Scientists develop new opioid painkiller that could help prevent lethal overdoses

(The Washington Post)

A new model for developing painkillers could help cut opioid related overdoses, which killed around 2,000 people in the UK last year and which account for the deaths of 91 people every day in the states.

Research published in the journal Cell on Thursday demonstrates it is possible to develop opioids which relieve pain as effectively as morphine but without stopping or slowing breathing.

There were 3,744 drug poisonings in the UK in 2016 and 2,038 involved an opiate, most commonly heroin or morphine, Office for National Statistics figures show.

But in America prescription opioid addiction has devastated communities and left hundreds of thousands dead, with Donald Trump declaring it a public health emergency earlier this year.

The crisis in America has spiralled since the 1990s with overprescribing of addictive medications, high levels of chronic pain, and an influx of potent synthetic opioids, like fentanyl.

Fentanyl is usually used to relieve pain in terminally ill patients and, at a strength 100 times that of heroin, is a growing cause of deaths in the UK.

Researchers at The Scripps Research Institute, Florida, have spent two decades looking at ways of unlinking opioids’ effects on two pathways – the “G protein” pathway, which is thought to mediate pain-relief and the “beta-arrestin2” pathway, which is linked to breathing suppression.

In the last six years they have developed 500 potential new painkiller drugs compounds, 60 of which impact more on one or the other pathway.

In this group they identified key drugs, all of which were able to be absorbed to the brain, and were all as potent as morphine “if not more so”, but some of which only impacted one pathway.

The compounds that were more lethal in mice primarily acted on the beta-arrestin2 pathway, and this is the primary way that fentanyl acts – possibly explaining its high rate of overdose.

The study says that the findings “strongly support the hypothesis that compounds that do not recruit beta-arrestin2 may prove to be safer than current clinical opioids.”

However it has still not been conclusively proven that beta-arrestin2 is the main cause of breathing suppression – there could be other factors at play.

It’s also yet to be seen if drugs that avoid this pathway have other side-effects, such as being more addictive or causing constipation, that would undermine their lower overdose risk.

But the drugs identified in the research have the largest separation of pain and breathing suppression of any study so far, the authors conclude: “Our hope is that this work may aid the pharmaceutical development of safer alternatives to current opioid therapeutics.”

One in four adults in England prescribed potentially addictive drugs last year

13 per cent of the adult population in England were given opioids in the past year
13 per cent of the adult population in England were given opioids in the past year(Reuters)

Almost 12 million adults in England were prescribed potentially addictive drugs such as sleeping pills and painkillers last year, with half taking them long-term, a new report says.

A review of five classes of medicines – including anti-anxiety drugs, anti-depressants and opioid painkillers – found some patients struggle to come off them, suffering suicidal thoughts and anxiety as a result.

Public Health England (PHE), which led the government-ordered review, said a helpline must be set up to help the millions of people who may be dependent on the drugs.

Since at least a decade ago, more people are being prescribed potentially addictive medicines and for longer periods of time, it said.

Pain relief caused by SARS-CoV-2 infection may help explain COVID-19 spread

SARS-CoV-2, the virus that causes COVID-19, can relieve pain, according to a new study by University of Arizona Health Sciences researchers.

The finding may explain why nearly half of people who get COVID-19 experience few or no symptoms, even though they are able to spread the disease, according to the study’s corresponding author Rajesh Khanna, PhD, a professor in the College of Medicine — Tucson’s Department of Pharmacology.

“It made a lot of sense to me that perhaps the reason for the unrelenting spread of COVID-19 is that in the early stages, you’re walking around all fine as if nothing is wrong because your pain has been suppressed,” said Dr. Khanna. “You have the virus, but you don’t feel bad because you pain is gone. If we can prove that this pain relief is what is causing COVID-19 to spread further, that’s of enormous value.”

The paper, “SARS-CoV-2 Spike protein co-opts VEGF-A/Neuropilin-1 receptor signaling to induce analgesia,” will be published in PAIN, the journal of the International Association for the Study of Pain.

The U.S. Centers for Disease Control and Prevention released updated data Sept. 10 estimating 50% of COVID-19 transmission occurs prior to the onset of symptoms and 40% of COVID-19 infections are asymptomatic.

