Category: 3. Business

Greek researchers have made breakthroughs in the development of small-cell lung cancer, aiming to eradicate the disease globally.

For that purpose, the study was presented at one of the renowned clinical studies on lung cancer at the American Society of Clinical Oncology (ASCO).

The masterpiece got published in the prestigious New England Journal of Medicine.

It was a major advancement in this virulent form of disease that accounts for 15-20% of lung cancer diagnosis in Greece and all across the globe.

However, the clinical results focus on a new class of biotechnology developed drug known as “T-cell engager”, which will play a pivotal role in strengthening the immune system. The desired results declared that 40% reduction occurs in the relative risk of death from this deadly disease.

Most importantly, the new treatment demonstrated that it retains better tolerance than traditional chemotherapy. It reduces the symptoms of breath, coughing and chest pain.

The novel immunotherapy approach has received massive appreciation and got approval in several countries including the United States, Japan, Brazil, Canada, UK and approval in Europe and Greece to be expected by 2026.

Dr Mountzios said, “Greece played a leading role in this significant scientific milestone.”

He further explained, “Our international recognition clearly demonstrates the capabilities of Greek research centers, which stand on par with the best global institutions in terms of infrastructure, and access to cutting-edge treatments. This marks a defining moment for Greece Oncology.”

This approach may play a crucial role in dealing with other tumors apart from lung cancer, where immune system and metabolic reprogramming are major barriers to its treatment.

With more clinical trials, it could evolve into a flexible platform for combination therapies, pushing current limitations in cancer care and making a new era of immune restoration.

The FDA has granted orphan drug designation to the GPRC5D-targeted monoclonal antibody SAR446523 as a potential therapeutic option for patients with relapsed or refractory multiple myeloma.¹

SAR446523 is an investigational IgG1-based monoclonal antibody that also features an engineered fragment crystallizable domain intended to enhance antibody-dependent cell-mediated cytotoxicity. The agent is currently being evaluated in a first-in-human phase 1 trial (NCT06630806) in this patient population.

“The orphan drug designation is a significant milestone in our ongoing efforts to develop innovative treatments in multiple myeloma,” Alyssa Johnsen, MD, PhD, global therapeutic area head of Immunology and Oncology Development at Sanofi, stated in a news release. “This underscores our commitment to multiple myeloma, a disease for which we have acquired strong expertise with the development of another widely used and approved immunotherapy treatment.”

What’s Behind the Phase 1 Trial?

The first-in-human study includes both dose-escalation and -optimization portions, and investigators are enrolling patients at least 18 years of age with a documented diagnosis of multiple myeloma who have measurable disease.² All patients must have an ECOG performance status of 0 or 1 and adequate organ and bone marrow function.

In the dose-escalation portion, at least 3 prior lines of therapy are required for enrollment; patients must have disease that is either relapsed or refractory to those prior therapies, or they need to be intolerant to them. Prior exposure to GPRC5D- and BCMA-directed therapy is allowed in this portion of the study.

During dose optimization, at least 3 prior lines of therapy are required, and patients need to be relapsed or refractory to an immunomodulatory drug, proteasome inhibitor, anti-CD38 monoclonal antibody, and anti-BCMA targeting therapy. Intolerance to these treatments also allows patients to enroll. In this portion of the study, prior GPRC5D-directed therapy is not permitted.

In both portions of the study, patients are being excluded if they have primary systemic and localized amyloid light chain amyloidosis; active polyneuropathy, organomegaly, endocrinopathy, myeloma protein, and skin changes syndrome; or active plasma cell leukemia. Those with central nervous system involvement or with clinical signs of meningeal involvement of multiple myeloma are also excluded. Other key exclusion criteria comprised systemic therapy within 14 days before the first study treatment, prior treatment with natural killer cell–engaging therapy within 90 days of first study treatment, and significant concomitant illness.

Up to 6 dose levels of SAR446523 are being evaluated during dose escalation with the goal of determining the maximum administered dose, maximum tolerated dose, and recommended dose range for regimens that will be tested in dose optimization.

During dose optimization, patients will be randomly assigned 1:1 to receive SAR446523 at one of the selected doses established in part 1 of the study, with the goal of determining the recommended phase 2 dose.

