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Pharmacist

The goal is to ease workloads of overburdened pharmacists. But the company was also sued by a small Iowa pharmacy alleging that CVS violated antitrust laws. Among other news: Google led a funding round for Midi Health;

CVS Promises To Boost Hiring, Avoids A Repeat Pharmacists Strike

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Pfizer

The first treatment for acute migraine made by Pfizer Ltd has been recommended by the National Institute for Health and Care Excellence (NICE).

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Pfizer's first acute migraine treatment recommended by NICE

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Outsourcing-pharma

Japan

Pfizer is partnering up with Boston synthetic biology firm Ginkgo Bioworks, plus other life sciences news you may have missed.

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The Petri Dish: Ginkgo, Pfizer sign $331M deal; Leqembi OK'd in Japan

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The Business Journals

Boston

Decade

Pfizer chief executive Albert Bourla said artificial intelligence has the potential to accelerate everything from scanning for drug targets to enrolling patients in clinical trials.

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Pfizer chief, visiting Boston, sees ‘scientific renaissance’ over coming decade

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The Boston Globe

The large-cap biotechnology: pharmaceutical preparations company is based in the United States, and over the last twelve months it has returned a dividend yield of 5.0%. Pfizer has trailing twelve months earnings per share (EPS) of 3.76, which at today's ...

What Pfizer (PFE) Investors Need to Know Today

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Challenges

Across social media, people have expressed frustrations about the issues with booking appointments -- and taking time out of their day -- only for the provider, including major pharmacy chains like Walgreens and CVS, to cancel appointments at the last minute, citing issues such as a lack of doses.

COVID Vax Appointment Cancelled? New Shot Rollout Faces Challenges

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MedPage Today

Vaccine

host

MU Health Care received 1,500 COVID-19 Pfizer doses ahead of Saturday's drive-thru COVID and flu vaccination event.

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MU Health Care to host drive-thru for updated COVID vaccine rollout

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KOMU

Subsidiary

Pfizer Inc's PFE subsidiary Biohaven Ltd BHVN released an updated investor presentation with additional data on the company's BHV-1300 program, a potential competitor to FcRn-targeting therapeutics such as argenx's SE ARGX Vyvgart, Immunovant Inc's ...

Stock

What's Going On Pfizer's Subsidiary Biohaven Stock?

Benzinga.com

Pfizer, Inc. (NYSE: PFE) shares are trading lower Wednesday. The stock hit a new 52-week low and has fallen more than 24% over the last year. What To Know:

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Why Pfizer Stock Hit A New 52-Week Low Today

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As cold and flu season approaches, medical experts urge people to stay healthy with boosters. Vaccine companies Moderna and Pfizer told the Globe they’ve shipped millions of vaccines, but New England residents have had trouble obtaining them. Here are some tips for finding and receiving a vaccine without paying out of pocket or waiting for months.

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Your guide to getting a COVID booster this fall

Get the most recent info and news about Pfizer on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Pfizer Inc. discovers, develops, manufactures, markets, distributes, and sells biopharmaceutical products worldwide. It offers medicines and vaccines in various therapeutic areas, including cardiovascular metabolic and pain under the Eliquis, Chantix/Champix, and Premarin family brands; biologics, small molecules, immunotherapies, and biosimilars under the Ibrance, Xtandi, Sutent, Inlyta, Retacrit, Lorbrena, and Braftovi brands; and sterile injectable and anti-infective medicines under the Sulperazon, Medrol, Zithromax, Vfend, and Panzyga brands. The company also provides medicines and vaccines in various therapeutic areas, such as pneumococcal disease, meningococcal disease, tick-borne encephalitis, and COVID-19 under the Prevnar 13/Prevenar 13 (pediatric/adult), Nimenrix, FSME/IMMUN-TicoVac, Trumenba, and the Pfizer-BioNTech COVID-19 vaccine brands; biosimilars for chronic immune and inflammatory diseases under the Xeljanz, Enbrel, Inflectra, and Eucrisa/Staquis brands; and amyloidosis, hemophilia, and endocrine diseases under the Vyndaqel/Vyndamax, BeneFIX, and Genotropin brands. In addition, the company is involved in the contract manufacturing business. It serves wholesalers, retailers, hospitals, clinics, government agencies, pharmacies, and individual provider offices, as well as disease control and prevention centers. The company has collaboration agreements with Bristol-Myers Squibb Company; Astellas Pharma US, Inc.; Myovant Sciences Ltd.; Akcea Therapeutics, Inc; Merck KGaA; Valneva SE; BioNTech SE; and Syapse, Inc. Pfizer Inc. was founded in 1849 and is headquartered in New York, New York.

