conclusion of case study on tata nano

Tata Nano's Failure To Attract Customers [Tata Nano Case Study]

Devashish Shrivastava

Tata Nano is a compact vehicle that was produced and showcased by Indian automaker Tata Motors , principally in India, as a modest back-engined hatchback expected to speak to current riders of bikes and bikes — with a dispatch cost of Rs 1,00,000  or $2500. Delays during the production line migration from Singur to Sanand, early cases of the Nano bursting into flames, the impression of the vehicle being perilous, and compromise in quality due to cost slicing are some of factors behind Tata's failure to attract Indians.

Here we present the case study of tata motors – nano case and find out why did Tata Nano Failed and never gained traction despite being termed as the 'people's car'.

Tata Motors anticipated creation of 250,000 every year at dispatch. This didn't happen. Only 7591 were sold for the model year 2016-17. In 2017, Tata Motors said assembling would proceed because of Tata's passionate promise to the project. In 2018, Cyrus Mistry, previous Chairman of the Tata Group, called the Tata Nano a venture in progress with China, with a generation overhaul scheduled in May 2018.

Tata Motors' Nano Initiative Initial Effects Of Tata Nano Marketing And Business Strategy Of Tata Nano Why Tata Nano Failed? Tata Nano's Attempted Comeback FAQs

Tata Motors' Nano Initiative

After effectively propelling the ease of Tata Ace truck in 2005, Tata Motors started the advancement of a reasonable vehicle that would speak to the numerous Indians who ride motorcycles . The price tag of this nitty-gritty auto was brought somewhere around getting rid of the most superfluous highlights, diminishing the measure of steel utilized in its development, and depending on ease of Indian labor.

The superfluous highlights include the evacuation of the traveller's side wing mirror, having one wiper sharp edge, having just three fasteners for every wheel, and the expulsion of the fuel filler top from the fuel tank. The presentation of the Nano got much media consideration because of its low cost of Rs. 100,000. The vehicle was promoted as "The People's Car".

Initial Effects Of Tata Nano

A report by the Indian rating office CRISIL figured the Nano would extend the country's vehicle advertising by 65%, in any case, starting late 2012. However, deals in the initial two monetary years after the vehicle's divulging stayed unfaltering at around 70,000 units. Tata still proposed the ability ability to deliver the vehicle in a lot bigger amounts, somewhere in the range of 250,000 every year, if the need arise.

It was foreseen that its 2009 presentation would significantly influence the trade-in vehicle market, and costs dropped 25–30% before the launch. Sales of the Nano's closest rival, the Maruti 800, fell by 20% promptly following the disclosing of the Nano.

It is obscure if the Nano has lastingly affected the costs of and interests for close substitutes. In July 2012, Tata's Group administrator- Ratan Tata , who resigned in January 2014, said that the vehicle had huge potential while conceding that the early open doors were squandered because of starting problems. Due to the business drops, just a solitary unit was delivered in June 2018.

Marketing And Business Strategy Of Tata Nano

India is an organization with a larger part of its populace dwelling as a low pay gathering. Purchasing a vehicle is still a fantasy for many families in India. In the year 2008, Ratan Tata reported the dispatch of its new vehicle, which would be a progressive item in the car division. He called it a 1 Lakh rupee vehicle and the same title was used for promotions .

The fantasy of a middle and even lower white collar class family unit to have their very own vehicle woke up with this declaration from Tata Motors. It was hailed to be cutting edge innovation and was commended by international media. An entirely unexpected story unfurled when the vehicle appeared on the streets.

India Customer's Needs

Ratan Tata's flash for the Nano came when he saw groups of 3-4 individuals utilizing a motorbike for transportation . There are a great many bicycles in India and it has constantly presented well-being worries for the general population. In blend with the poor street conditions in India and conveying a full family on a bike, bikers are subjected to numerous mishaps.

His strategical view drove the concept of a low cost vehicle and chose that the 1 Lakh Rupee sticker price will be appealing to the objective market.

Proclamation

At the point when Tato Nano was declared in the long stretch of January 2008, it was over-advertised to be the vehicle of each Indian. The cost of Nano was pegged at Rs. 1 lakh or $2500 by Ratan Tata, the Chairman of the Tata Group by then of time.

It turned into a fantasy vehicle for each individual having a place with the lower working class and even the lower class. Anyway, the service of the vehicle additionally raised worries about the blockage on streets that the vehicle would acquire as individuals began to utilize it for everyday transportation.

Strategic Plans

TATA Motors fundamentally segmented and focused on the following sections of the Indian population:

• The middle class- Fundamentally the lower white-collar class.
• Upper lower class- Normally the bike clients .
• Family with 3-4 individuals who have inconveniences while going on a 2-wheeler.

Affordability And Family Friendly Usage

Tata Nano being propelled in the Indian market was an opportunity for the normal man of India to fulfill his dream of owning a vehicle. The promotions and media productions featured the passionate remainder that demonstrated the joy of youngsters when they see a vehicle coming to their home, and the joy on the substance of the relatives who delineated a white-collar class gathering.

The battling Indian classes who had a month to month pay of under Rs.6000 every month and comprised more than 110 million families got the chance to dream of purchasing a vehicle. Mr. Ramesh Mangaleswaran, an accomplice of McKinsey and co., anticipated that in Mumbai alone the 2 million individuals who rode a cruiser ordinary would now attempt to lift themselves to purchase a Tata Nano.

It was expected that Tata Nano would make a progressive change in the way of life, uncommonly concerning the substitution of the regular man. It would turn into a face of the Indian lower class, just like the Bajaj Scooter at one time represented the white-collar class.

Why Tata Nano Failed?

Ratan Tata stayed faithful to his obligation and the Indian market saw Nano set on the streets in the long stretch of July 2009. At the start, the deals for the vehicle were high. It then began to decay on every month. There are a few reasons of failure which justify Tata Nano's downfall,

• Failure in marketing the vehicle was the main reason behind Tata Nano's Failure to attract customers .
• TATA nano promotes itself as the least expensive vehicle.
• No one needs to drive the least expensive vehicle.
• Purchasing a vehicle is identified with economic well-being and distinction in the public arena.
• "Cheap" and "lakhtakia" used in Tata Nano's advertising for advancement and showcasing all over India disturbed its image.
• The engine was an issue.
• There was a buzz in the universal media, "What if Nano becomes successful? It would mean an end to the second-hand car market ."

Problems With The Car

• Awful picture of the shoddy vehicle.
• Several cars caught fire. Thus, in spite of its low cost, people refrained from buying it .
• Media channels covering the news related to Tata Nano underestimated the vehicle. In any case, they were correct.
• The car was not fit for sloping territories.
• The motor used to make a great deal of clamor, and individuals even compared it with an auto.
• Insides were dull with inadequate leg space.
• The whole assemblage of Nano was light and prone to damage on even the slightest of knocks.

Other Reasons

• Tata Nano got around 200,000 appointments at first. This made Tata Motors complacent and it didn't bother about new advertising strategies . New publicity procedures were essential to keep the enthusiasm of the individuals unblemished. But it wasn't done. When the main flame episode was accounted for, the ad system then just looked responsive and upgraded rather then focusing on negative attention.
• Nano was viewed as an attraction for individuals who never thought of purchasing a vehicle. It was focused on engine cycle riders, recycled vehicle proprietors, and different families in the lower white collar class gathering. This prompted some degree of opposition. According to the intended interest group, the media and the general public acknowledged Tata Nano as a poor man's vehicle.
• Ratan Tata in his previous question and answer sessions referenced that he wanted to position Tata Nano as a 'reasonable, all climate family vehicle".
• At the point when Nano later raised its cost to conquer the negatives of the principal model, the cost turned out to be a lot higher. The top-end Model of Tata Nano (2014) was cited at an on-street cost of around Rs.2.6 lakhs in Bangalore . This sort of evaluation with the equivalent Nano model which the poor man likewise claimed, confounded the clients..

Tata Nano's Attempted Comeback

In the year 2013 Tata engines re-propelled Tata Nano with new components and publicity efforts. The re-dispatch concentrated on the following:

Focusing on the young people of the nation, the new Nano had extravagant settings like settings and shading blends, for example, ranch side or experience sports . The ads and crusades this time concentrated on the adroitness factor. The emphasis was, "Why not purchase a Nano when it gives everything at a deep discounted?" It additionally featured the rational advantage of Fuel productivity in another manner.

This time, Tata Nano pursued another sort of crusade altogether. They began to support programs on MTV that energized experience sports and stretched out the crusade on National TV.

Like the arrangement Roadies circulated on MTV where the members need to go on a Hero Karizma, they attempted to execute a comparable technique where the members were approached to traverse India in a Tata Nano. This validated the intense with which the vehicle was fabricated.

It concentrated on the passionate parts of a parent, and the car was promoted such that guardians can give their children a Tata Nano as opposed to giving them bicycles; a vehicle is more secure than a bicycle. This would likewise make them brilliant guardians.

Why did Tata Nano failed?

There are a few reasons of failure which justify Tata Nano's downfall,

• Failure in marketing the vehicle was the main reason behind Tata Nano Failure to attract customers.
• Tata Nano promotes itself as the least expensive vehicle.
• There was a buzz in the universal media, "What if Nano becomes successful? It would mean an end to the second-hand car market."

Why did Tata Nano fail to attract customers?

Delays during the production line migration from Singur to Sanand, early cases of the Nano bursting into flames, the impression of the vehicle being perilous, and compromise in quality due to cost slicing are some of factors behind Tata's failure to attract Indians. Also Tata nano promotes itself as the least expensive vehicle. No one needs to drive the least expensive vehicle.

Is Tata Nano still available?

Tata Nano is no longer available in the new car market.

Is Nano car still in production?

No Tata Nano is no longer manufactured.

What went wrong with Tata Nano?

Tata Nano got around 200,000 appointments at first. This made Tata Motors complacent and it didn't bother about new advertising strategies. New publicity procedures were essential to keep the enthusiasm of the individuals unblemished. But it wasn't done. When the main flame episode was accounted for, the ad system then just looked responsive and upgraded rather then focusing on negative attention.

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Marketing Case Study: The Story Behind Tata Nano’s Failure as a Brand

Table of Contents

The tata nano story, tata nano’s marketing strategy, customer response to tata nano, 5 reasons behind tata nano’s marketing failure.

Ratan Tata and his companies need no introduction. Despite being a successful brand across different sectors, its potentially revolutionary product, Tata Nano, was a failure. This blog will discuss the Tata Nano story and understand the reasons for Tata Nano’s failure as a brand.

We’ll also shed light on Tata Nano’s marketing strategy and why it backfired and led to shutting down the production within a decade.

