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

Transcription

1 Leveragig Meta Data Maagemet: Powerig Cogitive Automatio i Cliical Trial Processes Abstract Today, arti cial itelligece (AI) is trasformig every area of busiess, icludig life scieces, particularly i the cliical trials space. Numerous compaies are already well o their way to usig cogitive automatio for cliical trial set up, fraud detectio, pharmacovigilace, recommedatio egies, crop classi catio, ad so o. Systems such as atural laguage processig (NLP), machie learig (ML), ad eural etworks are becomig commercially viable solutios for decipherig cliical cotet ad simplifyig reportig for supportig busiess decisios ad esurig compliace. As a welcome cosequece to all the cliical stadard iitiatives which were maily targetig stadards goverace, this paper highlights how these iitiatives i combiatio with AI ca provide a real shot at optimizig cliical developmet processes. We explore how metadata maagemet ca play a foudatioal role i stadardizig goverace, while beig a key eabler for cogitive process automatio.

2 The Evolvig Compliace Ladscape The pharmaceutical busiess is a highly process-drive space, govered by striget local ad iteratioal regulatios pertaiig to drug discovery, developmet, ad testig. Compaies therefore are seekig ways to icrease trial ef ciecy ad improve outcomes while reducig overall cycle time. However, the diverse ature of trials across therapeutic areas is a primary obstacle towards optimizig the compaywide process. By early last year, there were over 200,000 registered cliical 1 research studies. Depedig o the therapy area ad the way the orgaizatio is structured, there are may variatios i terms of goverace frameworks ad protocols followed eve withi a orgaizatio. Without a stadard techology-drive process for settig up cliical trials, these studies will ru the risk of costly delays or fail to comply with regulatory guidelies for submissios. Over the last decade or so, the idustry has bee lauchig various stadardizatios such as the Cliical Data Iterchage Stadards Cosortium (CDISC), ad simpli catio such as the Trascelerate iitiatives. These are aimed towards harmoizig ad stadardizig electroic data submissios. Drive for Cogitive Process Automatio We have see compaies takig steps towards applyig AI based solutio across the etire scope of cliical activities. GlaxoSmithKlie, for example, uses AI for data maagemet2 mappig raw cliical iformatio per study data tabulatio (STDM) stadards. The compay met iitial success with 5060% accuracy ad has bee steadily improvig. 3 Proprietary techologies such as those from Ioplexus codeses critical life scieces iformatio to create a repository of busiess use cases, which make iformatio more actioable for the ed-user.

3 The fudametal compoets of such cogitive systems atural laguage processig (NLP), machie learig (ML), ad eural etworks are becomig icreasigly accessible to drug maufacturers. These ca be used to aalyse cliical data for ot oly supportig busiess decisios but also esurig compliace. For example, buildig protocol compoets ca be simpli ed by implemetig NLP. I tur, this will subsequetly help create other deliverables such as electroic case report le (ecrf) or aalysis pla while ML creates the metadata used for geeratig submissio packages. Dow stream Impact Deliverables Stadards Activity Certai factors, however, eed to be cosidered for effectively implemetig cogitive automatio i these areas (Figure 1). Study Desig Protocol Desig PRM, CPT Study Setup CRF Desig SAP / DPS CDASH, ODM No Existig Stadard Draft Protocol Database build metadata form to upload i edc Draft SAP/DPS CRF forms Aalysis/Shells SDTM Metadata ADaM Metadata TFL Setup SDTM/ADaM/TFL Study Executio Aalysis/Report Geeratio SDTM, ADaM Submissio Datasets, Reports Setup ectd Submissio Package (Datasets, Reports CSR) Submissio Package Figure 1: Scope for automatio across cliical trial lifecycle These iclude: Diverse stadards across cliical lifecycle ad therapeutic areas Durig the course of a cliical trial, multiple study artefacts covered by the electroic commo techical documet (ectd) guidelies such as study protocol, submissio dataset, ad cliical study reports (CSRs) eed to be drafted ad prepared for submissio to ethics committees ad review boards. Each submissio deliverable follows differet stadards from simple templates for protocols to highly structured SDTMs or aalysis data model (ADaM) for lig digs reports. These ca also be further categorized by differet attributes of the study. From the study sposors perspective, it is dif cult to have a thorough uderstadig of each stadard before

4 cocludig which of the stadards is applicable for the study. I case chages made by regulators to the existig guidelies are overlooked ad ot icorporated ito studies wherever applicable, it ca lead to massive rework ad delay trial report submissio. Lieage ad traceability Disjoited reportig stadards across multiple sources ad the lack of traceability or lieage for the ed to ed process also stad i the way of creatig a seamless process ow. Stadards for protocol are drive by the protocol represetatio model (PRM) withi which data collected for a study is classi ed as electroic data capture (edc) committed by third party vedors. While edc is govered by Cliical Data Acquisitio Stadards Harmoizatio (CDASH) guidelies or by iteral stadards, o-edc data is tracked ad maitaied i lie with data trasfer agreemets betwee sposor ad third party data vedors. Sice these stadards have to be stored discretely ad are ot liked together, it becomes dif cult to sychroise them i case studies are ameded at a later date. Uless stadard or guidelie updates are sychroized with dowstream processes, they are at a risk of failig to comply with established good practices (GxP). Cliical Developmet Lifecycle 4.0 If we are to build a cliical developmet lifecycle that is highly automated ad is capable of rapidly adaptig to regulatory ad iteral stadards, we will eed to follow a phased approach that comprises: 1. Stadards goverace through a metadata repository AI i cliical trials ca be fully utilized if it is built usig the metadata repository. For example, data from completed trials, supervised learig techiques ca be used to trai a AI system for geeratig mappig speci catio from edc to SDTM. Similarly, for creatig protocols, NLP could be leveraged to covert ustructured data ito structured, searchable data improvig overall quality. The rst step towards automatio would be to esure stadards compliace by sematically comparig source metadata with eterprise stadards both of which are beig available i the metadata repository. This repository will have library for each deliverable type, icludig CRF,

