AI advanced analytics and IoT sensor technologies

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AI Advanced Analytics and IoT Sensor Technologies

Three examples of how the usage of artificial intelligence, advanced analytics, and IoT sensor technologies are being used in pharmaceuticals manufacturing to improve the supply chain and quality challenges are:
  • Pfizer's collaborations with both IBM Watson Health and Concerto Health, to develop a cure for cancer.
  • Bayer and Merck Co.'s combined AI software being used to identify and zero in on patients of CTEPH from a large pool of patients with similar results.
  • AbbVie and AiCure's collaboration to test drug adherence, that resulted in 90% success.

Challenges in pharmaceutical supply chain management.

1. Drug Development time and cost:

  • Drug development takes around 12 years to be complete.
  • Only 10% of drugs are approved by the FDA.
  • Developing a single new drugs costs around $2.6 billion. (Source3)
  • Reducing the investment will result in slower development of drugs, thus slowing down the chain.
  • To reduce the time, more funding as well as mental faculties will be necessary. This places severe restrictions on faster drug developments.
  • AI can assess the large existing and oncoming pool of clinical and genomic data to find the best molecular combination for any specific disease.
  • AI and machine learning, if introduced, will cut down the average production time by months and also reduce the cost of drug development, which for the 10 biggest pharmaceutical firms is close to $70 billion annually.

2. Drug Trials:

3. Insights into rare diseases:

4. Quality Assurance:

Examples of Usage of AI, machine learning, advanced analytics in pharmaceutical manufacturing

1. Pfizer, cancer and AI:

  • Pfizer is a research-based global biopharmaceutical company.
  • Pfizer made a collaboration with IBM to use the Watson Health AI to further its oncology research.
  • According to the Pfizer President of Innovative Health, Albert Bourla, "the best way to get the body to fight a tumor is some combination of agents to spur the immune system into action. But possible combinations are countless, so the greatest challenge is to find ways to narrow the field and predict what combinations might be more effective. Thanks to Watson, which has been trained to analyze historical data, we’re trying to predict the winning combination."
  • IBM's Watson Health AI has already analyzed a lot of research data, which Pfizer can use to make non-obvious connections that could lead to combination medicines for cancer.
  • Pfizer also entered into a collaboration with Concerto Health AI a month ago.
  • These research collaborations will help Pfizer develop a cure for cancer faster.
  • Since the collaborations and the products are still in development, results from both partnerships have not been made available.

2. Bayer and Merck AI Partnership:

  • Bayer & Merck, are two of the largest pharmaceutical companies in the world.
  • Bayer and Merck have developed an AI tool to collect image data from patients' pulmonary vessels, lung perfusion, and cardiac check-ups. The tool is able to identify CTEPH patients using the available images.
  • This software was recently approved by the FDA.
  • CTEPH is a rare disease caused by blood clots that don't dissolve in the lungs. Its prevalence is 5 per million people worldwide.
  • CTEPH has similar symptoms to COPD and asthma. This makes it difficult for doctors to diagnose the disease.
  • The AI tool uses data sets to differentiate it from diseases with similar symptoms.
  • The AI software was approved a year ago and since the tool is still in development, no major breakthroughs or results have been shared with the scientific community.

3. Abbvie Collaborates with AiCure in using AI for Drug Adherence: (Source4, 10)

  • Abbvie is a major pharmaceutical company. It recently partnered with AiCure, a company that uses AI and machine learning for pharmaceutical analysis.
  • The goal of their collaboration was to analyze drug adherence.
  • Abbvie used AiCure's facial and image recognition algorithm to monitor the adherence.
  • This method resulted in an improvement of the adherence by over 90%.
  • Drug adherence is very important for pharmaceutical companies to ensure that drug tests went according to set standards.
  • The results of this collaboration prove that using AI can improve the trial's efficiency and correct procedural flaws and errors.
The interest in the use of AI, machine learning, advanced analysis, and IoT to enhance and better drug manufacturing and supply chain has increased in the last few years but is still a fairly recent phenomena. This creates limitations on the analysis of impact made by these new technological augmentations. These are due to three main reasons:
  • Long development time — Despite AI reducing the time taken in drug development, the calculated time still takes around 10 years.
  • High failure rate — The introduction of AI only reduces the error, but it doesn't eliminate it.
  • Trade secrets — Due to competition among companies, rival companies may not present the exact data and results in order to stay competitive in the market.