“This research raises the possibility that pain, as an early symptom of COVID-19, may be reduced by the SARS-CoV-2 spike protein as it silences the body’s pain signaling pathways,” said UArizona Health Sciences Senior Vice President Michael D. Dake, MD. “University of Arizona Health Sciences researchers at the Comprehensive Pain and Addiction Center are leveraging this unique finding to explore a novel class of therapeutics for pain as we continue to seek new ways to address the opioid epidemic.”

Viruses infect host cells through protein receptors on cell membranes. Early in the pandemic, scientists established that the SARS-CoV-2 spike protein uses the angiotensin-converting enzyme 2 (ACE2) receptor to enter the body. But in June, two papers posted on the preprint server bioRxiv pointed to neuropilin-1 as a second receptor for SARS-CoV-2.

“That caught our eye because for the last 15 years my lab has been studying a complex of proteins and pathways that relate to pain processing that are downstream of neuropilin,” said Dr. Khanna, who is affiliated with the UArizona Health Sciences Comprehensive Pain and Addiction Center and is a member of the UArizona BIO5 Institute. “So we stepped back and realized this could mean that maybe the spike protein is involved in some sort of pain processing.”

Many biological pathways signal the body to feel pain. One is through a protein named vascular endothelial growth factor-A (VEGF-A), which plays an essential role in blood vessel growth but also has been linked to diseases such as cancer, rheumatoid arthritis and, most recently, COVID-19.

Like a key in a lock, when VEGF-A binds to the receptor neuropilin, it initiates a cascade of events resulting in the hyperexcitability of neurons, which leads to pain. Dr. Khanna and his research team found that the SARS-CoV-2 spike protein binds to neuropilin in exactly the same location as VEGF-A.

With that knowledge, they performed a series of experiments in the laboratory and in rodent models to test their hypothesis that the SARS-CoV-2 spike protein acts on the VEGF-A/neuropilin pain pathway. They used VEGF-A as a trigger to induce neuron excitability, which creates pain, then added the SARS-CoV-2 spike protein.

“Spike completely reversed the VEGF-induced pain signaling,” Dr. Khanna said. “It didn’t matter if we used very high doses of spike or extremely low doses — it reversed the pain completely.”

Dr. Khanna is teaming up with UArizona Health Sciences immunologists and virologists to continue research into the role of neuropilin in the spread of COVID-19.

In his lab, he will be examining neuropilin as a new target for non-opioid pain relief. During the study, Dr. Khanna tested existing small molecule neuropilin inhibitors developed to suppress tumor growth in certain cancers and found they provided the same pain relief as the SARS-CoV-2 spike protein when binding to neuropilin.

“We are moving forward with designing small molecules against neuropilin, particularly natural compounds, that could be important for pain relief,” Dr. Khanna said. “We have a pandemic, and we have an opioid epidemic. They’re colliding. Our findings have massive implications for both. SARS-CoV-2 is teaching us about viral spread, but COVID-19 has us also looking at neuropilin as a new non-opioid method to fight the opioid epidemic.”

Co-authors on the paper from the Department of Pharmacology are: Aubin Moutal, PhD; Lisa Boinon; Kimberly Gomez, PhD; Dongzhi Ran, PhD; Yuan Zhou; Harrison Stratton, PhD; Song Cai, PhD; Shizhen Luo; Kerry Beth Gonzalez; and Samantha Perez-Miller, PhD. Co-authors from the Department of Anesthesiology with additional affiliations with the Comprehensive Pain and Addiction Center are Amol Patwardhan, MD, PhD, and Mohab Ibrahim, MD, PhD.

Story Source:

Materials provided by University of Arizona Health SciencesNote: Content may be edited for style and length.

