Swedish immuno-oncology specialist BioInvent International (Nasdaq Stockholm: BINV) announced a clinical trial collaboration and supply agreement with MSD International Business GmbH, a subsidiary of Merck &amp; Co (NYSE: MRK) for a Phase I/IIa study of its monoclonal antibody BI-1910 in combination ...

BioInvent announces new clinical trial collaboration with Merck

We think that this gap in valuation multiple will narrow in favor of Pfizer .

After A 30% Fall In A Year Is Pfizer Stock A Better Pick Over Merck?

The asset in question, dubbed STRO-003, targets the receptor tyrosine kinase-like orphan receptor 1 (ROR1) tumor antigen. ROR1 is overexpressed in various cancers—including solid tumors and blood cancers—and the target is also being chased by the likes of Boehringer Ingelheim and Merck &amp; Co.

Ipsen joins ADC race via $900M biobuck deal with Sutro, chasing same target as Merck, Boehringer

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Merck & Co. Inc. stock underperforms Monday when compared to competitors

Merck &amp; Co., Inc.&#x27;s stock price is up 20% YTD, supported by approvals, a positive outlook for 2024. Check out why I’m going with a buy rating for MRK stock.

Merck: Better Times Ahead On Approvals And Outlook (NYSE:MRK) | Seeking Alpha

Merck & Co., Inc. provides healthcare solutions worldwide. The company offers therapeutic and preventive agents for cardiovascular, type 2 diabetes, chronic hepatitis C virus, HIV-1 infection, intra-abdominal, fungal infection, insomnia, and inflammatory diseases; neuromuscular blocking agents; cholesterol modifying medicines; and anti-bacterial and vaginal contraceptive products. It provides products to prevent chemotherapy-induced and post-operative nausea and vomiting; treat non-small-cell lung, ovarian and breast, esophageal, thyroid, cervical, and brain cancers; and prevent diseases caused by human papillomavirus, as well as vaccines for measles, mumps, rubella, varicella, shingles, rotavirus gastroenteritis, and pneumococcal diseases. In addition, the company offers drugs for hepatocellular and merkel cell carcinoma; antibiotic and anti-inflammatory drugs for infectious and respiratory diseases, fertility disorders, and pneumonia in cattle, bovine, and swine; vaccines for poultry; parasiticides for sea lice in salmon; and antibiotics and vaccines for fish. Further, it provides companion animal products; diabetes mellitus treatment and anthelmintic products for dogs and cats; products to treat fleas, ticks, mosquitoes, and sandflies; horse fertility management products for swine; and dog, cat, and horse vaccines. Additionally, the company offers services and solutions that focus on engagement, clinical, and health analytics. Merck & Co., Inc. has collaborations with AstraZeneca PLC; Bayer AG; Eisai Co., Ltd.; Almac Discovery Ltd.; Skyhawk Therapeutics, Inc.; Ridgeback Biotherapeutics; Shanghai Junshi Biosciences Co., Ltd.; and FUJIFILM Corporation. It serves drug wholesalers and retailers, hospitals, and government agencies; managed health care providers; and physicians and physician distributors, veterinarians, and animal producers. The company was founded in 1891 and is headquartered in Kenilworth, New Jersey.

Merck

Elon Musk on Friday that he found the new Twitter CEO. The billionaire announced Linda Yaccarino as the new CEO of Twitter. said was the former head of global advertising and partnerships at NBC Universal. She was also the head of advertising and client partnerships at NBC Universal for eight years. Linda Yaccarino NBC Universal announced on Friday morning that Linda was leaving the company. She had been with NBC Universal for over a decade. https://twitter.com/elonmusk/status/1657050349608501249?embedable=true Elon Musk previously said that the new CEO would join in six weeks, and after her joining, he will step down as the CTO of but still be the executive head. Elon will now oversee products, software & sysops. Twitter Linda had been with NBC Universal for over a decade, and her work was to find and approach advertisers and improve collaboration with them. She was also the key figure in the launch of NBC’s ad-supported streaming service Peacock. She had previously worked for 19 years at Turner Entertainment and has been credited for increasing the company’s advertisement revenues. She is the perfect person to become Twitter’s CEO because Twitter is seeing a decline in ad revenues. Many advertisers like Coca-Cola, Jeep, Merck Unilever and have stopped their ads on Twitter since Elon Musk took over in October 2022 because of many of Musk’s many controversial actions like firing half the Twitter employees, banning journalists, restricting Substack links and introducing paid verification programme. Meta Elon Musk wants to reduce Twitter’s reliance on advertisers so it can freely support “free speech.” This is also one of the reasons brands are pulling out from Twitter. Elon Musk is also the CEO of the car manufacturing company and rocket manufacturer and launcher SpaceX. Tesla Both companies have seen a decline in stocks since Elon became the CEO of Twitter. Investors of both companies have been complaining since October last year that Elon is spending most of his time on Twitter and not focusing on his two main businesses. Linda has years of experience with advertisers and she can be useful to bring advertisers back to the platform and attract potential advertisers.

Who is Twitter's New CEO?

