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PE: Do you see the FDA placing any restrictions on the use of AI and machine learning as times goes on? What may prompt such actions?

Fakhouri: Like I mentioned during the keynote interview, we get asked, does FDA regulate large language models? Are you going to ban generative AI use? My response is that we typically don't regulate linear regression. We look at the data and the information that any modeling technique is producing, and we want to make sure that the information is trustworthy. So, I wouldn't say that we would be banning or prohibiting a certain AI or machine learning type of algorithm, what we're actually interested in is how robust how accurate, how credible, the information from these models is.

PE: What do you think the future may hold for AI and machine learning in pharma R&D in both the short- and long-term?

Fakhouri: We're actually very excited about AI use, I think we're seeing that it's increasing efficiencies in different parts of the drug development process. If you think about things such as discovery or protein folding, which again, is outside of what we normally look at, it could potentially cut the development time by years. This is all very exciting, because it could translate into faster, safe and effective drugs coming into the market. It can also fill in certain gaps for rare diseases, for example, where we can see a lot of potential use for AI to accelerate the development of drugs. In this type of situation, that's what I would say would be the long term. With the short term, I think what we're all doing, whether it's industry, whether it's the regulator's academia, is we're going through this adoption curve. You need to train your staff, you need to bring in the right expertise, and you need to develop the right tools to solve the right problems. That's going to take some time and that's why I think the short term uses of AI are going to be mostly low hanging type of fruits where you're increasing operational efficiency, but then that will translate into the development of safe and effective drugs faster.

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US Pharma and Biotech Summit 2024: Artificial Intelligence and Machine Learning Through the Eyes of the FDA Part II - Pharmaceutical Executive

NeuroSense Therapeutics is collaborating with PhaseV for insights into how to better design the protocol for the planned Phase 3 trial that will test PrimeC for amyotrophic lateral sclerosis (ALS).

A specialist in machine learning technology for clinical trials, PhaseV used data from the ongoing Phase 2b PARADIGM trial (NCT05357950) as input to a causal machine learning model. This is a form of artificial intelligence that can help unlock insights and identify features that may contribute to a treatment response.

As part of its independent analysis, the company found that PrimeC could work well in multiple subgroups of patients in the Phase 3 study, which should start in the coming months.

Being able to predict treatment outcomes in certain patients may help optimize the design of the upcoming trial by selecting the patients most likely to respond, while reducing costs.

ALS is a complex disease that manifests in unique ways in each patient. Although there is an improved understanding of the underlying mechanisms of ALS, therapeutic options remain limited, Raviv Pryluk, CEO and co-founder of PhaseV, said in a press release.

NeuroSense plans to submit an end-of-Phase 2 package for review by the U.S. Food and Drug Administration (FDA) and the European Medicines Agency, the FDAs European counterpart, and discuss the clinical protocol for the Phase 3 trial with the regulators.

There remains a critical need for new innovative approaches to address this devastating neurodegenerative disease, said Alon Ben-Noon, CEO of NeuroSense. We plan to continue to collaborate with PhaseV as we develop our Phase 3 trial.

PrimeC contains fixed doses of two FDA-approved oral medications: the antibiotic ciprofloxacin and celecoxib, a pain killer that reduces inflammation. Both are expected to work together to slow or stop disease progression by blocking key mechanisms that lead up to ALS, such as inflammation, iron accumulation, and RNA processing.

PARADIGM is testing a long-acting formulation of PrimeC in 68 adults with ALS who started to see symptoms up to 2.5 years before enrolling. While continuing their standard ALS treatments, the participants were randomly assigned to PrimeC or a placebo, taken as two tablets twice daily for six months.

An analysis of PARADIGMs per-protocol population 62 adults with ALS who adhered well to the clinical protocol showed a significant 37.4% reduction in functional decline, as measured by the ALS Functional Rating Scale-Revised (ALSFRS-R).