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  • "...when combined, key clinical health AI and machine learning applications can potentially create $150 billion in annual savings for the U.S. healthcare economy by 2026. Revenue in the AI health market is expected to reach $6.6 billion by 2021, a CAGR of 40% and in just the next five years, the health AI market will grow more than 10 times. And as many as 74% of life-sciences executives believe AI will result in significant change or even completely transform their industry within three years."
  • "AI powered analytics is perfect for pharma marketing departments because it can undertake large volumes of interconnected and complex judgmental decisions by sifting through a multiple of seemingly unrelated data sets, and done with a high degree of accuracy."
  • "To improve the hit-and-miss business of finding new medicines, GlaxoSmithKline recently unveiled a $43 million deal to expand its AI capabilities. Other pharmaceutical companies, such as Johnson & Johnson, Merck, Novartis, and Sanofi are also jumping on the AI bandwagon to streamline their drug development and drug discovery processes."
  • "This technology can automate steps of the clinical process and complete tasks in a matter of seconds, which if done manually can take humans days... ...we can pinpoint trial aspects that are of vital or strategic importance for humans to focus their attention upon, thereby enhancing our ability to make critical decisions on trial execution."
  • "...the goal is to learn about molecular features behind varying responses to flu shots out of data sets that are unmanageably large by traditional standards, according to release from the company."
  • "Another burgeoning area of drug research where AI could provide value is in the rare disease space, where many rare diseases may go undetected until late stage symptoms surface, upon which a proper diagnosis can be determined."
  • "Typically, physicians see only a small number of patients with rare diseases. Without having a significant research pool to work with, diagnosis and treatment take a considerable amount of time. ...with the breakthrough combination of RWD and machine learning, physicians have an intuitive predictive model, based from immense amounts of normalized data and associated computational intelligence, available to them for analysis to facilitate a better understanding and faster diagnosis of these rare diseases."
  • "By integrating AI with human-driven workflows, organizations will begin to function with more precision faster as a greater share of decision-making activity is transferred to AI applications."
  • "Reports suggest big data and machine learning in pharma and medicine could generate a value of up to $100 billion annually, based on better decision-making, and optimized innovation, improved efficiency of research /clinical trials, and new tool creation for physicians, consumers, insurers, and regulators"
  • "The growing complexity of the healthcare stakeholder ecosystem is driving the production of increasingly diverse, unstructured, and difficult-to-integrate data sources, which is making it harder for life-sciences companies to get access to insights that are essential to their brand’s success."
  • "...clinical researchers can increase safety by adapting to the impacts of an investigative product through remote monitoring and real-time data access. Researchers can then add treatment arms, increase sample size, modify consent forms, and have deep and immediate insights into potential adverse events."
  • "Pharmaceutical companies can leverage AI to get the right patients the right care at the right time."
  • "Depending on how we leverage our vast data sources to understand what treatments a patient will benefit the most from, AI can influence drug development strategies for pharma companies..."
  • "Through the use of big data and analytics, we can understand in what diseases a medicine is most likely to have an impact and how to design clinical trials with specific patient populations and endpoints in mind, ultimately speeding pipeline progression."
  • "Principally, the main benefit from AI is helping pharmaceutical companies understand data in real-time."
  • "When healthcare AI is used on billions of clinical diagnostic, or labs, records available in real time, it can map out specific and complex patient journeys with a high degree of accuracy. As such, it can identify relevant patient segments before a physician makes a treatment decision and, in some cases, before a therapy is even considered."
  • "AI and robotic process automation (RPA) is a perfect application to drug development not just because it is a highly regulated process and by nature quite structured with many repetitive task, but because it is a data-intensive activity."
  • "RPA and AI can be of value in all phases of drug development and trial execution where there is a need to make decisions that can be better informed by learning from available knowledge or data."
