Applied Technology Review : News

There is an increasing sense of optimism around nanotechnology in healthcare, which is bringing about significant innovations in the diagnosis, treatment, and prevention of diseases. The use of nanotechnology in healthcare is bringing in new frontiers across the life sciences sector. With the potential to manipulate matters at atomic levels, nanotechnology has a massive ability to revolutionize aspects of medical care, such as diagnostics, disease monitoring, vaccine development, surgical devices, regenerative medicine, and drug delivery. It is also opening avenues to better treatment choices for several diseases through innovative research tools that can be leveraged for drug discovery. Here are the major ways in which nanotechnology is revamping the future of healthcare: • Smart Pills Smart pills refer to nano-level electronic devices that are designed like pharmaceutical pills but perform advanced functions like sensing, imaging, and drug delivery. Nanotechnology helped in developing several smart pills. More advanced smart pills being developed, which, when ingested, examines the gases in the human gut to detect any disorders. Its sensors can detect the levels of oxygen and carbon dioxide in the body and the presence of harmful substances. Its uses include diagnosing gastrointestinal disorders, identifying malignant digestive organs, and tracking food sensitivities to allow personalized diet and nutrition plans. • Cancer Detection and Treatment A major problem with regular chemotherapy and radiation is the damage due to the body’s healthy cells during the treatment. New nanomedicine methods are being leveraged in the treatment of skin cancer, which allows efficient delivery of drugs and other therapeutic treatments tumor sites and target cells with low toxic side-effects. • Nanobots Nanobots are micro-scale robots, which serve as miniature surgeons. They can be inserted into the body to replace intracellular structures. They can replicate themselves to correct a deficiency in genetics or even eradicate illnesses by replacing DNA molecules. Nanobots are presently being tested to perform eye surgery through a microscopic needle inserted into the retina. Surgeons can direct this needle with a specialized magnetic field. Nanobots can also be leveraged to clear artery blockages by drilling through them. ...Read more

Breakthrough Energy Catalyst published a request for proposals for large-scale deep green tech projects based in Europe. The request will trigger investments in a portfolio of high-potential projects in the areas of clean hydrogen, sustainable aviation fuels, direct air capture, and long-duration energy storage It is the first milestone of the EU-Catalyst partnership, which was launched in November 2021 at COP26 in Glasgow by European Commission President Ursula von der Leyen, Bill Gates, the Founder of Breakthrough Energy, and European Investment Bank President Werner Hoyer. Between 2022 and 2026, the Partnership would raise USD1 billion to help speed the deployment and commercialization of new technologies that are likely to help Europe meet its climate targets by 2030 and achieve carbon neutrality by 2050. The authorities aim to take a bold step forward in attaining our climate targets with the EU-Catalyst collaboration along with a worldwide technological revolution, massive investments, more financial risk-taking, and game-changing breakthroughs, as well as regulations that encourage global public-private collaborations. Horizon Europe and the Innovation Fund, both managed by InvestEU, provide funds for the EU-Catalyst Partnership. Three euros of private money are projected to be leveraged for every euro of state funds. The EU-Catalyst Partnership programme complements the numerous efforts which are already underway under the European Green Deal (EGD) and the National Recovery and Resilience Plans (RRP) funded by NextGenerationEU. The European Commission, the European Investment Bank, and Breakthrough Energy Catalyst are all part of the EU-Catalyst cooperation. It was first announced during the Mission Innovation Ministerial Conference in June 2021 by Commission President Ursula von der Leyen. The partnership would then aid in achieving the European Green Deal's goals as well as the EU's 2030 climate goal. The alliance will bring public and private sector partners together to invest in large-scale demonstration projects. In the initiatives, the European Investment Bank and Breakthrough Energy Catalyst are poised to give equal amounts of grants and loans. Breakthrough Energy Catalyst will mobilize partners to invest in initiatives and/or purchase the ensuing green products by leveraging similar private resources and charity assets. Through InvestEU or at the project level, the EU-Catalyst cooperation would be open to national investments by the EU Member States, as well as Norway and Iceland.   ...Read more