Journal Reference:

  1. Aubin Moutal, Laurent F. Martin, Lisa Boinon, Kimberly Gomez, Dongzhi Ran, Yuan Zhou, Harrison J. Stratton, Song Cai, Shizhen Luo, Kerry Beth Gonzalez, Samantha Perez-Miller, Amol Patwardhan, Mohab M. Ibrahim, Rajesh Khanna. SARS-CoV-2 Spike protein co-opts VEGF-A/Neuropilin-1 receptor signaling to induce analgesiaPain, 2020; Publish Ahead of Print DOI: 10.1097/j.pain.0000000000002097

Cite This Page:

University of Arizona Health Sciences. “Pain relief caused by SARS-CoV-2 infection may help explain COVID-19 spread.” ScienceDaily. ScienceDaily, 1 October 2020. <>.

Older adults using cannabis to treat common health conditions

With growing interest in its potential health benefits and new legislation favoring legalization in more states, cannabis use is becoming more common among older adults.

University of California San Diego School of Medicine researchers report that older adults use cannabis primarily for medical purposes to treat a variety of common health conditions, including pain, sleep disturbances and psychiatric conditions like anxiety and depression.

The study, published online October 7, 2020 in the Journal of the American Geriatrics Society, found that of 568 patients surveyed, 15 percent had used cannabis within the past three years, with half of users reporting using it regularly and mostly for medical purposes.

“Pain, insomnia and anxiety were the most common reasons for cannabis use and, for the most part, patients reported that cannabis was helping to address these issues, especially with insomnia and pain,” said Christopher Kaufmann, PhD, co-first author of the study and assistant professor in the Division of Geriatrics and Gerontology in the Department of Medicine at UC San Diego.

Patients surveyed in the study were seen at the Medicine for Seniors Clinic at UC San Diego Health over a period of 10 weeks.

The researchers also found that 61 percent of the patients who used cannabis had initiated use after age 60.

“Surprisingly, we found that nearly three-fifths of cannabis users reported using cannabis for the first time as older adults. These individuals were a unique group compared to those who used cannabis in the past,” said Kevin Yang, co-first author and third-year medical student at UC San Diego.

“New users were more likely to use cannabis for medical reasons than for recreation. The route of cannabis use also differed with new users more likely to use it topically as a lotion rather than by smoking or ingesting as edibles. Also, they were more likely to inform their doctor about their cannabis use, which reflects that cannabis use is no longer as stigmatized as it was previously.”

Given the rise in availability of CBD-only products, which is a non-psychoactive cannabinoid in contrast to THC-containing products, the researchers said it is likely that future surveys will continue to document a larger proportion of older adults using cannabis or cannabis-based products for the first time.

“The findings demonstrate the need for the clinical workforce to become aware of cannabis use by seniors and to gain awareness of both the benefits and risks of cannabis use in their patient population,” said Alison Moore, MD, senior author and chief of the Division of Geriatrics in the Department of Medicine at UC San Diego School of Medicine. “Given the prevalence of use, it may be important to incorporate evidence-backed information about cannabis use into medical school and use screening questions about cannabis as a regular part of clinic visits.”

The researchers said future studies are imperative to better understanding the efficacy and safety of different formulations of cannabis in treating common conditions in older adults, both to maximize benefit and minimize harm.

“There seems to be potential with cannabis, but we need more evidence-based research. We want to find out how cannabis compares to current medications available. Could cannabis be a safer alternative to treatments, such as opioids and benzodiazepines? Could cannabis help reduce the simultaneous use of multiple medications in older persons? We want to find out which conditions cannabis is most effective in treating. Only then can we better counsel older adults on cannabis use,” said Kaufmann.

Story Source:

Materials provided by University of California – San Diego. Original written by Michelle Brubaker. Note: Content may be edited for style and length.

Journal Reference:

  1. Kevin H. Yang, Christopher N. Kaufmann, Reva Nafsu, Ella T. Lifset, Khai Nguyen, Michelle Sexton, Benjamin H. Han, Arum Kim, Alison A. Moore. Cannabis: An Emerging Treatment for Common Symptoms in Older AdultsJournal of the American Geriatrics Society, 2020; DOI: 10.1111/jgs.16833

Cite This Page:

University of California – San Diego. “Older adults using cannabis to treat common health conditions: Data indicates 61 percent of patients who used cannabis began after age 60.” ScienceDaily. ScienceDaily, 7 October 2020. <>.