Take a look at these statistics: Artificial intelligence can according to a recent study by Insider Intelligence reduce drug discovery costs by up to 70% AI can on clinical research while cutting the phase’s length by half save pharma $28 billion a year AI can spare pharma companies on R&D costs up to $54 billion annually It’s not surprising that pharmaceutical companies to cut the costs and time required for drug development. The global AI in pharma market and is estimated to surpass $9,241 million by 2030, growing at a CAGR of 29.4%. turn to medical AI services was valued at around $905 million in 2021 Motivated to learn more about how using artificial intelligence in the pharmaceutical industry can improve your drug development processes? Then continue reading. 5 top use cases of AI in the pharma sector You will notice that it’s common for pharma companies to team up with tech innovators to successfully deploy AI. Accenture conducted a survey where in profit after partnering with a tech vendor, with 76% of pharma executives citing effective partnership as a key success factor. 61% of the respondents reported at least a 5% increase Here are 5 top applications of artificial intelligence in pharmaceutics. Drug discovery The Congressional Budget Office reports that the R&D costs of , which includes research and clinical trials. developing a new drug can exceed $2 billion Deploying AI in pharma enables researchers to sift through enormous datasets, such as small molecule libraries and spot disease patterns, and learn which chemical compositions can be a good fit for various biological targets. AI can generate chemical compounds either as a text string or as a graph architecture. It’s important to validate the resulting compounds, as many of them will not make sense, could be toxic, or could contain a component that shouldn’t be a part of any drug. In addition to discovering candidate compositions, scientists can use AI algorithms to parse medical literature on how to best synthesize the drug and design clinical trials. Research shows that pharmaceutical artificial intelligence can , saving the pharma sector up to $26 billion in annual expenses. cut drug synthesizing and screening time by 50% There are many great examples of pharma companies to facilitate drug discovery. For instance, GSK, a British pharmaceutical company headquartered in London, partnered with California’s Vir Biotechnology during the pandemic with the help of AI and a human gene editing tool, CRISPR. Vir already had an antibody platform that it deployed to discover drugs for different respiratory pathogens in the past. And now, in this collaboration, they discovered sotrovimab, an antibody to neutralize COVID-19. deploying AI solutions to accelerate COVID-19 antibody discovery that binds to a SARS-CoV-2 epitope In another example of collaboration between Europe and the US, a French pharma and healthcare company Sanofi partnered with California-based biotech innovator Atomwise to discover and synthesize drug compounds for five different targets. Sanofi wanted to steer clear of the traditional drug discovery approach and for their innovation and AI capabilities. paid Atomwise $20 million upfront Clinical trials AI has many . One of them is identifying the right candidate participants. The technology can analyze patient data, genetic information, doctor notes, and other information, and pick people who are eligible for a particular trial. AI can even help decide on the optimal population size based on the existing description of similar trials. applications in clinical trials within their target time frame. due to recruitment-associated challenges. 86% of clinical trials fail to recruit enough patients One-third of phase Ⅲ clinical trials have to stop For instance, relies on analytics and natural language processing (NLP) to analyze patient information. The tool can handle unstructured data, like doctor’s notes, and produce an insightful patient summary. Clinical researchers use these highlights to select and recruit patients. IBM Watson As AI helps pharma companies to find patients, it also works the other way around. , a clinical trial patient recruitment platform, uses NLP to analyze their text and screen them for trial inclusion/exclusion criteria. It requires patients to answer a few simple questions on its platform and suggests a list of trials that the person can join. Antidote Drug manufacturing Deploying AI in the pharmaceutical industry offers multiple opportunities to improve the drug production process. The technology could: . AI can inspect drugs on the conveyor belt and spot defects, such as damaged packaging. Moreover, the technology can identify any potential issues by analyzing manufacturing data, like quality control tests. For instance, AstraZeneca employs looking for defects, while Merck applies AI to spot problems in vaccine vials. Assist in drug quality control machine learning to analyze drug images . AI can monitor equipment on the production line and identify potential defects through sensors that measure equipment vibration, temperature, sound, etc. This gives employees time to fix the device before it breaks down, halting production. Facilitate predictive maintenance . AI can analyze data on energy consumption, raw material waste, and other parameters and put forward recommendations on how to . Also, the technology can predict demand, so that pharma manufacturers avoid producing large quantities of drugs that will not be consumed and will otherwise go to waste. Cut down on material waste improve the manufacturing process Drug marketing The pharma sector largely depends on sales. Companies aim to reach as many customers as possible while offering a distinctive user experience and a customized approach. Artificial intelligence in pharma can facilitate drug marketing by: Comparing past marketing campaigns and identifying the most profitable approaches. The technology can also analyze customer engagement tactics and pick the most successful ones. Aggregating customer data in real time to understand their behavior and what they are looking for to create a tailored advertisement. Optimizing pricing of new drugs by considering all involved stakeholders and data on similar medications. Simulating different market scenarios by forecasting changes in demand, competitor behavior, etc. This allows pharmaceutical firms to be prepared for sudden landscape changes. Finding new consumers for existing drugs. For instance, for Chantix (a drug that helps people quit smoking). The tool analyzed data from the Centers for Disease Control and Prevention to identify previously untapped population segments. Pfizer relied on AI to spot and reach new potential customers Drug dosage optimization AI can analyze large quantities of unstructured patient data and calculate the optimal dosage of a particular drug for this person to achieve the best possible results with minimal side effects. Artificial intelligence models in the pharma industry can analyze the following information: Medical history, such as doctor’s notes, lab test results, genetic makeup Medical images, such as Magnetic Resonance Imaging (MRI) scans Biomarkers, such as protein levels and genetic mutations Drug characteristics, such as its metabolism Potential side effects of a drug and of similar drugs When the optimal dosage is calculated, the technology can monitor its effectiveness and make adjustments when needed. To give a real-life example, a California-based company Dosis built an AI-driven personalized medicine dosing platform that dialysis clinics can use to manage chronic drug intake. In his interview with HealthcareITNews, Dosis’ CEO Shivrat Chhabra mentioned this platform helped clients while improving patient outcomes. reduce drug consumption by 25% Challenges associated with implementing AI in pharma Some of these obstacles are specific to the field, and some are more general and apply to all projects involving this technology. One of the key challenges is the . This is particularly hard as the expenses associated with drug development are already rather high. You can turn to experienced AI consultants to learn how to cut down on costs and still get a viable product. enormous costs associated with artificial intelligence Here are other prominent challenges that you can face during pharmaceutical AI implementation. Data quality and quantity According to a recent study by McKinsey, the on the way to applying analytics in the healthcare field. lack of integrated data sources was the chief obstacle Pharma AI models typically require large datasets to learn. However, it’s a challenge to obtain a sufficient dataset for each disease, especially the rare ones. So, as training datasets are getting smaller, the data that an AI-powered drug development tool has to handle is rather complex. Think of patient data. It includes historical information, genetic makeup, doctor notes, medical scans, etc. Under these conditions, it’s a challenge to build accurate algorithms. When training data is lacking, it’s possible to use synthetic data generators for some pharma applications. For instance, suitable for pharmaceutical usage. Healthcare data is among the most sensitive data types, and privacy is of the essence in such applications. Synthetic datasets can solve this issue. As Andreas Ponikiewicz, VP of Global Sales at Mostly AI, puts it, Mostly AI claims it can generate data “With generative AI based synthetic healthcare data, that contains all the statistical patterns, but is completely artificial, the data can be made available without privacy risk.” Another option for acquiring data for experimenting with AI and pharma is to become a part of a specialized collaboration. For example, the Massachusetts Institute of Technology initiated the Machine Learning for Pharmaceutical Discovery and Synthesis Consortium. 13 pharma companies joined the consortium to design and build AI algorithms for small molecule discovery. You need to make sure that the data used within pharmaceutical applications is all realistic. But it’s rather costly to verify that, as it requires the intervention of human experts. Lack of interoperability and a unified data standard There are still multiple , which means that each hospital can adopt a standard of their choice for data storage and formatting. This makes it hard to integrate and use patient data needed for drug-related research from different medical facilities. healthcare IT standards and regulations These issues of AI in the pharma industry can be addressed on the governmental level. For instance, the Swiss Personalized Health Network (SPHN) is a health data unifying initiative by the Swiss government. The that streamlines medical data exchange among Swiss hospitals, research institutes, and regulatory bodies. SPHN was set to build a national infrastructure On an individual level, pharma researchers can benefit from platforms like , which uses NLP to scan and extract data from heterogeneous electronic health record (EHRs) systems. Deep 6 AI Algorithmic bias “All data is biased. This is not paranoia. This is fact.” – Dr. Sanjiv Narayan, professor of medicine at Stanford University. if their training dataset wasn’t representative of the target population. Data bias has specifically been a problem in the pharmaceutical and healthcare sectors. Research shows that only a offer evidence on covering the bias issue. AI-powered models can easily develop bias few AI-powered products submitted for FDA approval Some medical professionals if data scientists work more closely with clinicians and learn more about data while building the algorithms. They can request information, such as where the data came from and what was the original goal of gathering it. Then engineers can make tweaks to the algorithms to address any population misrepresentation. believe that it will help reduce bias Algorithms can also acquire bias as they continue to learn on the job. Hence, systematic audits are essential to ensure that all AI-based tools are still relevant and work as expected. Integration with existing systems Deploying AI in pharma implies integrating it with the existing platforms and applications. Many pharma companies still rely on outdated legacy systems that are not designed to work with AI or deal with a large amount of data. Such systems use their proprietary protocols and are hard to . integrate with modern applications Pharma companies that want to use modern technology alongside legacy systems can benefit from designed to fit seamlessly with the existing legacy systems. custom pharma software solutions The complexity of the pharmaceutical applications The use cases of artificial intelligence in the pharmaceutical industry are rather complex, and there is a large room for error in the predictions that the technology makes. Here is what makes pharma so intricate: Every patient has individual characteristics and many factors to consider in clinical trials. If you are developing a drug for liver-related issues, you need to find trial participants with no other health conditions that can influence and sway your results. The need to consider the interaction between different drugs as one person might be taking multiple drugs to treat different conditions. Disease variability as one medical condition can have several variants and manifest itself in different ways. Training datasets describing diseases and treatments are not balanced, which can force the algorithm to recommend the most frequently occurring solution even if it’s not the correct one. To sum it up Deloitte reports that have witnessed some progress over the past years. And the consultancy believes AI in pharma can change this. In addition to the applications mentioned above, , which is vital in this field. only a few of the 7,000 rare diseases that we know AI can help pharma companies achieve compliance If you want to incorporate this advanced technology into your business, you are likely to have to team up with a tech vendor of your choice. Also, it’s a good practice to: Make sure your training dataset is realistic, even if the verification process is expensive and requires human expert intervention Incorporate AI into your strategy instead of treating it as a side project Build strong AI skills or outsource this to dedicated teams Encourage a close collaboration between your data scientists and clinicians Beware of the latest regulations regarding using AI in pharma, as these are changing rapidly Build your own ethical expertise to address any concerns associated with AI and pay a special attention to privacy and security if you decide to collaborate with other players in the field and share your data Regularly monitor the algorithms’ performance for bias and inaccuracy, be it for disease discovery, recruitment of trial participants, or even drug advertising. For example, the University of California on social media and discovered that AI models tend to excessively recommend these drugs to Latino and African-Americans customers. conducted a study on advertising mental health medication When using AI to generate chemical compounds, always validate the results, as it can deliver toxic or otherwise unsuitable components Speaking of the future of artificial intelligence in the pharmaceutical industry, that will include the following players: PwC predicts the emergence of a new digital health ecosystem Solution vendors, who will offer personalized treatments, drug dosages, etc. Orchestrators, who can use AI and analytics to address patients’ needs Platform providers, who will mediate between the aforementioned players And according to the consultancy, firms who will still refuse to make AI a part of their operations will turn into a mere “contract manufacturers” for the rest of the ecosystem. So, if you haven’t yet considered enhancing your business processes with AI, this seems like a good time to experiment with the technology. Are you looking to save time and money on drug development and clinical trial organization? ! We will help you build and train AI models and integrate them seamlessly into your system. Drop us a line