This story was originally published on by and . and contributed reporting. ProPublica Isaac Arnsdorf Ryan Gabrielson Mollie Simon Caroline Chen President Joe Biden has to immunize every American against COVID-19, and his administration says it’s using the full force of the federal government to get the doses . There’s a reason he can’t promise them sooner. ordered enough vaccines by July Vaccine supply chains are extremely specialized and sensitive, relying on expensive machinery, highly trained staff and finicky ingredients. Manufacturers have run into intermittent shortages of key materials, according to the ; the combination of surging demand and workforce disruptions from the pandemic has caused delays of four to 12 weeks for items that used to ship within a week, much like what happened when consumers were sent scrambling for household staples like , and . U.S. Government Accountability Office flour chicken wings toilet paper People often question why the administration can’t use the mighty Defense Production Act — which empowers the government to demand critical supplies before anyone else — to turbocharge production. But that law has its limits. Each time a manufacturer adds new equipment or a new raw materials supplier, they are required to run extensive tests to ensure the hardware or ingredients consistently work as intended, then submit data to the Food and Drug Administration. Adding capacity “doesn’t happen in a blink of an eye,” said Jennifer Pancorbo, director of industry programs and research at North Carolina State University’s Biomanufacturing Training and Education Center. “It takes a good chunk of weeks.” And adding supplies at any one point only helps if production can be expanded up and down the entire chain. “Thousands of components may be needed,” said Gerald W. Parker, director of the Pandemic and Biosecurity Policy Program at Texas A&M University’s Scowcroft Institute for International Affairs and a former senior official in the Department of Health and Human Services office for preparedness and response. “You can’t just turn on the Defense Production Act and make it happen.” The U.S. doesn’t have spare facilities waiting around to manufacture vaccines, or other kinds of factories that could be converted the way last year. General Motors began producing ventilators The GAO said the Army Corps of Engineers is helping to expand existing vaccine facilities, but it can’t be done overnight. Building new capacity would take two to three months, at which point the new production lines would still face weeks of testing to ensure they were able to make the vaccine doses correctly before the companies could start delivering more shots. “It’s not like making shoes,” Dr. Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases, said in an interview with ProPublica. “And the reason I use that somewhat tongue-in-cheek analogy is that people say, ‘Ah, you know what we should do? We should get the DPA to build another factory in a week and start making mRNA.’ Well, by the time a new factory can get geared up to make the mRNA vaccine exactly according to the very, very strict guidelines and requirements of the FDA ... we already will have in our hands the 600 million doses between Moderna and Pfizer that we contracted for. It would almost be too late.” Fauci added that the DPA works best for “facilitating something rather than building something from scratch.” The Trump administration deployed the Defense Production Act to give vaccine manufacturers priority in accessing crucial production supplies before anyone else could buy them. last year And the Biden administration used it to help Pfizer obtain specialized needles that can squeeze a sixth dose from the company’s vials, as well as for two critical manufacturing components: filling pumps and tangential flow filtration units. The pumps help supply the lipid nanoparticles that hold and protect the mRNA — the vaccines’ active ingredient, so to speak — and also fill vials with finished vaccine. The filtration units remove unneeded solutions and other materials used in the manufacturing process. These highly precise pieces of equipment are not typically available on demand, said Matthew Johnson, senior director of product management at Duke University’s Human Vaccine Institute, who works on developing mRNA vaccines, but not for COVID-19. “Right now, there is so much growth in biopharmaceuticals, plus the pinch of the pandemic,” he said. “Many equipment suppliers are sold out of production, and even products scheduled to be made, in some cases, sold out for a year or so looking forward.” In the meantime, the shortage of vaccines is creating widespread frustration and anxiety as eligible people struggle to get appointments and millions of others wonder how long it will be before it is their turn. , the U.S. had distributed 72.4 million doses and administered 56.3 million shots, but fewer than 16 million people have received both of the two doses that the Pfizer and Moderna vaccines require for full protection. As of Feb. 17 The Biden administration has said it is increasing vaccine shipments to states by 20%, to 13.5 million doses a week, and encouraged states to give out all their shots instead of holding on to some for second doses. But now that second-dose appointments are coming due, many jurisdictions are having to focus on those and stepping back from vaccinating uninoculated people. Even as the total number of vaccinations increased last week, the number of first doses fell to 6.8 million people, down from 7.8 million three weeks ago, according to Centers for Disease Control and Prevention data. At best, it will take until June for manufacturers to deliver enough doses for the roughly 266 million eligible Americans