Ratan Tata is well known as a social entrepreneur as most of his ventures are for society. His businesses have strengthened the country’s economy and have provided employment opportunities to many across various sectors. Ratan Tata is a philanthropist and, along with the Tata Group, committed to donating ₹1500 crore to fight COVID-19.

Tata Motors is the automotive entity of the brand and made a splash back in 2008 with the revolutionary Tata Nano. The vision was to make cars available to the lower-middle-class masses. The brand realized that there was a huge gap in the automobile industry, and Tata Nano was born as a result. The car came with the vision of bringing comfortable and safe mobility to the masses and was introduced as “The People’s Car” at the 2008 Auto Expo in India.

Tata Nano was presented as a 1 Lakh rupee car—small but spacious with a capacity of 4 and no power steering, no airbags, and no A/C in the base model. It stood firmly with quality, mileage, and environmental standards for the price.

Now, let’s look at how Tata marketed its new product and what led to Tata Nano’s failure as a brand?

The target market was the middle-class and lower-middle-class sector, and hence the car was positioned and marketed as a “One Lakh Rupees Car.” Its price was its biggest USP.

Tata Nano was the most ambitious venture of the brand. To make it cost-effective, it was made of steel, was small, light-weight, and without luxury features. The marketing strategy was to stage it as an affordable car for the Indian masses.

  It was presented as a safer alternative to a bike or a scooter for an Indian middle-class family of 4 members. Nano never intended to compete with other car companies. The funky colors appealed to modern families and the spacious interiors offered comfort.

Tata Nano’s marketing strategy seemed to be perfect so far, then what went wrong?

Kunal Shah, the founder of Freecharge and Cred, often repeats this well-understood fact of how obsessed humans are with their social status. A car is a status symbol that everyone wants to own. The bigger and more luxurious the car, the higher they perceive themselves to be in society.

The major reason for Tata Nano’s failure is the perception it creates in the minds of its target audience. A car, back then, was a luxury to own, and not a commodity to use. Branding Nano as a “cheap car” killed the entitlement that was supposed to come with it, and hence people did not find it worth buying.

The pre-launch buzz around Tata Nano was huge. All the media houses, national and international, were extensively covering the Tata Nano story. The buzz publicized the product, but as a “cheap car.” But, Ratan Tata, in an interview, said that he always called it a “people’s car.” It was the media and unfortunately the company, that termed it as a “cheap car.”

Tata Motors aimed to produce 2,50,000 Nano cars annually whereas the opening sales were 30,000 (approx.) only. The highest ever sales of 74,527 were achieved in 2011-12, which went down to 7,591 in 2016-17. The plant assembled only 1 Nano in June 2018 and closed production.

Harvard Business School covers a case study on Tata Nano’s failure and inculpates the company’s marketing strategy for the failure. Let’s see the 5 reasons behind Tata Nano’s failure.

1. Invalid market analysis

It seems like the company had assumed what their market desired. They were so ambitious about the product launch that they forgot to empathize with their target market before positioning their product.

Their emotional attachment to the product didn’t let them skeptically analyze their strategy. Tata possibly thought that the car was a necessity, but it was actually a luxury.

If they had given it more thought, they’d have realized that their customer did not want “the cheapest car.” They were more comfortable living as they were than going for a cheap car. Thus, a comfortable, fully-functional car that came with a tag of “cheap” was not appreciated by the market.

2. The Lakhtakiya positioning

Tata Nano was often called “Lakhtakiya,” translated as “worth a lakh.” Tata Motors used the penetration pricing strategy. The company marketed it as the cheapest car in the world to make it accessible to the masses irrespective of their socio-economic background. The media hyped the positioning and it backfired.

A noble idea went kaput because it could not sell entitlement. People did not want to own an item known for its cheap cost. Also, with the tag of cheap, quality stereotypes are bound to arise. People assumed that cost-effectiveness comes with quality compromises, and hence Tata Nano could not find its footing.

3. Logic over emotion

Tata Nano’s marketing strategy was unable to touch the hearts of Indians. Buying decisions are mostly emotional rather than rational. The marketing campaigns lacked that emotional touch and focussed more on the features.

Vouching on features in the marketing was also necessary, as the company wanted to tell people that they are providing almost everything that cars usually have. But, somewhere, they could not bridge the emotional connection, and thus lost their grip on the market.

4. The hype

The Tata Nano launch was hugely hyped in 2008. And as people were waiting for the launch, Tata announced the shift of the brand’s production plant from Singur, West Bengal to Sanand, Gujarat on October 7, 2008.

The West Bengal government, led by Mamta Banerjee accused Tata Motors of land acquisition and initiated the “Save Farmland” movement with local farmers and environmental activists. Tata Motors had to leave the state and was welcomed by Gujarat to set up their plant at Sanand. But the delay dulled public enthusiasm.

The hype was a problem as it mainly revolved around the price of the Tata Nano and not around the value proposition.

5. Negative PR

The leftover image was destroyed by the negative PR it had attracted. Many times, the initial models of Tata Nano caught fire and burst into flames on the road. Some said it was because of the faulty wiring. Others said a foreign object had entered the exhaust system.

Also, its lightweight affected the stability of the car on highways. The engine was not powerful and no A/C was installed in the base model. All these things contributed to a poor-quality ride.

The on-road price of Tata Nano was 2.59 lakh rupees instead of 1 lakh, as promised. Its close competitor, Maruti 800 was priced at 2.88 lakh rupees.

Lastly, Tata Nano received a Zero-star adult protection rating in a crash test. It failed to meet the basic UN safety requirements, and thus the very purpose—the safety of the car was crushed.

All these incidents decided the fate of the car and soon enough the car faded away from the market.

Parting Words

Tata Nano, which started off with great aspirations, failed within a decade. It was clearly a marketing strategy mistake along with various shortcomings in the product. The company itself was responsible for Tata Nano’s failure, and they have accepted it.

The Sanand plant is now being used to create Tata Tigor and Tata Tiago. But Tata is very ambitious about its product Nano and is likely to relaunch it as an affordable electric vehicle in the coming future. Let’s see how will they brand it in the future.

Key Takeaways

• No matter how benevolent your product is, if it is not marketed well, it will fail miserably.
• Seeking market validation by empathizing with your audience is a must.
• Brand positioning creates the strongest perception about the brand. Make sure you do it correctly.
• Mostly, buying decisions are emotional and not practical.
• Introduce a prototype in the market, take feedback, and improve the product before the final launch.
• No matter how big a company or a personality you are, you can make mistakes.

The company aimed to produce 2,50,000 cars annually, but their early sales were 30,000 for 2009-10, 70,432 for 2010-11, and 74,527 for 2011-12 which, then, kept on decreasing year-on-year to finally get closed in 2018. They have never achieved their target. So, yes, Tata Nano was a flop.

Tata Nano was marketed as the cheapest car, thinking that it was enough to motivate people to buy it. But people did not want to be associated with a cheap car. Also, people thought that cost-effectiveness would come with quality compromises. Thus, bad marketing caused Tata Nano’s failure.

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Learning from Tata’s Nano Mistakes

• Matt Eyring

It’s been a rough season for Tata Motors’ much-publicized “people’s car,” the Nano. In November, while overall auto sales in India’s booming economy rose more than 22%, Tata sold only 509 Nanos, down precipitously from the 9,000 it sold the previous July, news that’s been trumpeted in disparaging headlines from New York to Sydney. There […]

It’s been a rough season for Tata Motors’ much-publicized “people’s car,” the Nano. In November, while overall auto sales in India’s booming economy rose more than 22%, Tata sold only 509 Nanos, down precipitously from the 9,000 it sold the previous July, news that’s been trumpeted in disparaging headlines from New York to Sydney .

• ME Matthew J. Eyring is the president of Innosight, a strategy innovation consulting and investment firm.

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Tata Nano’s Execution Failure: How the People’s Car Failed to Reshape the Auto Industry and Create New Growth

Author(s):  KIM, W. Chan, MAUBORGNE, Renée, BONG, Robert, JI, Mi

Case study trailer

This case and the accompanying three-part theory-based movie describe Tata Motors’ strategic move to create and launch the Tata Nano and the successes and setbacks of the Tata Nano team in actualizing this strategic move. The case and part-one of theory-based video first review how the Tata Nano was conceived based on noncustomer insights from an alternative industry – the two-wheeler market and how a strategic price was set against alternatives to capture the mass of target buyers. The case and part-two of the theory-based movie then show how the Tata Nano team complemented its compelling value proposition with a viable profit proposition by pursuing target costing to deliver exceptional   buyer utility   at the strategic price set. Finally, the case and part-three of theory-based movie examine different components of the Tata Nano’s people proposition to identify the major causes of the setback it experienced in executing the strategic move, illustrating the importance of matching strong value and profit propositions with an equally compelling people proposition to ensure the successful execution of a blue ocean strategy. The accompanying three-part theory-based movie, available in the Educators’ Space , which longitudinally tracks the Tata Nano strategic move from conception to execution, is based on first-hand research and face-to-face interviews. A comprehensive teaching note also accompanies the case.

Pedagogical objectives:

• To demonstrate how Tata Nano reconstructed market boundaries across alternative industries and created a commercially viable blue ocean opportunity by following the right strategic sequence.
• To highlight the importance of matching value and profit propositions with an equally strong people proposition in ensuring the successful execution of a blue ocean strategy.
• To review major blue ocean strategy concepts, frameworks and tools   in the course of analyzing the Tata Nano strategic move.

English: HBSP  |  Case Centre  |  INSEAD

Chinese: HBSP  |  Case Centre  |  INSEAD

Teaching Note

HBSP | Case Centre | INSEAD

English, Chinese: Available to download for free in the Educators’ Space

Lecture Slides

Tata Nano's Execution Failure: How the People's Car Failed to Reshape the Auto Industry and Create New Growth

This case analyses Tata Motors' strategic move to create and launch the Tata Nano, exploring the factors behind the project's earlier success and the reasons for its execution failure. It illustrates the importance of having a strong and aligned set of value, profit and people propositions in order to create and capture a blue ocean. The teaching note reviews how Tata Nano created its exceptional value proposition and attained a viable profit proposition by following the right strategic sequence, and then examines different components of Tata Nano's people proposition to identify the major causes of failure in executing its blue ocean strategy. The case comes with a teaching note, lecture slides and a three-part movie based on first-hand research and face-to-face interviews describing Tata Nano’s strategic move from conception to execution. The slides and videos can be downloaded for teaching purposes from www.blueoceanstrategy.com The case material is also available in Chinese.

