By Sneha S K and Puyaan Singh

Sept 2 (Reuters) -

Drug developers are increasing adoption of AI technologies

for discovery and safety testing to get faster and cheaper

results, in line with an FDA push to reduce animal testing in

the near future.

Within the next three to five years, using AI and cutting

back on animal testing could reduce timelines and costs by at

least half, according to 11 different experts from across

contract research firms, biotech companies and brokerages.

Drug development software maker Certara ( CERT ), and

biotechs such as Schrodinger and Recursion

Pharmaceuticals ( RXRX ) are already using AI to predict how

experimental drugs might be absorbed, distributed, or trigger

toxic side effects.

"We are getting to the point where we don't actually need to

do that (animal testing) anymore," said Patrick Smith, president

of drug development solutions at Certara ( CERT ), which works with

companies developing infectious diseases drugs such as

monoclonal antibodies for hepatitis B.

Recursion said its AI-based drug discovery platform took

just 18 months to move a molecule into clinical testing as a

cancer drug candidate, far faster than the industry average of

42 months.

Analysts at TD Cowen and Jefferies expect these AI-driven

approaches to cut costs and timelines by more than half, from

current estimates of up to 15 years and $2 billion needed to

bring a drug to market.

The shift also aligns with the FDA's vision of approaches

such as AI-driven technologies, human cell models and

computational models becoming the new standard, as the agency

plans to make animal studies the exception for pre-clinical

safety and toxicity testing in three to five years.

The new approaches are expected to ultimately lead to lower

drug prices as well, the U.S. Food and Drug Administration had

said in its April statement that outlined a road map for

companies to reduce reliance on animal testing, especially for

monoclonal antibody drugs.

Still, industry experts have said the new methods are

unlikely to fully replace animal testing.

Under current FDA requirements for monoclonal antibodies,

companies conduct studies in animals to test for any harmful

effects of a drug. These studies typically take between one to

six months, and require about 144 non-human primates on average,

at a cost of $50,000 each, according to the agency.

NEW APPROACH

Charles River, one of the world's largest research

contractors, is among the industry mainstays investing in AI and

the so-called "new approach methodologies".

These NAMs use AI, computer-based modeling and machine

learning as well as human-based models such as organs-on-chips

to predict how a drug might work in the body. An organ-on-a-chip

is a small device lined with living human cells that replicate

key functions of an organ.

Charles River's NAM portfolio already generates about $200

million in annual revenue.

SMALLER PLAYERS STEPPING IN

InSphero is testing safety and efficacy in 3D liver models -

where lab-grown liver microtissues help replicate the functions

of the organ.

New York-based Schrodinger combines physics-based

simulations with AI to predict drug toxicology.

But industry experts say in the near future, companies will

use a hybrid approach, reducing animal testing and supplementing

with data from these new methods.

"I don't think we'll get to a point immediately, in the near

term where all of a sudden, animal testing is gone entirely,"

said Brendan Smith, a life sciences and biotech analyst at TD

Cowen.