This document discusses the importance and various applications of artificial intelligence in the pharmaceutical industry. It begins with an introduction from Dr. Ruchi Tiwari on the uses of AI in R&D, drug development, diagnosis, disease prevention, epidemic prediction, remote monitoring, manufacturing, and marketing. The rest of the document provides more details on each of these areas, including examples of companies using AI for drug discovery, clinical trials, adherence monitoring, and data analysis. It also discusses challenges to AI adoption in pharma such as unfamiliarity, lack of infrastructure, and unstructured data formats. The overall message is that AI has great potential to improve efficiency and outcomes across the pharmaceutical industry.

1. Dr RUCHI TIWARI
PROFESSOR &
EXECUTIVE COMMITTEE MEMBER OF
RESEARCH AND INNOVATION CENTRE,
Pranveer Singh Institute of Technology, Kanpur
IMPORTANCE OF ARTIFICIAL
INTELLIGENCE IN PHARMACEUTICAL
INDUSTRIES

2. R&D
Drug Development
Diagnosis
Disease Prevention
Epidemic prediction
Remote Monitoring
Manufacturing
Marketing
Wrapping up
How is AI used in the pharmaceutical industry?
How does AI help in drug discovery?
Will AI lead to cheaper and better medications?
Table of Contents

3. “Computing systems that are able to engage in human-like
processes such as learning, adapting, synthesizing, self-correction
and use of data for complex processing tasks”.
In the pharmaceutical and healthcare industry, due to its
versatility and efficiency, AI can be adopted in most departments
to enhance data processing, accelerate procedures, as well as
create new knowledge. Specifically, in the pharmaceutical
industry, AI can be adopted in the development of new drugs,
improvement of existing ones, patient diagnosis as well as patient
care
AI

5. • According to Tractica, the
global artificial intelligence
software market is forecast
to grow from $10.1 billion in
2018 to $126 billion by 2025.

6. Pharma Industry in the Age of
Artificial Intelligence: The Future
is Bright

7. More importantly, executives
across the pharma industry
are looking at ways to
leverage AI in their line of
business, including healthcare
(or the biotech industry to be
more precise).
In addition, various big
pharmaceutical players are
already getting their feet wet
in the world of machine
learning and artificial
intelligence.

8. R&D
• Pharma companies around the world are
leveraging advanced ML algorithms and AI-
powered tools to streamline the drug
discovery process. These intelligent tools
are designed to identify intricate patterns in
large datasets, and hence, they can be used
to solve challenges associated with
complicated biological networks.

9. Drug
Development
• According to an MIT study, only 13.8%
of drugs are successful in passing
clinical trials. To top that, a pharma
company has to pay anywhere between
161 million to 2 billion dollar for a drug
to get through the complete process of
clinical trial and get FDA approval.
• This is the reason why pharma
companies are increasingly adopting AI
to improve the success rates of new
drugs, create more affordable drugs ad
therapies, and, most importantly,
reduce operational costs.

10. Diagnosis
INDUSTRIES
Doctors can use advanced Machine Learning
systems to collect, process, and analyze vast
volumes of patients’ healthcare data.
Healthcare providers around the world are
using ML technology to store sensitive patient
data securely in the cloud or a centralized
storage system. This is known as electronic
medical records (EMRs).

11. Disease Prevention
o Pharma companies can use AI to
develop cures for both known
diseases like Alzheimer’s and
Parkinson’s and rare diseases.
o Generally, pharmaceutical
companies do not spend their
time and resources on finding
treatments for rare diseases
since the ROI is very low
compared to the time and cost it

12. Epidemic prediction
AI and ML technologies feed on
the data gathered from disparate
sources in the Web, study the
connection of various geological,
environmental, and biological factors
on the health of the population of
different geographical locations, and
try to connect the dots between
these factors and previous epidemic
outbreaks.