A subgroup of those patients who were at a higher risk for rapid disease progression had the most clinical benefit, with those treated with PrimeC for six months showing a significant, 43% reduction in functional decline over a placebo. High-risk patients made up about half the adults in the Phase 2b trial.

Another subgroup of newly diagnosed patients whod had their first symptoms of ALS within a year of enrollment showed a 52% reduction in the rate of disease progression. This translated to a 7.76-point difference in favor of PrimeC on a maximum total of 48 points in the ALSFRS-R.

Through our initial collaboration with PhaseV, we gained an even greater understanding of the effect of PrimeC across multiple patient subgroups, Ben-Noon said. We will apply these insights to optimize the design of our Phase 3 study with the aim of maximizing meaningful clinical results that will differentiate PrimeC in the market.

Through a unique combination of causal [machine learning], real-world data, and advanced statistical methods, we confirmed the potential clinical benefit of PrimeC, Pryluk said. Our analysis predicted a high rate of success for PrimeC in the Phase 3 clinical trial for multiple recommended subgroups.

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Machine learning drafted to aid Phase 3 testing of ALS therapy PrimeC - ALS News Today

image:

Graphical abstract

Credit: Joe F. Bozeman III

A paradigm shift towards integrating socioecological equity into environmental data science and machine learning (ML) is advocated in a new perspective article (DOI: 10.1007/s11783-024-1825-2)published in the Frontiers of Environmental Science & Engineering. Authored by Joe F. Bozeman III from the Georgia Institute of Technology, the paper emphasizes the importance of understanding and addressing socioecological inequity to enhance the integrity of environmental data science.

This study introduces and validates the Systemic Equity Framework and the Wells-Du Bois Protocol, essential tools for integrating equity in environmental data science and machine learning. These methodologies extend beyond traditional approaches by emphasizing socioecological impacts alongside technical accuracy. The Systemic Equity Framework focuses on the concurrent consideration of distributive, procedural, and recognitional equity, ensuring fair benefits for all communities, particularly the marginalized. It encourages researchers to embed equity throughout the project lifecycle, from inception to implementation. The Wells-Du Bois Protocol offers a structured method to assess and mitigate biases in datasets and algorithms, guiding researchers to critically evaluate potential societal bias reinforcement in their work, which could lead to skewed outcomes.

Highlights

Socioecological inequity must be understood to improve environmental data science.

The Systemic Equity Framework and Wells-Du Bois Protocol mitigate inequity.

Addressing irreproducibility in machine learning is vital for bolstering integrity.

Future directions include policy enforcement and systematic programming.

"Our work is not just about improving technology but ensuring it serves everyone justly," said Joe F. Bozeman III, lead researcher and professor at Georgia Institute of Technology. "Incorporating an equity lens into environmental data science is crucial for the integrity and relevance of our research in real-world settings."

This pioneering research not only highlights existing challenges in environmental data science and machine learning but also offers practical solutions to overcome them. It sets a new standard for conducting research that is just, equitable, and inclusive, thereby paving the way for more responsible and impactful environmental science practices.

Frontiers of Environmental Science & Engineering

Experimental study

Not applicable

Bolstering integrity in environmental data science and machine learning requires understanding socioecological inequity

8-Feb-2024

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Bolstering environmental data science with equity-centered approaches - EurekAlert

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The next phase of technology has been established: machine learning and AI will revolutionize the world for the better. Although it might seem like these stocks are trading in a bubble, investors need to keep a discerning and keen long-term vision for these disruptive, emerging technologies. Some way or another, AI will grow to become a secular movement that nearly every industry, not every company in the world, will incorporate to increase productivity and efficiency.

Of course, anxiousness about the AI bubble is not unwarranted. Preparing a well-diversified portfolio of the right stocks is crucial to avoid such major drawdowns. Just because a company mentions AI doesnt mean it instantly becomes a good investment. Weve already seen this with pullbacks in industries like EVs and fintech. So, if you want to gain machine learning exposure in your portfolio, consider these three machine learning stocks to buy and thank us in the coming five or ten years.