age 16 and over, according to public statements by the companies. That includes expected deliveries of Johnson & Johnson’s one-dose vaccine, which is widely expected to win emergency authorization from the FDA shortly after a public advisory committee meeting on Feb. 26. But Johnson & Johnson has fallen behind in manufacturing. The company told the it will have only 2 million doses ready to go by the time the vaccine is authorized, whereas its $1 billion with HHS scheduled 12 million doses by the end of February. GAO contract It’s not clear what held up Johnson & Johnson’s production line; the company has benefited from first-priority purchases thanks to the DPA, according to a senior executive close to the manufacturing process. A Johnson & Johnson spokesman declined to comment on the cause of the delay, but said the company still expects to ship 100 million U.S. doses by July. Vaccine supply won’t cover all Americans until late spring, at best Public statements from vaccine developers Pfizer, Moderna and Johnson & Johnson illustrate how many people could be covered by the available U.S. supply from now until the end of the summer. Sources: , company press releases and statements, news reports, . Information as of Feb. 16. U.S. Census 2020 Demographic Analysis estimates GAO Moderna declined to comment on “operational aspects” of its manufacturing, but “does remain confident in our ability to meet contracted quantities” of its vaccine to the U.S. and other nations, a spokesperson said in a statement. Pfizer did not respond to ProPublica’s written questions. Ramping up production is especially challenging for Pfizer and Moderna, whose vaccines use an mRNA technology that’s never been mass-produced before. The companies started production even before they finished trials to see if the vaccines worked, another historic first. But it wasn’t as if they could instantly crank out millions of vaccines full blast, since they effectively had to invent a novel manufacturing process. “Putting together plans 12 months ago for a Phase 1 and 2 trial, and making enough to dose a couple hundred patients, was a big deal for the raw material suppliers,” said Johnson, the product manager at Duke University’s vaccine institute. “It's just going from dosing hundreds of patients a year ago to a billion.” Raw materials for the Pfizer and Moderna vaccines are also in limited supply. The manufacturing process begins by using common gut bacteria cells to grow something called “plasmids” — standalone snippets of DNA — that contain instructions to make the vaccine’s genetic material, said Pancorbo, the North Carolina State University biomanufacturing expert. Next, specific enzymes cultivated from bacteria are added to cause a chemical reaction that assembles the strands of mRNA, Pancorbo said. Those strands are then packaged in lipid nanoparticles, microscopic bubbles of fat made using petroleum or plant oils. The fat bubbles protect the genetic material inside the human body and help deliver it to the cells. Only a few firms specialize in making these ingredients, which have previously been sold by the kilogram, Pancorbo said. But they’re now needed by the metric ton — a thousandfold increase. Moderna and Pfizer need bulk, but also the highest possible quality. “There are a number of organizations that make these enzymes and these nucleotides and lipids, but they might not make it in a grade that is satisfactory for human consumption,” Pancorbo said. “It might be a grade that is satisfactory for animal consumption or research. But for injection into a human? That’s a different thing.” Johnson & Johnson’s vaccine follows a slightly more traditional method of growing cells in large tanks called bioreactors. This takes time, and the slightest contamination can spoil a whole batch. Since the process deals with living things, it can be more like growing plants than making shoes. “Maximizing yield is as much of an art as it is a science, as the manufacturing process itself is dependent on biological processes,” said Parker, the former HHS official. The vaccine developers are continuing to find tweaks that can expedite production without cutting corners. Pfizer is now delivering six doses in each vial instead of five, and Moderna has asked for permission to fill each of its bottles with 15 doses, up from 10. If regulators approve, it would take two or three months to change over production, Moderna spokesman Ray Jordan said on Feb. 13. “It helps speed up and lighten the logistical side of getting vaccines out,” said Lawrence Ganti, president of SiO2, an Alabama company that makes glass vials for the Moderna vaccine. SiO2 expanded production with in funding from the federal government last year, and Ganti said there aren’t any hiccups at his end of the line. $143 million Despite the possibility of sporadic bottlenecks and delays in the coming months, companies appear to have lined up their supply chains to the point that they’re comfortable with their ability to meet current production targets. Massachusetts-based Snapdragon Chemistry received almost $700,000 from HHS' Biomedical Advanced Research and Development Authority to develop a new way of producing ribonucleoside triphosphates (NTPs), a key raw material for mRNA vaccines. Snapdragon’s technology uses a continuous production line, rather than the traditional process of making batches in big vats, so it’s easier to scale up by simply keeping production running for a longer time. Suppliers have told Snapdragon that they have their raw materials covered for now, according to Matthew Bio, the company’s president and CEO. “They’re saying, ‘We have established suppliers to meet the demand we have for this year,’” Bio said. Photo by on Mufid Majnun Unsplash