13. Remote Monitoring
Remote monitoring is a breakthrough in the pharma and
healthcare sectors. Many pharma companies have already
developed wearables powered by AI algorithms that can remotely
monitor patients suffering from life-threatening diseases.
TENCENT HOLDINGS COLLABORATED
WITH MEDOPAD
Develop an AI technology
Remotely monitor patients with Parkinson’s
disease
Reduce the time taken to perform a motor function
assessment from 30 minutes to three minutes.

14. Monitor the opening
and closing motions of
the hands of a patient
Smartphone
camera will
capture
Determine the
severity of the
symptoms
Frequency and
amplitude of
the movement
Determine the
severity score
of the
patient’s
condition
Allowing
doctors to
change the
drugs as well as
the drug doses
remotely
conditions become
worse demanding a
treatment upgrade
ARRANGE A
CHECKUP

15. Manufacturing
Pharma companies can implement AI in the manufacturing
process for higher productivity, improved efficiency, and faster
production of life-saving drugs.
Quality control
Predictive maintenance
Waste reduction
Design optimization
Process automation

16. Marketing
Given the fact that the
pharmaceutical industry is a sales-
driven sector.
AI can help to map the customer
journey.
In this way, pharma companies can
focus more on those marketing
strategies that lead to most
conversions and increase revenues.

17. Process of AI adoption in the pharma
sector
Partnering and collaborating with
academic institutions that specialize in AI
R&D to guide pharma companies with AI
adoption.
Collaborate with companies that specialize
in AI-driven medicine discovery to reap
the benefits of expert assistance,
advanced tools, and industry experience.
Train R&D and manufacturing teams to
use and implement AI tools and
techniques in the proper way for optimal
productivity.

18. Major Areas Where AI
Can Be Seen as a
Positive Force in
Pharma

19. Discovery and Development of New
Drugs
Against the background,
there are a few
companies out there
which are using AI to
redefine pharma and cut
drug development times.
9
out
10
clinical
drugs
fail
to
make
it
to
trials
Lot
more
don’t
reach
[FDA]
approval
stage
Driving
the
costs
of
drug
discovery
and
development

20. Cyclica and Bayer AI Collaboration helps
both companies discover & design drugs
faster
In late 2018, Bayerannounced an artificial
intelligence collaboration with Cyclicato take its discovery
of peptide drugs to an advanced level.
They will also combine Cyclica’s Differential Drug Design
technology and AI platform to come up with multi-targeted,
state-of-the-art drug designs. This is a step up in the world of
drug discovery and investigation. On the other hand, Cyclica will
in return improve upon its integrated network of enabling AI-
powered innovations.

21. Bayer and Merck AI Partnership helps radiologists
identify patients with chronic pulmonary hypertension
faster
The tool employs machine
learning to comb through
image findings from
pulmonary vessels, lung
perfusion, and cardiac
check-ups, as well as the
clinical history of the
patient.
This way, radiologists will be able to analyze
these images quickly and efficiently to zero in
on patients with chronic thromboembolic
pulmonary hypertension (CTEPH).

22. The benefits of CTEPH Pattern Recognition
Artificial Intelligence Software are vast:
The AI helps radiologists
analyze diagnostic images
faster and identify patients
with CTEPH earlier, more
efficiently, and more reliably,
therefore enabling earlier
use of therapies
Faster and earlier diagnosis
of CTEPH patients means
more of them beat the
condition
The AI helps physicians
treating CTEPH in the
intricate diagnostic decision-
making process of chronic
thromboembolic pulmonary
hypertension.
The software will ultimately
help increase awareness of
this rare chronic condition

23. Novartis uses AI to get insights from
clinical trial data
• The scientists at the Novartis Institute of Biomedical
Research (NIBR) are using AI technology to gather,
analyze, and gain insights from clinical trial data from an
array of internal sources.
• Novartis is to keep track of trial enrolment, as well as a
predict associated costs and quality assurance. The
results have been quite surprising, with the Institute
reporting a 10-15 percent decrease in the patient
enrolment times, especially during early-stage clinical
trials.

24. Scientists at Novartis are
leveraging deep learning to
mimic how our eyes and brains
process photographic
information.
The computer “neural network”
predicted almost 100% the
results for cells treated with
100 mysterious compounds,
even at the various level of
dosage.