A Pill a Day: What AI Can REALLY Do in the Pharma Sector

Una pastilla al día: lo que la IA REALMENTE puede hacer en el sector farmacéutico

Une pilule par jour : ce que l'IA peut VRAIMENT faire dans le secteur pharmaceutique

ए पिल ए डे: एआई वास्तव में फार्मा सेक्टर में क्या कर सकता है

A Pill a Day: AI が製薬業界で本当にできること

Uma pílula por dia: o que a IA REALMENTE pode fazer no setor farmacêutico

Một viên thuốc mỗi ngày: AI THỰC SỰ CÓ THỂ LÀM GÌ TRONG NGÀNH DƯỢC PHẨM

一天一粒：人工智能在制药行业的真正作用

AI drug discovery is exploding. Overhyped or not, investments in AI drug discovery from $450 million in 2014 to a whopping $58 billion in 2021. All pharma giants, including Bayer, AstraZeneca, Takeda, Sanofi, Merck, and Pfizer, have stepped up spending in the hope to that will bring cost efficiency, speed, and precision to the process. jumped create new-age AI solutions Traditional drug discovery has long been notoriously difficult. It takes and costs to bring a new drug to the market. And this is only the case for drugs that succeed in clinical trials (only one in ten does). at least 10 years $1.3 billion Hence, the interest in finding new ways we discover and design drugs. AI has already helped identify promising candidate therapeutics, and it didn’t take years, but months or even days. In this article, we will explore how AI drug discovery is changing the industry. We will look at success stories, AI benefits, and limitations. Let’s go. How drugs are discovered The drug discovery process typically starts with scientists identifying a target in the body, such as a specific protein or hormone, that is involved in the disease. Then they use different methods to find a possible solution, a drug candidate, including: : Scientists can screen libraries of compounds (natural products or chemicals) they made before, to check if any of them have the desired activity or interaction with the target. Screening existing compounds : They can use computer modeling and simulation to develop novel chemical compounds that can do the job. This approach is used to create small molecule drugs, which are chemically synthesized compounds less than 1,500 daltons in size. De novo drug design : Researchers can also generate biological molecules like antibodies, enzymes, or proteins to act as drugs. This involves isolating or synthesizing molecules from living organisms that can interact with the target. Compared with small molecules, such molecules are typically larger and more complex. Biologics : Scientists can take a look at compounds that were developed for something else and see if they have therapeutic potential for the disease in question. Repurposing Once a potential drug candidate (called lead compound) is found, it is tested in cells or animals, before moving on to clinical trials which include three phases, starting with small groups of healthy volunteers, and then proceeding to larger groups of patients suffering from the specific condition. How AI is applied that involve using sophisticated computational methods to mimic elements of human intelligence such as visual perception, speech recognition, decision-making, and language understanding. Artificial Intelligence covers various technologies and approaches AI began back in the 1950s as a simple series of “if, then rules” and made its way into healthcare two decades later after more complex algorithms were developed. Since the advent of deep learning in the 2000s, have expanded. AI applications in healthcare A few AI technologies are empowering drug design. Machine Learning focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed. Machine learning (ML) ML solutions encompass a diverse array of branches, each with its own unique characteristics and methodologies. These branches include supervised and unsupervised learning, as well as reinforcement learning, and within each, there are various algorithmic techniques that are used to achieve specific goals, such as linear regression, neural networks, and support vector machines. ML has many different application areas, one of which is in the field of AI drug discovery where it enables the following: Virtual screening of compounds to identify potential drug candidates Predictive modeling of drug efficacy and toxicity Identification of new targets for drug development Analysis of large-scale genomic and proteomic data collected from living organisms (DNA sequences, gene expression levels, protein structures, etc.) Optimization of drug dosing and treatment regimens Predictive modeling of patient responses to treatment Deep Learning Deep Learning (DL) is a subset of ML based on using artificial neural networks (ANNs). ANNs are made up of interconnected nodes, or “neurons,” that are connected by pathways, called “synapses.” Like in the human brain, these neurons work together to process information and make predictions or decisions. The more layers of interconnected neurons a neural network has, the more “deep” it is. Unlike supervised and semi-supervised learning algorithms that can identify patterns only in structured data, DL models are capable of processing vast volumes of and make more advanced predictions with little supervision from humans. unstructured data In AI drug discovery, DL is used for: Improved virtual screening of compound libraries to identify hits with a higher probability to bind to a target Image-based profiling to understand disease-associated phenotypes, disease mechanisms, or a drug’s toxicity More accurate prediction of how a drug will be absorbed, distributed, metabolized, and excreted from the body (pharmacokinetic properties) Prediction of drug-target interactions and binding affinity Prediction of the structure of proteins that account for most of the currently identified drug targets Generation of novel drug-like compounds with the desired physical, chemical, and bioactivity properties and protocol design Automation of clinical trial processes Natural Language Processing (NLP) NLP relies on a combination of techniques from linguistics, mathematics, and computer sciences, including DL models, to analyze, understand, and generate human language. AI drug discovery research often uses NLP to extract information from both structured and unstructured data to accomplish the following: Text mining of scientific literature to identify associations between chemical/drug entities, their targets, and novel disease-related pathways Extracting structured information from unstructured electronic health records (EHRs), such as patient demographics, diagnoses, and medications Identifying adverse drug events by analyzing text data from social media, news articles, and other sources Determining clinical trial eligibility criteria based on protocols and matching patients to trials Summarizing drug information Why AI drug discovery is the talk of the town now In the last couple of years, companies across the pharmaceutical sector have taken steps to incorporate AI into their research methods. This includes building in-house AI teams, and , backing startups with an AI focus, and teaming up with technology firms or research centers. hiring AI healthcare professionals data analysts A combination of factors is driving this trend. The increasing power of computers and new AI developments Recent tech advances have shifted the traditional focus of AI drug discovery research. As the majority of companies in the sector (around 150 in 2022 according to ) continue to be busy with designing small molecules, which are easy to represent computationally and compare at scale, there is also a growing interest in new applications of AI in drug discovery. BiopharmaTrend AI Report Many companies are beginning to embrace AI for designing biologics (77 companies) and discovering biomarkers that indicate the presence or progression of a disease (59). Others are focused on building all-embracing AI drug discovery platforms, identifying new targets, or