Why Making Vaccine Doses Is a Slow Process

Dividend stocks are a great way to generate income from your investments while also benefiting from capital appreciation. However, not all dividend stocks are created equal. Some may offer high yields but low growth prospects, while others may have erratic payouts or poor stock performance. To help you find the best dividend stocks under $50 in 2023, we have screened for companies that have: : This is an arbitrary threshold, but it helps us focus on stocks that are more accessible and less expensive than some of the high-priced dividend aristocrats or blue chips. A current share price below $50 This is also a subjective cutoff, but it ensures that we get a decent return on our investment. A yield above 2% is also higher than the average yield of the S&P 500 index, which is currently around 1.3%. A dividend yield above 2%: This shows that the company has a long-term commitment to rewarding shareholders with consistent and growing dividends. It also indicates that the company has a stable and profitable business model that can withstand various economic cycles and competitive pressures. A history of increasing dividends for at least 10 years: This demonstrates that the company has been able to grow its earnings over time, which supports its dividend payments and future growth prospects. A positive earnings growth rate also implies that the company has a competitive advantage or a strong market position in its industry. A positive earnings growth rate over the past five years: This measures how much of the company’s earnings are paid out as dividends. A lower payout ratio means that the company retains more earnings for reinvestment or debt reduction, which enhances its financial flexibility and sustainability. A higher payout ratio means that the company has less room to grow or maintain its dividends in case of an earnings decline. A reasonable payout ratio below 80%: Based on these criteria and a few more important ones listed by five of the best dividend stocks under $50 that you can explore right now. Disclaimer: The information provided in this article is for informational purposes only and e. Investing involves risk and you should carefully consider all risks and uncertainties before making any investment decisions. We do not guarantee the accuracy or completeness of the information presented and we are not responsible for any errors or omissions. You should consult with a financial advisor before making any investment decisions. should not be construed as professional investment advic List of best dividend stocks under $50 in 2023 (NYSE: PFE) Pfizer Pfizer is one of the world's largest pharmaceutical companies, with a of drugs and vaccines for various therapeutic areas. The company has been in the spotlight for its COVID-19 vaccine, which has generated strong sales and profits in 2021 and 2022. diversified portfolio Pfizer has a long history of paying and growing dividends, with 11 consecutive years of annual increases. The current dividend yield is 4%, and the payout ratio is 54%. The company has also delivered an impressive earnings growth rate of 36% over the past five years. Pfizer expects to grow its revenue by 6% to 9% annually through 2025, driven by its core products and pipeline opportunities. The company also plans to return more cash to shareholders through dividends and share buybacks. (NYSE: T) AT&T AT&T is a leading telecommunications company that provides wireless, broadband, video, and media services to millions of customers. The company also owns WarnerMedia, which includes HBO, CNN, Warner Bros., and other entertainment assets. AT&T has been paying dividends since 1984 and has increased them for 36 consecutive years. The current dividend yield is a whopping 6%, and the payout ratio is 58%. The company has also grown its earnings by an average of 5% per year over the past five years. AT&T is undergoing a major transformation as it plans to spin off WarnerMedia and merge it with Discovery (NASDAQ: DISCA) in mid-2023. The deal will create a new media giant that will compete with Netflix (NASDAQ: NFLX) and Disney (NYSE: DIS). AT&T will focus on its core connectivity business and reduce its debt load. AT&T expects to maintain its dividend at current levels after the spin-off and grow it modestly over time. The company also expects to generate strong free cash flow and invest in its wireless network and fiber broadband expansion. (NASDAQ: TXN) Texas Instruments Texas Instruments is one of the world's largest companies that designs and manufactures chips for various applications such as industrial equipment, automotive systems, personal electronics, communications devices, and more. semiconductor Texas Instruments has been paying dividends since 1962 and has increased them for 18 consecutive years. The current dividend yield is 2.8%, and the payout ratio is only 46%. The company has also achieved an impressive earnings growth rate of 21% per year over the past five years. Texas Instruments benefits from its diversified end markets, strong competitive position, high-quality products, efficient manufacturing capabilities, and robust cash generation. The company expects to grow its revenue by mid-to-high single digits annually over the long term and return more than 100% of its free cash flow to shareholders through dividends and share buybacks. (NYSE: XOM) Exxon Mobil Exxon Mobil is one of the largest oil and gas companies in the world, with operations spanning exploration, production, refining, chemicals and renewables. The company has been recovering from the impact of the COVID-19 pandemic on oil demand and prices, as well as facing pressure from investors and regulators to address climate change risks. Exxon Mobil has been taking steps to improve its profitability and sustainability by cutting costs, divesting non-core assets, investing in low-carbon technologies and increasing shareholder returns. The company expects to grow its earnings by more than four times by 2027, compared to its pre-pandemic level in 2019, while also reducing its greenhouse gas intensity by up to 20% by 2025. emissions Exxon Mobil pays a quarterly dividend of $0.87 per share, which translates to an annual yield of about 3.5% at the current stock price. The company has raised its dividend for 39 consecutive years, making it another long-standing dividend aristocrat. Exxon Mobil trades at a fair valuation of 15 times forward earnings, which is slightly above the industry average of 14 times. The company also generates solid cash flow and has a manageable debt-to-equity ratio of 0.8. (NASDAQ: CSCO) Cisco Systems Inc Cisco is a leading provider of networking equipment and software for enterprises and consumers. The company has been paying dividends since 2011 and has increased them every year since then. The current dividend yield is 3.05% and the payout ratio is 49%. Cisco has also grown its earnings per share (EPS) by an average of 9.6% annually over the past five years. The stock is trading at $48.19 as of March 22, 2023. Conclusion In conclusion, dividend stocks are a reliable way to generate income while benefiting from capital appreciation. However, not all dividend stocks are created equal. To find the best dividend stocks under $50, we have used specific criteria such as a current share price below $50, a dividend yield above 2%, a history of increasing dividends for at least 10 years, a positive earnings growth rate over the past five years, and a reasonable payout ratio below 80%. Based on these criteria, Pfizer, AT&T, Texas Instruments, and Exxon Mobil are some of the best dividend stocks under $50 in 2023. These companies have a long history of paying and growing dividends, a stable and profitable business model, and a strong market position in their respective industries. Moreover, they offer decent dividend yields and a potential for future growth, making them a good investment opportunity for income-seeking investors. Disclaimer: The information provided in this article is for informational purposes only and e. Investing involves risk and you should carefully consider all risks and uncertainties before making any investment decisions. We do not guarantee the accuracy or completeness of the information presented and we are not responsible for any errors or omissions. You should consult with a financial advisor before making any investment decisions. should not be construed as professional investment advic

