To interpret and adequately test the results of the algorithms, medical scientists must be knowledgeable about the analytical methodologies required but not necessarily fluent in them.

Drug Discovery Datasets

Data is pivotal for successful AI and machine learning deployments. Large-scale datasets help to build models for machine learning that can evaluate whether molecules have investable potential.

Researchers can quantify the strength of molecules in binding to a protein. Specific drug interactions and combinations do not react favourably and must be avoided by patients. Vast data volumes help to identify the positive and negative combinations quickly.

Embedding AI Within Drug Discovery

AI represents a new era in drug discovery. Companies will need to couple a clear vision with a healthy amount of ambition to be successful.

Choose an area to apply AI and be specific about the improvements you want to see. Decide whether to alter the discovery program using an AI-first model or to utilise AI for optimising the present discovery process.

Further, it can be challenging to scale AI. Teams frequently adhere to well-established procedures and feel at ease using instruments with a long history of success. Businesses need to demonstrate their commitment to AI by focusing on complete processes and thoroughly reevaluating current operating methods.

It’s best to include the entire organisation in the AI journey. In order to overcome hesitancy, management should emphasise the transformational vision, share value proofs and lessons from inside teams, and gradually develop a wave of enthusiasm.

AI is here for good, and it is for us to harness its power. What can be better than using AI to cure previously unsolvable diseases?

Artificial Intelligence and Biomedicine

Posted on March 17, 2023 by Jean Chalopin

Two unlikely interweaving sciences, artificial intelligence and biomedicine, have changed our health and lives. These two sciences have now intertwined further, aiding scientists, medical professionals, and, ultimately, all of us to improve our ongoing health so we can live better lives. This article will introduce some of the ways these two sciences are working together to solve medical mysteries and problems that have plagued us for generations.

Combining With Artificial Intelligence

The field of biomedical sciences is quite broad, dealing with several disciplines of scientific and medical research, including genetics, epidemiology, virology, and biochemistry. It also incorporates scientific disciplines whose fundamental aspects are the biology of health and diseases.

In addition, biomedical sciences also aim at relevant sciences that include but are not limited to cell biology and biochemistry, molecular and microbiology, immunology, anatomy, bioinformatics, statistics, and mathematics. Because of this wide breadth of areas that biomedical sciences touches, the research, academic, and economic significance it spans are broader than that of hospital laboratory science alone.

Artificial intelligence, applied to biomedical science, uses software and algorithms with complex structures designed to mirror human intelligence to analyse medical data. Specifically, artificial intelligence provides the capability of computer-trained algorithms to estimate results without the need for direct human interactions.

Some critical applications of AI to biomedical science are clinical text mining, retrieval of patient-centric information, biomedical text evaluation, assisting with diagnosis, clinical event forecasting, precision medicine, data-driven prognosis, and human computation.

Medical Decision Making

The Massachusetts Institute of Technology has developed an AI model that can automate the critical step of medical decision-making. This process is generally a task for experts to identify essential features found in massive datasets by hand.

The MIT project automatically identified the voicing patterns of patients with vocal cord nodules (see graphic below). These features were used to predict which patients had or did not have the nodule disorder.

Vocal nodules may not seem like a critical medical condition to identify. However, the field of predictive analytics has increasing promise, allowing clinicians to diagnose and treat patients. For example, AI models can be trained to find patterns in patient data. AI has been utilised in sepsis care, in the design of safer chemotherapy regimens, to predict a patient’s risk of dying in the ICU or having breast cancer, among many others.

Optoacoustic Imaging

At the University of Zurich, academics use artificial intelligence to create biomedical imaging using machine learning methods that improve optoacoustic imaging. This technique can study brain activity, visualise blood vessels, characterise skin lesions, and diagnose cancer.

The quality of the images rendered depends on the number of sensors used by the apparatus and their distribution. This novel technique developed by Swiss scientists allows for a noteworthy reduction in the number of sensors needed without reducing the image quality. This allows for a reduction in the costs of the device and increases the imaging speed allowing for improved diagnosis.

To accomplish this, researchers started with a self-developed top-of-the-end optoacoustic scanner with 512 sensors, which produced the highest-quality images. Next, they discarded most of the sensors, leaving between 32 and 128 sensors.

This had a detrimental effect on the resulting image quality. Due to insufficient data, different distortions appeared on the images. However, a previously trained neural network was able to correct for these distortions and could produce images closer in quality to the measurements obtained with the 512-sensor device. The scientists stated that other data sources could be used and enhanced similarly.

Using AI to Detect Cancerous Tumours

Scientists at the University of Central Florida’s Computer Vision Center designed and trained a computer how to detect tiny particles of lung cancer seen on CT scans. These were so small that radiologists were unable to identify them accurately. The AI system could identify 95% of the microtumors, while the radiologists could only identify 65% with their eyes.

This AI approach for tumour identification is similar to algorithms used in facial recognition software. It will scan thousands of faces, looking for a matching pattern. The University group was provided with more than 1000 CT scans supplied by the National Institutes of Health with the Mayo Clinic collaboration.