The pharmaceutical industry has been dominated by large pharmaceutical companies, often known as “big pharma”. This was for a very good reason. Developing drugs is incredibly expensive, time-consuming, and risky. Pharmaceutical companies spend hundreds of millions of dollars and years discovering new drugs, testing them, and then seeking regulatory approval. However, the majority of promising drug candidates fail to obtain regulatory approval because they do not have the necessary level of clinical benefit or have unacceptable side-effects. Artificial intelligence (AI) is changing the landscape by shortening discovery times whilst reducing the number of failed drug candidates.   In recent years, AI has become ubiquitous with modern businesses. Far from the realms of science fiction, almost every sector and industry has been changed in some way using AI to automate previously manual processes that took humans far longer to carry out. From finance to agriculture, AI has been implemented to assist humans in their work, improving accuracy, decision making, and time efficiency. The healthcare and especially the health tech industries are no different. Previously, healthtech companies developed traditional software technology to remind patients to take pills, facilitate virtual doctor’s appointments or allow those with diabetes to track blood sugar levels. Although these software applications are entirely useful, AI has now swept in and provided an entirely new and exciting opportunity for healthtech companies to interact with the pharma pipeline. Most importantly, the computing power of AI algorithms has specifically impacted the way healthtech companies can now enter the lucrative drug discovery, drug repurposing, and personalised medicine markets. The growth of AI healthtech startups has given rise to a need for patenting of not just the computer software but also inventions derived using the software to protect startups from losing out on monetising their innovations. However, using AI to help facilitate invention or innovation has become a contentious issue in recent months with the DABUS AI inventor patent cases receiving media attention on the issue as to whether an AI platform can be named as an inventor in a patent application – the answer was a firm “No”! The important thing to note is that in most cases in healthtech AI is not actually inventing but rather facilitating and speeding up innovation. There is no question that you can patent the insights that AI provides. Check Out This:  EdTech Startups in Europe The high barrier of entry to the pharma pipeline has been broken down by the introduction of AI that can do much of the leg-work operating on huge data sets using the power of modern computer processors, and at a fraction of the cost. What previously took the likes of AstraZeneca and GlaxoSmithKline thousands of iterations using hundreds of pharmacists and lab hours can now be done by a handful of data scientists and pharmacists with a computer and access to appropriate data sets. The ability to patent computer assisted discoveries allows AI startups in this field to quickly and securely monetise them to allow the company to become revenue generating. AI has allowed these startups to process vast amounts of patient data and drug data to find new drug treatments. For example, AI can be used to design the ideal structure for a completely new drug, by crunching data regarding the biological target. Al can also be used to match a disease with an unmet need with already-approved drugs, by analysing the complex pharmacology of drugs and the physiology of a disease. As every drug and disease has a profile, the computer can match the disease with a possible treatment. What the computer can do is match these elements rapidly and without stopping, whilst possibly learning which criteria are the most important. The silico data that AI provides may not necessarily yield new drug candidates, but there is no doubt it aids the drug discovery process by narrowing down the possible candidates and thus reducing the workload for the pharmacologists. It is an important tool. The drug candidates that may be identified by AI still require real world testing, but the time to reach this point is shortened. Once the drug candidate has been identified and verified in the lab, patent applications can be filed in the usual way. This combination of real-world data and a patent application has significant value and can be taken to a large pharmaceutical company for partnering, for example. Big pharma are often best placed to finance the large scale clinical trials needed before a drug can be approved. By using this strategy, both the tech startups and the big pharma “win”. The tech startup is able to deliver a partnerable asset in a realistic timescale (that often ties in with the investors’ requirements) and the big pharma saves money and time that they would have otherwise have needed to spend in early stage research (which for big pharma can be very costly due to the methods they use). Entry for tech startups funded by venture capital to do drug discovery using AI is now far lower. Previously companies were having to raise millions of pounds just to get to the stage where it had a potential drug candidate. Investors faced the prospect of putting in large sums of money and gambling that an effective drug was found. Often this didn’t happen, and the investors would lose everything. Now with the use of AI, investors can fund a startup business with a much lower level of capital and with increased confidence that the technology is going to deliver effective solutions.  These new technologies are also applicable to vaccine development. Traditionally, vaccine development is very slow and very difficult, especially for certain viruses. Despite this, AI is still being trialled in the search for vaccines, with some early success being shown. The key with AI is that the name somewhat misconstrues what it actually is. At present, AI is a complex algorithm or set of algorithms that churn through vast amounts of data to provide outcomes or insights. It is a tool. It does not answer a question, because it does not know what the question is. It does not invent. It assists pharmacists and data scientists in faster innovation to make discoveries. It is important to train the machine on reliable data and this is why it is vital that data scientists are involved in training the algorithms on good, unbiased data. Large medical research institutions, including the NHS, have loads of health data to mine. These data can help them train the algorithms to spot patterns in certain data sets of certain cohorts of patients. However, should the wrong or incomplete data sets be used to train the algorithms then the outcomes will be unreliable. There is a clear need for personalised medicine and one way to rapidly achieve this is through AI. Access to huge data sets and the ability to sift through vast quantities of it rapidly means that healthtech companies are able to develop personalised drug therapies. By looking at data for specific cohorts of people, AI algorithms are able to stratify patient populations and personalise therapies. Ultimately, the large pharmaceutical companies will start to recruit the sort of people at these healthtech businesses. They will also start to partner with digital innovation specialists outside of the business that can broaden or deepen the expertise in handling data to find these inventions. If a pharmaceutical company fails to develop a digital technology division or capacity they will be left behind. AI has already changed the way many businesses operate and has successfully proven itself as indispensable in modern business. Now, AI is set to change the pharmaceutical industry through rapidly increasing the speed and range of drug discovery, supporting clinical trials, and driving personalised medicine, and allowing smaller healthtech firms to thrive alongside big pharma. ...Read more