The incidence of dose-limiting toxicities is the primary end point in dose escalation. Overall response rate (ORR) is serving as the primary end point for dose optimization. Secondary end points include safety (both parts), ORR (dose escalation only), very good partial response or better rate, clinical benefit rate, time to response, progression-free survival, and minimal residual disease status.

The study was initiated in October 2024 and is currently enrolling patients at 9 locations in the United States, Canada, Australia, and Italy. Investigators will enroll an estimated 82 patients, and the estimated primary completion date of the study is November 2028.

References

1. Sanofi’s SAR446523, a GPRC5D monoclonal antibody, earns orphan drug designation in the US for multiple myeloma. News release. Sanofi. July 30, 2025. Accessed August 8, 2025. https://www.sanofi.com/en/media-room/press-releases/2025/2025-07-30-05-00-00-3123737
2. A study to investigate the safety and efficacy of SAR446523 injected subcutaneously in adult participants with relapsed/​refractory myeloma. ClinicalTrials.gov. Updated April 23, 2025. Accessed August 8, 2025. https://clinicaltrials.gov/study/NCT06630806

Honda dealerships in Pakistan have announced immediate delivery of the Honda Civic, coupled with multiple financing and payment plans to accommodate a range of buyers.

Availability and Dealership Network

Honda Khair in Karachi and Hyderabad is offering ready delivery for various Civic variants. Customers can confirm availability or book through the dealerships’ dedicated contact lines.

• Honda Khair (Karachi): 0336-2323612, 0300-2006736, 0321-8210390
• Hyderabad Honda: 0321-2005298, 0301-8378823
• UAN: 0304-1117723

Pricing Structure

According to Honda Atlas Cars Pakistan, the Civic is priced at:

• Civic Oriel: Rs. 8,834,000 (ex-factory)
• Civic RS: Rs. 10,100,000 (ex-factory)

Market data shows the Civic Oriel available from Rs. 8.659 million and the RS variant close to Rs. 9.899 million, depending on the dealership and variant choice.

Financing and Installment Options

Zero-Markup Plan

A limited-time 0% interest plan requires an upfront payment of approximately Rs. 4.4 million, with the remainder payable over up to 18 months. The package includes insurance, a tracker, and a four-year warranty.

Standard Financing

For a Civic Oriel priced at Rs. 8.659 million:

• Down payment: 20% (about Rs. 1.73 million)
• Financed amount: ~Rs. 6.93 million
• Estimated monthly payment (5-year term): Rs. 150,000–180,000, subject to prevailing interest rates.

Islamic Financing

HBL’s Islamic Car Finance offers Musharakah-based financing with tenures ranging from one to five years, comprehensive Takaful coverage, and flexible repayment options.

Vehicle Specifications

The Honda Civic features a 1.5-liter petrol engine paired with an automatic transmission, delivering fuel economy between 11 and 14 km/l. Delivery timelines can extend up to 11 months for certain variants, though immediate delivery is available under the current dealership offer.

Maggiulli’s insights are rooted in data and everyday observation, but he believes the upper middle class is going through an “existential crisis,” as he noted on his blog “Of Dollars and Data.” He talked to Fortune about what he thinks is going on: “The economy wasn’t built to handle this many people with this much money,” he said, hinting at his research on what he calls the new economic classes of the United States.

In “The Wealth Ladder,” Maggiulli proposes a new, data-backed framework for thinking about affluence. It’s a much bigger topic than just Level 4. He divides American households into six wealth levels, ranging from under $10,000 (Level 1) to $10 million-plus (Level 5 and beyond). The most populous segment is Level 3—those with $100,000 to $1 million in wealth—but he says that Level 4, the so-called “upper middle class,” is notable for its rapid growth and unique challenges.

Maggiulli’s analysis shows the angsty, existential Level 4 was just 7% of the country in in 1989, but as of 2022/23, that had shot all the way up to 18%. Admittedly, inflation means that a millionaire in the late ’90s would have a net worth of around $2 million, also as of 2022/23. But still, he says, this economic class is much bigger than it used to be, especially since the pandemic, and he thinks it’s “starting to have all these impacts throughout the rest of the economy.”