creating ontologies — structured representations of relationships between different entities such as chemical compounds, proteins, and diseases. Widening access to AI tools As the shortage of AI talent shows no sign of abating, the entry barriers to AI drug discovery have actually reduced. Tech vendors and pharma giants are releasing increasingly sophisticated AI platforms, including ready-to-use no-code and drag-and-drop systems that enable non-AI experts to integrate artificial intelligence into their research. These developments are playing a major role in the accelerated adoption of AI by the industry. AI-enabled success stories AI drug discovery projects pursued in academia and the industry have already produced the first successful results across the value chain of drug discovery. Examples include: DeepMind has built the AI system AlphaFold that can predict a protein’s 3D structure from its one-dimensional amino acid sequence in seconds rather than months or years that it would normally take. The system was used to predict over 200 million protein structures belonging to animals, plants, bacteria, fungi, and other organisms. University of Washington researchers have developed a deep learning model that uses gaming computers to calculate protein structures within 10 minutes. Deep Genomics has used AI technologies to screen more than 2,400 diseases and 100,000 mutations to predict the exact disease-causing mechanism in a Wilson disease mutation and create a DG12P1 drug in 18 months. Aladdin has released a proprietary AI drug discovery platform for commercial use in virtual screening, hit-to-lead, lead optimization, and the preclinical phase. This platform helped Aladdin identify a number of drug compounds for a potential treatment of age-related diseases. IBM has developed the Watson system with cognitive computing capabilities that is used by the pharmaceutical industry for matching patients to the right-fit clinical trials for their condition. In a clinical trial for breast cancer, the platform demonstrated an increase of 80% in enrollment and a reduction in trial matching time. It has taken less than three months for AbCellera to develop a monoclonal antibody for neutralizing viral variants of COVID-19 and obtain approval from the US Food and Drug Administration (FDA). BenevolentAI has combined its knowledge graph with AI tools to uncover baricitinib as a potential COVID-19 treatment in several days. BioXcel Therapeutics has accelerated the discovery of dexmedetomidine as a sedative for patients with schizophrenia and bipolar disorders. The company obtained FDA approval for its proprietary sublingual film of dexmedetomidine (IgalmiTM) in less than four years after its first-in-human trials. Using AI, Exscientia has designed three small molecules to enter clinical trials over the span of two years (for the treatment of Alzheimer’s disease psychosis, obsessive-compulsive disorder, and immuno-oncology). In early 2023, Insilico reported positive topline results in a Phase 1 clinical trial of the first AI-designed novel molecule for an AI-discovered novel target to treat idiopathic pulmonary fibrosis (IPF). In 2021, reached the clinical stage, with their therapy areas including COVID-19, oncology, and neurology. 13 AI-derived biologics Benefits and challenges in AI drug discovery AI is a powerful tool that holds the promise of revolutionizing the pharmaceutical industry. With its ability to analyze vast amounts of data and make predictions, artificial intelligence can help researchers overcome the obstacles that have long hindered the drug discovery process by enabling: Reduced timelines for discovery and preclinical stages More accurate predictions on the efficacy and safety of drugs New, unanticipated insights into drug effects and diseases New research lines and new R&D strategies Cost savings through quicker analysis and automation According to Insider Intelligence, AI can save the pharmaceutical industry up to 70% of drug discovery costs. The potential of AI in drug discovery is truly exciting, but there are a few roadblocks that need to be tackled first to exploit it to the fullest. Data When it comes to AI, it always comes down to input data. Data silos and legacy systems that wouldn’t allow their consolidation are big hurdles to AI research in any domain. In the pharmaceutical industry, the problem may be even more pronounced. , be it results from clinical studies or de-identified patient information, while the troves of data they have may provide answers to questions that the original researcher never considered. Pharmaceutical companies have traditionally been bad at sharing data When it ultimately comes to sharing data, it’s often incomplete, inconsistent, or biased, used for predicting protein-ligand binding affinities that are crucial for drug discovery. In some cases, the data may not even be reflective of the entire population and the AI model may fall short in real-world scenarios. as is the case with datasets Complexity The sheer complexity of biological systems makes AI-enabled analysis and predictions of time and spatial changes in their behavior hard. There is a vast number of complex and dynamic interactions within biological systems where each element such as proteins, genes, and cells can have multiple functions and be affected by multiple factors, including genetic variations, environmental conditions, and disease states. Interactions between different elements can also be non-linear, meaning that small changes in one element can have a great impact on the entire system. For instance, a single gene that controls cell division can be responsible for the growth of a tumor, or interactions between multiple proteins can lead to the development of highly specific and complex structures such as the cytoskeleton of a cell. Another challenge is a lack of qualified staff to handle AI drug discovery tools. Interpretability The use of neural networks in AI drug discovery has pushed the boundaries of what is possible, but a lack of their interpretability poses a significant challenge. Referred to as black boxes, such AI models might produce the most accurate predictions possible but even engineers can’t explain the reasoning behind them. This is particularly challenging in deep learning, where the complexity of understanding the output of each layer escalates as the number of layers grows. This lack of transparency can lead to flawed solutions and reduce trust in AI among researchers, medical professionals, and regulatory bodies. To address this challenge, there is a growing need for the development of explainable, trustworthy AI. Wrapping up New drugs that are changing the game for patients continue to emerge. Just 15 years after HIV was identified as the cause of AIDS in the 1980s, the pharmaceutical industry has developed a multi-drug therapy that allows people affected by the virus to live a normal life span. Novartis’ Gleevec prolongs the lives of leukemia patients. Incivek from Vertex Pharmaceuticals has doubled hepatitis C cure rates. Keytruda from Merck reduces by 35% the risk of cancer coming back after patients had surgery to excise melanoma. But not all new drugs are created equal. conducted in Germany has revealed that only 25% provided significant advantages over existing treatments. The remaining drugs yielded either minimal or no benefits, or their impact was uncertain. A recent analysis of over 200 new medicines Given the costly and time-consuming nature of drug discovery, it’s clear the pharmaceutical industry needs major changes. And that’s where AI drug discovery could play a role. There is every chance that artificial intelligence can make a transformational contribution going beyond accelerating time-to-clinic. Thinking about your own AI drug discovery project? . With years of experience in creating AI solutions for healthcare, we are your right partner. Drop us a line

