Author: Yuri Zamostin

June 19, 2024 By Miranda Schmalfuhs DIA 2024: Machine Learning and Simulations to Facilitate Clinical Trials In a session at the 2024 DIA Global Annual meeting in San Diego, CA, Raviv Pryluk, CEO & co-founder, PhaseV; Sam Miller, head of strategic consulting, Exploristics; and Andrew Stelzer, head of business development, Unlearn.ai addressed a number of ways simulations are currently being utilized in clinical research. Young Adults with Type 1 Diabetes Have Suboptimal Glucose Control Researchers suggest younger people with diabetes need more education and better support to manage their diabetes. Establishing a Data Ecosystem Using Automation to Optimize Regulatory-Compliant Clinical Trials Significant advances in technology are providing new opportunities to collect and utilize real-world data to establish better clinical trial protocols that have a patient-centric focus and the ability to transform healthcare. However, it is important to understand how such applications work and their impact on safety. This webcast series uncovers the concerns of safety professionals and other stakeholders involved in the development and implementation of clinical trial protocols in the digital era and highlights methods to best apply digital data into routine practice to best optimize trial success and adhere to strict regulatory guidance. How A.I. Is Revolutionizing Drug Development In high-tech labs, workers are generating data to train A.I. algorithms to design better medicine, faster. But the transformation is just getting underway. New Study Finds That Empowering Primary Care Providers with Data-Driven Cognitive Screening Tools Increases Identification of Cognitive Impairment A new study “Self-administered gerocognitive examination (SAGE) aids early detection of cognitive impairment at primary care provider visits” published today in Frontier in Medicine by researchers at The Ohio State University Wexner Medical Center finds that the practical and brief SAGE test was easily incorporated into primary care providers’ visits. This screening tool resulted in a significant increase in the detection of cognitive concerns. Silence Therapeutics plc on LinkedIn Discover how Craig Tooman, President and CEO, navigated the #biotechsector’s post-pandemic funding challenges to strengthen Silence’s position in the #siRNAdrug field in this interview with Pharmaceutical Executive : https://bit.ly/45r9zOF Have news you want us to share in Pharma Pulse? Reach out to Social Media Editor Miranda Schmalfuhs

A recent report from Accenture analyzes the value and impact AI can have on drug research and development. By Veronica Salib, Associate Editor Published: 22 Jul 2024 Last month, Accenture published a report titled “Reinventing R&D in the age of AI,” outlining how biopharmaceutical companies have been and can continue to leverage artificial intelligence (AI) and other intelligent technologies in the drug and therapeutic research and development pipeline. Kailash Swarna, a managing director and Accenture Life Sciences Global Research and Clinical lead, sat down with PharmaNewsIntelligence to discuss various facets of the report and explain how companies can effectively incorporate AI to address ongoing challenges in research and development and get a return on their technological investments. “Data and analytics are going to play a vital role in advancing drug development across the board from early research through late-stage clinical development,” said Swarna. “The key challenges for the industry that we are all aware of is that it still takes too long and costs too much to bring a medicine to market and patients. As a premier technology firm, we believe it is incumbent on us to bring the best of what data analytics and technology can do for drug discovery and development across the board.” Accenture conducted a series of in-depth interviews with leaders at biopharma companies to better understand the role of AI and its potential in drug development and discovery. During the CEO forum, which occurs the day before the JP Morgan conference, Accenture conducts closed-door interviews with these leaders. “For the first time, we saw technology rise to the top as a key area of opportunity and concern for biopharma,” noted Swarna, as he revealed that was how Accenture’s report started. Key Challenges in Research and Development Acknowledging the ongoing challenges in this field is important to understanding the opportunities for AI to improve research and development. The drug research and development landscape presents many difficulties. Although the complexity and magnitude of these challenges change based on the company and environment, Swarna offered some insight into the significant challenges faced by this industry. Scientific Growth “The biggest challenge is both an opportunity and a challenge. We see that there’ve been tremendous advances in biology and basic disease biology. We understand human disease biology better than we’ve ever done as a global scientific community working to address unmet needs in disease areas,” he stated, suggesting that one challenge is keeping up with scientific growth. “Science has grown dramatically in terms of its impact on patients and the benefit for patients, but we’ve not been able to keep pace with the growth in science in terms of executing on that scientific progress and being able to shorten the time and the cost associated with bringing therapeutics and devices to patients.” Swarna referred to the mechanics of clinical trials as “fraught,” meaning data management challenges and other complexities littered throughout the trial process. For example, clinical trials often take a long time to execute. While significant resources across the industry have been allocated to shorten the time it takes to get a drug to market, it can still take over a decade and billions of dollars. Macroeconomics In addition to keeping up with advancing science, drug research and development firms are also challenged by macroeconomic conditions, including reimbursement challenges and the Inflation Reduction Act in the US. “It’s changing how companies think about their portfolios and the molecules as well as the disease areas they will focus on,” Swarna emphasized. “There’s