The existential crisis of the upper middle class in the 21st century

This demographic expansion, Maggiulli says, has sparked unexpected economic side effects, from crowded airport lounges to bidding wars for housing and luxury amenities. ““The economy wasn’t built to handle this many people with this much money,” he observes, linking “scarce resource” frustrations to the surging population of affluent Americans. “They’re all competing for a small pool of resources,” he says.

The weirdest thing, Maggiulli says, is that these people are objectively very successful. “They’ve done well in life … but on a relative basis in the United States, the competition for these higher-end goods is very high, so now it feels like we’re all canceling each other out with all this extra wealth.” Wealthy level 4 Americans could always move somewhere else, where their money would go much further, but they are mostly staying in the U.S., where they don’t feel like the millionaires that they’ve become.

It really is different from the late ’90s to now, Maggiulli says, adding that in terms of purchasing power, an American with a net worth of $1 million back then would rank in the top 5% of wealth, whereas that status in the 2020s belongs to someone worth $4 million. “There’s so much wealth being created that the upper end is seeing this competition like never before,” he adds.

UBS Global Wealth Management noticed a similar trend in its 2025 edition of the Global Wealth report, seeing a dramatic rise in the “everyday millionaire,” or EMILLI. At the dawn of the millennium, there were just over 13 million EMILLIs worldwide, UBS found, but that number had shot up to nearly 52 million—a more than fourfold increase in less than 25 years. Even after adjusting for inflation, the number of EMILLIs has more than doubled in real terms since 2000. “There’s a good portion of [these Level 4, everyday millionaires] that feel like they don’t have enough,” Maggiulli told Fortune, “and they feel like they’re just getting by, even though statistically they’re in the top 20% of U.S. households.”

Maggiulli’s remarks recall those of Charlie Munger, Warren Buffett’s long-time right-hand man at Berkshire Hathaway, who died in 2024. The previous year, in his last appearance at the annual meeting for his newspaper holding company, Daily Journal, Munger sounded a similar tune about things being ever better but people feeling ever worse. “People are less happy about the state of affairs than they were when things were way tougher,” Munger said, then made a striking comparison. “It’s weird for somebody my age, because I was in the middle of the Great Depression when the hardship was unbelievable.” Munger said he was powerless to change how unhappy people felt “after everything’s improved by about 600% because there’s still somebody else who has more.”

The importance of assets

Maggiulli’s analysis extends to the composition of wealth across classes: “The poor own cars, the middle class own homes, and the rich own businesses.” He stresses the “rich” in America tend to hold assets like businesses and stocks, not just real estate or commodities. To truly shift up levels, the kind of assets you own really matters.

Nick Maggiulli

Maggiulli told Fortune about the long-anticipated “Great Wealth Transfer,” when baby boomers pass on their $124 trillion fortunes to the Gen Xers and millennials now in or entering midlife. As baby boomers age, their assets are expected to flow into Gen X and eventually millennials, a process he frames as “very normal.” But he cautions that much of this wealth is tied up in illiquid assets like real estate, potentially distorting Americans’ perception of their own affluence.

He’s also candid about what he calls the “broken housing market.” Even affluent adults are forced into renting more often than not: In fact, Maggiulli’s research shows there have never been so many millionaire renters before. Maggiulli says if it seems like economic conditions have driven many Americans to postpone homeownership, he would know, because he’s one of them. “What that means for me personally is that I’m just gonna be renting for a lot longer,” Maggiulli tells Fortune, “because it doesn’t make sense to buy, especially where rates are, prices, everything.” The housing market as currently constructed just “doesn’t add up” for his situation.

For Maggiulli, the key takeaway is adaptability. He analogizes personal finance to fitness: “You can imagine a fitness instructor giving different advice to someone who’s morbidly obese versus someone who’s a well-trained athlete.” Likewise, financial strategies must shift as individuals progress up the “wealth ladder.” This particular ladder isn’t one that you’re meant to keep climbing forever, but a very large ladder with a lot of plateaus on it, some where you stay forever. He says you need to step back and reassess: “Do I need to keep climbing? Is this right for me?”

