Clinical trials take up the last half of the 10 – 15 year, 1.5 – 2.0 billion USD, cycle of development just for introducing a new drug within a market. 1. AI and its Evolution 2. AI in Clinical Trials To Continue Reading: https://bit.ly/2W01UDQ Contact Us: Website : https://bit.ly/33Fwsye Email us: sales.cro@pepgra.com Whatsapp: +91 9884350006

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Clinical Trial Design and Artificial Intelligence
Dr. Nancy Agens, Head,
Technical Operations, Pepgra
Sales.cro@pepgra.com
I. INTRODUCTION
Clinical trials take up the last half of the 10
– 15 year, 1.5 – 2.0 billion USD, cycle of
development just for introducing a new drug
within a market. Therefore, any clinical
trials services that end in failure tends to
translate not just in losses in terms of
investment for the clinical trial but also
takes into its ambit the costs incurred as
preclinical development costs. This renders
the loss for every failed clinical trial at 800
million to 1.5 billion USD(Harrer et al.,
2019). Keeping these factors in mind, it
would be beneficial to explore how
Artificial Intelligence or AI as it is popularly
known can be effectively utilized to re-
mould the key phases of a clinical trial
design with a view to augment the rate of
success in the trial.
Figure 1 pharma drug development cycle
SourceHarrer et al.,(2019)

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II. FACTORS CAUSING FAILURES IN
CLINICAL TRIALS AND HOW AI CAN
HELP
The main two factors that lead to
failure within clinical trials services pertains
to the selection of patient cohorts and the
mechanisms used for recruiting cohorts. The
said two factors are not enough to bring in
the most appropriate patients to the trial in a
timely manner, along with the absence of a
technical infrastructure to match the
intricacies of executing a clinical trial(Fogel,
2018). This is particularly true in phases that
come later particularly, where there is a lack
of an effective and reliable protocol for
adherence, clinical endpoint detection
systems and patient monitoring, In such
scenarios, AI can prove to be extremely
beneficial in circumventing such shortfalls
of the present clinical trial design.
Specifically, deep learning and machine
learning would be in a position to
automatically seek out meaningful patterns
from huge datasets such as speech, text or
images. Natural language processing (NLP)
as well as Human Machine Interfaces
(HMIs) enables a seamless transfer of
information amongst humans and
computers(Nadkarni et al., 2011).
III. AI AND ITS EVOLUTION
AI in the domain of medicine has a
history which goes back to the early 1970s
when proficient systems such as MYCIN
had been initially introduced for providing
support to diagnostic decisions(Shortliffe,
1984). Nonetheless, early systems of
medical AI depended largely on experts
from the medical domain for training
computers by encrypting clinical knowledge
as logic based rules for particular situations
or scenarios. Systems of such kind were
wrought with the limitation wherein it was
intensive to labour and required a lot of time
for the construction process. Also, once it
was developed there were also considerable
challenges with regards to any future
updates as such systems were largely
rigid(McCauley & Ala, 1992). Highly
advanced systems of machine learning
which had the ability to train itself to imbibe
such rules by recognizing and weighing in
pertinent aspects from data like medical
images, unstructured texts and electronic
health records made an entrance only in the
90s and 2000s. However, the medical
domain was slow in terms of adapting such
systems mainly owing to the absence of data
that was extensively available. This was
further aggravated by the fact that such early
techniques warranted intense feature
engineering efforts that involved severe
commitments from experts within the
medical domain(Niu et al., 2011). Over the
years, the situation improved tremendously
especially in recent times, mainly due to two
key factors. First and foremost, the domain
of AI as such experienced transformational
developments, especially in deep learning
and machine learning techniques which
were facilitated through hardware
enhancements and extensive datasets for
training(Wang et al., 2019). Secondly, data
in the medical domain became largely
available and easily accessible in digital
formats. Credit goes to innovative
developments in technology and also to
initiatives on public policy such as the
Electronic Records Meaningful Use
Programs within the United States of
America.
IV. AI IN CLINICAL TRIALS
The figure above envisions the key
techniques through which AI can be
introduced within the tenets of clinical
design. There are three key themes within
the design which comprises of; cohort
composition, patient recruitment and patient

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monitoring. These are founded on the
features of the patient in terms of eligibility,
suitability, empowerment, enrollment and
motivation. This is also inclusive of aspects
pertaining to trial such as; adherence
control, endpoint detection and retention of
patients.
Figure 2 AI for clinical trial design
SourceHarrer et al.,(2019)
An array of methodologies for design has
been utilized to execute functionalities of the
target. Functionalities such as these are
facilitated through distinct combinations
from the three key AI technologies – deep /
machine learning, human machine interfaces
and reasoning wherein every single
technology evaluates a particular set of
patient and functionality specific sources of
data. The pertinent enhancement which is
introduced through such type of an
execution on the outcome of the study is
signified by the length of the horizontal lines
within the color bar code beneath the key
outcome features. Every study design
application that is based on AI directly
hinges on the quantum and quality of data it

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can tap into, and thus is confronted with the
similar basic challenges.
REFERENCES
[1] Fogel, D.B. (2018). Factors associated with clinical
trials that fail and opportunities for improving the
likelihood of success: A review. Contemporary
Clinical Trials Communications. [Online]. 11. pp.
156–164. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S245186
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[2] Harrer, S., Shah, P., Antony, B. & Hu, J. (2019).
Artificial Intelligence for Clinical Trial Design.
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