The global quest to use a person’s breath analysis for rapid, inexpensive and accurate early-stage testing for cancer and other diseases has taken a leap forward.

In a new paper in the British Journal of Cancer, Flinders University researchers have reported significant progress in developing a method to test exhaled breath profiles which accurately differentiate head and neck cancer from non-cancer patients.

The Australian researchers collected breath samples from 181 patients suspected of having early-stage head and neck squamous cell carcinoma (HNSCC) before any treatment began.

“We sought to determine the diagnostic accuracy of breath analysis as a non-invasive test for detecting head and neck cancer, which in time may result in a simple method to improve treatment outcomes and patient morbidity,” says lead researchers Dr Roger Yazbek and Associate Professor Eng Ooi.

Worldwide, head and neck cancer accounts for 6% of all cancers, killing more than 300,000 people per year globally. Tobacco, alcohol and poor oral hygiene are known major risk factors for this cancer.

A surge in human papilloma virus (HPV)-associated head and neck cancers is seeing these cancers affecting a much younger population, the researchers say.

Current therapies are effective at treating early-stage disease, however late-stage presentations are common, and often associated with poor prognosis and high treatment-related morbidity.

In the Australian study, a selected ion flow-tube mass spectrometer was used to analyse breath for volatile organic compounds. Using statistical modelling, the Flinders researchers were able to develop a breath test that could differentiate cancer and control (benign disease) patients, with an average sensitivity and specificity of 85%.

Diagnosis was confirmed by analysis of tissue biopsies.

“With these strong results, we hope to trial the method in primary care settings, such as GP clinics, to further develop its use in early-stage screening for HNSCC in the community,” says co-lead author Dr Nuwan Dharmawardana.

Story Source:

Materials provided by Flinders UniversityNote: Content may be edited for style and length.

Journal Reference:

  1. Nuwan Dharmawardana, Thomas Goddard, Charmaine Woods, David I. Watson, Eng H. Ooi, Roger Yazbeck. Development of a non-invasive exhaled breath test for the diagnosis of head and neck cancerBritish Journal of Cancer, 2020; DOI: 10.1038/s41416-020-01051-9

Cite This Page:

Flinders University. “Promising breath-test for cancer: Potential for early warning of head, neck cancer.” ScienceDaily. ScienceDaily, 5 October 2020. <>.

Pain Medications

Over-the-Counter Pain Relievers

Over-the-counter (OTC) pain relievers include:

Both acetaminophen and NSAIDs reduce fever and relieve pain caused by muscle aches and stiffness, but only NSAIDs can also reduce inflammation (swelling and irritation). Acetaminophen and NSAIDs also work differently. NSAIDs relieve pain by reducing the production of prostaglandins, which are hormone-like substances that cause pain. Acetaminophen works on the parts of the brain that receive the “pain messages.” NSAIDs are also available in a prescription strength that can be prescribed by your physician.

Using NSAIDs increase the risk of heart attack or stroke and have also been known to cause stomach ulcers and bleeding. They can also cause kidney problems.

Topical pain relievers are also available without a doctor’s prescription. These products include creams, lotions, or sprays that are applied to the skin in order to relieve pain from sore muscles and arthritis. Some examples of topical pain relievers include AspercremeBenGayIcy Hot, and Capzasin-P.

Prescription Pain Relievers

Prescription pain relievers include:

What Are Corticosteroids?

Prescription corticosteroids provide relief for inflamed areas of the body by easing swelling, redness, itching and allergic reactions. Corticosteroids can be used to treat allergiesasthma and arthritis. When used to control pain, they are generally given in the form of pills or injections that target a certain joint. Examples include: prednisoneprednisolone, and methylprednisolone.

Prescription corticosteroids are strong medicines and may have serious side effects, including:

To minimize these potential side effects, corticosteroids are prescribed in the lowest dose possible for as short of a length of time as needed to relieve the pain.

What Are Opioids?