Alex Bryson, professor of Quantitative Social Science at University College London, told Fortune something similar in an interview about his research into 21st century labor markets, social mobility, and young workers. “People at that time in their lives, when they’re looking to build careers and move on and acquire property and, you know, all the the ladder-type things … it feels as if, perhaps, for some of them, somebody’s removed some of the rungs on that ladder.” Bryson added that “we haven’t necessarily got the same career structures and patterns” in the current economy as in the past.

Maggiulli says he’s not advocating through his book for people to choose one particular path or another, but to be aware of their wealth and their trajectory. “I think a lot of people get there, and they say, ‘Wait, do I want to keep going down this path? Or maybe I can take my foot off the gas and choose a different path where money is not the only thing I’m focusing on.’”

For this story, Fortune used generative AI to help with an initial draft. An editor verified the accuracy of the information before publishing. 

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Global rice prices have tumbled to their lowest level in eight years, in a blow to many farmers across Asia, as record harvests and the ending of export bans in India flood the market with supply.

Export prices for Thai 5 per cent broken white rice, the global benchmark, have dropped to $372.50 per tonne in recent days, a 26 per cent decline since late last year and their lowest level since 2017. That extends a slide that began after India, the world’s largest exporter, started lifting restrictions on shipments in September 2024.

The UN’s All Rice Price index is down 13 per cent this year, according to the body’s Food and Agriculture Organization.

“It’s that simple: there’s just too much stock,” said Samarendu Mohanty, director of the Centre for Sustainable Agriculture and Development Studies at Professor Jayashankar Telangana State Agricultural University. “India’s rice production last year was a record . . . The crop they just planted is going to be another record crop.”

The price decline marks a sharp reversal from early last year, when rice soared to its highest level since 2008 after India introduced a series of export curbs. That sparked a wave of panic buying among consumers and prompted protectionist measures in other producing countries.

India’s policy shift late last year, following a record harvest in 2023-24 that swelled government inventories, was “the main reason” for the dramatic drop in prices, said Oscar Tjakra, senior analyst at Rabobank.

“This comes on top of strong production in Thailand and Vietnam, which has taken global rice output to a record high this marketing year,” he added. 

Demand, meanwhile, has fallen. Indonesia, one of the biggest buyers, frontloaded imports last year and has not re-entered the market in 2025. The Philippines has banned imports until October to protect domestic prices during its main harvest.

“Indonesia is out, the Philippines is out — there’s no demand for white rice right now,” Mohanty said.

India’s unusually strong supply position reflects advances in the country’s agriculture, he said. Almost all the farms in the country’s main rice-growing regions have irrigation systems now, making production more resilient to drought and increasingly erratic monsoons, he said. “India has monsoon-proofed rice production.”

Farmers are also increasingly buying new seeds each season, which boosts yields, and expanding acreage of rice, thanks to the country’s minimum support price system and state bonuses, which helps shield farmers from global price swings. “Farmers know paddy is the most attractive crop. You get an MSP, you get a bonus, and it’s less risky,” said Mohanty. 

Growers in most other Asian countries have no such protection as global prices plunge, said Tjakra. “Low prices will erode farm earnings, which is particularly challenging with higher input costs and inflation.”

For consumers, however, the slump offers welcome relief after several years of high food prices. In countries that depend on rice imports, cheaper prices can help ease headline inflation and household budget pressures.

Despite the sharp price fall so far this year, there could be further to go, said Mohanty. “I see another 10 per cent downside,” he said. “There are no buyers out there.”

He estimates the Indian government’s warehouses held as much as 60mn tonnes of rice in May — up to 15mn above the average for recent years. With another bumper crop under way, New Delhi has been offloading stocks into the domestic market and even into ethanol production — at lower prices than for human consumption — to free space ahead of the next harvest.

“We are going into [a period of] low commodity prices,” said Mohanty. “I don’t see the trend reversing for at least the next two years, unless there’s a war or some other major shock.”

At its live-streamed launch on Thursday in the US, GPT-5 was touted by OpenAI as its “smartest, fastest, most useful model yet, and a major step towards placing intelligence at the centre of every business”.