Dividend Stocks Under $50 to Consider in 2023

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

In the US, the Food and Drug Administration (FDA) acts as a primary entity setting standards for compliance in pharma. In the EU, the European Medicines Agency (EMA) keeps an eye on drug quality. These and many other organizations around the globe make sure to promote public health by evaluating, regulating, and supervising all things pharma. The following factors, among others, are prompting regulatory bodies to introduce pharmaceutical compliance programs: The growing pressure on the healthcare and pharmaceutical industries to ensure patient safety and protect public health The rising amount of counterfeit products on the market Rising drug development, testing, and distribution costs The need for facilitating compliance in pharmaceutical industry with applicable laws The need for maintaining consistent quality standards across different regions and markets. Compliance programs aim to promote transparency, safety, and efficacy in the industry. However, achieving compliance in pharma can be complex and costly. Adopting , companies can gain a valuable ally in their efforts to achieve and maintain compliance. Below, we share our experience crafting pharma and and highlight key ways in which technology streamlines pharmaceutical compliance. pharmaceutical technology solutions life science software Four ways in which technology streamlines compliance in pharma 1. Overcoming silos , , , and other technologies can help pharma companies consolidate data that was previously stored across siloed systems. Cloud-based data management data warehousing big data analytics Consolidated data storage allows pharmaceutical companies to gain a 360-degree view of their data and detect compliance issues early on. One example of a company leveraging modern tech to improve pharma compliance by boosting data management is Pfizer. The leading pharma corporation relies on an that consolidates clinical trial data from multiple sources for a single, unified view. It conveniently tracks the progress of clinical trials, identifies trends and patterns, and ensures that trial data is accurate and up-to-date. electronic document management system Another example comes from Johnson & Johnson, which uses a that integrates all enterprise data coming from different sources. The solution facilitates the company’s efforts in achieving compliance in pharma by maintaining accurate and up-to-date records of all activities related to drug development, testing, and distribution. This includes tracking all stages of drug development, including research, , regulatory approval, and post-market surveillance. cloud-based data management system clinical trials 2. Enabling advanced reporting By relying on new-gen reporting tools, pharmaceutical companies can now generate customized reports of any complexity (in real time as well). This helps them gain deeper insight into their data, identify potential pharma compliance risks, and take proactive measures to address them. Moreover, technology tools can help pharma companies avoid duplicate or contradictory reports across different departments or regions, ensuring that the data submitted to regulatory bodies . is consistent and accurate The Jonhson & Johnson’s data management system mentioned above is a worthy example of how technology facilitates pharma compliance through efficient adverse event reporting. The Johnson & Johnson’s solution allows the company to efficiently track and report adverse events and side effects, ensuring that all necessary information is collected and submitted to regulatory bodies in a timely manner. Another global pharmaceutical company, Sanofi, uses to facilitate their efforts in achieving compliance in pharma. Technology tools allow the company to generate customizable reports that meet the specific requirements of each regulatory body they deal with. All reports are generated automatically, saving time and minimizing the potential for human error. advanced reporting 3. Facilitating business processes Another use case of technology in facilitating pharma compliance is improving business processes. Specifically, technology can help streamline and even automate the following tasks: Administration, tax, and : accounts payable, payment processing, bank reconciliation, and tax reporting finance Purchasing: expense analysis, expense report management, procurement data management, supplier relationship management, and contract management : demand forecasting, inventory and assets management Supply chain Human resources: travel and expense management, employee data management, time record validation, and others. Many pharma companies across the globe are leveraging the power of tech to facilitate and . For example, Novo Nordisk, a multinational pharmaceutical company headquartered in Denmark, uses technology to automate indirect tax compliance management across more than 50 countries the company operates in, which significantly improves their pharma compliance management programs. automate mundane workflows The solution is integrated with the company’s ERP system, which helps quickly identify the potential areas of non-compliance. It also provides real-time visibility into the company’s tax position, enabling Novo Nordisk’s executives to make more informed decisions and optimize their tax strategy. Another example of relying on business process optimization for maintaining compliance in pharma comes from AstraZeneca. The global pharmaceutical company has adopted that allows them to effectively manage their global vendor relationships, monitor vendor performance, and keep an eye on the vendors’ compliance with regulatory requirements. The system maintains a central database of vendors and their associated compliance documentation, including qualifications, certifications, and financial records. a vendor management system 4. Speeding up drug development The growing use of technology helps pharma companies reduce the time and cost associated with bringing new drugs to the market. The ways in which modern tech allows speeding up drug development — while helping maintain pharma compliance — are many: Advanced data analytics and machine learning tools can accelerate and identification of potential drug candidates drug discovery Using computer simulations and digital twins can help predict