1) To demonstrate how Tata Nano reconstructed market boundaries across alternative industries and created a commercially viable blue ocean opportunity by following the right strategic sequence. 2) To highlight the importance of matching value and profit propositions with an equally strong people proposition in ensuring the successful execution of a blue ocean strategy. 3) To review major BOS concepts, frameworks and tools in the course of analyzing the Tata Nano strategic move.

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Tata Nano's Execution Failure: How the People's Car Failed to Reshape the Auto Industry and Create New Growth (Chinese)

By   W. Chan Kim ,  Renée Mauborgne ,  Robert Bong ,  Mi Ji

W. Chan Kim

Renée Mauborgne

Robert bong.

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Case Study Tata Nano

The Tata Nano, hailed as the "people's car," has garnered significant attention and scrutiny since its inception. This essay delves into a comprehensive analysis of the Tata Nano case study, examining its conception, marketing strategies, challenges, and implications for the automotive industry.

The conception of the Tata Nano represents a bold and innovative approach to addressing the transportation needs of the masses in India. Conceived by Ratan Tata, the chairman of Tata Motors, the Nano aimed to provide an affordable and accessible mode of transportation for millions of people across India. Priced at around $2,500, the Nano was positioned as the world's cheapest car, challenging conventional notions of automotive design and affordability.

From a marketing perspective, the Tata Nano presented both opportunities and challenges. On one hand, the Nano's ultra-low price point appealed to price-sensitive consumers in emerging markets, positioning Tata Motors as a pioneer in inclusive innovation. The company leveraged extensive marketing campaigns and strategic partnerships to promote the Nano as a symbol of aspiration and economic empowerment.

However, the Nano's marketing efforts were not without controversy. Critics argued that Tata Motors failed to effectively communicate the value proposition of the Nano beyond its affordability. Concerns over safety, quality, and environmental impact plagued the Nano's reputation, leading to perceptions of the car as a "cheap" and inferior alternative to traditional automobiles.

Furthermore, the Tata Nano faced numerous operational and logistical challenges that hindered its widespread adoption and success. Production delays, quality control issues, and supply chain disruptions contributed to underwhelming sales figures and eroded consumer confidence in the Nano brand. Despite initial optimism and enthusiasm surrounding its launch, the Nano failed to meet sales targets and fell short of expectations.

The case of the Tata Nano holds several implications for the automotive industry and beyond. Firstly, it underscores the importance of understanding the needs and preferences of target markets in driving product innovation and market penetration. The Nano's failure highlights the limitations of a one-size-fits-all approach to product development and marketing in diverse and dynamic markets.

Additionally, the Tata Nano case study serves as a cautionary tale for companies seeking to disrupt established industries with disruptive technologies. While the Nano's affordability and accessibility were laudable goals, Tata Motors underestimated the complexities and challenges inherent in producing and marketing a revolutionary product on a mass scale.

In conclusion, the Tata Nano case study offers valuable insights into the intricacies of product development, marketing, and innovation in the automotive industry. Despite its noble intentions and ambitious aspirations, the Nano's journey is a testament to the inherent risks and uncertainties associated with disruptive innovation. As companies continue to navigate the evolving landscape of consumer preferences and technological advancements, the lessons learned from the Tata Nano case study remain relevant and instructive.

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Tata Steel, a renowned global steel company, has a rich history dating back to 1907 when it was established in India. Over the years, Tata Steel has grown to become one of the world's top steel producers, with a strong presence in various countries. The company's success can be attributed to its commitment to innovation, sustainability, and quality. One of the key factors contributing to Tata Steel's success is its focus on innovation. The company has continuously invested in research and development to enhance its product offerings and manufacturing processes. By embracing new technologies and practices, Tata Steel has been able to stay ahead of the competition and meet the evolving needs of its customers. This commitment to innovation has helped Tata Steel maintain its position as a leader in the steel industry. In addition to innovation, Tata Steel places a strong emphasis on sustainability. The company is dedicated to reducing its environmental impact and promoting sustainable practices throughout its operations. Tata Steel has implemented various initiatives to minimize waste, conserve resources, and reduce carbon emissions. By prioritizing sustainability, Tata Steel not only contributes to environmental conservation but also enhances its reputation as a responsible corporate citizen. Furthermore, Tata Steel's unwavering commitment to quality has been a driving force behind its success. The company adheres to stringent quality standards at every stage of the production process, ensuring that its products meet the highest levels of excellence. Tata Steel's dedication to quality has earned it the trust and loyalty of customers worldwide, establishing the company as a preferred supplier in the steel industry. In conclusion, Tata Steel stands out as a prime example of a company that excels in innovation, sustainability, and quality. Through its relentless pursuit of excellence, Tata Steel has established itself as a global leader in the steel industry, setting high standards for others to follow. With its strong foundation built on innovation, sustainability, and quality, Tata Steel is well-positioned to continue thriving in the competitive global market....

• Corporations
• Entrepreneurs
• Management Accounting
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Case Study Of Tata Motors Acquisition Jaguar

Case Study of Tata Motors Acquisition of Jaguar In the competitive landscape of the automotive industry, mergers and acquisitions are strategic moves employed by companies to expand their market share, gain technological expertise, and diversify their product portfolios. One such significant acquisition was Tata Motors' purchase of Jaguar Land Rover (JLR) in 2008. This acquisition marked a pivotal moment in Tata Motors' history, transforming it from a predominantly domestic player into a global automotive powerhouse. Tata Motors' acquisition of Jaguar Land Rover was a bold strategic move aimed at catapulting the company onto the global stage. Prior to the acquisition, Tata Motors was primarily known for producing commercial vehicles and passenger cars in the Indian market. However, with the purchase of JLR, Tata Motors gained access to two iconic British luxury brands renowned for their craftsmanship, innovation, and heritage. This acquisition instantly elevated Tata Motors' status in the automotive industry and provided it with a foothold in the lucrative luxury car segment. One of the key challenges faced by Tata Motors following the acquisition was integrating Jaguar Land Rover into its existing operations while preserving the unique identity and brand value of both marques. The company had to navigate cultural differences, streamline operations, and leverage synergies between Tata Motors and JLR to drive growth and profitability. Despite initial skepticism from industry observers, Tata Motors successfully managed the integration process, demonstrating its ability to manage a diverse portfolio of brands effectively. The acquisition of Jaguar Land Rover also presented Tata Motors with opportunities to leverage JLR's expertise in design, engineering, and technology to enhance its own product offerings. By leveraging JLR's capabilities, Tata Motors was able to enhance the quality and performance of its vehicles, strengthen its competitive position, and expand its global footprint. Additionally, the acquisition provided Tata Motors with access to JLR's advanced research and development capabilities, enabling it to stay ahead of evolving market trends and consumer preferences. In conclusion, Tata Motors' acquisition of Jaguar Land Rover was a strategic masterstroke that transformed the company's trajectory and positioned it as a global player in the automotive industry. Through effective integration, strategic collaboration, and leveraging synergies, Tata Motors successfully capitalized on the acquisition to drive growth, innovation, and profitability. The case study of Tata Motors' acquisition of Jaguar Land Rover serves as a testament to the power of strategic foresight, effective management, and bold decision-making in driving organizational success in a dynamic and competitive business environment....

• Corporate Social Responsibility (CSR)
• Global Economy

Case Study Of Tata Consultancy Services

Tata Consultancy Services (TCS) stands as a beacon of innovation and excellence in the realm of information technology and consulting services. Established in 1968, TCS has burgeoned into one of the largest IT services firms globally, with a significant presence in numerous countries. This case study delves into the core strategies, milestones, and factors contributing to TCS's remarkable success in the competitive IT industry. At the heart of TCS's success lies its unwavering commitment to innovation and adaptability. From its inception, TCS has consistently embraced emerging technologies and trends, positioning itself as a pioneer in the IT sector. By fostering a culture of innovation and investing heavily in research and development, TCS has been able to stay ahead of the curve and deliver cutting-edge solutions to its clients. The company's ability to anticipate market shifts and proactively innovate has been instrumental in its sustained growth over the decades. Moreover, TCS's focus on building long-term client relationships has been a cornerstone of its business strategy. Rather than viewing projects as one-off transactions, TCS emphasizes partnership and collaboration, striving to understand clients' unique needs and challenges deeply. By leveraging its extensive industry expertise and global talent pool, TCS delivers tailored solutions that drive tangible business outcomes for its clients. This client-centric approach not only fosters trust and loyalty but also positions TCS as a strategic partner rather than a mere service provider. Furthermore, TCS's commitment to corporate social responsibility (CSR) has played a pivotal role in shaping its corporate identity and reputation. As a responsible corporate citizen, TCS actively engages in various initiatives aimed at social welfare, environmental sustainability, and community development. Whether it's through philanthropic endeavors, employee volunteer programs, or sustainable business practices, TCS demonstrates its dedication to making a positive impact beyond the realm of business. This emphasis on CSR not only aligns with TCS's core values but also enhances its brand image and fosters goodwill among stakeholders. In conclusion, Tata Consultancy Services exemplifies the essence of success through innovation, client-centricity, and corporate responsibility. By staying true to its founding principles while continuously evolving to meet the evolving needs of the digital era, TCS has carved a niche for itself as a global leader in the IT services industry. As technology continues to redefine the business landscape, TCS remains poised to navigate the challenges and seize the opportunities that lie ahead, reaffirming its position as a beacon of excellence in the world of information technology....

• Technology Companies

Case Study: Tata Nexon

In the dynamic landscape of the automotive industry, Tata Motors has emerged as a formidable player with its innovative and reliable vehicles. Among its impressive lineup, the Tata Nexon stands out as a testament to the company's commitment to excellence in design, performance, and safety. This compact SUV has garnered significant attention and acclaim, setting new standards in its segment. Firstly, the Tata Nexon boasts a striking and contemporary design that captivates onlookers and exudes a sense of confidence on the road. Its bold stance, sleek lines, and signature Humanity Line grille give it a distinctive appearance that instantly grabs attention. The dual-tone color options further enhance its visual appeal, allowing customers to express their individuality. Moreover, thoughtful design elements such as projector headlamps, LED taillights, and roof rails not only elevate the aesthetics but also contribute to the vehicle's functionality and convenience. In terms of performance, the Tata Nexon delivers a thrilling driving experience powered by advanced technology and robust engineering. Equipped with a range of petrol and diesel engines, including the efficient Revotron and Revotorq units, it offers impressive power and torque for both city commutes and highway journeys. The availability of manual and automatic transmission options ensures smooth and responsive gear shifts, enhancing the overall driving dynamics. Additionally, features like multi-drive modes provide drivers with the flexibility to tailor the vehicle's performance to their preferences and driving conditions, further enhancing the driving experience. Safety is paramount in the design and development of the Tata Nexon, making it one of the safest vehicles in its class. Built on a high-strength steel platform, it offers robust structural integrity and crash protection, earning top ratings in global safety assessments. The incorporation of advanced safety features such as dual front airbags, ABS with EBD, rear parking sensors, and ISOFIX child seat mounts provides occupants with peace of mind and reassurance on every journey. Furthermore, the Nexon comes equipped with advanced technologies like Electronic Stability Program (ESP), Hill Hold Control, and Brake Assist, further enhancing its safety credentials and ensuring confident handling in various driving conditions. In conclusion, the Tata Nexon exemplifies Tata Motors' commitment to innovation, quality, and customer satisfaction. With its striking design, exhilarating performance, and best-in-class safety features, it has set a new benchmark in the compact SUV segment, earning accolades from customers and industry experts alike. As Tata Motors continues to push the boundaries of automotive excellence, the Nexon remains a shining example of its unwavering pursuit of perfection....