AI Drug Discovery: How It’s Changing the Game

Descubrimiento de fármacos por IA: cómo está cambiando el juego

Découverte de médicaments par IA : comment cela change la donne

एआई ड्रग डिस्कवरी: हाउ इट्स चेंजिंग द गेम

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Descoberta de drogas de IA: como isso está mudando o jogo

Khám phá thuốc AI: Nó đang thay đổi cuộc chơi như thế nào

人工智能药物发现：它如何改变游戏规则

The New York Times recently proclaimed It describes Google’s “dread” of interfaces based on the species of artificial intelligence called large language models (LLM), such as ChatGPT, that threaten to displace its dominance in search. . A New Chat Bot Is a ‘Code Red’ for Google’s Search Business Search proved the key factor in the modern economy, propelling far beyond that of the previous most valuable commodity in the world: oil and gas. the value of data Per the Times: “Over the past three decades, a handful of products like Netscape’s web browser, Google’s search engine and Apple’s iPhone have truly upended the tech industry and made what came before them look like lumbering dinosaurs. “Three weeks ago, made its case to be the industry’s next big disrupter." an experimental chat bot called ChatGPT The prime directive for Silicon Valley, coined by the late Andy Grove (former CEO of Intel), still applies: “ .” Yet… rumors of Google’s death are greatly exaggerated. Only the paranoid survive The purported panic fosters a misunderstanding of how Google operates, why it dominates, and how programs such as GPT3 and ChatGPT operate. #A beginner’s guide to Google’s (and LLMs) inner workings Google has the world’s largest (mega-billion dollar) supercomputer, built out of networked Linux devices and storage devices.  Reuters that Google was going to spend an additional $13B in 2019 just augmenting capacity. It keeps this beast fed by regularly crawling the web and by attracting a steady stream of user content including the world’s email, videos, geolocation data, and much much more data. . estimated By one estimate, at least an exabyte It is practically impossible for a mere AI compute function to overcome Alphabet’s massive advantages from its installed base. Unless Google fumbles. Highly unlikely. ChatGPT has a different sort of compute challenge resulting in a different architecture. This keeps it from being an immediate threat to Google. It is predicated on large scale linear algebra operations and can get by with a much smaller data set. Furthermore, the corpus it learns, as opposed to what it searches, is fixed. Searchwise, that’s a fatal limitation. While ChatGPT can search for more recent information about the pandemic, GPT3 knows nothing about  COVID-19. It was trained on a pre pandemic corpus. This prevents it from playing David to Google’s Goliath. AI interfaces, indeed, could create a threat to attracting a stream of user content. That is the case, however, only if the purveyors of LLM-based interfaces, like OpenAI, are already prepared to store, safeguard, and monetize that data. Currently none are so prepared. Furthermore, these programs give no particular advantage to finding data in the middle of a video or detecting piracy of video content.  Remember, Alphabet, Google’s parent, owns YouTube, the Fort Knox of video. #The future of search There are new approaches on the horizon to expanding what is searchable. These almost certainly will prove of great value for accessing the many specialist data repositories that are currently effectively unsearchable by Google’s search engine. GitHub, the go to repository of software code, is a great example. Even if there are in the world, that’s a tiny drop in the bucket of the world’s online population. 30M programmers and software developers Yet, software developers, on average, control vastly more deployable capital than the average citizen of planet Earth. This is not necessarily through their personal wealth. They exercise their market power  through their collective influence on their clients and employers. As such, they, their clients and employers are more able and more willing to pay for expanded capacity to search repositories like GitHub, which is currently only searchable via metadata and social means. Code is opaque to automated investigation of its structure and behavior. There are even fewer biologists and chemists actively involved in drug development. That said, they very likely control even more deployable capital than software developers (on average). Companies like Pfizer, Lily, Merck, and AstraZeneca have enormous capital. They are even more able, and certainly willing, to pay for expanded search capabilities. They are especially keen because it is significantly less expensive to run a computer search than it is to run a physical assay. The potential of the Kyoto Genes and Genomics database, for example, is largely untapped because we cannot see into the dynamics of the data stored there. That’s a spectacularly valuable niche application.  It is, however, irrelevant to the vast majority of rank-and-file web-searchers who vastly outnumber data specialists. For the foreseeable future, the expansion of search will not be in consumer markets. It will be in technical specialist, government, and enterprise markets. One of the first things specialists (like the lead author here) do is to test ChatGPT on common sense reasoning over specialist data sets. At this it fails . miserably #Back to the future Google’s past could be a map to AI’s future. Google missed video search.  It paid for that by with $1.65B in a stock-for-stock swap in 2006.  Great move, as YouTube is , two orders of magnitude greater than what they paid for it. Good deal all around, but to the point Google expanded its search hegemony. buying YouTube now estimated to be worth $180B In , America’s premier futurist, George Gilder, has suggested the end of big data as a siloed commodity behind the firewalls of the big five. Supposedly, this will be due to developments of decentralizing technologies, such as blockchain. Indeed, the co-author’s review of Life After Google suggested that innovation rather than regulation is a better way of domesticating big data. Life After Google However, we can’t look to LLMs as the relevant innovation. While the computer architectures of LLMs and other similar AI-based approaches are not the same as Google’s, they are still massively centralized and run counter to the decentralizing trend. They may yet extend the life of centralized big data, until scalable decentralizing technologies, now on the horizon, come to fruition. Apple, Amazon, Alphabet (Google’s holding company),  Meta (Facebook’s holding company), and Microsoft today are the Masters of the Digital Universe. That won’t last. Eventually, sooner than you might think, they are likely .  The penalty for failure to innovate is severe. to go the way of Kodak. Xerox or Blockbuster As the Greek’s taught us, – extreme wealth – tends to lead to – overweening pride.  Which is then, always, slain by the goddess . oblos hubris Nemesis Google’s reported “dread” at the advent of ChatGBT is very much a bullish, not bearish, signal.  It implies that Google’s leadership has not succumbed, and does not appear at risk of soon succumbing to . hubris Only the paranoid survive. By Lucius Gregory Meredith and Ralph J. Benko

