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CHINESE PHARMA FIRMS TARGET THE GLOBAL MARKET

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A new Chinese drug for colorectal cancer could mark an important milestone

WALK into the Shanghai laboratories of Chi-Med, a biotech firm, and you encounter the sort of shiny, cutting-edge facilities common in any major pharma company in America, Europe or Japan. Chi-Med has just had positive results in a late-stage trial of its drug for colorectal cancer, which is called Fruquintinib. If the drug is approved both in China and in Western markets it could be the very first prescription drug to be designed and developed entirely in China that will be on a path to global commercialisation.

Given China’s ageing population, higher incomes and rising demand for health care it is clear why innovation in drugs is a priority for the country. Its national market for drugs has grown rapidly in recent years to become the world’s second-largest. It could grow from $108bn in 2015 to around $167bn by 2020, according to an estimate from America’s Department of Commerce. By comparison, America spends about $400bn a year on drugs.

Chinese firms mainly sell cheap, generic medicines that earn only razor-thin margins. The pharma industry is extremely fragmented, with thousands of tiny manufacturers and distributors. That helps explain the limited amount of finance that is available for investment in new medicines. Most Chinese pharma firms devote less than 5% of sales to R&D, according to a report last year from the World Health Organisation (big global drug firms typically spend 14%-18% of sales on R&D). And the bulk of that spending goes to research into generics.

But things are changing quickly. The government is encouraging the industry to consolidate, chiefly by raising standards for the quality of new medicines. It is also improving the country’s regulatory infrastructure, which should make it more efficient, and faster, to develop drugs. The value of deals in the health-care sector has been increasing as a result. ChinaBio, a research firm, reckons that over $40bn of foreign and local money went into the life sciences in China in 2016. In the same year just three Chinese biotech firms—CStone, Innovent and Ascletis—together raised more than $500m of financing.

Another boost is the arrival of talent from abroad, whether Chinese-born executives returning with a Western education or Westerners with experience of multinational pharmaceutical firms. Christian Hogg, the boss of Chi-Med—which was founded in 2000, has eight drugs in clinical development and listed on the NASDAQ stock exchange in 2016—used to work at Procter & Gamble, a global consumer-goods firm. Samantha Du, the firm’s very first scientific officer, was formerly an executive at Pfizer, an American pharma giant. Now known as the godmother of Chinese biopharma, she used to manage health-care investments for Sequoia Capital, a Silicon Valley venture-capital firm. In 2013 she helped found Zai Lab, which licenses late-stage drugs from Western pharma companies to develop and sell in China. Zai Lab also aims to develop innovative medicines in immuno-oncology.

Another firm attracting attention is BeiGene, an oncology firm based in Beijing, which has four clinical-stage drug candidates and which raised $158m in an IPO last year. Chi-Med’s Fruquintinib may even be beaten in the race to approval in America and Japan by a cancer drug called Epidaza from Chipscreen Biosciences of Shenzhen. China approved it in 2015.

It is too early to say whether these innovative firms will remain rarities. Only a few large ones have emerged, since the industry is resisting consolidation. But the size of the local market will itself help the industry grow. And developing a drug in China is far cheaper than it is in America or Europe. Given the outrage at the high cost of drugs in America, in particular, there is every incentive for Chinese firms to develop medicines for the global market.

source:http://www.economist.com/news/business/21718937-new-chinese-drug-colorectal-cancer-could-mark-important-milestone-chinese-pharma-firms?fsrc=scn/li/te/bl/ed/abetterpillfromchinachinesepharmafirmstargettheglobalmarket

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Innovations

HERE’S WHAT GOOGLE MAPS LOOKS LIKE RUNNING ON APPLE CARPLAY

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With iOS 12, Apple is giving third-party apps more flexibility and new capabilities within CarPlay. As an example, for the first time, you can use other apps besides Apple Maps as your preferred navigation software for Apple’s in-car platform. With that change now possible, Google Maps and Waze are both planning to support CarPlay and have begun beta testing.

Unfortunately it’s not a beta test most of us can join, so you’ll have to wait for the proper release before you can use either of these in your own vehicle through CarPlay. But some early screenshots posted by 9to5Mac provide a good preview of how Google Maps and Waze will look once that happens.

Both apps are already available on Android Auto, so the developers behind each app are well familiar with the basics of optimizing their navigation for an in-car display: make the icons big, text readable, and directions… well, accurate. Apple Maps continues to get better and better, but G Maps and Waze each have their own strengths.

Google Maps utilizes Google’s own traffic and mapping data, which you might trust a bit more than Apple’s — even now. It also syncs up with your saved places. And Waze is pretty unrivaled when it comes to warning you about accidents or, for those who go heavy on the gas pedal, nearby police. Google Maps still looks like a Google app while following CarPlay’s UI guidelines.

There’s no official word on exactly when the CarPlay versions of Google Maps and Waze will widely roll out to users. But with iOS 12 widely launching on Monday — you can already install it now, remember — hopefully it won’t be long before this beta graduates to a full update.

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MANAGING THE EVOLVING SKIES

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Unmanned aircraft system traffic management (UTM), the key enabler

Aerial technology is transforming the way people perceive aviation. Elevated vehicles, including passenger and delivery drones, have the potential to address today’s urban congestion, improve logistics, and create new products and markets. With the growing demand for unmanned aerial vehicles (UAVs) across commercial and non-commercial markets, the skies will get busier. An upshot would be to manage and maintain an increasingly diverse airspace, while keeping all the air traffic safe and efficient.