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Case Study on Tata Motors and Ford Motors Mergers and Aquisition

Tata Motors and Ford are two prominent companies in the automotive industry that have attracted significant attention due to their strategies, successes, and challenges. This case study aims to explore the key aspects of these companies, including their backgrounds, market positions, strategies, and notable developments. Tata Motors, headquartered in India, has emerged as one of the leading automobile manufacturers globally, with a diverse portfolio ranging from passenger vehicles to commercial trucks. Established in 1945, Tata Motors has a rich history deeply intertwined with India's industrial growth and economic development. One of Tata Motors' notable achievements was the acquisition of Jaguar Land Rover (JLR) in 2008, marking its entry into the luxury car segment and expanding its global footprint. On the other hand, Ford, an American multinational corporation founded in 1903, holds a prominent position in the automotive industry. Ford has been instrumental in revolutionizing automobile manufacturing with innovations such as the assembly line, which significantly increased production efficiency. Over the years, Ford has built a strong presence worldwide, manufacturing a wide range of vehicles, including cars, trucks, and SUVs. However, the company has faced challenges, particularly during economic downturns and shifts in consumer preferences. Despite their differences in origin and market focus, Tata Motors and Ford share some similarities in their strategic approaches. Both companies have emphasized innovation and sustainability in their product development initiatives. Tata Motors, for instance, has invested in electric and alternative fuel vehicles to address environmental concerns and changing regulatory requirements. Similarly, Ford has introduced hybrid and electric models as part of its commitment to reducing emissions and embracing technological advancements. Moreover, both Tata Motors and Ford have recognized the importance of international expansion to sustain growth and competitiveness. Tata Motors' acquisition of JLR provided access to premium brands and established markets, enabling the company to diversify its revenue streams and enhance its brand image. Similarly, Ford has pursued global expansion strategies, establishing manufacturing facilities and distribution networks in key emerging markets to capitalize on growing demand for automobiles. In conclusion, the case study on Tata Motors and Ford highlights the diverse yet interconnected nature of the automotive industry. Despite facing challenges and undergoing transformational changes, both companies continue to innovate, adapt, and strive for excellence in a competitive global market. By understanding their backgrounds, strategies, and notable developments, stakeholders can gain valuable insights into the dynamics shaping the future of the automotive sector....

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Case Study Of Tata Docomo

Tata Docomo, a joint venture between Tata Teleservices and NTT Docomo, entered the Indian telecommunications market in 2008 with a unique approach aimed at disrupting the industry. This case study delves into Tata Docomo's strategies, challenges, and outcomes, offering insights into its journey. One of Tata Docomo's key strategies was its innovative pricing model, which introduced the concept of "pay-as-you-use" and "per-second billing" to the Indian market. This approach resonated with consumers seeking flexibility and transparency in their mobile plans. By offering customizable plans and transparent billing, Tata Docomo differentiated itself from competitors and appealed to a broad spectrum of customers, including cost-conscious users and heavy data consumers. Despite its innovative offerings, Tata Docomo faced challenges in a fiercely competitive market dominated by established players. The company had to contend with aggressive pricing strategies and extensive network coverage from competitors. Additionally, regulatory hurdles and spectrum allocation issues posed further obstacles to Tata Docomo's growth ambitions. To address these challenges, Tata Docomo focused on enhancing its network infrastructure and expanding its reach across the country. The company invested significantly in upgrading its technology and rolling out new services to improve the quality of its offerings. By prioritizing customer experience and network reliability, Tata Docomo aimed to carve out a niche for itself in the highly competitive telecommunications landscape. However, despite its efforts, Tata Docomo struggled to gain significant market share and sustain profitability over the long term. The intense competition and pricing pressures exerted by rivals took a toll on the company's financial performance. In 2017, Tata Teleservices announced the merger of its consumer mobile business with Bharti Airtel, marking the end of Tata Docomo's independent operations. The case of Tata Docomo highlights the complexities of operating in the telecommunications industry, where innovation and competitive pricing are crucial for success. While Tata Docomo introduced groundbreaking concepts and endeavored to meet customer needs, it ultimately faced formidable challenges that led to its consolidation within the industry. Nonetheless, the legacy of Tata Docomo serves as a testament to the importance of innovation and agility in navigating dynamic market environments....

Nano gene case study

Nano Gene, a biotechnology company specializing in gene therapy, embarked on a groundbreaking project aimed at developing a novel treatment for a rare genetic disorder. The disorder, known as familial hypercholesterolemia (FH), results in abnormally high levels of cholesterol in the blood, leading to severe cardiovascular complications. This case study delves into Nano Gene's journey, from the initial research phase to the successful clinical trials of their innovative gene therapy. The project began with extensive research into the underlying genetic mechanisms of FH. Nano Gene's team of scientists meticulously studied the genetic mutations responsible for the disorder, aiming to identify a target gene for therapeutic intervention. Through advanced genomic analysis and experimental studies, they pinpointed a specific gene variant implicated in the regulation of cholesterol metabolism. With the target gene identified, Nano Gene proceeded to develop a sophisticated gene therapy approach. Leveraging cutting-edge nanotechnology, the company engineered specialized nanoparticles capable of delivering therapeutic genes to targeted cells within the body. These nanoparticles were designed to penetrate cell membranes efficiently and deliver the therapeutic payload directly to the affected cells, minimizing off-target effects and maximizing treatment efficacy. The next critical phase of the project involved preclinical testing to assess the safety and efficacy of the gene therapy approach. Nano Gene conducted comprehensive animal studies to evaluate the therapeutic potential of their nanoparticles in a relevant disease model of FH. The results of these studies were promising, demonstrating significant reductions in cholesterol levels and improvements in cardiovascular health indicators. Buoyed by the success of the preclinical studies, Nano Gene proceeded to initiate clinical trials to evaluate the gene therapy's safety and effectiveness in human patients with FH. The trials involved rigorous testing protocols and close monitoring of participants to ensure both safety and regulatory compliance. Over the course of several phases, the clinical trials yielded encouraging results, with participants experiencing significant reductions in cholesterol levels and improvements in overall health outcomes. In conclusion, Nano Gene's case study exemplifies the power of innovative biotechnology in addressing complex genetic disorders. Through meticulous research, technological innovation, and rigorous clinical testing, the company successfully developed a groundbreaking gene therapy for familial hypercholesterolemia. This case study underscores the immense potential of gene therapy in revolutionizing healthcare and offering hope to patients with rare genetic diseases....

• Famous Artists
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• History of the United States
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Case Study : Europa Airlines ( Ea ) Case Study

Europa Airlines, a prominent player in the airline industry, faced a pivotal moment in its history with the EA case study. This case study delves into the challenges, decisions, and outcomes of Europa Airlines as it navigated through a complex operational landscape. The EA case study highlights the strategic choices made by Europa Airlines and the implications of those choices on its market position, profitability, and overall sustainability. The EA case study begins with Europa Airlines recognizing the need to streamline its operations and enhance efficiency to remain competitive in a rapidly evolving industry. Facing stiff competition and changing consumer preferences, Europa Airlines embarked on a journey to optimize its resources, improve customer experience, and drive profitability. This necessitated a thorough analysis of its operations, including fleet management, route optimization, and cost structure. One of the key decisions highlighted in the EA case study was Europa Airlines' investment in modernizing its fleet. Recognizing the importance of fuel efficiency, passenger comfort, and environmental sustainability, Europa Airlines strategically upgraded its aircraft to newer, more advanced models. This decision not only reduced operating costs but also enhanced the overall travel experience for passengers, thereby strengthening Europa Airlines' competitive position in the market. Furthermore, the EA case study sheds light on Europa Airlines' approach to route optimization and network expansion. By leveraging data analytics and market insights, Europa Airlines identified lucrative routes and untapped markets, allowing for strategic route expansion and increased market penetration. This strategic expansion enabled Europa Airlines to capture new revenue streams while effectively managing operational costs, thereby driving sustainable growth and profitability. Additionally, the EA case study underscores Europa Airlines' commitment to customer-centricity and innovation. By investing in technology-driven solutions such as online booking platforms, mobile check-in services, and personalized customer experiences, Europa Airlines aimed to enhance customer satisfaction and loyalty. This focus on innovation and customer experience not only differentiated Europa Airlines in a crowded marketplace but also fostered long-term relationships with its passengers. In conclusion, the EA case study provides valuable insights into Europa Airlines' journey towards operational excellence, strategic decision-making, and sustainable growth. By prioritizing efficiency, innovation, and customer-centricity, Europa Airlines successfully navigated through challenges and emerged as a resilient player in the airline industry. The lessons learned from the EA case study serve as a testament to Europa Airlines' adaptability and commitment to delivering value to its stakeholders in an ever-changing business environment....