Is Google Doomed?  Only the Paranoid Survive….

Straits Research Latest 2022 Report: The Global Microcephaly Industry Analysis Research Report represents a comprehensive study on the Microcephaly industry including current trends and status. The report can help to understand the market in-depth and strategize for business expansion accordingly in the future. In the strategic analysis process, it gives insights from marketing channel and market positioning to potential growth strategies, providing in-depth analysis for new entrants or exists competitors in the now and in the future. Microcephaly Industry Analysis Aimed to offers the most segmented consumption and sales data of downstream consumption fields and competitive landscape in various regions and countries around the globe, this report analyses the latest market data from the primary and secondary authoritative sources also. Global Microcephaly Industry Analysis is expected to grow at a during the forecast period 2019–2026. CAGR 8.6% Market Key Drivers, Restraints, and Opportunities: On the contrary, technological advancements are expected to open lucrative opportunities for the market players in the future. Get Exclusive Sample of Report on Microcephaly Industry Analysis is Available Here @ https://straitsresearch.com/report/microcephaly-market/request-sample Cumulative Impact of COVID-19 on Microcephaly Industry Analysis: COVID-19 is a unique global public health emergency that has affected almost every industry in the world, and the long-term effects are predicted to influence the industry growth during the estimated period. Our ongoing research amplifies our research framework to ensure the enclosure of underlying COVID-19 issues and potential paths forward. The report delivers insights on COVID-19 considering the changes in consumer behavior and demand, purchasing patterns, re-routing of the supply chain, dynamics of present market forces, and the significant involvements of governments. The updated study offers market insights, industry analysis, estimations, and forecasts, considering the COVID-19 impact on the market. Global Microcephaly Industry Analysis is Segmented Based Segmentation and Region. Congenital, Postnatal Magnetic Resonance Imaging (MRI), Head Computed Tomography (CT) Scan, Ultrasound Test, Other Tests Diagnostic Centers, Hospitals, Ambulatory Surgical Centers, Others End User By Onset Type, By Diagnosis, By End User, Do You Have Any Query Or Specific Requirement? 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Click Here to Access the Full Report @ https://straitsresearch.com/report/microcephaly-market Table of Contents of Microcephaly Industry Analysis: It includes key vendors covered, key market segments, the scope of products offered in the global Flanged Heaters market, years considered, and study objectives. Additionally, it touches on the segmentation study offered in the report on the basis of the market segments. Study Coverage: It gives an overview of key studies of industry, market growth rate, competitive landscape, key restraints, market drivers, key trends, and industry issues, swot analysis, and macroscopic indicators of the market. Executive Summary: Here, the report offers information regarding to import and export details, production, revenue, market sales, and key vendors of all regional markets studied. Production by Region: Every vendor profiled in this section is studied on the basis of SWOT analysis, their products, market production, value, capacity, and other vital factors. Profile of Manufacturers: Click Here to Access the Toc of Microcephaly Industry Analysis Report @ https://straitsresearch.com/report/microcephaly-market/toc About Us: StraitsResearch.com is a leading market research and market intelligence organization, specializing in research, analytics, and advisory services along with providing business insights & market research reports. Contact Us: Email: sales@straitsresearch.com Address: 825 3rd Avenue, New York, NY, USA, 10022 Tel: +1 6464807505, +44 203 318 2846 Website: https://straitsresearch.com/ List of Similar Industry Reports: https://thegameoflife-de.mn.co/posts/21232855?utm_source=manual https://beyondher.mn.co/posts/21232904?utm_source=manual

