Leveraging Meta Data Management: Powering Cognitive Automation in Clinical Trial Processes

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Abstract

Today, articial intelligence (AI) is transforming

every area of business, including life sciences,

particularly in the clinical trials space. Numerous

companies are already well on their way to using

cognitive automation for clinical trial set up, fraud

detection, pharmacovigilance, recommendation

engines, crop classication, and so on. Systems

such as natural language processing (NLP),

machine learning (ML), and neural networks are

becoming commercially viable solutions for

deciphering clinical content and simplifying

reporting for supporting business decisions and

ensuring compliance.

As a welcome consequence to all the clinical

standard initiatives which were mainly targeting

standards governance, this paper highlights how

these initiatives in combination with AI can

provide a real shot at optimizing clinical

development processes. We explore how

metadata management can play a foundational

role in standardizing governance, while being a

key enabler for cognitive process automation.

The Evolving Compliance Landscape

The pharmaceutical business is a highly process-driven space,

governed by stringent local and international regulations

pertaining to drug discovery, development, and testing.

Companies therefore are seeking ways to increase trial

efciency and improve outcomes while reducing overall cycle

time. However, the diverse nature of trials across therapeutic

areas is a primary obstacle towards optimizing the company-

wide process.

By early last year, there were over 200,000 registered clinical 1

research studies. Depending on the therapy area and the way

the organization is structured, there are many variations in

terms of governance frameworks and protocols followed even

within an organization. Without a standard technology-driven

process for setting up clinical trials, these studies will run the

risk of costly delays or fail to comply with regulatory guidelines

for submissions.

Over the last decade or so, the industry has been launching

various standardizations such as the Clinical Data Interchange

Standards Consortium (CDISC), and simplication such as the

Transcelerate initiatives. These are aimed towards harmonizing

and standardizing electronic data submissions.

Drive for Cognitive Process Automation

We have seen companies taking steps towards applying AI

based solution across the entire scope of clinical activities. 2GlaxoSmithKline, for example, uses AI for data management -

mapping raw clinical information per study data tabulation

(STDM) standards. The company met initial success with 50-

60% accuracy and has been steadily improving.

3 Proprietary technologies such as those from Innoplexus

condenses critical life sciences information to create a

repository of business use cases, which make information more

actionable for the end-user.

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The fundamental components of such cognitive systems –

natural language processing (NLP), machine learning (ML), and

neural networks – are becoming increasingly accessible to drug

manufacturers. These can be used to analyse clinical data for

not only supporting business decisions but also ensuring

compliance. For example, building protocol components can be

simplied by implementing NLP. In turn, this will subsequently

help create other deliverables such as electronic case report le

(eCRF) or analysis plan while ML creates the metadata used for

generating submission packages.

Certain factors, however, need to be considered for effectively

implementing cognitive automation in these areas (Figure 1).

These include:

n Diverse standards across clinical lifecycle and

therapeutic areas

During the course of a clinical trial, multiple study artefacts

covered by the electronic common technical document (eCTD)

guidelines such as study protocol, submission dataset, and

clinical study reports (CSRs) need to be drafted and prepared

for submission to ethics committees and review boards. Each

submission deliverable follows different standards – from

simple templates for protocols to highly structured SDTMs or

analysis data model (ADaM) for ling ndings reports. These

can also be further categorized by different attributes of the

study. From the study sponsors perspective, it is difcult to

have a thorough understanding of each standard before

Figure 1: Scope for automation across clinical trial lifecycle

Activity

Sta

ndard

sD

elivera

ble

sD

ow

n s

tream

Impact

Study Design Study Setup Study Execution

Protocol Design CRF Design SAP / DPS SDTM/ADaM/TFLAnalysis/Report

Generation

PRM, CPT CDASH, ODMNo ExistingStandard SDTM, ADaM eCTD

CRF formsAnalysis/Shells

SDTM MetadataADaM

MetadataTFL Setup

Submission Package

Draft ProtocolDatabase buildmetadata form

to upload in eDC Draft SAP/DPS

SubmissionDatasets, Reports

Setup

Submission Package(Datasets, Reports

CSR)

concluding which of the standards is applicable for the study.

In case changes made by regulators to the existing guidelines

are overlooked and not incorporated into studies wherever

applicable, it can lead to massive rework and delay trial report

submission.

n Lineage and traceability

Disjointed reporting standards across multiple sources and the

lack of traceability or lineage for the end to end process also

stand in the way of creating a seamless process ow.