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• Published: 23 July 2024

NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study

• Shan He 1 , 2 , 3 ,
• Karam Nader 2 ,
• Julen Segura Abarrategi 2 ,
• Harbil Bediaga 3 ,
• Deyani Nocedo-Mena 4 ,
• Estefania Ascencio 1 , 2 , 3 ,
• Gerardo M. Casanola-Martin 1 ,
• Idoia Castellanos-Rubio 2 ,
• Maite Insausti 2 , 5 ,
• Bakhtiyor Rasulev 1 ,
• Sonia Arrasate 2 &
• Humberto González-Díaz 2 , 6 , 7  

Journal of Nanobiotechnology volume  22 , Article number:  435 ( 2024 ) Cite this article

Metrics details

Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe 3 O 4 _A and Fe 3 O 4 _B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe 3 O 4 _A and Fe 3 O 4 _B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

Introduction

Neurodegenerative Diseases (NDs) constitute a diverse set of conditions marked by the gradual deterioration and loss of neurons in various regions of the nervous system. These diseases pose a significant challenge to global health because their incidence is increasing. With the expansion of the aging population, the World Health Organization anticipates a threefold increase worldwide in the number of individuals affected by neurodegenerative disorders over the coming three decades. Although the precise mechanisms driving NDs are not fully elucidated, researchers suggest a multifaceted interplay involving genetic, epigenetic, and environmental factors. Presently, there are no established treatments capable of slowing, halting, or preventing the progression of any NDs [ 1 , 2 ]. For example, diseases like Alzheimer´s and Parkinson´s, which have been recognized for over a century, continue to lack a cure [ 3 , 4 , 5 ]. Some promising lines of research for the treatment of neurodegenerative disorders are: gene therapy [ 6 ], development of neuroprotective mimetic peptides [ 7 ], repurposing (or reevaluation) of known drugs [ 8 ], among others [ 9 ].

One challenge is the interaction between NPs and components of the immune system. Over the past ten years, research has demonstrated that although NP can be toxic, advances in nanotechnology have enabled the modification of these materials. These modifications can either prevent interaction with the immune system or specifically target it. When nanoparticles are used for medical purposes that do not aim to activate or suppress the immune system, it is beneficial to avoid any immune system interaction [ 10 ]. For instance, NPs can be engineered by coating them with poly(ethylene glycol) (PEG) or other polymers, creating a hydrophilic layer that conceals them from the immune system’s detection [ 11 ]. Another challenge to be addressed in the treatment of neurodegenerative disorders lies in the passage of therapeutic agents through the Blood-Brain Barrier (BBB) to reach the Central Nervous System (CNS). To overcome these obstacles, research efforts are directed towards both the development of new drugs and the exploration of innovative drug delivery methods, including targeted nanocarriers [ 12 ]. Some of these approaches are: nanobodies [ 13 ], nano-antibodies, nano-metal particles (gold, silver, iron oxide) [ 14 ] and lipid nanoparticles (nanoliposomes) [ 15 ]. These nano-approaches applied to drug R&D as innovative delivery systems for NDs face inherent challenges. Therefore, we find ourselves with arduous experimental work associated with high costs, low stability profiles, short useful lives, and inconsistency between and within production batches [ 16 ].

In this sense, Machine Learning (ML) techniques can be useful for analyzing, predicting, and selecting the optimal delivery nano-system to treat neurodegenerative diseases (Nanoparticle Neuronal Disease Drug Delivery systems, in the future “N2D3 systems”). ML has been successfully used for the prediction of biomedical properties of NPs of medical interest. These studies include the influence of particle physicochemical properties on cellular uptake, cytotoxicity, molecular loading, and molecular release, as well as manufacturing properties such as NP size and polydispersity [ 17 , 18 ]. In the efforts to design new N2D3 systems, a ML algorithm needs to analyze multiple output properties (IC 50 , K i , etc.) of a broad range of N2D3 systems with different transported substances (drugs), nanocarriers, coatings, etc., under various conditions such as cell lines and organisms (labels) [ 19 ]. On the other hand, Gajewicz et al. [ 20 ] have recently discussed the lack and/or dispersion (different sources of information) of nanotoxicity data with special emphasis on the low variety of drugs transported by the current N2D3 systems in contrast to the high number of free drugs assays [ 21 , 22 , 23 , 24 , 25 ]. Consequently, in order face N2D3 systems design problem a ML should be multi-output (able to predict multiple outputs), read-across species (able to infer properties for different species), multi-label (able to consider multiple cell lines, etc.), and able to consider multiple sources of information at the same time. With this purpose our group introduced the Information Fusion (IF) + Perturbation-Theory (PT) + Machine Learning (ML) algorithm. IFPTML gets information from different sources (Drugs assays, NP assays, Proteomics, Metabolic networks, etc.), and carry out an IF process, later uses PT operators to quantify all the variability of the data, and last use ML algorithms to seek a predictive model and predict new N2D3 systems. In the case of specific applications to Nanoscience the algorithm has been called as the NANO.PTML algorithm. NANO.PTML algorithm have been applied successfully before to different types of NP systems [ 26 , 27 , 28 , 29 ].

In this paper, firstly we are going to use NANO.PTML algorithm to find a new ML model able to predict new N2D3 systems. Furthermore, in order to illustrate the applicability of the NANO.PTML model in practice we reported an additional computational-experimental case of study. In this case of study, firstly we carried out the synthesis and characterization of two new NPs with potential application in the development of N2D3 systems. Next, we used the NANO.PTML model to carry out a simulation of the outcomes for 500,000 different assays of N2D3 systems based on the two NPs reported. These predictions involve different combinations of up to 123 drugs, 53 cell lines, 16 NP coats, 5 NP core types, 5 NP shapes. The outcome of this experiment serves as guidance for the identification of promising N2D3 systems and gaining insights into their behaviors across different cell lines, coating agents, among others, which could offer valuable guidance for future studies. NANO.PTML model predictions and its experimental validation could offer a promising alternative to traditional trial-and-error methods and pave the way for more efficient N2D3 systems for neurodegenerative diseases.

Materials and methods

Experimental methods.

The products, iron(III) chloride, 1-octadecene, oleic acid, dibenzyl ether, Chloroform and Cetyl Trimethyl Ammonium Bromide (CTAB) were purchased in Sigma-Aldrich. Sodium oleate, ethanol, hexane and tetrahydrofuran were purchased in TCI, PanReac, Honeywell and Emplura, respectively.

Experimental characterization

X-ray diffraction (DRX) patterns of Fe 3 O 4 hydrophobic NPs were performed using a PANalytical X’Pert PRO diffractometer equipped with a copper anode (operated at 40 kV and 40 mA), diffracted beam monochromator and PIXcel detector. Scans were collected in the 10 − 90° 2θ range with a step size of 0.02° and scan step speed of 1.25 s. The amount of organic matter in the Fe 3 O 4 hydrophobic NPs was determined via thermogravimetric measurements (TGA), performed in a NETZSCH STA 449 C thermogravimetric analyser, by heating 10 mg of dry samples at 10 °C/min in Ar atmosphere. Dynamic Light Scattering (DLS) and Zeta potential (ζ) measurements of the NPs functionalized with CTAB were performed in a Zetasizer Nano-ZS (Malvern Instruments). The measurements were carried out at 25 °C after an equilibrium time of 1 min for 0.05 mg·mL − 1 Fe 3 O 4 @CTAB aqueous dispersions. For each sample, 10 runs of 10 s were performed with three repetitions. A Phillips CM200 Transmission Electron Microscopy (TEM) with an accelerating voltage of 200 kV and a point resolution of 0.235 nm was used to analyse the morphology of the samples. Magnetic measurements as a function of the magnetic field M(H) at Room Temperature (RT) were obtained in a Vibrating-Sample Magnetometer (VSM) by measuring the magnetization of the dried hydrophobic nanoparticles and normalizing the magnetization value per unit mass of inorganic matter.

Preparation of Fe 3 O 4 nanoparticles

Two different Fe 3 O 4 nanoparticles (NPs) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor which was previously prepared from iron(III) chloride and sodium oleate mixed in a mixture of solvents (hexane, ethanol and distilled water). The synthesis process of both the FeOl precursor and the Fe 3 O 4 NPs was formerly analyzed and optimized throughout different works [ 30 , 31 , 32 ].

For this work two different samples composed of NPs of similar average dimension (≈ 20 nm) but different morphology (cuboctahedral and octahedral) have been prepared (samples Fe 3 O 4 _A and Fe 3 O 4 _B). Sample Fe 3 O 4 _B was prepared by mixing 10 mmol of the previously prepared FeOl precursor with 20 mL of 1-octadecene, 10 mL of dibenzyl ether and 6.4 mL of oleic acid and heating the mixture until reflux (around 320 ºC). The resulting hydrophobic NPs of Fe 3 O 4 coated with oleic acid were washed by centrifugation 3 times (at 9500 rpm) with ethanol and tetrahydrofuran and, finally, they were collected in chloroform and stocked in the fridge 4 ºC. Fe 3 O 4 _A was similarly prepared, but in this case the synthesis process was scaled to double to analyze the effect of this synthetic parameter in the features of the NPs.

In order to make the as-synthesized hydrophobic NPs (Fe 3 O 4 _A and Fe 3 O 4 _B) soluble in water (Fig.  1 a) a coating approach based on previously refined protocol was carried out [ 33 ]. In this case, instead of using the poly(maleic anhydride-alt-1-octadecene) (PMAO) polymer for the coating the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was used (Fig.  1 b). A CTAB solution in chloroform was added to a 1 mg/mL stock solution of NPs (maintaining a ratio of 100 molecules per nm 2 of Fe 3 O 4 NP surface). After stirring the mixture for 15 min, the solvent was evaporated under vacuum and the nanoparticles were dispersed in chloroform. This process was repeated three times, and the last redispersion was carried out using distilled H 2 O. Finally, the two samples functionalized with CTAB (Fe 3 O 4 _A@CTAB and Fe 3 O 4 _B@CTAB NPs) were further washed with distilled water (3 times) by centrifugation to remove the excess of CTAB that was not attached to the surface of the NPS. The scheme of the NPs coating process has been displayed in Fig.  1 .

(a) Hydrophobic Fe 3 O 4 NPs coated with oleic acid, and (b) hydrophilic Fe 3 O 4 NPs coated with oleic acid and a layer of CTAB

Computational methods

In a previous work, we collected three datasets from different databases. The first dataset (Dataset 01) from ChEMBL, with information from preclinical trials of different NDDs, was merged with Dataset 02 built from NP data collected from the literature. As a result, three large subsets (Subset 1, Subset 2, Subset 3) with different variables were obtained, from which the best IFPTML model for the effective N2D3 systems was obtained [ 34 ]. In this work we reprocessed all the information with Python algorithms in order to obtain open access code for this problem for the same time. To construct the IFPTML models, we followed the sequential steps outlined in Fig.  2 , which illustrates the overall workflow of the computational procedures employed in this study. Additionally, to facilitate comprehension, each step was annotated with a corresponding enumeration (e.g., 2.2.1, 2.2.2).