drug behavior before it is tested live Electronic health records and other patient data management systems can help identify eligible patients for clinical trials and speed up recruitment Wearable devices and monitoring technology can provide real-time data on patients in clinical trials, reducing the time and cost of collecting data manufacturing equipment can accelerate the production of drug prototypes while guarding drug quality Internet of Things-powered tools can automate repetitive tasks in drug development, freeing up researchers’ time Robotic process automation (RPA) can provide scalable and cost-effective storage and processing of large amounts of data, providing for faster analysis and decision-making. Cloud computing technologies One example of a company that has already discovered the value of tech in speeding up drug development is GSK. The British pharmaceutical and biotechnology company to test drug compounds more quickly, efficiently, and accurately. The company also relies on AI to identify potential drug candidates, significantly reducing the time it takes to move them to clinical trials. uses artificial intelligence Another example of a company using tech to improve pharma compliance comes from Novartis. The Swiss pharma corporation uses AI to analyze vast amounts of data from its drug discovery and development programs. Specifically, they apply AI to analyze patient data, preclinical data, and genetic information to identify potential drug targets and optimize drug candidates. Novartis also uses AI to design new drug compounds with specific properties and to . predict how those compounds will interact with biological targets Barriers to using technology for pharma compliance management While technology can be extremely helpful in achieving compliance in pharma, there are several barriers that companies must overcome in order to tap into the full value of tech. One of the most significant ones is the lack of digital maturity within the sector. Digital maturity refers to the extent to which an organization is relying on digital tools and technology. Many pharma companies still depend on legacy systems, which can be difficult to integrate with modern technology solutions. A among global pharma leadership highlights the following reasons for the low digital maturity in the sector: survey conducted by DT Сonsulting Improving the sector’s digital maturity and streamlining pharma compliance efforts calls for sector-wide change and requires addressing the following bottlenecks: The perceived risk associated with adopting new technology The pharma industry is highly regulated, and any change to processes or systems must be carefully evaluated and tested to ensure compliance with regulatory requirements. This cautious approach can lead to slow decision-making, resulting in delays in the adoption of new technology. : To minimize the risks associated with adopting technology solutions, we advise you to collaborate with regulatory authorities and engage with them early in the transformation process in order to ensure pharmaceutical compliance and get a buy-in. Solution Another solution to consider is starting your digital transformation initiative with a pilot study. Conducted in a controlled environment, it could help you test new technologies, as well as demonstrate their compliance and safety. The lack of technology standardization in the sector Many pharma companies have grown through mergers and acquisitions, resulting in a patchwork of legacy systems and processes that are often difficult to integrate. This can make it challenging to implement new technology solutions, as they may not be compatible with the existing systems. : To overcome this issue, we recommend running a thorough assessment of current systems and processes to identify potential bottlenecks, prioritizing the implementation of technology solutions that are compatible with existing systems, and resorting to iterative implementation to ensure seamless integration of all technology components. Solution The lack of interoperability The pharma sector is highly dependent on data. Data silos and the lack of interoperability can hinder the adoption of technology and pose a challenge to maintaining compliance in pharma. Different departments within a pharma company may use different systems or even different versions of the same system, making it challenging to consolidate data and gain a holistic view of the organization’s operations. : To ensure , consider developing a comprehensive data strategy that would define data governance, ownership, and standardization policies. It also pays off to conduct regular data audits to identify data silos and inconsistencies. Solution data integrity and software interoperability The lack of digital skills and expertise within the industry The pharma sector has traditionally been slow to embrace new technology and may lack the necessary resources, skills, and expertise to implement digital solutions. : To close the expertise gap, pharmaceutical companies can either invest in recruiting experienced professionals who can lead the implementation of the new technology or turn to external consultants with the expertise developing solutions for pharma to provide strategic and technology leadership. Solution On a final note Even though historically, the pharmaceutical sector has been rather slow in adopting technology for better pharma compliance management, the outlook is changing. Regulatory authorities themselves prompt the industry to jump on the innovation bandwagon. In the US, the FDA created the emerging technology program that encourages innovative approaches to pharmaceutical product design and manufacturing. Similarly, the Process Analytical Technologies (PAT) team established by the EMA and the MHRA (Medicines and Healthcare products Regulatory Agency) leads innovation in the EU and UK. The success stories of early adopters prove: technology solutions do make it easier to achieve and maintain compliance in pharma. Still, to reap the benefits, it is crucial to approach the implementation of novel solutions cautiously and with the potential challenges in mind.To learn more about how technology solutions can help your company achieve and maintain compliance, get in touch with our technology consultants. Looking for ways to streamline pharma compliance? Contact ITRex for help. Also published . here

