May 7, 2017 7:00 pm JST

AI gives medical research a shot in the arm

IBM's Watson is among the cutting-edge tech being used to develop new treatments

DAISAKU YAMASAKI, Nikkei Biotech deputy editor

The Watson IoT Center in Munich, Germany © AP

TOKYO "Beyond the pill" is becoming a popular catch phrase among executives at major U.S. and European drug makers, and for good reason.

 

Having developed drugs for lifestyle-related diseases like diabetes and high blood pressure, as well as antibiotics and other remedies for infectious diseases, companies are now shifting their attention to developing medicines for ailments that are much more difficult to treat. The probability of success, once said to be on the order of 1 in 10,000, has fallen to 1 in 30,000 since the turn of the century.

Development costs have soared, with a single new drug now costing around $2.55 billion to bring to market, according to a 2014 paper by Tufts University in the U.S. Drug makers have turned to repeated mergers and acquisitions to take advantage of economies of scale and boost their research budgets, and many are snapping up promising-looking products being developed by startups.

At the same time, aging populations and the rising cost of medical care, particularly in developed countries, mean drug prices will not be able to rise enough to compensate for the skyrocketing cost of development.

That's where "beyond the pill" comes in. By utilizing artificial intelligence, big data and the latest in information and communication technology, pharmaceutical companies are looking for new ways not only to develop drugs, but also to diagnose and treat patients.

SMARTER RESEARCH In August 2014, IBM and Baylor College of Medicine in the U.S. set tongues wagging when they discovered six previously unknown enzymes that activate a gene known as p53, which controls tumor growth. In a typical year, about one such enzyme is discovered, so coming across six at once caused quite a stir.

Moreover, the discovery was not made in the usual way -- by a team of researchers repeatedly conducting experiments -- but rather by utilizing Watson, a computer system developed by IBM.

Capable of retaining and structuring information and able to answer questions posed in natural language, Watson gained fame when it beat the champion of American quiz show "Jeopardy!" in 2011. It has begun to find commercial application, for instance, at corporate call centers, where it can enable staff to provide a consistent level of support despite individual differences in experience or knowledge.

In the fields of medicine and medical care, Watson is helping doctors make more accurate diagnoses by recording and organizing more than 24 million medical records.

To discover the p53-related enzymes, Baylor and IBM first used Watson to pick out 20,000 designated keywords from 70,000 papers dealing with the gene. Analyzing the relationship among these words and their frequency of occurrence across all the papers led to the discovery that although there were no papers indicating a relationship between the six enzymes and p53, such a relationship was likely to exist.

Discovering candidates for new drugs is just one use for AI and big data. As observational equipment becomes more advanced, the volume of data these devices produce is growing at a lightning pace. The challenge is mining this mountain of information for useful insights.

To tackle this challenge, LPixel, founded by members of the University of Tokyo's Laboratory of Plant Cell Biology in Totipotency, developed IMACEL image analysis software.

IMACEL takes image data stored in the cloud and automatically calculates the number of cells contained in the image, as well as changes in their volume, movement, luminance and so on. This information is then compiled as quantitative data, allowing researchers to easily gather data on cell death and cell cycles.

The strength of the company's image analysis software comes from its use of AI. When counting cells of a certain type by hand, there is always the risk of human error -- one researcher might see two cells where another sees just one. By designating the type of cell to be counted when the image is registered, the software can use machine learning to understand and analyze what researchers are looking for and classify different cell types more accurately.

In 2016, the company established a joint venture in Singapore with iGroup, which represents academic publications in Hong Kong. It is also pursuing business development overseas, especially elsewhere in Asia. LPixel has also developed a system that can tell whether an image has been altered. This product has been sold to research institutions in such places as South Korea and Taiwan. These institutions have reportedly made inquiries regarding IMACEL as well.

"In surveys we've done, 90% of researchers said they need image processing, but just 1% had properly studied it," said LPixel CEO Yuki Shimahara. "Moreover, the volume of image data continues to increase with each passing year. Our AI-powered systems are so simple even a grade schooler could use them."

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