IFPTML detailed information-processing workflow. Step 2.2.1 and 2.2.2 Data collection. Step 2.2.3 Data pre-processing and Information Fusion (NP and NDDs assay). Step 2.2.4 Objective and reference functions definition. Step 2.2.5 PTO calculation

NP cytotoxicity dataset

Simultaneously, the dataset of preclinical assays for cytotoxicity/ecotoxicity of NPs were collected from 62 papers. (step 2.2.2 in Fig.  2 ). This dataset contained 260 preclinical assays for 31 NPs, resulting in an average of approximately 8.39 assays per NP. Furthermore, the dataset covered a wide range of NP properties, including morphology, physicochemical properties, coating agents, assay duration, and measurement conditions. These properties were represented as discrete variables ( c nj ) used to characterize the conditions and labels of each assay. We categorized all specific conditions of each assay into a general vector c nj = [c n1 , c n2 , c n3 ….c nmax ]. These variables were biological activity parameters (c n0 ), cell lines utilized in assays (c n1 ), NP shapes (c n2 ), measurement conditions (c n3 ), and coating agents (c n4 ). Please see more details about the dataset content in the Supporting Information SI00.docx, 1.1.1. NP cytotoxicity dataset.

NDDs dataset from ChEMBL

At first, 4403 preclinical assays of Neurodegenerative Disease Drugs (NDDs) were downloaded from the ChEMBL database (step 2.2.1. in Fig.  2 ) [ 35 , 36 , 37 ]. The dataset comprised 2566 different NDDs, with an average of around 1.71 assays per drug. Additionally, we defined as categorical variables (c dj ) the conditions which covered biological activity parameters (c d0 ), target proteins associated with NDDs (c d1 ), cell lines used in NDDs assays (c d2 ), and organisms involved (c d3 ). The nature and quality of the data were also defined as categorical variable, including type of target (c d4 ), type of assay (c d5 ), data curation (c d6 ), confidence score (c d7 ), and target mapping (c d8 ). Additionally, the database provided molecular descriptors ( D dk = [D d1 , D d2 ]) to characterize the chemical structure of NDDs compounds. Specifically, two types of molecular descriptors were used for each compound: the logarithm of the n-Octanol/Water Partition coefficient (LOGP i ) and the Topological Polar Surface Area (PSA i ). Please see more details about the dataset content in the Supporting Information SI00.docx, 1.1.2. NDDs dataset from ChEMBL.

IF process drug nanoparticle delivery system (DNDS) pair resampling

Initially, we utilized the objective value v ij to formulate the IFPTML model. The IFPTML model involved two types of observed values, denoted as v ij (c d0 ) and v nj (c n0 ), corresponding to both NDDs and NPs. Additionally, we established the target function by employing the descriptor vectors denoted as D dk (for the drugs) and D nk (for NPs) as input variables in the AI/ML model. In order to simulate a real experiment with the N2D3 systems system, we prioritize certain properties while reducing others. To do this, we defined the desirability value as d(c d0 ) = 1 or d(c n0 ) = 1. This value d(c d0 ) = 1 when we needed to maximize the value of v ij (c d0 ) or v nj (c n0 ), otherwise d(c d0 ) = -1 or d(c n0 ) = -1. On the other hand, we used the cutoff to rescale the parameters of v ij (c d0 ) and v nj (c n0 ) to achieve the observed functions f(v ij (c d0 )) obs and f(v nj (c n0 )) obs . These values were obtained as: f(v ij (c d0 )) obs = 1, if v ij (c d0 ) > cutoff and d(c d0 ) = 1 or v ij (c d0 ) < cutoff and desirability d(c d0 ) = -1, f(v ij (c d0 )) = 0 otherwise. Please see more details in the Supporting Information SI00.docx, 1.1.3. IF process DNDS pair resampling.

Definition of objectives and reference functions

Another input variables of the IFPTML model is the reference/objective function, defined as f(v ij (c d0 ), v nj (c n0 )) ref . The f(v ij (c d0 ), v nj (c n0 )) ref function defines the expected probability f(v ij (c d0 ), v nj (c n0 )) ref = p(f(v ij (c d0 ), v nj (c n0 )) ref = 1) of getting the desired activity for a particular property obtained. The reference function f(v ij (c d0 ), v nj (c n0 )) ref , is calculated as the number of positive outcome n(f(v ij (c d0 )) = 1) (for drugs) and n(f(v nj (c n0 )) = 1) (for NPs) divided by the total number of cases for the NDDs and NP systems individually. These functions are characterized as: f(v ij (c d0 )) ref = p(f(v ij (c d0 )) ref = 1) = n(f(v ij (c d0 )) ref = 1)/n(c n0 ) j and f(v nj (c n0 )) ref = p(f(v nj (c n0 )) ref = 1) = n(f(v nj (c n0 )) ref = 1)/n(c n0 ) j . Please see more details in the Supporting Information SI00.docx, 1.1.4. Definition of objectives and reference functions Fig.  3 .

Workflow for the observed and reference function definition (see details in supporting information)

PTO calculation

Ifptml n2d3 systems data analysis phases.

The dataset in study was formed by structural descriptors vectors denoted as D nk and D dk , for each NPs [ 38 , 39 , 40 ] and NDDs [ 35 , 41 , 42 , 43 ]. Furthermore, we defined assay condition vectors as c nj and c dj to denote each label for both NPs and NDDs. For more detail information about the structural descriptors and assay condition vectors, refer to the Supporting Information SI00.docx, 1.1.5. PTO calculation (IFPTML N2D3 systems data analysis phases).

Preprocessing of PT data

The IFPTML study incorporates all vectors c dj and c nj , representing the non-numerical experimental conditions and labels for both NDDs and NP preclinical assays. Subsequently, we calculated the Perturbation Theory Operators (PTOs), taking into account the Moving Average (MA) of NDDs and NP (see, Eq.  1 and Eq.  2 ). The PT initiates with the experimental/observed value of an already known activity and adds the perturbations/variations to the system [ 26 , 27 , 44 , 45 , 46 , 47 ]. For more detail information, refer to the Supporting Information SI00.docx, 1.1.5. PTO calculation (Preprocessing of PT data).

NANO.PTML models training and validation overview

In developing the model using ML techniques, each sample case is categorized into either the training (subset = t) or validation (subset = v) series. The assignment process of cases should be random, representative, and stratified [ 48 , 49 ]. Subsequently, we divided the cases into three equal parts for subset = t (training) and one-quarter for subset = v (validation) for the whole dataset. It is important to note that the 75% and 25% proportion kept between training and validation [ 48 ]. Additionally, the performance of the NANO-PTML models was evaluated using different statistical metrics, particularly Sensitivity (Sn) and Specificity (Sp) [ 50 , 51 ]. For more detail information, refer to the Supporting Information SI00.docx, 1.1.5. PTO calculation (NANO.PTML models training and validation overview).

NANO.PTML simulation of experimental case of study

We conducted a computational analysis to illustrate the applicability of the NANO.PTML model in an example of a real wet-laboratory setting. In this context, we predicted the Fe 3 O 4 -core based NPs with CTAB as the coating system, as reported in the experimental part here. To create a more ambitious prediction experiment, we added multiple combinations of Fe-based cores, coatings, cell lines, and shapes. Particularly, this prediction dataset was formed by diverse combinations of up to 123 drugs, 53 cell lines, 16 coats, 5 NPs core and 5 NP shapes. The NPs core studied were CoFe 2 O 4 , ZnFe 2 O 4 , Fe 3 O 4 , Fe 2 O 3 and Fe. Additionally, the cell lines used in the cytotoxicity predictive study were L929 (M), HepG2 (H), A549 (H), among other. On the other hand, the organisms used in the eco-toxicity computational study were Vibrio fischeri , Oryzias latipes (embryos) , etc. Furthermore, there were different NP shapes such as irregular, elliptical, etc. Finally, the NP coating agents studied in this research were Polyvinyl alcohol (PVA), Polyvinylpyrrolidone (PVP), CTAB, potato starch (PS), PEG-Si(OMe) 3 (PEG), etc. For more detail information of simulation experiment, refer to the Supporting Information SI00.docx, 1.1.5. PTO calculation (NANO.PTML simulation of experimental case of study).

Results and discussion

Ai/ml python computational models.

In order to design AI/ML models for predicting the NP system as a neurodegenerative drug carrier, the Scikit-Learn module in Python [ 52 ] was used to identify the best AI/ML estimator. In this context, linear and non-linear classifiers were employed, specifically, Linear Discriminant Analysis (LDA) [ 53 ], Decision Tree (DT) [ 54 ], Random Forest (RF) [ 55 ], k-Nearest Neighbor (kNN) [ 56 ], and Gradient Boosting (GB) [ 57 ]. Additionally, the Expert-Guided Selection (EGS) [ 34 ] approach was employed to identify the most significant variables capable of defining the NANO.PTML system. The variables utilized for these models were considered crucial for describing the NANO.PTML system: ΔDPSA( c I ) dj (deviation of topological Polar Surface Area) for neurodegenerative drug and for NPs as drug carrier the variables including ΔDt( c III ) nj (deviation of NP safety time), ΔDLnp( c III ) nj (deviation of NP length), ΔDVnpu( c III ) nj (deviation of NP core volume), ΔDVxcoat( c III ) nj (deviation of McGowan volume), and ΔDVvdwMGcoat( c III ) nj (deviation of van der Waals volume from McGowan volume) were taken into account. Table  1 ; Fig.  4 presented the statistical parameters obtained by linear and non-linear models. The results showed that the DT classifier exhibited a good fit in both the training and validation sets, with Specificity (Sp) values of 96.4/96.2 and Sensitivity (Sn) values of 79.3/75.7, respectively. Another important statistical parameter included is the Mathew’s Correlation Coefficient (MCC) values [ 58 ], giving 0.6722/0.6401 in training/validation series.

Summary of the statistical parameters obtained for the linear and non-linear NANO.PTML models. ( A ) Training set and ( B ) Validation set

After tuning the hyperparameters to develop the DT algorithm which play a crucial role in determining its performance and behavior [ 59 ]. The best combination found were the following; The ccp-alpha parameter, set to 0.0, controls the complexity of the tree by correcting excessive branching and preventing overfitting. The class-weight parameter assigns weights to different classes within the dataset, in this case we set class 0 at 40% and class 1 at 60%, addressing potential imbalances in class distribution. The choice of criterion as “gini” indicates the use of Gini impurity as the measure of split quality, influencing how the tree partitions the feature space. Furthermore, max-depth is set to 15, limiting the depth of the tree to prevent it from growing overly complex and overfitting to the training data. The max-features and max-leaf-nodes parameters, both set to “None”, which allow the tree to explore all available features and leaf node possibilities, respectively, without imposing additional constraints. The min-impurity-decrease set at 0.0 defines the minimum impurity decrease required for a split, regulating the tree’s growth. The min-samples-leaf and min-samples-split, both set to 5 and 2 respectively. These parameters establish the minimum number of samples required in a leaf node or for a node split, contributing to the ability of generalizing the tree and avoiding it from being overly specific to the training data. The min-weight-fraction leaf was set to 0.0, indicating that it was not applied, while the random-state was set to 42, ensuring reproducibility of results across different runs of the model. Finally, splitter as “best” indicates that the best split at each node is determined based on the chosen criterion, enabling optimal tree construction. Further information about these parameters can be found in the documentation provided by the Scikit-learn library [ 52 ]. The hyperparameter used for LDA, kNN, etc. can be found in Table S1 Supporting Information SI00.docx.

