AI如何让老药新用?(3)

At the dawn of modern genetic research, almost no one anticipated the enormous complexity in the biology of disease. Many researchers thought the genome would be a kind of instruction manual for the body.

Pioneers such as Celera Genomics' Craig Venter and Francis Collins of the National Institutes of Health were celebrated as "gene hunters," a term that evoked crusaders scouring the globe for that one "silver bullet" gene that would explain-and facilitate a cure for-a given disease.

To some extent, these researchers found real treasure. Geneticist Nancy Wexler, for example, spent years in Venezuela compiling family trees of those affected by Huntington's disease, a rare, inherited condition.

Her work led to the discovery of the mutation in a single gene that predicts whether an individual will contract the condition. But scientists soon realized that genetic maps were less like an instruction manual and more like the parts catalog you get with Ikea furniture.

What's more, researchers discovered other catalogs that added complex variables to the relationships between genes and disease- for example, the proteome, the proteins encoded by DNA, and the transcriptome, all the nucleic acids that convert DNA into proteins.

The morning-after disappointment has proved wrenching, as researchers learned that complex diseases, such as cancer and Alzheimer's, didn't yield to a single gene. (Even Huntington's, its gene identified, has remained untreatable. )

Today, Cohen and others see a link between the obsession with simplicity and a decline in drug discovery. That decline shows itself in the 1-in-10 success rate for FDA approval of new therapies; in spiraling costs for drug development

(what a Tufts study recently identified as "the $2.6 billion pill"); and in the soaring prices of the few treatments that break new ground, such as the $475,000 cost of a course of treatment with Novartis's leukemia drug Kymriah.

More recently, researchers have begun to grapple with biological complexity with the help of the science of networks. That science's chief evangelist is Albert-Laszlo Barabasi,

a professor at Northeastern University whose 2014 book Linked popularized the notion that network theory can explain numerous fields, from fashion trends to sexual relations to disease.