The new AI model features improved performance across coding, maths, writing, health and visual perception, among others. OpenAI described it as “a unified system” that features a built-in “thinking” function, with the ability to automatically switch between “standard” and “deep thinking” modes based on factors such as conversation, task types and query complexity.

The new system is “like a PhD-level expert in anything, any area”, OpenAI CEO Sam Altman said at the launch.

GPT-5 “doesn’t come with revolutionary breakthroughs; it lacks memorable characteristics”, Zhang said.

Thin films with a high technical specification have many applications, including within lithium-ion batteries^1,2, solar panels³ and polymer electrolyte membrane fuel cells^4,5. Roll-to-roll slot die coating is a widely used technology for the industrial scale manufacture of thin films, which involves pumping a fluid through a slot in a metal block onto a moving substrate^4,6. The high line speed, high material utilization and ability to pre-select coating thickness are factors which have contributed to roll-to-roll slot die coating becoming ubiquitous in state-of-the-art thin film manufacture⁷.

Slot die coating has many adjustable process parameters, which influence the coating formed. These parameters, as illustrated in Fig. 1, include substrate velocity, coating gap, shim thickness and composition of coating solution^4,8. Slot die coating forms defect-free coatings using parameter sets within the operating window⁴. Outside of this window, the coating process is susceptible to significant defects such as ribbing, dripping and air entrapment. However, even when remaining within the operating window, different sets of coating parameters give different coating properties^9,10. The wet coating thickness, for example, can vary within the operating window depending on the ratio of pump rate to substrate velocity⁴. However, more subtle features such as coating uniformity and edge quality also depend on the process conditions used^10,11. Schmitt et al. termed regions within the operating window with a high coating uniformity, the quality window¹⁰.

Many theoretical models can predict the operating window for slot die coating such as those by Ruschak¹², Higgins and Scriven¹³ and Yamamura¹⁴. However, to the best of the authors’ knowledge, there is no efficient theoretical understanding or analytical models, of how process parameters affect features of the coating within the operating window. This is noteworthy as coating properties such as unexpected coating thickness deviations and coating uniformity have a large influence on the performance of subsequent devices¹⁵. For example, a high coating uniformity is essential for Li-ion battery electrodes as it minimizes rejection rate and has a large influence on the electrochemical performance of the electrode^1,16. As a specific example of this, Mohanty et al. found that non-uniform Li-ion NMC electrode coatings gave poor rate capability, especially at higher rates, and a lower Coulombic efficiency¹⁷. Coating uniformity is also an important coating feature for organic photovoltaic devices, with a higher uniformity resulting in improved device performance¹⁸.

Despite the dogma that slot die coating provides perfect thickness control, in reality small changes in the coating width can occur depending on the process parameters used, which in turn alters the coating thickness¹⁹. This effect is due to the non-Newtonian behavior of polymeric coating solutions²⁰. The coating’s thickness determines the energy density of a Li-ion battery, with a thinner than expected electrode giving a lower cell energy density²¹. Additionally, a thicker than expected electrode may lead to mass transport limiting charging/discharging rates. These differences in coating thickness are particularly significant for industrial operators in applications which utilize stripe coatings or with strict thickness requirements.

The lack of theoretical modeling and understanding of how input parameters impact these fundamental coating properties in slot die coating⁴ means that operators currently optimize production through iterative, trial and error adjustments^1,11. The large amount of process parameters and competing outputs exacerbate the complexity of this optimization and make trial and error unlikely to result in the coating process being truly optimized. This has cost implications, due to the waste material produced and production time lost during this laborious process. Additionally, this type of optimization leaves potential device performance improvements untapped.

Despite computer aided optimization having been implemented in a wide variety of manufacturing processes in other sectors, such as additive manufacturing^22,23 and pharmaceutical manufacturing²⁴, it is not routinely applied in industrial roll-to-roll slot die coating lines. Harnessing computer-aided optimization in this context could unlock significant cost and performance benefits for a wide range of devices. Furthermore, such an approach has the potential to provide valuable insights into the relationship between key coating parameters and the resultant properties of the coating.