Figure  5 depicts the structure of the decision tree, comprising 3249 nodes with a depth of 15 layers and terminating in 1625 leaf nodes. Final predictions or decisions are made based on the input data [ 60 ]. To facilitate better understanding of this tree plot, we have focused the explanation on a tree depth of 2 layers, resulting in 4 leaf nodes, which collectively form 7 main families. This analysis involved input variables such as ΔDVvdwMGcoat( c III ) nj , f(v ij (c d0 ),v nj (c n0 )) ref , and ΔDLnp( c III ) nj . Full information of the description for each family can be seen in Table  2 . For example, in family i, composed by NPs with lower McGowan volume deviation than Families v-vii and lower prior probability of activity than families ii-iv.

Plot for DT structure

Overall, this implies smaller NPs, possibly with lower polarizability, and lower expected biological property values suggesting overall reduced drug-NP activity likelihood. The 0.4% of cases are predicted as class 1. Consequently, NPs in this family should not be short-list for assay according to the DT model. However, on the right section of the DT, family ii, composed by NPs with higher McGowan volume deviation than Family i and lower prior probability of activity than families iii and iv. General, this indicates larger NPs, possibly with higher polarizability, and lower expected biological property values for Drug and NP suggesting overall increased activity likelihood. The 1.5% of cases are predicted as class 1. Therefore, NPs in this family should not be short-list for assay according to the model. However, families iii and iv yielded more promising results, with 4% and 3.3% of class 1, respectively. Family iii suggests smaller NPs, possibly with lower polarizability and low to medium expected biological property values, indicating an overall reduced likelihood of drug-NP activity. Conversely, family iv suggests larger NPs with higher polarizability. Medium to high biological property values indicate a higher likelihood of drug-NP activity.

Another statistical metric used in this study is the Area Under Receiver Operating Characteristic (AUROC), for both training and validation set, see Fig.  6 [ 48 ]. A high AUROC value indicates better overall performance of the model in terms of its ability to correctly classify instances from both classes. An AUROC of 1.0 represents a perfect classifier, while an AUROC of 0.5 indicates a classifier that performs no better than random guessing [ 48 ]. The highest AUROC values, 0.97 − 0.96, are obtained by the DT algorithm, which accordingly matches the results of Sn/Sp in the training/validation set. Whereas, the LDA algorithm is not among the top-performing classifiers, with AUROC values ranging from 0.73 to 0.74.

AUROC exploration of NANO.PTML models in both training (A) and validation (B) set

Contrast with earlier AI/ML algorithms

Other research jobs have showed in the recent investigation a wide variety of problems relating with NPs and/or NDDs discovery, see Table  3 . Actually, the majority of these researches explore the cytotoxicity of NP assays or NDDs against a large number of species by applying NANO.PTML models. Nevertheless, to the best of our knowledge, there are not study that includes both NP and NDDs component simultaneously or the opportunity of developing N2D3 systems. For example, Kleandrova et al. developed an combined QSTR-perturbation model to simultaneously explore ecotoxicity and cytotoxicity of NPs under different experimental conditions, including diverse measures of toxicities, multiple biological targets, compositions, sizes and conditions to measure those sizes, shapes, times during which the biological targets were exposed to NPs, and coating agents [ 44 ]. The model was obtained from 36,488 cases of NP-NP pairs. Nevertheless, in this research Kelandrova et al. is only restricted to the study of ecotoxicity and cytotoxicity of NPs and does not contemplate the data about NDDs components. Similarly, Cordeiro et al. built up the QSAR-perturbation model which involves 5520 cases (NP–NP pairs). The aim of this model is the simultaneous prediction of the ecotoxicity of NPs against several assay organisms (bio-indicators), by considering also multiple measures of ecotoxicity, as well as the chemical compositions, sizes, conditions under which the sizes were measured, shapes, and the time during which the diverse assay organisms were exposed to nanoparticles [ 40 ]. As the previous model, they do not take into account the NDDs biological activity. On the other hand, Luan et al. generated the mx-QSAR model from 4915 cases of multiple assays of neurotoxicity/neuroprotective effects of drugs. In addition, the model was trained with a dataset which involved diverse assay endpoints of 2217 compounds. Each compound was assayed in at least one out of 338 assays, which included 148 molecular or cellular targets and 35 standard type measures in 11 model organisms (including human).Unlike previous models, this mx-QSAR algorithm contained information NDDs, however, it does not consider the NP as part of this system [ 61 ]. In this paper, we developed an innovative system including both NP and NDDs components. The results of the NANO.PTML-DT was quite satisfactory Sp values of 96.4/96.2 and Sn values of 79.3/75.7 in training and validation series including 375 K and 125 K cases, respectively. Other research with similar scope as the present work, García et al. built up the LDA linear model in order to predict the results of 42 different experimental tests for GSK-3 inhibitors with heterogeneous structural patterns. GSK-3β inhibitors are interesting candidates for developing anti-Alzheimer compounds among others urgent diseases. These authors obtained Sn/Sp ≈ 90% in training/validation series [ 62 ]. On the other hand, Ferreira da Costa et al. constructed LDA model so as to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions involved in the dopamine pathway. They obtained Sn/Sp ≈ 70–91% in both training and validation series [ 63 ]. However, it is worth mentioning that the contract of statistical parameters between the model of this work and the previous one is not informative at all due to the fact that the design of each model is specific to the problem to be dealt with.

Experimental study of new system

Characterization of fe 3 o 4 nanoparticles.

Initially the hydrophobic NPs (samples Fe 3 O 4 _A and Fe 3 O 4 _B) have been structurally, morphologically and magnetically characterized (Table  4 ). Both samples present the inverse spinel structure of magnetite (Fe 3 O 4 , S.G. Fd-3 m) with no traces of secondary phases. The crystallite sizes of the samples were calculated from the maximum diffraction peak (311) of X-ray powder diffraction patterns using Scherrer’s equation. The calculated crystallite sizes of the two samples are around 24 nm and are compatible with the average physical size determined by TEM analysis (see Table  4 ; Fig.  7 ). The rather good agreement between the two techniques (DRX and TEM) indicate that the NPs of both samples are composed of single nanocrystals. In relation to the morphology of the NPs, sample Fe 3 O 4 _A is composed of NPs with more facets (cuboctahedrons), while the NPs of sample Fe 3 O 4 _B present octahedral-like shape as it can be seen in Fig.  7 a) and b), respectively.

TEM micrographs of (a) Fe 3 O 4 _A (cuboctahedral) and (b) Fe 3 O 4 _B (octahedral) nanoparticles. Large scale bars: 100 nm. Zoom scale bars: 10 nm, (c) a representative Electron Diffraction (ED) pattern corresponding to sample Fe 3 O 4 _A and (d) M (H) curves of both samples at room temperature

The magnetization dependence with the magnetic field (M(H)) in the two samples has been carried out by DC Magnetometry at RT. The M(H) curves of Fig.  7 d display saturation magnetizations (M S ) of 88 and 91 Am 2 /kg Fe3O4 , respectively, which proves the high quality of the magnetite phase and the purity of the inorganic core. After coating the hydrophobic NPs with CTAB, both samples (Fe 3 O 4 _A@CTAB and Fe 3 O 4 _B@CTAB NPs) become highly soluble in water as it is shown by the Z potential values, which are positive due to the cationic nature of the CTAB molecule (see Table  4 ; Fig.  7 b). Regarding the degree of agglomeration of the NPs in water dispersion, it can be claimed that these NPs are arranged in small clusters (2–5 NPs) because they present moderate hydrodynamic diameters (see Table  4 ) in comparison to the average diameter of a single NP determined by DRX and TEM.

This experimental section is focused specifically on the NP core of Fe 3 O 4 with two shapes (cuboctahedral and octahedral) and on the CTAB coating. We performed a computational analysis to demonstrate the practical application of the NANO.PTML model using a real-world wet-laboratory scenario. Additionally, we carried out a simulation experiment that try to mimic this experimental part. For this purpose, we created a prediction dataset with various combinations of NP systems including NP cores, coating agents, cell lines, shapes, and anti-neurodegenerative drugs linked with certain coatings. It is important to note that the total number of combinations, considering NP cores, cell lines, shapes, coating agents, and anti-neurodegenerative drugs, amounted to N tot = n(NP cores) · n(cell lines) · n(NP shapes) · n(NP coats) · n(drugs) = 5 · 53 · 5 · 16 · 123 = 2,607,600 assays. Performing all these combinations in a wet-laboratory is impractical, time-consuming, and resource-intensive. Even with expert criteria, the number of assays remains unmanageable for study. Therefore, the NANO.PTML-DT approach is introduced to address this issue by reducing the number of assays and serving as a guide for the experimental part, highlighting the most promising combinations within the NP systems as drug carriers for neurodegenerative diseases.

Experimental vs. computational illustrative case of study

Nano.ptml-dt simulation experiment.

In this section, a computational case study was presented to simulate the Fe 3 O 4 _A@CTAB and Fe 3 O 4 _B@CTAB NPs from the experimental study detailed in this paper (Fig.  8 ). The aim of this simulation experiment was to forecast the best combination of the NPs core vs. cell lines (cytotoxicity or ecotoxicity) vs. shapes vs. coating agents as mentioned in the previous section. In this scenarios, we created a total of 500,000 assays as new prediction dataset, which was formed by up to n(NPs core) = 5, n(cn1 = cell lines) = 53, n(cn2 = NP shapes) = 5, n(NPs coat) = 16 and n(drugs) = 123.

Workflow of experimental illustrative simulation experiment using NANO.PTML approach

On the other hand, the DT model was selected due to the good performance of the statistical parameters in both training and validation set, as shown in Table  1 . The probability p(NANO.PTML in ) c nj values were acquired with NANO.PTML in system. The heatmap shown in Fig.  9 illustrates the findings using a 3-color scale based on probability values: the green zone represents a high probability range, the yellow zone signifies a moderate to low probability range, and the red zone indicates a very low predicted probability. Assays that had never been reported previously or had very low representation in the original dataset, as well as insignificant combinations of NP systems were depicted in white to prevent overestimation in the results. Additionally, the columns of this heatmap represented the NP core, cell lines, and NP morphology. The column for cell lines was further categorized into cytotoxicity and ecotoxicity. The rows of the heatmap corresponded to the NP coats studied, arranged based on their MacGowan volume_n values. Furthermore, the heatmap contained information regarding the frequency of each combination appearing in both the columns and rows within the prediction dataset.