Opioids are narcotic pain medications that contain natural, synthetic or semi-synthetic opiates. Opioids are often used for acute pain, such as short-term pain after surgery. Some examples of opioids include:

How Chemotherapy Works

Chemotherapy is one of the most common treatments for cancer. It uses certain drugs to kill cancer cells or to stop them from growing and spreading to other parts of your body. Your doctor might prescribe chemo by itself or with surgery or radiation therapy. You might also take newer kinds of cancer-fighting drugs along with chemotherapy.You can take chemo as pills or shots. You might go to a clinic or hospital so you can get the drugs through an IV, what doctors call an infusion.

To help your body regain strength and grow new, healthy cells, you might take the drugs over a few weeks. You might take doses every day, every week, or every month. It depends on the type of cancer you have and how severe it is.

Your cancer doctor, called an oncologist, may prescribe one chemo drug or a mix of different ones, depending on: Contact Here Buy cancer drugs

  • Your type of cancer
  • Whether you’ve had cancer before
  • If you have other health problems like diabetes or heart, kidney, or liver disease

Why You Need Chemotherapy

Even after surgery to remove a tumor, your body might still have cancer cells. These cells can grow new tumors or spread the cancer to other parts of your body.

Chemotherapy drugs help destroy, shrink, or control those cells. It might also treat symptoms the cancer causes, like pain. You might also get chemo to shrink a tumor before your doctor removes it in surgery.

How It Works

Chemotherapy drugs work in a few different ways. They can:

  • Kill both cancerous and healthy cells
  • Fight only cancer cells
  • Keep tumors from growing blood vessels, which help them thrive
  • Attack the cancer cells’ genes so the cells die and can’t grow into new tumors

Common Chemotherapy Drugs

There are dozens of chemotherapy drugs that doctors can prescribe. They’re often divided into groups based on how they work and what they’re made of. Each group of drugs destroys or shrinks cancer cells in a different way.

  • Some drugs damage the DNA of cancer cells to keep them from making more copies of themselves. They are called alkylating agents, the oldest type of chemotherapy. They treat many different types of cancer, such as leukemialymphomaHodgkin’s diseasemultiple myeloma, and sarcoma, as well as breast, lung, and ovarian cancers. Some examples of alkylating agents are cyclophosphamidemelphalan, and temozolomide. As they kill bad cells, though, they can also destroy your bone marrow in the process, which can cause leukemia years later. To lower this risk, you can take the drugs in small doses. One type of alkylating agent — platinum drugs like carboplatincisplatin, or oxaliplatin — has a lower risk of causing leukemia.
  • One type of chemo drug interferes with the normal metabolism of cells, which makes them stop growing. These drugs are called antimetabolites. Doctors often use them to treat leukemia and cancer in the breasts, ovaries, and intestines. Drugs in this group include 5-fluorouracil, 6-mercaptopurine, cytarabinegemcitabine, and methotrexate, among many others.
  • Anthracycline chemotherapy attacks the enzymes inside cancer cells’ DNA that help them divide and grow. They work for many types of cancer. Some of these drugs are actinomycin-D, bleomycindaunorubicin, and doxorubicin, among others. High doses of anti-tumor antibiotics can damage your heart or lungs. So your doctor will have you take them for a short time.
  • Drugs called mitotic inhibitors stop cancer cells from making more copies of themselves. They can also stop your body from making the proteins that cancer cells need to grow. Doctors might prescribe them for breast and lung cancers and types of myeloma, leukemia, and lymphoma. Mitotic inhibitors include docetaxelestramustinepaclitaxel, and vinblastine.
  • Another type of medicine, called topoisomerase inhibitors, also attacks enzymes that help cancer cells divide and grow. They treat some types of leukemia and cancer of the lung, ovaries, and intestines, among other types. This group of medicine includes etoposideirinotecanteniposide, and topotecan. Some of them, though, may raise your odds of getting a second cancer a few years later.
  • Steroids are drugs that act like your body’s own hormones. They are useful in treating many types of cancer, and they can keep you from having nausea and vomiting after a round of chemo. They can also prevent allergic reactions to some of the drugs. Some of the steroids your doctor might prescribe are prednisonemethylprednisolone, and dexamethasone.

Predictive pricing hits the bigtime in chemicals

Niels Bohr, the pioneering Danish physicist and Nobel Prize winner in 1922, once remarked, “Prediction is very difficult, especially if it’s about the future.”1 In almost a century since, the frontiers for predicting the future have accelerated exponentially, thanks to the commoditization of computing technology, advancements in data harnessing capabilities and algorithms, and increased maturity of predictive models.