Microcephaly Industry Analysis; Technology Advancement, Trends and Growth 2029

We found drug ads targeted at users interested in everything from bourbon to therapy By Colin Lecher If you’re a drug manufacturer looking for patients, one company has become a major destination in the past few years: Facebook. The social media giant, through its power to target users based on their interests, is especially attractive to pharmaceutical companies looking to sell drugs to potential patients. The Washington Post that health and pharmaceutical companies spent almost $1 billion on just Facebook mobile ads in 2019. The draw? Unlike a traditional TV or radio ad, Facebook’s ad categories help those companies target their drug ads at users who likely suffer from a specific illness the drug treats. reported last year And data from The Markup’s Citizen Browser project—which collects Facebook data from thousands of users—shows how precise and wide-ranging that targeting is. Though Facebook does not offer advertisers categories that explicitly identify people’s health conditions, The Markup identified dozens of ads for prescription pharmaceuticals targeted at people with “interests” in topics like “bourbon,” “oxygen,” and “Diabetes mellitus awareness.” Indeed, The Markup found, “awareness” of a disease is a frequent proxy for illness in targeting decisions made by advertisers. Zejula, a drug manufactured by pharmaceutical giant GlaxoSmithKline, for example, is prescribed to treat advanced ovarian cancer. We found the drug targeted at users who Facebook determined had shown an interest in “cancer awareness.” Piqray, another cancer treatment, manufactured by Swiss company Novartis, was shown to users with an interest in “National Breast Cancer Awareness Month.” Several drugs were targeted at either “Diabetes mellitus awareness” or “Diabetes mellitus type 2 awareness.” “Stroke awareness?” Brilinta, from AstraZeneca. “Multiple sclerosis awareness?” Mayzent and Kesimpta, two other products from Novartis. “Chronic obstructive pulmonary disease awareness?” Trelegy, another GSK product. We also found drugs targeted by close proxies for other health conditions. Ads for Latuda, an antipsychotic from the company Sunovion used in the treatment of bipolar depression, were shown to users with an interest in the Depression and Bipolar Support Alliance, a nonprofit support group. We also found it targeted at users interested in therapy and the National Alliance on Mental Illness. Anoro, a GSK drug also used to treat chronic obstructive pulmonary disease, an inflammatory lung disease caused by emphysema and chronic bronchitis, was targeted at users interested in cigarettes, or just “oxygen.” Ads for Nurtec, a Biohaven Pharmaceuticals product used for treating migraines, were targeted at users who showed an interest in the “international classification of headache disorders,” a medical scale of headaches. The cancer treatment Keytruda, from Merck, was targeted at an especially wide array of users, including those interested in the chemical industry, Corona beer, and bourbon. In at least one case, we found, the interest targeted was barely even a proxy. Syprine, a Bausch Health drug used to treat a rare genetic condition called Wilson’s disease, was targeted at users who’d signaled an interest in “genetic disorder.” It’s not clear exactly how Facebook decides that a person is interested in topics such as “oxygen” or “genetic disorder.” The company only places some on how users can target health conditions. Advertisers can’t use “sensitive” personal health data to target their ads, meaning a pharmacy, for example, couldn’t upload a list of customers with a specific illness to market to them on Facebook. very specific limits The company has also broadly defended its health privacy practices, that said that “privacy and safety are particularly important for people dealing with health conditions.” releasing a statement in 2019 “We do not use medical history to inform the interest categories we make available to advertisers,” Facebook spokesperson Tom Channick said in a statement to The Markup. “Instead, people are placed into interest categories based on their activity on Facebook, including the pages they like or the ads they click on.” GSK, AstraZeneca, Bausch Health, Merck, Biohaven, and Sunovion, manufacturers of some of the drugs we saw ads for, didn’t respond to requests for comment on their Facebook advertising practices. Jamie Bennett, a spokesperson for Novartis, said the company’s “goal is to reach audiences where it matters” and that it follows all regulations, both legal and those set by services like Facebook. The potential audience size for these ads can be massive. Facebook’s ad-buying system says the number of users who have shown an interest in cancer awareness, for example, is nearly 203 million. Other categories are more narrow. Facebook’s system said the audience for users with an interest in the depression and bipolar support alliance was a relatively slim 250,000 users. Kirsten Ostherr, director of the Medical Futures Lab at Rice University, said there are both practical and moral problems with advertising around such medical conditions. If a work colleague walks by your computer screen and sees a targeted ad for a lung cancer drug, for example, he or she could deduce you have the condition. “Even if the advertiser doesn’t know exactly who you are,” Ostherr said, “if these ads are popping up on your screens everywhere, that potentially exposes information about you.” “If these ads are popping up on your screens everywhere, that potentially exposes information about you.” Kirsten Ostherr, Rice University Beyond that, targeting products by surveilling the sick inherently exploits vulnerable users’ privacy, Ostherr said. “One of the things that is most insidious about this to me is it’s really targeting and exploiting people who are in this vulnerable state and who have found real benefit from online patient communities,” she said. Legally, aside from providing a way for advertisers to disclose side effects, Facebook is under little obligation to shield its users’ health information. The United States’ health privacy law, the Health Insurance Portability and Accountability Act, or HIPAA, only applies to a handful of entities that directly deal with patient health information. Ostherr said the vagueness of Facebook’s targeting could be a “legally defensive maneuver” protecting it from liability in the future by keeping them as distant from protected health data as possible. In 2016, a lawsuit filed against Facebook claimed that the company had unlawfully invaded users’ privacy by tracking their visits to sensitive health-related websites. included a now-outdated list of health targeting options on Facebook, which included options like “cardiac arrest,” “renal failure,” and “West Nile virus.” (Facebook denied the allegations, calling the suit “ ”) The suit an attack on the way the Internet works. Courts have so far in the dispute, saying its users must agree to its advertising practices when they sign up for the service and that health information isn’t necessarily more private than other types of data. sided with Facebook But the data Facebook does have can be enormously sensitive, as some independent researchers have shown. In 2019, researchers from the University of Pennsylvania and Stony Brook University that they were able to predict whether a Facebook user had any of 21 health conditions through language in status updates alone. reported Many patients may have looked to Facebook for community and help in the past year of pandemic-enforced isolation. When someone finds support for an illness on social media, Ostherr said, it’s unlikely that the health data they’re giving away is at the forefront of their minds. “Their priority really is not being tracked from one website to another,” Ostherr said. “Their priority is surviving.” Originally published as " “ with the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license How Big Pharma Finds Sick Users on Facebook

How Facebook Serves Targeted Ads by Big Pharma

Where would you invest in an endless world of the pandemic? Pharmaceuticals, 5G network, Apple stock, cloud storage? Well, if you believed in no end to lockdowns, you’d probably invest in all the above and diversify. That’s precisely what Buffett and his team at Berkshire Hathaway have done. So, let me walk you through Buffett’s eight most significant moves during the last quarter: 1.Dumping bank stocks Buffett continues along with the trend from the second quarter. He’s only added to 9.2% to Bank of America but drastically reduced all other JPMorgan, Wells Fargo, and BNY Mellon positions. He’s bearish on the US financials, and as I wrote in , he probably understands that “the great reset” and the emergence of digital assets and CBDCs will disrupt the traditional banks. my last article 2.Buying pharmaceuticals Four out six new buys for the quarter were pharmaceutical companies. Berkshire has made new positions in Merck, Pfizer, Bristol Myers Squibb, and AbbVie.  They invested approximately 1.8 billion in each one of them, which leads me to conclude that he’s merely diversifying into the sector. This allocation is essentially buying him insurance against the pandemic. And it’s an excellent vaccine play as well. 3.Trimming his position in Apple Buffett reduced the Apple position by 3.7%. This was the most impactful move in Berkshire’s portfolio in the third quarter. But overall, he’s still extremely bullish on Apple. It takes almost half of his portfolio. Holding Apple stock during global lockdowns is one of the most brilliant plays. 4.Buying T-Mobile T-Mobile is one of the largest mobile network providers focusing on rolling out the 5G network. After it acquired Sprint, it became the second-largest mobile network in the US. So, if you believed in a prolonged pandemic, you’d want to own a piece of a company providing a fast connection to the confined population. 5.Selling his gold This move is surprising. First, Buffett shocked the world by ; now, he’s quickly selling half of the position. Both things are the most unlike him. If I had to speculate, I would say he’s finally realized that Bitcoin is better than gold. Popular television host and Bitcoin proponent Max Keiser recently that “Buffett will start panic-buying bitcoin at $50,000, just like Peter Schiff.” buying into gold tweeted 6.Snowflake Snowflake is a data platform built for the cloud dealing with giants like Microsoft, Amazon, or Salesforce. Snowflake makes it easier to work from home. One can surmise that he’s making a bet on the unending pandemic and the consequent growth of cloud-based data warehousing. But it could have been just an opportunistic trade since Buffett bought the shares in the private placement for only at $120. That’s about a half-price from the current market price. 7.Bullish on Japan Berkshire acquired a little more than a 5% stake in five Japanese companies: Itochu, Marubeni, Mitsubishi, Mitsui, and Sumitomo. He made these investments over the previous year, so he likely purchased just a small fraction during the third quarter. Japan has been one of the most rational countries in their approach to Covid-19. Japan has never imposed lockdowns and will likely continue with its model in the future.  That will provide the Japanese economy with an edge over the economies suffering from the draconian measures. So what does it mean for investors? Many investors look to Buffett's wisdom when it comes to investing in turbulent times. Obviously, the Oracle of Omaha does not think the worst of the pandemic is behind us. As he once said, “predicting rain doesn't count, building the ark does.” So go ahead and build your ark too.

