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what’s happening in any body differs sharply
from the “on/off ” model of one gene turning on
a set of symptoms, technology can help drug
developers wrestle with the complexity.
Last, but hardly least, these A.I.-driven
efforts offer a glimmer of economic hope. In
an era in which the cost of drug development
is a daunting obstacle, smart algorithms may
someday enable medical stakeholders to derive
more value from the trillions of dollars that
have already been spent on drug research. In
theory, with repurposing, “you don’t need to
design [new] drugs,” Cohen avers. “My feeling
is that with 50 drugs, we can treat everything.”
That would mean changing yet another
definition: the meaning of “discovery.”cess is something Cohen has done before. He was a cofounder of
Millennium Pharmaceuticals, a U.S. oncology-drug maker that
helped develop the multiple-myeloma treatment Velcade.
Cohen is bullish that Pharnext can be successful with A.I.,
but he is also aware of the technology’s limitations. Google’s
AlphaZero, an A.I. program, was able to beat the world’s human
masters at the Chinese strategy game Go, without using any prior
human knowledge. But as Cohen points out, Go has a finite set of
rules, which AlphaZero knew completely. In biology, thanks in part
to pleiotropy, the rules are not fully known—and may never be.
But thoughtfully designed A.I. has enabled Pharnext to build
models around the rules that are known and make choices accord-
ingly. Out of the universe of 10,000 known drugs, the company’s
discovery model takes in an assortment of 2,000 that are both out
of patent and “marketed”—that is, already judged both therapeuti-
cally effective and safe enough to be sold to the public.
To develop its CMT drug, Pharnext first spent about a year
assembling its network model for the disease—a framework
comparable to GNS’s Parkinson’s map, showing how nervous
and muscular problems “cascade” from the relevant gene muta-
tion. Based on this mechanism, the computer model arrived at
a short list of 57 candidate drugs that addressed various points
in the cascade. Pharnext tested those drugs one by one in vitro,
generating a shorter list of 22 to be tested in mice, which finally
yielded the three-drug combination that went to human clinical
trials. The recent positive Phase III results confirmed that the
PXT3003 cocktail is acting at various points in the cascade.
Without the A.I. model, many more years of preclinical test-
ing would have been required beyond the three years it took
Pharnext, says Cohen. “With 2,000 drugs [to start with], I could
produce all possible combinations, a billion possibilities” to test
in vitro. That’s a recipe for countless false positives and dead
ends—years of frustration, for now forestalled.
P
HARNEX T ’S SHARES, which trade on the Paris stock
exchange, have more than doubled since October’s
Phase III results announcement. The company has
spent about 120 million euros ($135 million) over the
past decade on research and development—a very modest figure
by pharma standards. It has never made a profit, but analysts
estimate that if PXT3003 reaches the market, revenue—9 million
euros in 2018—could soar starting in 2020. (GNS Healthcare is
privately held and does not disclose spending or revenue.)
Beyond possible victories for investors, the advances at
Pharnext and GNS point the way to A.I.’s growing up—and phar-
macology along with it. The ability to reason about causality, and
to explore counterfactual questions, is a threshold that users of
artificial intelligence have long sought to cross. The computer
models at these startups are making a foray in that direction as
they manage and tame a bewildering number of variables.
Even the underlying definition of disease may evolve. As scien-
tists are learning, these definitions have been overly simplistic. A
study in the journal Bioinformatics last year noted that attempts
to treat tumors are hampered by the fact that genetic mutations in
cancer are “fundamentally heterogeneous”: What appears as one
disease, or class of disease, in fact contains few commonalities and
many differences from patient to patient. As it becomes clear that
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PHARNEXT ASSEMBLES
A DATABASE OF 2,000
DRUGS THAT HAVE
ALREADY BEEN
SAFETY-
TESTED AND
APPROVED
TO TREAT
OTHER
DISEASES.
AN A.I. MODEL COMPARES
THOSE DRUGS TO A “MAP”
OF THE EFFECTS OF CMT,
YIELDING 57 CANDIDATES
FOR MORE TESTING.
IN VITRO TESTING YIELDS A
SHORT LIST OF 22 DRUGS FOR
FURTHER TESTING IN MICE.
A THREE-DRUG COMBINATION, PXT3003,
CLEARS THE THRESHOLD FOR HUMAN TRIALS.
the big sortPharnext uses an A.I. model to search through a
database of existing drugs to find new therapeutic
combinations—a process that can shave years
off the drug-development process. Here’s how it
helped the company develop PXT3003, a com-
pound to treat CMT.