One area where prediction is now widely used is in pricing, especially in business-to-consumer (B2C) customer segments, where it is applied to improve margins and expand market share.

Companies that initially experimented with predictive pricing often leveraged it for customer centricity efforts, with customer personas becoming a key driver for price setting. This escalated the deployment of predictive analytics and machine learning models at a commoditized cost to capture supply and demand trends from a customer perspective and drive sales.

Today, B2C companies capture large volumes of customer, market and product data and its associated drivers and use this information to set price, plan supply strategy and shape demand forecasts. As just one example, Amazon has built and deployed dynamic (predictive) pricing models that make pricing and supply-demand planning both seamless and real time.2

Fortunately, chemical companies can mirror the B2C companies that are accessing on-demand computing and application services in the cloud and on-premise for data management, machine learning, artificial intelligence, predictive analytics and visualization. Furthermore, vendors are integrating these services into enterprise ecosystems, which makes it even easier for chemical companies to kickstart, test and extend predictive pricing programs for specific business use cases—from sandbox to industrial scale.

Chemical companies join predictive pricing trend

More recently, the need for predictive pricing in chemicals—which is still mainly a business-to-business (B2B) industry—has emerged due to four converging factors. The first is fierce competition in base and performance chemicals markets, including new customers, global competitors, cost-effective feedstocks and a dynamic supply chain. The second is the shift to customer centricity, which is shaping chemical product and end-use application development. Third is the growth in e-commerce channels designed to serve customers with an order-to-door experience. And last is the increasing business prioritization to manage and maximize margin.

Given these demands as well as the growing use of real-time predictions to make decisions in sectors such as financial, aviation and logistics, predictive pricing can play a central role for doing business in the chemical industry.

Steps to forge ahead

Sales, marketing and planning teams of chemical suppliers of all types can undertake a digital transformation journey using predictive pricing. Case in point: Accenture helped one global supplier capture and transform market pricing and driver data from sales personnel in the field into margin improvement. The goal was also to improve the company’s existing operational structure such as sales, supply and planning processes within global regions.

Accenture developed a machine learning/artificial intelligence model to predict pricing of a high-margin base chemical in a dynamic and highly competitive region. Key steps included:

  1. Capture pricing drivers (e.g., global, macro, strategic, supply-demand, feedstock)
  2. Identify dynamics of drivers
  3. Quantify and validate driver relevance
  4. Select drivers for building the model
  5. Build, train, predict and back-test price up to a six-month time horizon

Working together, we helped the company deliver a predictive pricing model with an accuracy of more than 80 percent in comparison to industry price benchmarks and validated it with regional market prices. The model was adopted to ultimately achieve the following key benefits:

Why every chemical business is a digital business and data is the new Permian

In their book, Prediction Machines: The Simple Economics of Artificial Intelligence, University of Toronto economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb posit that the principal benefit of Artificial Intelligence (AI) is that it significantly lowers the cost of making accurate predictions. They argue that when the cost of something comes down, we use more of it and

Figure 1 shows chemical industry disruptions, starting with plastics in the 1950s, oil shocks in the 1970s and 1980s and recently shale. Disruption occurs about every 20 years, looking ahead it appears to be shifting to data and digital technologies.

apply it in ways not thought of before. Basic principles of economics would suggest, therefore, that the use of AI is set to witness exponential growth. And, the results of Accenture’s 2018 Technology Vision survey support this as 68 percent of respondents indicate that their organization plans to invest in AI over the next year.1

So how might the chemical industry use AI to automate and augment prediction-making capabilities?

In the course of making decisions when running a chemical company, workers, managers and executives make scores of predictions every day, sometimes several times a day. For example:

  • A formulation chemist predicts how a change in the recipe might affect the functionality of the formulation before making the change.
  • A product manager predicts how a five cent per pound increase in price will be received by the customer and competitors before deciding on the pricing action.
  • A talent manager predicts how a candidate will perform and fit into the culture of the organization when making a hiring decision.
  • A maintenance worker predicts the likelihood of the continued functioning of a piece of equipment before deciding whether to let it run or take it down for maintenance.
  • A treasurer predicts future cash nee
  • ds when deciding how to fund or where to invest excess cash.