a whole retooling of the industry in terms of thinking about the impact of the macroeconomic conditions, reimbursement, and the economics of bringing drugs to market. That’s a second driver.” Technology Optimization The final challenge is optimizing technology in research and development. Many companies have invested in various technologies across the industry. “Technology investments that companies have made have been very important and very useful investments in individual areas, but bringing them all together and systematically utilizing data from early research through to late stage development and regulatory submission [is a challenge], he explained. “There’s a lot that can be done. There’s a lot of opportunity to unify all of that.” Reinventing Research and Development with AI The report focuses on the idea that technological intervention can reinvent and improve the research and development pipeline. “Unlike other previous technology movements, this is not about an individual technology. Generative AI and analytics are not just about one technology implementation. It is about a systematic rethinking of the processes and the data flow and the investments that companies make in technology to really go end-to-end early research, early discovery through to late-stage development and beyond,” he said. He reiterated that technology reinvention is about enterprise-wide strategy and implementation. Responsible AI However, reinvention requires companies to consider the potential challenges that AI may present. “We have a very robust framework at Accenture that we call our responsible AI framework,” noted Swarna. Responsible AI is an industry-wide term that considers how AI can be used effectively and how certain challenges that are inevitable with AI, like bias and security, can be addressed. Accenture considers multiple factors regarding bias, which means choosing the right patient populations to study and understanding how bias might have impacted existing data that inform ongoing research. In addition, the company considers security issues, including intellectual property protection and patient privacy, in its responsible AI framework. “Protecting patient privacy and being compliant with all the regulations around patient privacy around the world is really important for us to think about [to use] technology in the right way inside an organization because this is a very powerful technology, and [without] guardrails around how it’s used, we could run into data loss or data breaches.” Measuring ROI Swarna explained how companies can ensure that the technologies they deploy effectively and positively impact their research and development life cycle. “We’ve developed a rubric for being able to do that,” he noted. “The one thing that is potentially different in our industry from other industries is that we are a long cycle industry.”…

Contributed Commentary by Wing Lon Ng, Director of AI Engineering, IQVIA July 19, 2024 | The rapid advancements in artificial intelligence, particularly generative AI (GenAI), are enabling broader technology acceleration and driving a significant transformation in the healthcare industry. This democratization of AI capabilities opens the door to widespread innovation across clinical trials and drug development and represents a unique change in thinking compared to previous technological advancements.  AI streamlines and automates numerous processes in clinical research and trials. It enhances patient recruitment, improves data analysis and enables predictive modeling, all of which are key areas of impact. AI can analyze vast amounts of data much faster than humans to identify potential patients. Algorithms can analyze complex datasets, identify patterns, and predict outcomes much faster than humans.   The addition of GenAI to this technology mix is proving particularly valuable in accelerating trial design and execution. For example, GenAI can rapidly generate draft protocols by analyzing historical trial data, automating document creation and enabling real-time trial adjustments based on emerging data. These AI-powered capabilities are cutting down planning time from days to minutes for many trial-related tasks.   Beyond these efficiency gains, GenAI facilitates smarter, data-driven decisions throughout the clinical trial lifecycle. AI can simulate different trial scenarios, recommend optimal strategies and surface key insights from massive datasets. This allows sponsors to make faster, more informed choices about trial design, site selection, and patient recruitment.  Most importantly, GenAI has the potential to make clinical trials more responsive to patient needs. AI-powered matching can connect patients to highly relevant trials. Personalized treatment plans can be developed based on individual patient data. Additionally, AI-enabled remote monitoring allows for less disruptive trial participation.  Addressing Challenges While the potential benefits of implementing GenAI in clinical trials are significant, several crucial hurdles must be overcome to ensure the safe and effective use of this technology in such a critical domain.  Data quality and standardization represent a primary concern. Clinical trial data often comes from diverse sources, with varying formats and levels of completeness. For GenAI to function effectively, it requires high-quality, consistently formatted data. Achieving this standardization across different healthcare systems, research institutions and geographical regions is a formidable task that requires coordination and investment.  Regulatory compliance adds another layer of complexity. The pharmaceutical and healthcare industries are heavily regulated, and for good reason—patient safety is paramount. Integrating GenAI into clinical trial processes must align with existing regulatory frameworks, which may not yet be fully adapted to these emerging technologies. This necessitates ongoing dialogue between AI developers, healthcare professionals and regulatory bodies to establish proper guidelines and standards.  The risk of AI “hallucinations” is particularly concerning in clinical trials. These instances, where AI generates plausible but incorrect information, could lead to serious consequences if not properly identified and managed. In a field where decisions can directly impact patient health and the validity of scientific research, even small errors can have significant repercussions.  