How Novel Technology Boosts Compliance in Pharma

Cómo la tecnología novedosa impulsa el cumplimiento en la industria farmacéutica

Comment une nouvelle technologie stimule la conformité dans l'industrie pharmaceutique

नई तकनीक फार्मा में अनुपालन को कैसे बढ़ावा देती है

新しいテクノロジーが製薬業界のコンプライアンスをどのように後押しするか

Como a nova tecnologia aumenta a conformidade na indústria farmacêutica

Công nghệ mới lạ tăng cường tuân thủ trong dược phẩm như thế nào

新技术如何提高制药业的合规性

, or is a community using crypto and web3 to address some of the deep-seated problems in science. In January, I had the good fortune to attend the conference to find out more. Ever since then, I’ve been reflecting on what I’ve learned, the challenges and opportunities facing DeSci, and how I think the space could develop. DeSci ‘Decentralized Science’ DeSci London My conclusions are somewhat heretical: DeSci is right about the problems facing science, but web3 is only a small part of the solution. If we care about impact, we should loosen the blockchains. I’d like to explain how I got here, why I don’t think it’s oxymoronic for DeSci to dabble in other technologies, and suggest practical ways science can embrace the values of crypto, without getting so tangled in web3. For an intro and other perspectives on DeSci, checkout: Ethereum.org’s definition A Guide to DeSci, the Latest Web3 Movement Decentralised Science — a cure for the Science sector’s woes? Bright Sparks: DeSci London In-spite of the headwinds of a global economic slowdown and (another) cryptocurrency crash, the decentralised science community appears to be growing, and the DeSci London conference is a testament to the the movement’s vigour. Hosted in the prestigious Crick institute, the organisers put together an impressive range of speakers and backers, pulling hundreds of guests across two days. My highlights from the event included a keynote presentation by ; a great demo from ; an investor panel hosted by Local Globe’s ; and a disease workshop featuring the pioneering . The conference also featured a with and Vibe’s Alok Tayi lateral.io Julia Hawkins Ethan Perlstein DeSci Podcast premier Vincent Weisser . Vitalik Buterin The quality of guests was also striking, with many accomplished scientists, engineers, entrepreneurs, and investors. It might seem odd to laud the of a community, but the tone and quality of conversations in DeSci feels very different from those I witnessed during the crypto manias of 2017/18 and 2020/22. seriousness In the end, although I remain skeptical about many of the ideas falling under the DeSci umbrella, the community’s talent is undeniable. But if these sparks are to kindle a flame, and then a in science, passion and expertise must rapidly translate into revolution impact. The tail wags the Dog Listening to the presentations, talking with guests, and reading around DeSci, two themes leap out: Science is broken, and Crypto is the answer. (i) (ii) The first claim is compelling, and is by no means exclusive to the movement. Whether it’s the allocation of funding, the accessibility of data, the management of IP, or the undue influence of institutions, . there are a web of problems hobbling our ability to make scientific progress It often felt jarring when a conversation or presentation would turn to ledgers, smart contracts, DAOs, or decentralised compute. and the analysis would retreat to shibboleths like ‘centralisation is bad’. But is blockchain the answer? always Suddenly the would seem more important than the technology problem itself, It is as if the tail were wagging the dog. There are good reasons to think crypto can play a role in improving science, and you might puzzle at my complaining about the prominence of web3 in a community dedicated to decentralisation… But before going all-in, we need to be realistic about what the technology can and can’t do. Here is none other than Vitalik Buterin, in : 2017 https://twitter.com/VitalikButerin/status/940744724431982594?embedable=true In the thread he goes on: How many dapps have we created that have substantial usage? …How much value is stored in smart contracts that actually do anything interesting?… …The answer to all of these questions is definitely not zero, and in some cases it’s quite significant. But not enough to say it’s $0.5T levels of significant. Not enough. 5 years later the market cap of crypto is over 1 trillion, and while the technology has advanced, how much has really changed? Not enough. Observing DeSci, I’m left with the impression of a community comprised of talented (and frustrated) people who have coalesced around the of decentralisation, but who risk being constrained, and ultimately short-changed by the reality of web3. ideal Blockchains haven’t scaled, decentralised software development remains difficult and niche, and most importantly of all: wallets, smart contracts and DAOs are near-impenetrable for the average user. The Uncanny Valley of Progress In other sectors, have argued that our present institutions are not just flawed, but , and that we need new technology to overturn a corrupt order. Speaking with members of the DeSci community, the view is less militant: As one presenter put it: crypto maximalists irredeemable web3, they argue, is critical as an accelerator and enabler. “The world is too complex for legacy systems and coordination mechanisms” To my mind, this is a misapprehension. We don’t web3 to build ‘Decentralised Autonomous Organisations’ (DAOs), create transparency, or develop innovative reward systems. need We have many examples of independent, decentralised organisations that thrive without cryptographic governance — just look at open source projects like the . Equally, , and the show that open access to information is possible without the need for convoluted decentralised compute or storage. And finally, we have innumerable examples of companies, individuals and institutions working together using contracts and the legal system to create value for all. linux kernel Wikipedia PubMed NIH Genome library Indeed, what’s instructive about these examples, is that although they are non-web3, they have made extensive use of to solve many of the problems crypto advocates care about: hashes, public keys, and signatures to ensure the integrity of files, and of course SSL to validate the identity of websites. . crypto graphy They use these tools because they add real value to their users and mission The stated goals of DeSci include I only argue that as we make technology choices, we should be optimising for these goals, . Whether it’s choosing between Postgres vs MongoDB, or OAuth vs Web3, the benefits must justify the cost. accelerating scientific collaboration, increasing access to research data, and improving economic incentives. first ✅ IP-NFTs Let’s say we’re running a university lab and have identified a potential treatment for an important disease. We want to hand-off the asset to industry