25. Boehringer + Bactevo AI Partnership to improve
the quality & speed of drug discovery
Totally Integrated
Medicines Engine
platform (TIME) – to boost
the efficiency, speed, and
quality of drug discovery
from small molecule lead
compounds.
It essentially brings
together the powerful
drug research experience
at Boehringer and state
of the art TIME drug
discovery platform to
discover new medicines
for ALS, Parkinson’s
disease and Alzheimer’s
disease.

26. Verge Genomics uses AI to speed
up drug discovery during
preclinical trials
• Verge Genomics brings together
breakthroughs and innovations in
genomics, machine learning, and
neuroscience to deliver a new approach
to discovering new drugs and therapies
for brain disorders.
• If Verge’s machine learning-driven
approach works as intended, it will
reduce drug development process for
discovering several different life-saving
therapies for brain diseases like ALS,
Alzheimer’s disease, Autism, and
Parkinson’s disease, just to mention a
few.

27. Nuritas + BASF AI Partnership to
develop novel peptides from natural
food
BASF will use Nuritas AI and DNA analysis capabilities
to predict, analyze, and validate peptides from natural
sources. The main goal of BASF is to discover and
deliver to the market peptide-based therapies that’ll
help treat conditions like diabetes.

28. Drug-Adherence & Dosage
Abbvie partnered with New York-based AiCure
 to enhance drug trial vigilance and improve drug
adherence.
 In this collaboration, Abbvie used facial and image
recognition algorithm of AiCure mobile SaaS platform to
monitor adherence. To be more specific, the patients take
a video of themselves swallowing a pill using their
smartphones, and the AI-powered platform confirms that
indeed the correct person swallowed the right pill. And
the results were amazing, improving adherence by up to
90%.
 https://youtu.be/xBpvK_VxiXM

29. Bayer Collaborates with
Genpact to use AI to Improve
Pharmacovigilance
• Genpact’s AI solution has been used severally in
clinical trials to change the dosage given to
specific patients to optimize the results. Bayer
takes advantage of Genpact’s
Pharmacovigilance Artificial Intelligence (PVAI)
to not only monitor drug adherence but also
detect potential side effects much earlier.

30. Using AI To Make Sense of Clinical Data & to
Produce Better Analytics
When it comes to clinical trial matching, many companies are
working with IBM Watson to make sense of better data. These
companies include Highlands Oncology Group, Mayo Clinic,
Perficient Partners, Medtronic, Illumina, Pfizer, Merck & Co., and
Bristol-Myers Squibb, just to name a few.
Apple’s Researchkit makes it easy for people to enrol in clinical
trials and studies without having to go through physical enrolment.
It’s a clinical research ecosystem designed around its two flagship
products, the iPhone and the Apple Watch. Duke University, for
instance, uses patient data collected by these Apple devices and AI-
driven facial recognition algorithm to identify children with autism.

31. + OWKIN
Speed up drug discovery,
development, and trials
+ FLATIRON Accelerate cancer research
and improve patient care
+ SYAPSE
ROCHE
AI-powered healthcare
software and precision
medicine
+ GNS HEALTHCARE Big data analytics company

32. NOVARTI
S
Massachusetts
Institute of
Technology
IBM Watson
Quantumblack Intel

34. Challenges to
Adoption of AI in
Pharma

35.  The unfamiliarity of the technology
 Lack of proper IT infrastructure
 Much of the data is in a free text
format
AI is already redefining biotech and pharma. And
ten years from now, Pharma will simply look at
artificial intelligence as a basic, everyday,
technology. The only question is how long will
pharma executive wait till they jump on the
wagon and leverage AI to improve their
operational efficiency, outcomes and profits.

36. “Machine learning is
pointing us to new
therapeutic
possibilities with
unprecedented
efficiency … And it
has an unparalleled
ability to teach us
about how our drugs
are working,”
Head of Informatics
for Chemical Biology
and Therapeutics at
NIBR, Jeremy Jenkins.