To address these challenges, implementing robust safeguards is crucial and requires a multidisciplinary approach, involving collaboration between AI developers, healthcare professionals, ethicists and regulatory experts. Cross-checking mechanisms, where AI-generated information is verified against multiple reliable sources, can help catch potential errors or inconsistencies.   The Importance of AI “Explainability”  Mandatory explanations for AI recommendations provide transparency into the AI’s decision-making process and help human experts evaluate the suggestions’ validity. As organizations increasingly rely on AI for critical decisions, there is a growing need for these systems to be not only accurate, but also explainable.   Explainable AI (xAI) refers to the ability of these technologies to clearly communicate to users why a particular decision or outcome was reached. Explainability has become an important factor. The opacity of certain machine learning models, dubbed “black boxes,” is important, especially in understanding how AI models arrive at decisions. Black box models are historically closed, powered by deep neural networks and sophisticated algorithms. By nature, the outputs are accurate but not explained. While this is acceptable for many applications, when it comes to predictive modelling and diagnostics in clinical trials, drug development and healthcare, it is imperative that the decisions made by AI are understood, transparent, and can be verified. Understanding the reasoning behind a certain diagnosis or prognosis is not only a matter of curiosity in important medical settings when judgments might be life or death. It is a vital necessity for trust and responsibility.  It’s here that “white box” models that not only deliver correct predictions but also provide insights into how these predictions are created are key. This multidisciplinary strategy combining advances in model interpretability, feature importance analysis, and a commitment to ethical AI practices is required to achieve this transparency.   The “why” is crucial to achieve both explainability and accuracy in AI to build user trust and identify potential biases effectively. Explainability and interpretability provide clearer insights into how AI models arrive at their conclusions, making it easier for human experts to validate and trust the output.  By carefully navigating these hurdles and implementing appropriate safeguards, the integration of GenAI into clinical trials can be achieved responsibly, potentially leading to more efficient, accurate, and innovative research processes in healthcare.  Human-in-the-Loop Remains Key While GenAI can process vast amounts of data and identify patterns beyond human capacity, the final decisions in clinical trials should always involve human judgment. This “human-in-the-loop” approach is essential as it ensures that ethical considerations, contextual nuances, and the potential real-world implications of decisions are thoroughly evaluated.  It must be emphasized that the goal is to augment rather than replace human expertise: AI recommendations still require human review and approval, which is important when combining the strengths of machine learning with human judgment. It recognizes that while AI systems can process vast amounts of data and identify patterns with remarkable speed and accuracy, they lack the nuanced understanding, ethical reasoning, and contextual awareness that humans possess.  Integrating AI recommendations with human review and approval leverages the strengths of both machine learning and human judgment. AI can rapidly analyze complex datasets, generate insights and propose…

By Andrew Bolt, principal, and Amy Cheung, principal, Deloitte Consulting LLP In today’s challenging economic environment, leaders in life sciences are often faced with the difficulty of executing flexible and adaptable clinical trials that can serve the unique needs of various stakeholder groups in a cost-effective manner. This is not an easy task given that clinical trials are inherently labor intensive, complex, and regulated. Novel digital technologies, automation tools, and patient-experience solutions have helped mitigate manual activities, and in turn, reduce overall cycle time and cost. However, these digitization tools have historically had only an incremental, not transformational, impact on clinical trials. Generative artificial intelligence (AI) might finally be the technology innovation that can be a transformative lever. Notably, generative AI has the potential to slow rising costs and transform the landscape of clinical development by accelerating tasks across the clinical lifecycle. That could help bring a greater share of services back within the four walls of biopharma companies, while improving experiences for internal resources and patients alike, and ultimately contributing to more efficacious therapies. Three processes that could be improved with generative AI Generative AI is already transforming the way life sciences organizations decide which disease areas to invest in. It is also being used to identify targets, develop molecules, streamline and accelerate clinical trials, and submit findings for regulatory approval (see Can Life Sciences companies unlock the full value of GenAI?). Here is a look at the role generative AI might play in several aspects of a clinical trial: Three considerations to ensure the effective use of generative AI Many of the life sciences leaders we talk to are excited by the potential of applying generative AI to clinical trials. They are also understandably concerned about managing risks to quality and employee experience. To that end, we provide the following considerations: Combining GenAI with other digital platforms such as machine learning and predictive analytics could create an end-to-end business value stream—from clinical study startup through clinical study closeout. Acknowledgments: Aditya Kudumala and Adam Israel