so that it can be developed, while retaining a slice of the IP, so that we can share in any potential upside. This is precisely the challenge DeSci company is tackling, and they have done so using web3 — Their service seeks to pair a traditional legal contract with an IP-NFT primitive. This answers the obvious question: “Would Pfizer care that the university holds a virtual token entitling them to 5% of revenue?” — Molecule but not only web3. hybrid approach Not unless they also hold a corresponding legal contract! Discord > DAOs 🤷 Let’s apply the same reasoning to DAOs . They are at-once ultra-secure, ultra-transparent, ultra- (in the true, web3 sense) cool …and so hard to understand that most people cannot join or engage with them. These tools do not add enough value to scientists or scientific research. Moreover, their complexity is a barrier to the wider understanding and adoption of DeSci’s principles. An unfortunate irony, since one of the rallying cries of DeSci is to obstacles. I experienced this many times speaking with non-technical attendees in London. Several, including patient advocates, were clearly baffled by the jargon and just wanted to know more about the . …And the same can be said of decentralised compute and storage: byzantine remove outcomes These deep and challenges are seen across the crypto ecosystem, but is science the best sector in which to pioneer the solutions? UX DX all Instead, I want to argue that It is in these chatrooms that like-minded researchers will connect, collaborate and move science forward. the discord servers of DeSci DAOs are far more interesting and important than their smart contracts and tokens. Engagement is everything. Tokens & Fiat Economic opportunity sits alongside idealism as the other engine of DeSci. 2020–22 saw a boom in web3 investment from venture capital firms, and many of the players in the space receive funds from crypto orgs such as . Protocol Labs The arguments against crypto tokens and especially ‘utility-tokens’ are well-rehearsed (e.g. and ), and I won’t repeat them. Instead I’ll simply observe that you can’t mint a token or NFT and declare success — where is the impact? Creating communities and platforms is hard graft, it requires people, and can’t be accomplished by . here here fiat Summary Technical purity is a false economy when it is purchased at the cost of impact. I see a risk that many DeSci outfits will get trapped in an uncanny valley of progress: Nobody will care that a research org has a smart-contract constitution if it only has 10 members. Likewise, nobody will care that your data is hosted on IPFS, if the experience is indistinguishable from S3. The opportunity costs we pay for these optimisations is vast and unnecessary. We should leverage web3 like any other technology — when the benefits justify the costs. So, how can web3 benefit Science? With caveats, and with emphasis on mainstream adoption. 1. New Funding Mechanisms As described there are interesting opportunities to change how scientific research is funded using crypto principles. These opportunities are not on the web3 stack, but since web3 has formalised many of the concepts, such as , it may play a helpful role. is a company exploring this space. eloquently elsewhere, dependent quadratic funding VibeBio But we must remember that This means outcomes such as: the objective is not to allocate funding via blockchain, it is to make science better. Increasing the total amount of funding available. Widening the pool of funders and recipients. Shortening allocation time. Enabling more useful research to be done (e.g. replication studies). Success must be judged on these terms. Organisations who allocate funds could do so with the help of web3 scaffolding, but these new funding systems . Without engagement from existing institutions, DeSci’s impact will be very limited indeed. must also be backed up by robust legal contracts if they are to gain traction 2. Intellectual Property As with funding, this opportunity has been . There is every reason to think that IP-NFTs (exemplified by the work of ), could change the landscape of scientific innovation by providing paved roads for the allocation and management of IP. This kind of approach might even be extended to patient data. well-explained by others Molecule And again, it must be understood that these opportunities are only possible with a hybrid approach: Off-chain legal contracts supported and augmented by on-chain smart contracts. . Web3 must be a , not an tool end KPIs for DeSci Given the above, why don’t we create some KPIs for DeSci’s adoption and impact? Here are some potential examples, assuming a bio-tech lens: Funding allocated per quarter ($M) Institutional funding via DeSci ($M) Phase IV > III > II > I clinical trials funded per quarter Questions Answered > Papers Published Replication Studies as a % of all studies Number & Value of IP-NFTs purchased by Pharma Discord Monthly Active Users On-chain transactions per quarter, by DAO What about web3 data storage? Peer Review? Reputation? At best, these web3 solutions would be ‘nice to have’. But when we step back and look at the bottlenecks and challenges facing scientific research as a whole, none of them come close to high-priority. And so even if the technology could be made to work, and even if they gained adoption, none would be high-impact or high-value. The future of Science: beyond web3 The ideology of decentralisation has so many exciting applications that it’s easy to get lost down a crypto rabbit hole. We can get carried away talking about new structures and rulesets, that we don’t pay enough attention to the of science itself: being in the lab, running experiments, testing new techniques and technologies, processing data, coordinating with others, and writing up results. work Yet, these are all things DeSci advocates have every right to care about. Web3 can help support alignment, but it doesn’t collaboration, nor can it increase the yield of a plasmid, or boost the speed of a data pipeline. Whether it’s in pure software, AI, or robotics, there are opportunities for technology to accelerate scientific research beyond web3. Transforming science depends on us seizing types. deliver huge both </ Opinion>. Responses, rebuttals, corrections and counterfactuals welcomed! Also published . here

How Badly Does Science Need Crypto?

¿Qué tanto necesita la ciencia las criptomonedas?

À quel point la science a-t-elle besoin de la cryptographie ?

विज्ञान को क्रिप्टो की कितनी बुरी तरह आवश्यकता है?

科学はどれくらい暗号を必要としていますか?

Quão mal a ciência precisa de criptografia?

Khoa học cần tiền điện tử đến mức nào?

科学对加密的需求有多严重？

Does Google’s Ad Business Fund Disinformation? The Truth

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

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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

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