Source: Ascannio / Shutterstock.com

Palantir (NYSE:PLTR) went from a meme stock to a legitimate business, earning hundreds of millions each year in profits. The stock is trading right at the average analyst price target of $21.45 and has a street-high price target of $35.00. This high-end target represents a more than 60% upside from the current price.

This stock has been polarizing on Wall Street since its direct listing debut in September 2020. While the first few years were a roller coaster ride for investors, the stock is earning legitimate backing through its machine-learning integrated production deployment infrastructure. Additionally, the hype doesnt get any more legit than Stanley Druckenmiller, who disclosed that he bought nearly 770,000 shares in the recent quarter! For those who dont know him, Druckenmiller has long supported the ML revolution, with NVIDIA (NASDAQ:NVDA) being his most recent win during its massive rally over the past year.

The problem with Palantir has always been its valuation. Currently, shares trade at 21x sales and 65x forward earnings. Nonetheless, growth prospects are looking strong now, with revenue growing at a five-year compound annual growth rate (CAGR) of 12% and a three-year CAGR of 21%. As multiples begin to compress, investors should consider Palantir to be a legitimate money-making contender in the ML space.

Baidu (NASDAQ:BIDU) is a Chinese technology company that recently amassed over 200 million users on its new Ernie AI chatbot. This year, the stock is down by about 4.0% as Chinese stocks have lagged the broader rally in US equities. Nonetheless, Wall Street has maintained an average analyst price target of $153.36, about 40% higher than the current price.

Baidu recently made headlines after reporting it was interested in partnering with Tesla (NASDAQ:TSLA) to use its robotaxis in China. As China looks to get its hands on some for immediate rollout, investors should keep their eyes peeled for the unveiling of the CyberCabs in America this August. Not only will this potentially be one of the strongest new channels for revenue growth for both these companies, but Baidus race to get first movers advantage could solidify it as a leader in the Chinese automobile space.

As with many Chinese ADR stocks, the multiples for BIDU are low. For example, its P/E ratio of 9.79x is sitting 25% lower than its sectors median! On top of such a discounted valuation, Baidu has maintained a strong 10-year revenue CAGR of 14%. Baidu looks like a bargain for investors who can tolerate the risk that comes with Chinese stocks.

Micron Technologies (NASDAQ:MU) is an American chip maker with a major surge in demand due to AI and machine learning technology. Analysts are bullish on MU, with 28 of 31 recommendations coming in May as a Buy or Strong Buy rating. The average analyst price target is $145.52, nearly 15% higher than the current price.

This chip maker has already hit new all-time highs this month and is seeing revitalized product demand. This growth potential has largely been attributed to Micron being one of three companies in the world that make DRAM memory chips. These chips allow for storing massive amounts of data, which will help accelerate the training of AI and machine learning technologies. These DRAM chips account for 71% of Microns revenue as of Q2 2024, which bodes well for the stocks upward momentum.

Usually, when a stock trades at all-time highs, its valuations also stretch. Thats not exactly true for Micron, as shares are trading at just 7.5x sales and 17x forward earnings. As revenue growth accelerates, Micron sticks out as one of the more under-the-radar ways to gain exposure to AI and potentially join the million-dollar club.

On the date of publication, Ian Hartana and Vayun Chugh did not hold (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

Chandler Capital is the work of Ian Hartana and Vayun Chugh. Ian Hartana and Vayun Chugh are both self-taught investors whose work has been featured in Seeking Alpha. Their research primarily revolves around GARP stocks with a long-term investment perspective encompassing diverse sectors such as technology, energy, and healthcare.

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Machine Learning Stocks to Buy That Are Millionaire-Makers: May - InvestorPlace

In the ever-evolving landscape of manufacturing, especially electronics assembly, AI-driven robotics is transforming the nature of quality control.

By using deep learning, these systems can detect patterns and defects that humans cannot, which provides ample opportunity for companies to leap ahead of their competition.