The software designed to identify cancer tumours used machine learning to ignore benign tissues, nerves, and other masses encountered in the CT scans while analysing the lung tissue.

AI-Driven Plastic Surgery

With an always-increasing supply of electronic data being collected in the healthcare space, scientists realise new uses for the subfield of AI. Machine learning can improve medical care and patient outcomes. The analysis made by machine learning algorithms has contributed to advancements in plastic surgery.

Machine learning algorithms have been applied to historical data to evolve algorithms for increased knowledge acquisition. IBM’s Watson Health cognitive computing system has been working on healthcare applications related to plastic surgery. The IBM researchers designated five areas where machine learning could improve surgical efficiency and clinical outcomes:

Aesthetic surgery
Burn surgery
Craniofacial surgery
Hand and Peripheral Surgeries
Microsurgery

The IBM researchers also expect a practical application of machine learning to improve surgical training. The IBM team is concentrating on measures that ensure surgeries are safe and their results have clinical relevance–while always remembering that computer-generated algorithms cannot yet replace the trained human eye.

The researchers also stated that the tools could not only aid in decision making, but they may also find patterns that could be more evident in minor data set analysis or anecdotal experience.

Dementia Diagnoses

Machine learning has identified one of the common causes of dementia and stroke in the most widely used brain scan (CT) with more accuracy than current methods. This is small vessel disease (SVD), a common cause of stroke and dementia. Experts at the University of Edinburgh and Imperial College London have developed advanced AI software to detect and measure small vessel disease severity.

Testing showed that the software had an 85% accuracy in predicting the severity of SVD. As a result, the scientists assert that their technology can help physicians carry out the most beneficial treatment plans for patients, swiftly aiding emergency settings and predicting a patient’s likelihood of developing dementia.

Closing Thoughts

AI has helped humans in many facets of life, and now it is becoming an aid to doctors, helping them identify ailments sooner and determine the best pathways to tackle diseases. AI performs best with larger data sets, and as the volume of data increases, the effectiveness of AI models will continue to improve.

The current generation of machine models uses specific images and data to solve defined problems. More abstract use of big data will be possible in the future, meaning that more extensive data sets of disorganised data will be combined, and high-quality computers (potentially quantum computers) will be allowed to make new inferences from those data sets.

For example, when multiple tests like blood pressure, pulse-ox, EKG, bloodwork, and other tests, including CT and MRI scans, are all combined, the models may see things that doctors did not piece together. This is when machine learning will take medicine to the next level, providing even more helpful information to doctors to help us live longer and healthier lives.

Next Generation DNA Sequencing

Posted on January 3, 2023January 3, 2023 by Jean Chalopin

A sequence tells a scientist the genetic information carried on a particular DNA or RNA segment. For example, the sequence can be used to determine where genes are located on a DNA strand and where regulatory instructions turn those genes on or off.

In the mid-90s, colleges started teaching their undergraduates about DNA sequencing, with DNA sample amplification tech as the new kid on the block. The Human Genome project was ongoing, and the first human sequence had yet to be completed.

Twenty-five years later, DNA sequencing is done regularly for many and has helped dramatically with medical and forensic needs. We are now entering a whole new era of sequencing that is the “next generation.” Let’s look at this change and how this generation alters science and medicine.

What Is Next Generation DNA Sequencing?

Next generation DNA sequencing (NGS) started gaining notoriety in the early 2010s and is a term that describes DNA sequencing technologies that have revolutionized genomic research.

Original DNA Sequencing

To understand NGS, we need to understand the original type of DNA sequencing.

First, a DNA strand was copied to create enough material. Then one by one, the base pairs were determined using gels with capillaries that pulled them through using electricity, the chain-termination method, or as it is commonly known, Sanger sequencing.

The Human Genome Project used Sanger sequencing, which multiple international teams utilized to decipher the human genome, taking 13 years and $3 billion to produce the final draft released in 2003. By 2008, using several NGS techniques, the famous discoverer of DNA, James Watson’s genome sequence, was provided to him on a hard drive at an estimated cost of $1 million.

In 2011, Apple co-founder and billionaire Steve Jobs had his DNA sequence done to help in his cancer fight for $100,000. Using NGS, a lab can now sequence an entire human genome in only one day at the cost of $100 (Ultima Genomics).

Source: National Human Genome Research Institute

How NGS Works

NGS involves a stepwise process (four total steps) that breaks up the sample (DNA or RNA) and sequences the parts simultaneously to get faster results.

The process is generally as follows:

1. Sample preparation involves fragmenting DNA/RNA into multiple pieces (millions for the human genome) and then adding “adapters” to the ends of the DNA fragments.

2. Cluster generation is where the separated strands are copied millions of times to produce a larger sample.

3. Sequencing the libraries: each of the strands is sequenced with unique fluorescent markers.

4. A genomic sequence is formed by reassembling the strands using data analysis techniques.

In principle, the NGS concept is similar to capillary electrophoresis (gels used to sequence DNA in Sanger sequencing). The critical difference is that with NGS, because the fragments are broken up, the sequences of millions of fragments are obtained in a massively parallel fashion, improving accuracy and speed while reducing the cost of sequencing.