These predictions are not pulled out of thin air. They are made on the basis of data and models that analyze this data. The challenge, however, is that the data sets are incomplete and the models not necessarily reliable because they are built on these limited datasets. For example:

  • Forecasting prices: A product manager for polyester makes her price prediction based on several data sets: the price of ethylene glycol and
  • terephthalic acid, industry cost curves, polyester operating rates, etc. Price forecasts made with this limited data set tend to be directionally correct, at best, over the long run. This is because other variables like the price of cotton, short-term fashion trends (e.g., rise of athleisure wear), disposable income, shipping rates or inventory positions also affect price. But this data is either hard to obtain or difficult to factor into predictions.
  • Creating formulations: A formulation chemist who develops an additive package for a new engine oil specification relies on historical recipes to come up with initial formulations. He must deal with perhaps 20 to 25 complex components that react with each other and collectively produce the desired characteristics in the additive package. Once the potentially viable samples are developed, he runs expensive engine trials that generate a vast amount of quantitative and qualitative data (e.g., visual inspection of piston head). The process of developing a new package thus involves a lot of trial and error, and informed guesswork.

To understand why the use of AI is set to explode, it is important to first understand what has happened in recent years to the costs of data and computing power, two key inputs for making predictions.

  • Data: The biggest change is that it is now easy and inexpensive to gather and store vast amounts of data. The cost of Internet of Things (IoT) sensors has plummeted, lowering the cost of data gathering, and the cost of storage has dropped because of the cloud.
  • Computing power: Over the last 25 years, computing power per dollar has increased by an order of magnitude every four to five years; some estimates even suggest by as much as a factor of 10.2

Let us revisit the two examples discussed earlier in this new world of cheap, virtually limitless data sets and very low cost of computing power.

  • Forecasting prices: The product manager for polyester now uses an AI assistant that continuously accesses—in addition to the company’s proprietary supply curves and demand projections—several additional information sources, such as Chicago Board of Trade cotton futures, news reports on fashion trends, the ICIS3 feed on plant outages across the polyester value chain and shipping prices. Machine learning algorithms continuously detect patterns in this vastly expanded data set to make increasingly accurate predictions. With this information, the product manager can continuously improve her ability to price product to optimize profitability.The AI agent does what it does well—processes vast amounts of data, detects patterns and makes predictions. The product manager does what humans do best—exercise judgement on how to translate the predictions into pricing actions. In other words, a crucial function in the future of running the polyester business has become “digital.”
  • Creating formulations: The formulation chemist feeds the new engine oil specifications into an AI aide—that is, a “digital chemist.” The AI-based assistant accesses all the recipes ever created in the company’s history. The “digital chemist” is connected to the company’s laboratory information system and taps into patent and CAS4 databases. Years of quantitative and qualitative engine test data have also been ingested.In a matter of minutes, the machine learning algorithm driving the digital chemist recommends three recipes that have the highest likelihood of meeting the new specifications. Now the formulation chemist only needs to run three engine trials instead of dozens, thereby saving the cost of developing a new recipe and significantly accelerating the speed to market. Again, a crucial function in R&D has become “digital.”

As these two examples illustrate, chemical companies can apply AI throughout the organization and across every function to significantly improve the quality of critical decisions that are made every day. And when the use of AI to enhance decision-making becomes pervasive across a company, a chemical business becomes a digital business!

There is another way to think about this. The chemical industry has long thought in terms of value chains, and chemical companies have mastered the art of creating value by managing the physical flows in a value chain.

As they go digital and apply AI, however, chemical companies must master the art of creating value by tapping into the vast data flow that accompanies the physical flow. This data flow has always existed, but what has changed, as highlighted above, is the cost of gathering and processing the data. Deploying AI across the business represents numerous opportunities to monetize this data flow every bit as well as, if not better than, mastering the physical flows. And when a chemical business attains mastery over the data flow it become a digital business!