How Buffett Is Hedging Against The Endless Pandemic

In June 2017, a variant of the Petya malware worm, NotPetya, wreaked global havoc. Global enterprise networks from Ukraine to the shores of New Jersey’s commercial shipping depots were eviscerated. The worm, which basically locked companies out of their own platforms without recourse, was developed (allegedly) by the Russian cyber-warfare branch (Sandworm) to target Ukraine in the countries’ ongoing stealth war. However, the worm quickly spread like a plague to systems running a distinct accounting software platform -- condemning enterprise networks to one path, restart from scratch. The fallout was an estimated in damages. In its wake, global shipping behemoth Maersk was left to reboot almost its entire network, as were companies like pharmaceutical giant Merck and FedEx subsidiary TNT Express. $10 billion And however lucky other firms may feel to have dodged such a fatal bullet, collateral effects of cyber warfare may become the new normal. After all, there are no geopolitical boundaries in digital space. The problem is that cyber warfare appears only to be gaining momentum. Fought behind computer screens, digital highways, and silicon, the conflicts are invisible but include the likes of China, the US, and Russia. According to recent book on cyberwarfare, every person’s device is now part of the security perimeter for an organization or network. Pair that notion with the galvanization of the IoT sector, and the picture of a landscape ripe for ratcheting up the cyber hacks begins to crystallize. Dr. Chase Cunningham’s So how do enterprises respond? How do they avoid being the collateral victim of the next NotPetya? Quite simply, fight technology with technology. Then, one of the ancillary advantages will be enterprises building network infrastructure congruent with the rise of the edge computing paradigm. The Phoenix for Cyberwarfare Consequences If state-backed hacking groups are constructing devastating worms that can spread like a viral pandemic, enterprises need to have the ability to: Spin up and manage networks with speed and ease. Begin using encryption by default Spinning up networks with volumes of endpoints quickly remains a legitimate problem in enterprise and cloud computing -- malware like NotPetya aside. Many systems are not congruent with the variety of new IoT and edge devices, nor are they capable of supporting an expanding generation of devices necessary to reach the full potential of IoT technology. For context, Maersk, a global shipping giant and the centerpiece of Wired Magazine’s coverage on NotPetya was still running Windows 2000 on some of its systems. Microsoft hasn’t supported Windows 2000 for years. The costs of outdated systems and ones susceptible to outages and hacks continue racking up. IT downtime costs businesses more than $700 million per year already, . according to the IHS Those costs, in many cases, result from far less onerous causes than cyber warfare products. Maybe it’s just a in a Cloudflare update or an internal mistake by an IT employee. bad snippet of code Cloud computing needs to revamp its core infrastructure. It needs to be protective against large-scale cyber warfare forces and bring cloud computing to the level that an IoT revolution requires. One of the optimal methods is by welding together IPv6 and Segment Routing -- called SRv6. One emerging internet startup, NOIA, refers to itself as building the “Waze” of Internet traffic, enabling enterprises to spin up and manage networks via an intuitive UI rapidly. They are already working with and expect their model to become popular because of its costs, performance, and management ease. firms like Oracle In the context of malware like NotPetya, should an enterprise fall victim to the collateral effects of a state-sponsored malware (almost certain to happen again), the firm will need to repair its core network rapidly. Even more so if that enterprise consists of thousands of user endpoints, those pesky security perimeters detailed by Dr. Cunningham. For example, stories from inside Maersk during NotPetya that were anonymously elicited to Wired Magazine indicate a laborious effort of thousands of employees, along with consulting giant Deloitte, to revamp Maersk’s multi-national global shipping logistics operations. NotPetya had destroyed the company’s entire set of domain servers save for one computer that happened to be offline at the time in Ghana. Like a Phoenix, Maersk was able to re-emerge from the ashes, but only at an exorbitant cost and effort. Public, decentralized infrastructure solutions like NOIA Network would be a boon for future instances of what Maersk experienced. Instead of waiting for the worst to upgrade their cloud computing security and Windows 2000-infused logistical hubs, enterprises would be prepared for the vociferous spread of malware. Networks could be preserved via DLT’s distributed infrastructure, and rapidly spun up again in the case of catastrophic losses. But more to the point of security; encryption, along with new developments like , are the future of enterprise security. Zero Trust Security Default Encryption Encryption has myriad benefits, but it is primarily to protect against third-party eavesdropping and black hat threats concurrently. Cryptocurrencies, blockchains, and zero-knowledge technology have pushed the notion of encryption into a golden age, but it may be the current backdrop giving it the extra nudge for more consideration by enterprises. Whether its government surveillance, hacker threats, or the security concerns of companies promoting work-from-home policies, encryption is set to take a major role this decade. Encryption by default gives enterprises peace of mind knowing that their network is automatically secure. Within the context of the growth of IoT (e.g., the security perimeters of a network), think about how much private data can be mined via a single vulnerability. Instances of hacks exposing user data populate the headlines seemingly every day. End-to-end encryption from the cloud infrastructure to the edge devices themselves is rapidly becoming a necessity. Otherwise, firms will face similar circumstances of Maersk, who downloaded the infected accounting malware on a single computer that eventually brought down its entire network. If there’s a demonstration of how negative convexity wreaks havoc on interconnected systems -- it doesn’t really get any better than Maersk. The type of intrusion from NotPetya is different than the typical BGP hijacks and service disruptions that NOIA Network solves. Still, it serves as a reminder about how a single vulnerability can upend everything. Combining NOIA with concepts like Zero Trust Networks, which assumes there is no implicit trust granted to user accounts of a network, and enterprises can make significant progress in the race to more performant, secure infrastructure. The premium on encryption and clever security models will only increase from here. The initial exploration of better cloud computing may have been focused on the rise of IoT and how companies can cater to a changing market, but it will be security threats that force their hand. If enterprises prepare for the worst (e.g., NotPetya) first, deploying more agile performant networks, then the benefits of being up to par on IoT requirements will follow. NotPetya is a warning of what’s possible when malicious technology escapes its cage, so enterprises need to meet those looming threats with technological ingenuity of their own.

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MERCK

merck.com

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Since 1891

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MERCK

EVERGREEN INDEX #6864

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