Unmanned aircraft system traffic management (UTM) can play the role of a “key enabler” in the future of UAVs and presents significant business opportunities to main stakeholders. The global UTM market, valued at about US$538 million in 2018, is expected to grow at a compounded annual growth rate (CAGR) of over 20 percent during the period 2019 to 2025.

Our study explores the challenges and solutions that are critical to the success of all UAV ecosystem stakeholders. It also discusses how a UTM system can ensure safe and efficient operations and is a critical requirement for the future of elevated mobility.

 

 

Read more:  https://www2.deloitte.com/global/en/pages/energy-and-resources/articles/managing-evolving-skies.html?id=gx:2sm:3li:4UTM18::6Energy_and_Resources:20180717093000:Global&linkId=54344340

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Innovations

MOST OF AI’S BUSINESS USES WILL BE IN TWO AREAS

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While overall adoption of artificial intelligence remains low among businesses (about 20% upon our last study), senior executives know that AI isn’t just hype. Organizations across sectors are looking closely at the technology to see what it can do for their business. As they should—we estimate that 40% of all the potential value that can created by analytics today comes from the AI techniques that fall under the umbrella “deep learning,” (which utilize multiple layers of artificial neural networks, so-called because their structure and function are loosely inspired by that of the human brain). In total, we estimate deep learning could account for between $3.5 trillion and $5.8 trillion in annual value.

However, many business leaders are still not exactly sure where they should apply AI to reap the biggest rewards. After all, embedding AI across the business requires significant investment in talent and upgrades to the tech stack as well as sweeping change initiatives to ensure AI drives meaningful value, whether it be through powering better decision-making or enhancing consumer-facing applications.

Through an in-depth examination of more than 400 actual AI use cases across 19 industries and nine business functions, we’ve discovered an old adage proves most useful in answering the question of where to put AI to work, and that is: “Follow the money.”

The business areas that traditionally provide the most value to companies tend to be the areas where AI can have the biggest impact. In retail organizations, for example, marketing and sales has often provided significant value. Our research shows that using AI on customer data to personalize promotions can lead to a 1-2% increase in incremental sales for brick-and-mortar retailers alone. In advanced manufacturing, by contrast, operations often drive the most value. Here, AI can enable forecasting based on underlying causal drivers of demand rather than prior outcomes, improving forecasting accuracy by 10-20%. This translates into a potential 5% reduction in inventory costs and revenue increases of 2-3%.

While applications of AI cover a full range of functional areas, it is in fact in these two cross-cutting ones—supply-chain management/manufacturing and marketing and sales—where we believe AI can have the biggest impact, at least for now, in several industries. Combined, we estimate that these use cases make up more than two-thirds of the entire AI opportunity. AI can create $1.4-$2.6 trillion of value in marketing and sales across the world’s businesses and $1.2-$2 trillion in supply chain management and manufacturing (some of the value accrues to companies while some is captured by customers). In manufacturing, the greatest value from AI can be created by using it for predictive maintenance (about $0.5-$0.7 trillion across the world’s businesses). AI’s ability to process massive amounts of data including audio and video means it can quickly identify anomalies to prevent breakdowns, whether that be an odd sound in an aircraft engine or a malfunction on an assembly line detected by a sensor.

Another way business leaders can home in on where to apply AI is to simply look at the functions that are already taking advantage of traditional analytics techniques. We found that the greatest potential for AI to create value is in use cases where neural network techniques could either provide higher performance than established analytical techniques or generate additional insights and applications. This is true for 69% of the AI use cases identified in our study. In only 16% of use cases did we find a “greenfield” AI solution that was applicable where other analytics methods would not be effective. (While the number of use cases for deep learning will likely increase rapidly as algorithms become more versatile and the type and volume of data needed to make them viable become more available, the percentage of greenfield deep learning use cases might not increase significantly because more established machine learning techniques also have room to become better and more ubiquitous.)

We don’t want to come across as naïve cheerleaders. Even as we see economic potential in the use of AI techniques, we recognize the tangible obstacles and limitations to implementing AI.  Obtaining data sets that are sufficiently large and comprehensive enough to feed the voracious appetite that deep learning has for training data is a major challenge. So, too, is addressing the mounting concerns around the use of such data, including security, privacy, and the potential for passing human biases onto AI algorithms. In some sectors, such as health care and insurance, companies must also find ways to make the results explainable to regulators in human terms: why did the machine come up with this answer? The good news is that the technologies themselves are advancing and starting to address some of these limitations.

Beyond these limitations, there are the arguably more difficult organizational challenges companies face as they adopt AI. Mastering the technology requires new levels of expertise, and process can become a major impediment to successful adoption. Companies will have to develop robust data maintenance and governance processes, and focus on both the “first mile”—how to acquire data and organize data efforts—and the far more difficult “last mile,” how to integrate the output of AI models into work flows, ranging from those of clinical trial managers and sales force managers to procurement officers.

While businesses must remain vigilant and responsible as they deploy AI, the scale and beneficial impact of the technology on businesses, consumers, and society make pursuing AI opportunities worth a thorough investigation. The pursuit isn’t a simple prospect but it can be initiated by evoking a simple concept: follow the money.

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