As AI continues to integrate into manufacturing, its role in ensuring stringent quality standards, enhancing inspection capabilities, and maintaining compliance with IPC (Institute for Printed Circuits) standards becomes increasingly vital.

AI-driven robotics significantly enhance quality control by using advanced algorithms and machine learning to detect defects more accurately than human inspectors.

These systems can analyse large volumes of data in real time, identify patterns, and make decisions based on stringent quality standards. This is important in the automotive, pharmaceutical, and electronic assembly industries, where even minor defects can lead to substantial issues.

The AI algorithms that work on modern quality control depend upon machine learning and deep learning. Machine learning involves the development of algorithms and data sets that allow AI to perform tasks without explicit instructions.

With machine learning, AI can learn and make decisions on data independently. Deep learning is a type of machine learning that uses layers of networks to interpret complicated data. This enables AI to understand and learn from images, text, video, and audio.

Machine learning and especially deep learning is being used to train AI on large data sets so that it can detect patterns and anomalies. This is then applied to quality control.

For example, in the electronics manufacturing industry, AI can be trained to detect soldering defects, missing components, and alignment issues. These algorithms continuously learn from new data, improving their accuracy over time.

One of the significant advantages of AI-driven robotics is the ability to process and analyse data in real time. High-speed cameras and sensors capture detailed images and measurements of products as they move through the production line.

AI systems then quickly analyse this data, instantly identifying defects and deviations from quality standards. This immediate feedback allows for quick corrective actions, reducing the amount of quality issues in manufacturing.

AI systems excel in pattern recognition, which is crucial for detecting defects that may not be obvious to human inspectors. For example, AI can identify subtle variations in texture, colour, or shape that could indicate a potential issue.

This is particularly important in industries like electronics and automotive manufacturing, where precision and consistency are absolutely vital. Pattern recognition capabilities ensure that even the smallest defects are detected and addressed promptly.

IPC is a trade association that standardises the assembly and production of electric equipment and assemblies and it is the bedrock behind the success of modern electronics manufacturing. AI robotics poses a number of significant opportunities to further enhance IPC moving forward:

The Future of Quality Control in Electronics Manufacturing: As AI-driven robotics evolve, their role in quality control will expand. Future advancements may include sophisticated machine learning models for predictive maintenance, reducing downtime and enhancing production efficiency.

As AI-driven robotics continue to evolve, their role in quality control within electronics manufacturing is ready for significant expansion in several different ways.

Combining Internet of Things (IoT) devices with AI-driven robotics will revolutionise quality control. IoT sensors can monitor production environments in real time, providing data that AI systems analyse to maintain optimal conditions.

Predictive maintenance, powered by this integration, will allow for timely repairs, minimising production disruptions.

AI systems that learn and adapt to new products and manufacturing methods offer greater flexibility and efficiency. These adaptive systems can adjust in real-time based on data analysis, ensuring quality control processes evolve with production innovations.

Advanced machine learning models will continually improve from new data, enhancing their defect detection and process optimisation capabilities.

Predictive analytics will become integral to quality control. By analysing historical and real-time data, AI can foresee potential issues, enabling proactive measures. This reduces unexpected downtime and optimises production schedules, leading to more efficient operations.

Future quality control will see increased collaboration between humans and robots. Collaborative robots (cobots) will handle repetitive or hazardous tasks, while humans focus on complex and creative quality control aspects.

This synergy will enhance productivity and job satisfaction, creating a more skilled workforce that can adapt to advanced technologies.

There can be no debate that AI-driven robotics is revolutionising the nature of quality control in manufacturing, especially for electronics assembly. These systems provide consistent, precise inspections and reduce human error to ensure strict adherence to IPC standards.

As AI technology continues to develop, the integration of IoT devices, adaptive quality control systems, and predictive analytics will further increase efficiency and reduce downtime.

Human-robot collaboration will also play a key role, combining the strengths of both to achieve higher productivity and quality standards.

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AI-Driven Robotics and Quality Control: Transforming Electronics Assembly and Manufacturing - Robotics and Automation News