NANO.PTML systems experiment simulation

The prediction was carried out taking into account the cytotoxicity and eco-toxicity. It is crucial the study of the cytotoxicity as NPs are increasingly employed in medical diagnostics and therapies to enhance our comprehension, detection, and treatment of human diseases. The exposure of NPs in consumer products or their use in emerging biomedical applications, such as drug delivery, biosensors, [ 69 ] or imaging agents, [ 70 ] entails direct ingestion or injection into the body [ 71 ]. Additionally, the study of eco-toxicity is critical for assessing their impact on ecosystems, wildlife, and human health [ 72 ]. It helps in understanding how NPs interact with the environment, entering food chains and potentially affecting biodiversity.

In this context, the outcomes of the DT model highlighted certain NANO.PTML systems as promising candidates for further investigation. Interestingly, the high prediction value of Lycopersicon esculentum proved to be a favorable ecotoxicity cell line, exhibiting high probability values with the majority of coating systems. Contrarily, the least propitious cell lines were Danio rerio (embryos) , Danio rerio (juvenile) , Danio rerio (adults) , Oryzias latipes (adults), Ceriodaphnia dubia (neonates), Daphnia pulex (adults), Chlorella sp. , and Scenedesmus sp. , which yielding in medium to low probability values. On the other hand, one more important characteristic is MacGowan volume which has been widely used in many areas to estimate the physicochemical and biochemical properties of molecules, [ 73 , 74 ] specifically for CTAB, PS, and PEG as coating agents, with an exception in PVA. The combination of elliptical-shaped NPs with PVA as a coating agent in cytotoxicity cell lines appears to be a promising candidate for further synthesis. Another important factor is the type of the cell line which obtained higher probability value with cytotoxicity. It is important to note that all predictions generated by this method should be approached with caution and necessitate experimental validation. The NANO.PTML-DT method holds potential for accelerating experimental studies and offering cost-effective preliminary results for a vast database of NANO.PTML systems. This methodology presents an effective and robust tool for guiding experimental research, offering an alternative to laborious trial-and-error testing.

General applications of NANO.PTML-DT model

The NANO.PTML model has different types of applications in various stages of N2D3 system development, as shown in Fig.  10 . It includes the selection of new cores, coats, or drugs. In all these cases, the N2D3 systems can be optimized in terms of drug activity and NP system safety (cytotoxicity and ecotoxicity). The first three applications involve the selection of input variables. In the NP core scanning stage, researchers can select different types, sizes, and shapes. In the NP coats scanning stage, they can select up to 16 coating agents, such as CTAB, PVA, PVP, etc. In the drug scanning stage, they can carry out NDDs synthesis modifications, repurposing, and patent greening. The synthesis modifications refer to the prediction of new N2D3 systems (different coats, cores) for new drug structures with potential NDDs activity [ 75 ]. Repurposing refers to the prediction of new NDDs for N2D3 systems from already known drugs with other activities [ 76 ]. Patent greening applications refer to the prediction of new N2D3 systems (different coats, cores) for already known NDDs [ 77 ]. In all these cases, the outcomes predicted by the NANO.PTML model can optimize NP safety and/or biological activity of NDDs. To make these predictions, we have to change the values of different input variables. In Fig.  10 , we highlighted the input variables that need to be changed to make predictions for different applications. For details about the variables, see AI/ML Python Computational Models section. The variables in these four stages can be changed one by one according to the researchers’ needs; however, they can also be changed simultaneously. For example, in the simulation experiment shown in Fig.  9 , we created a total of 500,000 assays in which up to 123 drugs, 53 cell lines, 16 NP coats, 5 NP shapes, and 5 NP cores were changed at the same time.

Others applications of NANO.PTML model

Conclusions

The NANO.PTML model, which integrates NDDs and NP models, offers a practical solution for developing new NP system as drug carriers for neurodegenerative diseases. It effectively addresses the challenge of exploring numerous NP and NDDs compound combinations. The best-performing AI/ML model, using the DT algorithm, achieved high Sp (96.4%/96.2%) and Sn (79.3%/75.7%) in training and validation, with AUROC values of 0.97 and 0.96. Chemically synthesized Fe 3 O 4 NPs were structurally characterized and coated with CTAB to enhance water solubility. We illustrated an example of the IFPTML-DT model application in a real experiment (reported here). To do this, we performed an experimental simulation using a large prediction dataset including 500,000 cases/empirical experiments similar to NPs studied in the experimental part. This simulation experiment showed that certain NP systems as promising candidate for further investigation, highlighting the Lycopersicon esculentum cell line for ecotoxicity studies according green section of Fig.  9 . The MacGowan volume was significant for certain coating agents (CTAB, PS, PEG) but not for PVA. Overall, the NANO.PTML model expedites experimental research and provides reliable initial findings, reducing the reliance on time-consuming wet-lab procedures.

Data availability

The datasets generated and/or analyzed during the current study are available in the Figshare repository, DOI: 10.6084/m9.figshare.25450291. On the other hand, the code of the NANO.PTML models was uploaded to a GitHub repository and is available free for use by researchers. For the NANO.PTML models code the link is: https://github.com/she012/NANO.PTML-project .

Abbreviations

Artificial Intelligence refers to the use of computers to simulate intelligent behavior with minimal human involvement [ 78 ]

Area Under Receiver Operating Characteristic is commonly used to assess the accuracy of diagnostic tests. A ROC curve that is closer to the upper left corner of the graph indicates higher test accuracy, as this point represents a sensitivity of 1 and a false positive rate of 0 (specificity of 1) [ 79 ]

Blood-Brain Barrier is a selective permeability barrier formed by the blood vessels that vascularize the central nervous system [ 80 ]

Central Nervous System is the brain and spinal cord. This system is responsible of receiving, processing, and responding to sensory information [ 81 ]

Cetyl Trimethyl Ammonium Bromide is a cationic surfactant

Dynamic Light Scattering measures changes in scattering intensity over time caused by particles moving randomly due to Brownian motion [ 82 ]

Drug Nanoparticle Delivery System

X Ray Diffraction has become a widely used method for examining crystal structures and atomic spacing [ 83 ]

Decision Tree is a hierarchical decision support model that employs a tree-like structure to represent decisions and their potential outcomes [ 54 ]

Gradient Boosting is an ensemble machine learning technique that sequentially combines the predictions of multiple weak learners, usually decision trees [ 57 ]

Information Fusion is the process of integrating data from different sources

K-Nearest Neighbors is a popular machine learning technique which based on the principle that data points which are close to each other are likely to share similar labels or values [ 84 ].

Linear Discriminant Analyses is a machine learning method that aims to identify a linear combination of features that effectively distinguishes between two or more classes of objects or events [ 85 ]

Neurodegenerative Diseases

Moving Average is a method used to analyze data points by calculating a series of average values from various subsets of the entire dataset [ 86 ]

Mathew’s Correlation Coefficient is a metric for binary classification that evaluates predictions by considering true positives, true negatives, false positives, and false negatives[87

Machine Learning is the science of developing algorithms and statistical models that enable computer systems to perform tasks without explicit instructions, relying instead on patterns and inferences [ 88 ]

• Nanoparticle

Neurodegenerative Disease Drugs

Nanoparticle Neuronal Disease Drug Delivery systems

Poly(Maleic Anhydride-alt-1-Octadecene)

Perturbation Theory involves starting with a simple system for which a mathematical solution is already known. Then, an additional perturbation is introduced to represent a weak disturbance to the system [ 89 ]

Perturbation Theory Operators is the linear and non-linear transformations of moving average. For example, the deviation of the moving average

Radom Forest is a popular machine learning technique that combines the collective predictions of numerous decision trees to generate a combined outcome [ 90 ]

Room Temperature

Transmission Electron Microscopy is a method of microscopy where a specimen is illuminated with a beam of electrons, allowing the formation of an image as the electrons pass through the specimen [ 91 ]

ThermoGravimetric Analysis is a method used to assess the thermal stability of materials, including polymers, by measuring changes in weight as a function of temperature [ 92 ]

Vibrating-Sample Magnetometer is a device used to measure the magnetic properties of a sample while it undergoes perpendicular vibrations within a uniform magnetic field [ 93 ]

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Acknowledgements

Mrs. Arrate Bañeres is acknowledged for management support.

We are grateful to receive the financial support from grants Basque Government / Eusko Jaurlaritza (IT1558-22), SPRI ELKARTEK grants AIMOFGIF (KK-2022/00032), Ministry of Science and Innovation (PID2022-137365NB-I00), and Eusko Jaurlaritza, LANBIDE, INVESTIGO Grants, IKERDATA 2022/IKER/000040 funded by NextGenerationEU funds of European Commission. We also want to acknowledge the Spanish Ministry of Science and Innovation for financial support under grant No. PID2022- 136993OB-I00 (AEI/FEDER, UE), funded by MCIN/AEI/ 10.13039/ 501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union”. This work was also supported in part by the U.S. Department of Energy (DOE) Grant DE-SC0022239. The work used resources of the Center for Computationally-Assisted Science and Technology (CCAST) at North Dakota State University (Fargo, ND USA), which was made possible in part by the U.S. National Science Foundation (NSF) MRI Award No. 2019077.

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Shan He, Estefania Ascencio, Gerardo M. Casanola-Martin & Bakhtiyor Rasulev

Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain

Shan He, Karam Nader, Julen Segura Abarrategi, Estefania Ascencio, Idoia Castellanos-Rubio, Maite Insausti, Sonia Arrasate & Humberto González-Díaz

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ICR, MI, SA, and HGD conceived the presented idea. JSA collected the dataset. SH, GMCM, BR, SA and HGD implemented the idea computationally, performed the computations and analysis. KN, ICR and MI performed the nanoparticle synthesis experiments. GMCM, ICR, MI, BR, SA and HGD supervised the findings of this work. All authors discussed the results and wrote the manuscript with input of all authors. All authors read and approved the final manuscript.

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Correspondence to Idoia Castellanos-Rubio or Sonia Arrasate .

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He, S., Nader, K., Abarrategi, J.S. et al. NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study. J Nanobiotechnol 22 , 435 (2024). https://doi.org/10.1186/s12951-024-02660-9

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Received : 18 April 2024

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Published : 23 July 2024

DOI : https://doi.org/10.1186/s12951-024-02660-9

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