Hitesh Chopra, PhD,a Annu, PhD,b Dong K. Shin, PhD,b Kavita Munjal, PhD,c Priyanka, PhD,d Kuldeep Dhama, PhD,e and Talha B. Emran, PhDf,g Author information Article notes Copyright and License information PMC Disclaimer Associated Data Data Availability Statement Abstract Clinical trials are the essential assessment for safe, reliable, and effective drug development. Data-related limitations, extensive manual efforts, remote patient monitoring, and the complexity of traditional clinical trials on patients drive the application of Artificial Intelligence (AI) in medical and healthcare organisations. For expeditious and streamlined clinical trials, a personalised AI solution is the best utilisation. AI provides broad utility options through structured, standardised, and digitally driven elements in medical research. The clinical trials are a time-consuming process with patient recruitment, enrolment, frequent monitoring, and medical adherence and retention. With an AI-powered tool, the automated data can be generated and managed for the trial lifecycle with all the records of the medical history of the patient as patient-centric AI. AI can intelligently interpret the data, feed downstream systems, and automatically fill out the required analysis report. This article explains how AI has revolutionised innovative ways of collecting data, biosimulation, and early disease diagnosis for clinical trials and overcomes the challenges more precisely through cost and time reduction, improved efficiency, and improved drug development research with less need for rework. The future implications of AI to accelerate clinical trials are important in medical research because of its fast output and overall utility. Keywords: artificial intelligence, application, clinical trial, difficulties Introduction Highlights Artificial intelligence (AI) is a field of computer science that tries to figure out how the human brain solves problems and makes decisions1. It has been around for almost 100 years, and the use of AI is not what makes it new. Over the past few decades, drug development has become more complex, and AI has been used to help with that, though this is not talked about much. A famous example is using AI models to help figure out how the structure of chemical molecules affects how they work in living things1,2. They are important for finding new drugs and help scientists predict how a potential drug will work in the body. Even though their estimates are limited by what the models can do, they have made the process of finding new drugs much more efficient by letting scientists focus on possible drugs that have a better chance of fighting a certain disease3,4. The problems we are attempting to tackle now include fighting considerably more complicated illnesses with more precision, safety, and effectiveness than was possible in the past. Fortunately, we live in a time where not only is there a plethora of data on human biology but also the capacity to analyse enormous volumes of that data, all owing to cheap and powerful technology. Although AI’s ability to tackle these complicated illnesses has grown, so has the challenge of doing so. Using massive volumes of genetic, phenotypic, and chemical data, we can now create a whole virtual world centred on drug development, complete with in-silico models that replicate human illness. Due to their reliance on a single preset hypothesis, traditional illness discovery approaches often miss identifying traits that may be identified using computational methods and algorithms. Multiple targets might be considered at once as we assess therapy options. As human beings, we are incapable of multitasking. AI helps bridge that gap, but it still relies on humans for direction. In recent years, the coronavirus disease 2019 (COVID-19) outbreak has pushed the pharmaceutical business to go through more digital change5. More people are interested in using AI and big data analytics across the pharmaceutical value chain, from drug development and clinical study design all the way to sales and marketing. This is because ultralarge datasets are now available, and technology is getting better. In the past 3–4 years, there has been more interest in using AI to find new drugs. This is shown by the growing number of start-ups working in this area, the growing number of agreements for drug development, and the record amounts of funding. Most drugs made with AI are still in the early stages of development, but there have been some big steps forward recently. For example, the first drug made with AI is now in clinical studies, and a drug already on the market is being used to treat COVID-19. In the past few years, AI has caught the attention and interest of people who work in medical technology. This is because a number of companies and big research labs have been working to make AI technologies ready for clinical use6,7. AI, also known as Deep Learning (DL), Machine Learning (ML), or Artificial Neural Networks (ANNs), can now help doctors in the real world for the first time. These tools could change the way clinicians do their jobs and make them more productive while also improving care and patient turnover. AI for drug discovery is a technology that uses machines to mimic human intelligence in order to solve difficult problems in the process of making new drugs. Adopting AI solutions in the clinical trial process gets rid of possible problems, shortens the time it takes to run a clinical trial, and makes the process more accurate and productive. Life science industry players are becoming more interested in using these advanced AI solutions in the drug development process. In the pharmaceutical industry, it helps find new chemicals, find treatment targets, and make more personalised medicines. AI systems used for drug development can be a good way to learn more about how to find drugs to treat and lessen the effects of a number of chronic diseases. For instance, NVIDIA Corporation released Clara Holoscan MGX in March 2022 so that real-time AI apps could be made and used8. Clara Holoscan MGX expands the Clara Holoscan platform to offer an all-in-one, medical-grade standard design and long-term software support to speed up innovation in the medical device market. This will help the company improve the AI it uses for treatment, diagnosis and finding new drugs. In May 2022, BenevolentAI, a…