NGS’ Impact

Compared to the conventional Sanger sequencing method’s capillary electrophoresis, NGS’ short-read massively parallel sequencing technique is a fundamentally different approach that revolutionizes our sequencing capabilities, launching the second generation of sequencing methods.

NGS allows for the sequencing of both DNA and RNA at a drastically cheaper cost than Sanger sequencing, and it, therefore, has revolutionized the studies of genomics and molecular biology.

NGS’ Advantages

Because NGS can analyze both DNA and RNA samples, it’s a popular tool for functional genomics. In addition, NGS has several advantages over microarray methods.

· A priori knowledge of the genome or of any genomic features is not a requirement.

· NGS offers single nucleotide resolution, which detects related genes and features, genetic variations, and even single base pair differences. In short, it can spot slight differences in code between two samples.

· NGS has a higher dynamic signal range, making it easier to read.

· NGS requires less DNA or RNA as an input (nanograms of material are sufficient).

· NGS has higher reproducibility. Because of its other advantages, the chance of an error between repeated tests is reduced.

Most Common NGS Technologies

Three sequencing methods are and were widely used that fall under the NGS umbrella:

· Roche 454 sequencing (discontinued in 2016). This method uses a pyrosequencing technique that detects a pyrophosphate release. It uses bioluminescence (a natural light signal). Broken-up DNA stands had unique markers attached.

· Illumina (Solexa) sequencing. The Illumina process simultaneously identifies DNA base pairs. This is done as each base emits a different and unique fluorescent signal, continuously added to the nucleic acid chain.

· Ion Torrent (Proton/PGM) sequencing. This kind of sequencing measures the direct release of positive Hydrogen protons when incorporating individual base pairs. They are released when added by a DNA polymerase. The Ion Torrent method differs from the previous two methods because it is not using a light measurement to do the sequencing.

How Is NGS Being Used?

The advent of NGS has changed the biotechnology industry. There are now new questions that scientists can ask and get the answers to that were either cost-prohibitive or the samples needed were more significant than the available material. The main applications possible with NGS include:

· Rapidly sequencing the whole genome of any life form, from prions and RNA viruses to individual humans and other mammals.

· Utilize RNA sequencing to discover novel RNA variants and splice sites.

· Quantify mRNAs for gene expression.

· Sequence cancer samples to study rare variants, specific tumor subtypes, and more.

· Identify novel pathogens (such as viruses in bats).

What Can NGS Do?

Notable organizations, such as Illumina, 454 Life Sciences, Pacific Biosciences, and Oxford Technologies Nanopore, are working on getting prices down so nearly anyone can get sequencing done. For example, Ultima Genomics has claimed a cost of $100 for its sequencing. Now, companies are marketing benchtop sequencing platforms that will bring these advances to as many labs as possible.

The Illumina NextSeq Sequencer (above) is a benchtop system that can do nearly any task except “Large Whole-Genome Sequencing.” However, there is a cost of $210,000-335,000.

We expect NGS to become more efficient and affordable over time, and these cost reductions will revolutionize several genomics-related fields. Currently, all NGS approaches demand “library preparation” after the DNA fragmentation step, where adapters are attached to the ends of the various fragments. That is generally followed by a DNA amplification step to create a library that can be sequenced with the NGS device.

As we know more about different DNA molecules, we can develop ways to fight disease through gene therapy or particular drugs. This knowledge will help change our way of thinking about medicine.

Third Generation Sequencing

A new class of sequencing tech, called third-generation sequencing or TGS, is being developed. These technologies can sequence single DNA molecules without the amplification step, producing longer reads than NGS.

Single-molecule sequencing was started in 2009 by Helicos Biosciences. Unfortunately, it was slow and expensive, and the company went out of business in 2012. Nonetheless, other companies saw the benefit and took over the third-gen space.

Pacific Bioscience has its “Single-Molecule Sequencing in Real Time (SMRT),” and Oxford Nanopore has nanopore sequencing. Each can produce long reads of 15,000 bases from a single DNA or RNA molecule. This evolution means smaller genomes can be produced without the biases or errors inherent to amplification.

Closing Thoughts

The DNA sequence is a simple format in which a broad range of biological marvels can be projected for high-value data collection. Over the past decade, NGS platforms have become widely available, with the costs of services lowering by orders of magnitude, much faster than Moore’s law, democratizing genomics, and putting the tech into the hands of more scientists.

Third generation sequencing will require robust protocols and practical data approaches. The coming expanse of DNA sequencing will require a complete rethinking of experimental design. Still, it will accelerate biological and biomedical research, enabling the analysis of complex systems inexpensively and at scale. We can then fight and prevent genetic diseases before they become realized issues.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. Using this, you agree that the information does not constitute investment or financial instructions. Do research and reach out to financial advisors before making any investment decisions.

The views, thoughts, and opinions expressed in this text are solely the authors’ views, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.