  • "AI could both supplement human workflows and replace others. This hybridized approach to AI implementation for drug trials could reduce the time it takes drugs to reach the market, or rather speed up the process, thus reducing costs. If AI reduced the cost of a drug trial by just 10%, that’s $160 million saved. The hope is that would also translate to lower drug costs that deliver new treatments and cures seven to eight months sooner, allowing us to identify and produce even more treatment/cures faster."
  • "With the quantity and complexity of data available today, it’s just not possible for life-sciences companies to interpret these data manually... Advanced analytics platforms with AI are necessary to integrate this data and extract timely, relevant, and predictive insights."
  • "Pharma companies are considering AI to help them design new drugs, select genomic mutations, find patterns in real-world data to evaluate safety of their products and improve their understanding of the therapeutic market."
  • "The main challenges related to Quality Assurance processes are: spotting early indicators of production problems, identifying slowdowns and potential failures before they occur, and saving resources and time to optimize operational efficiency."
  • "According to Forbes, automating quality testing with machine learning can increase defect detection rates by up to 90%."
  • "The latest figure from the Tufts Center for the Study of Drug Development is that developing a new drug costs an average of $2.6 billion. The average time to market for a new drug is about twelve years. About 10% of drug candidates make it from Phase 1 testing to market."
  • "Berg Health, which is gathering massive amounts of data from patients with diseases like prostate cancer in order to identify new targets and develop new drugs."
  • "Pfizer now has over 150 AI projects underway. But few of them are at the core of drug development."
  • "We liked one example of AI-related “augmentation” rather than automation at Flatiron Health. This data-driven cancer treatment company, now a part of Roche, has a group of “abstractors” whose job involves extracting key information from physicians’ notes and other unstructured data in electronic health records. Flatiron is using natural language processing AI tools not to eliminate the job, but to increase its productivity."
  • "A multi-facetted biotech firm based in Toronto, Cyclica is redefining drug discovery and development by equipping pharma with AI-augmented and cloud-based platforms that improve how scientists design, screen, and customize drugs."
  • "Novartis, one of the largest global pharma companies by revenue, sales and market capitalization, is at the forefront of using AI to redefine drug development."
  • "The scientists at the Novartis Institute of BioMedical Research (NIBR) are using AI technology to gather, analyze, and gain insights from clinical trial data from an array of internal sources."
  • "The end-game at Novartis is to keep track of trial enrollment, as well as a predict associated costs and quality assurance. The results have been quite surprising, with the Institute reporting a 10-15 percent decrease in the patient enrollment times, especially during early-stage clinical trials."
  • "The brainchild of two well-known AI experts, Alice Zhang and Jason Chen, Verge Genomics brings together breakthroughs and innovations in genomics, machine learning, and neuroscience to deliver a new approach to discovering new drugs and therapies for brain disorders."
  • "If Verge’s machine learning-driven approach works as intended, it will reduce drug development process for discovering several different life-saving therapies for brain diseases like ALS, Alzheimer’s disease, Autism, and Parkinson’s disease, just to mention a few."
  • "IBM Watson is an AI platform that proven quite handy for big pharma. When it comes to clinical trial matching, many companies are working with IBM Watson to make sense of better data. These companies include Highlands Oncology Group, Mayo Clinic, Perficient Partners, Medtronic, Illumina, Pfizer, Merck & Co., and Bristol-Myers Squibb, just to name a few"
  • "Pharmaceutical giant Pfizer in late 2016 announced a collaboration that will utilize IBM Watson for Drug Discovery. Pfizer is using IBM’s AI technology on its immuno-oncology research, a strategy of using a body’s immune system to help fight cancer. Based on our research, this appears to be one of the first significant uses of Watson for drug discovery. The move was a big public announcement for both Pfizer and IBM."
  • "Pfizer President of Innovative Health Albert Bourla told Wired the best way to get the body to fight a tumor is “some combination of agents to spur the immune system into action. But possible combinations are countless, so the greatest challenge is to find ways to narrow the field and predict what combinations might be more effective. Thanks to Watson, which has been trained with historical data, we’re trying to predict the winning combination.”"