5 SDTM, ADaM, amog others. This will also provide the basis for uderstadig the impact that a chage i oe deliverable will have o aother. Stadards would also eed to be maitaied across differet levels spaig multiple therapeutic areas ad lifecycle stages per the Cliical Data Iterchage Stadards Cosortium (CDISC). 2. Cogitive process automatio Oce the metadata registry is up ad ruig, AI ca be leveraged ot just for preparig deliverables, but also esurig that they are kept up to date. Such activities ca iclude: Study setup AI ca use historic data ad well de ed otologies to create complete or partial deliverables such as protocol sets from a topic library or electroic case report forms (ecrfs) based o protocols. Study coduct ad closure As the study data starts owig i ear real-time, this eormous volume of iformatio eeds to cleased ad trasformed for dowstream aalysis. AI-based usupervised learig methods that have bee traied usig sematic represetatios of data alog with correspodig cotextual iformatio ca be used to create a library of rules that ca be applied to icomig data. Such rules ca be used for goverig evaluatig ad scrubbig data ad ca create dowstream artefacts like a mappig documet to lik ecrfs with SDTMs. Study aalysis ad submissio This is a particularly complex stage of a study ad typically ivolves extesive statistical aalysis ad medical regulatory cotet creatio. AI-based supervised learig methods ca assist huma agets i this regard by ruig quality aalysis o arratives. This ca help trial operators idetify ad acquire missig iformatio or eve ru aalysis o mock shell table listig gures (TLF) per eterprise stadards for geeratig ad submittig reports to regulatory authorities improvig turaroud time as well as quality.

6 The Road Ahead We believe that the key to improvig ad optimizig cliical developmet process is to create a cetralized AI-based data maagemet platform that feeds off a eterprise-wide metadata maagemet repository. The ef ciecy of such a solutio ca be further ampli ed by implemetig AI to optimize process iterdepedecies ad exterally depedet risks like cliical trial recruitmet. Refereces 1. CliicalTrials.gov, Trials, Charts, ad Maps, accessed April 27, 2018, 2. Forbes, Bitig The Data Maagemet Bullet At GlaxoSmithKlie, Jauary 8, 2018, accessed April 27, 2018, 3. YourStory, How Ioplexus uses smart ad o-edge techology to help its pharma cliets d relevat aswers hidde i ustructured data, April 10, 2018, accessed April 30, 2018,

7 About The Authors Devraj Goulikar, Life Scieces Platform Solutios Lead, TCS Devraj is the Life Scieces Platform Solutios Lead at Tata Cosultacy Services. He is resposible for coceptualizig ad deliverig platform-based solutios for the Life Scieces uit. He has bee with TCS for over 22 years ad holds a master s degree i techology from IIT Bombay. Charusheela Thakur, Domai Cosultat, Life Scieces Platform Solutios, TCS Charusheela is a Domai Cosultat for the Life Scieces Platform Solutios team ad has bee with TCS for over five years. She has bee workig i the pharmaceutical idustry for 16 years ad specializes i areas such as cliical trial reportig, stadardizatio, ad aalytics. She has a master s degree i statistics from the Uiversity of Mumbai. Cotact Visit the Life Scieces & Healthcare page o lshcip.pmo@tcs.com Subscribe to TCS White Papers TCS.com RSS: Feedburer: Tata Cosultacy Services is a IT services, cosultig ad busiess solutios orgaizatio that delivers real results to global busiess, esurig a level of certaity o other firm ca match. TCS offers a cosultig-led, itegrated portfolio of IT ad IT-eabled, ifrastructure, egieerig ad assurace services. This is delivered through its uique Global Network Delivery ModelTM, recogized as the bechmark of excellece i software developmet. A part of the Tata Group, Idia s largest idustrial coglomerate, TCS has a global footprit ad is listed o the Natioal Stock Exchage ad Bombay Stock Exchage i Idia. For more iformatio, visit us at All cotet / iformatio preset here is the exclusive property of Tata Cosultacy Services Limited (TCS). The cotet / iformatio cotaied here is correct at the time of publishig. No material from here may be copied, modified, reproduced, republished, uploaded, trasmitted, posted or distributed i ay form without prior writte permissio from TCS. Uauthorized use of the cotet / iformatio appearig here may violate copyright, trademark ad other applicable laws, ad could result i crimial or civil pealties. Copyright 2018 Tata Cosultacy Services Limited TCS Desig Services I M I 07 I 18 About Tata Cosultacy Services Ltd (TCS)