Standards for protocol are driven by the protocol

representation model (PRM) within which data collected for a

study is classied as electronic data capture (eDC) committed

by third party vendors. While eDC is governed by Clinical Data

Acquisition Standards Harmonization (CDASH) guidelines or by

internal standards, non-eDC data is tracked and maintained in

line with data transfer agreements between sponsor and third

party data vendors. Since these standards have to be stored

discretely and are not linked together, it becomes difcult to

synchronise them in case studies are amended at a later date.

Unless standard or guideline updates are synchronized with

downstream processes, they are at a risk of failing to comply

with established good practices (GxP).

Clinical Development Lifecycle 4.0

If we are to build a clinical development lifecycle that is highly

automated and is capable of rapidly adapting to regulatory and

internal standards, we will need to follow a phased approach

that comprises:

1. Standards governance through a metadata repository

AI in clinical trials can be fully utilized if it is built using the

metadata repository. For example, data from completed trials,

supervised learning techniques can be used to train an AI

system for generating mapping specication from eDC to

SDTM. Similarly, for creating protocols, NLP could be leveraged

to convert unstructured data into structured, searchable data –

improving overall quality.

n The rst step towards automation would be to ensure

standards compliance by semantically comparing source

metadata with enterprise standards – both of which are

being available in the metadata repository. This repository

will have library for each deliverable type, including CRF,

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SDTM, ADaM, among others. This will also provide the basis

for understanding the impact that a change in one

deliverable will have on another.

n Standards would also need to be maintained across different

levels – spanning multiple therapeutic areas and lifecycle

stages per the Clinical Data Interchange Standards

Consortium (CDISC).

2. Cognitive process automation

Once the metadata registry is up and running, AI can be

leveraged not just for preparing deliverables, but also ensuring

that they are kept up to date. Such activities can include:

n Study setup – AI can use historic data and well dened

ontologies to create complete or partial deliverables such as

protocol sets from a topic library or electronic case report

forms (eCRFs) based on protocols.

n Study conduct and closure – As the study data starts

owing in near real-time, this enormous volume of

information needs to cleansed and transformed for

downstream analysis. AI-based unsupervised learning

methods that have been trained using semantic

representations of data along with corresponding contextual

information can be used to create a library of rules that can

be applied to incoming data. Such rules can be used for

governing evaluating and scrubbing data and can create

downstream artefacts like a mapping document to link

eCRFs with SDTMs.

n Study analysis and submission – This is a particularly

complex stage of a study and typically involves extensive

statistical analysis and medical regulatory content creation.

AI-based supervised learning methods can assist human

agents in this regard by running quality analysis on

narratives. This can help trial operators identify and acquire

missing information or even run analysis on mock shell table

listing gures (TLF) per enterprise standards for generating

and submitting reports to regulatory authorities – improving

turnaround time as well as quality.

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The Road Ahead

We believe that the key to improving and optimizing clinical

development process is to create a centralized AI-based data

management platform that feeds off an enterprise-wide

metadata management repository. The efciency of such a

solution can be further amplied by implementing AI to

optimize process interdependencies and externally dependent

risks like clinical trial recruitment.

References1. ClinicalTrials.gov, Trials, Charts, and Maps, accessed April 27, 2018,

https://clinicaltrials.gov/ct2/resources/trends#RegisteredStudiesOverTime

2. Forbes, Biting The Data Management Bullet At GlaxoSmithKline, January 8, 2018,

accessed April 27, 2018,

https://www.forbes.com/sites/tomdavenport/2018/01/08/biting-the-data-

management-bullet-at-glaxosmithkline/2/#3a6ccd0f7122

3. YourStory, How Innoplexus uses smart and on-edge technology to help its pharma

clients nd relevant answers hidden in unstructured data, April 10, 2018, accessed

April 30, 2018, https://yourstory.com/2018/04/innoplexus-uses-smart-edge-

technology/

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About The Authors

Devraj Goulikar, Life Sciences

Platform Solutions Lead, TCS

Devraj is the Life Sciences

Platform Solutions Lead at Tata

Consultancy Services. He is

responsible for conceptualizing

and delivering platform-based

solutions for the Life Sciences

unit. He has been with TCS for

over 22 years and holds a

master’s degree in technology

from IIT Bombay.

Charusheela Thakur, Domain

Consultant, Life Sciences

Platform Solutions, TCS

Charusheela is a Domain

Consultant for the Life Sciences

Platform Solutions team and has

been with TCS for over five

years. She has been working in

the pharmaceutical industry for

16 years and specializes in areas

such as clinical trial reporting,

standardization, and analytics.

She has a master’s degree in

statistics from the University of

Mumbai.

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