The literature has documented some instances of computer-assisted optimization for roll-to-roll slot die coating^16,25,26. However, there are no reports linking fundamental process parameters to critical coating properties, highlighting a significant gap in the understanding of slot die coating within its operating window. Additionally, there has not been any utilization of a machine learning model to provide experimental improvements in coating features. This disconnect highlights a divide between the theoretical modeling and practical application, raising concerns about the effectiveness or applicability of the previously reported methods.

Machine learning-based surrogate optimization is well-suited for slot die coating, due to the process’s complexity and the numerous interacting inputs and outputs. Surrogate modeling involves constructing computationally efficient approximations of approximate complex, computationally expensive, or time-intensive simulations using experimental data and is well known for capturing intricate non-linear relationships^16,27. Surrogate models are particularly effective when analytical models are unavailable.

There are a range of analytical approaches and multivariate modeling tools that can be used for modeling industrial processes. For instance, multiple linear regression (MLR), polynomial regression (PR)²⁸, principal component analysis (PCA), latent variable model (LVM), orthogonal partial least squares (OPLS)²⁹, Gaussian process (GP), and Artificial Neural Networks (ANN)³⁰. Among these, Radial Basis Function Neural Networks (RBFNNs), are a notable machine learning modeling method due to their ability to accurately describe complex nonlinear relationships while maintaining computational efficiency. With its universal approximation capability³¹, RBFNNs excel at modeling complex systems with high accuracy and efficiently capturing local variations in data.

However, alternative surrogate modelling methodologies also warrant consideration. For example, Gaussian Process regression is widely recognized for its flexibility and ability to provide uncertainty estimates, making it a strong candidate for problems where quantifying prediction confidence is important. Similarly, Support Vector Regression, which adapts support vector machine methodology to regression tasks, is known for its robustness in high-dimensional spaces and often achieves good generalization on relatively small datasets³². Comparative studies have demonstrated that while RBFNNs typically offer fast training and effective local modelling, Gaussian Process models may outperform them in terms of predictive error under certain circumstances, particularly with small datasets or when uncertainty quantification is required. Furthermore, recent research has shown that SVR can deliver competitive performance, though its accuracy might be surpassed by RBFNNs and Gaussian Process models under some conditions, such as in delamination detection in composite laminates³³. Therefore, incorporating comparative analysis with these surrogate modelling techniques can further reinforce the strengths and appropriate use-cases for RBFNNs, highlighting the need for model selection based on the specific characteristics of the data and the modelling objectives.

Although gradient-based optimization techniques can effectively explore solution spaces, they have inherent limitations such as generalization challenges and a lack of performance guarantees. Surrogate-assisted evolutionary computation addresses these issues by using computationally efficient models to estimate fitness functions in evolutionary algorithms. This approach is particularly valuable for complex optimization problems with computationally expensive objective functions. By employing surrogate models like RBFNNs, this approach significantly reduces the need for costly experimental evaluations, accelerating both exploration and exploitation of the parameter space. Additionally, acquisition functions further balance exploration and exploitation, making this method particularly effective for high-dimensional optimization tasks.

Reference Vector Guided Evolutionary Algorithm (RVEA), a flexible and scalable meta-heuristic evolutionary optimization method³⁴, is well-suited for complex processes, that traditional multi-objective problem algorithms struggle with in terms of performance and diversity maintenance³⁵. RVEA utilizes a uniform distribution of reference vectors – directions in the input parameter space- to guide the search for optimal solutions. This method ensures a diverse set of solutions spread across the Pareto front, which represents the set of optimal trade-offs between conflicting objectives. Additionally, RVEA’s adaptive angle penalty mechanism dynamically adjusts selection pressure, maintaining a balance between convergence and diversity, making it well-suited for complex, high dimensional problems.

This article presents an experimentally validated optimization approach for roll-to-roll slot die coating, combining a RBFNN surrogate model with RVEA. The coating composition used resembles many industrially relevant coatings, such as slurries for lithium ion batteries and solar cells^1,36, making insights gained applicable across a wide range of fields. This article aims to promote the widespread implementation of machine learning-based optimization in roll-to-roll slot die coating, offering potential improvements in cost, performance and process understanding.