|3rd December 2020|
|Understanding Data Democratization in Clinical Research Landscape - Advantages, Challenges & Way Forward.
Session Chair: Debu Baruah, Manager, Software Development, IQVIA
|Data democratization could be the next-generation solution for healthcare industry to step-up and be an active partner with patients by making data accessible to relevant stakeholders, inclusive of non-technical user of information systems. Current landscape of clinical research demands patients to be evermore empowered and equipped with data, technology, and expertise to take charge and manage their own health care. Data democratization also opens opportunities to access real-world patient experiences and to feed them back into clinical trial systems. Recent global health crisis has challenged the “well-oiled” traditional systems and the rendered volatility is pushing the clinical research professionals across globe to reinvent how they manage, distribute, and interpret data while safely freeing information from the silos into a borderless ecosystem hoping to make faster and flowless healthcare decisions. Cutting-edge technologies are breaking down silos and incentivizing transparent data-sharing practices. However, there are cultural, ethical, technological, and data privacy legislation related challenges on which the clinical research industry needs to work together. Space of data democratization needs strong leadership and pre-competitive collaboration of AROs, CROs, patients, sponsors, and other allied stakeholders to understand how it may revolutionize and reshape the conventional ways of managing clinical research data. Lack of common and shared best practices on how stakeholders should collaborate to consume, analyze, and interpret the democratized pool of data is posing a formidable challenge as well as an opportunity to explore pioneering solutions. This session will provide a common platform for different stakeholders of this industry to kick start collaboration and pave a path towards sustainable data democratization solutions|
|4th December 2020|
|Leap from traditional Data Management to Data Science: Understanding guiding principles related to Data Science. Tips and Tricks to implement concepts such as Metadata consolidation (PRM to eCTD), Visualization considering ICH E6 R2 framework a practical insight
Session Chair: Raghavendra Kalmadi - IQVIA, Tushar Sakpal -TCS,M Srinivasan-Cognizant
|Clinical landscape has a varied spectrum of stakeholders, standards, domains (CRAs, Data Managers, Data Reviewers, PVs etc.) who have diversified interests on outcome of Clinical trial and its data. Protocol being basis for conduct of clinical trial. This forum will address need of extracting elements of protocol information to be consumed as standards for downstream applications as well as act as precursor for activities down the line. This will not only have cascading effect on activities such as Common protocol template to auto eCRF creation to SDTM, ADAM & TLF creation using the metadata structure. Apart from documenting and creating flawless framework for data traceability and audit trail it will also define how data science methodologies can facilitate for this automation. A reference to Transcelerate Digital Data Flow guidance document will be made to articulate the concepts. Further taking CDISC standards and also technological integrations which would help EMR/EHR integration and also effectively use controlled terminologies and coding dictionaries for uniformity and consistency. Data its representation as well as preempting risk through various data analytics and visualization assist decision making as well as taking calibrated decision in enhancing safety of patients.|
|Session 1: Track 1|
|Metrics / KPIs used in CDM to Track & Monitor Progress of Study / Program
Session Chair: Susan Korah- AVP and Delivery Head, Life Sciences,TCS & Nagendra Kumar -Director, DM, ICON PLC
|“Technology has enabled us to track and measure every aspect of operations. Metrics and KPI’s plays vital role for success of any business. Keeping this in mind about current scenario of virtual working, metrics and KPI’s are even more crucial to understand Performance / Trends / Improvement areas helping to drive efficiency in Operations.
It’s of paramount importance to understand and differentiate KPI’s vs Metrics. This session not only would help in understanding the latest advancement in tracking and monitoring the metrics along but will also elude you with effective decision making based on the metrics”
|Session 1 : Track 2|
|Evolution of Monitoring Approach (2.0):
Session Chair: Abby Abraham-Global Head of Data Science, George Clinical
|Manual and laborious traditional monitoring approaches have evolved over recent years. ICH-E6(R2) amendment and new regulatory expectations are gradually pushing organizations to embrace risk-based quality management (RBQM) that leverages data-driven monitoring approaches. COVID-19 pandemic has driven the industry to increase the adoption of novel systems to transform paper-based practices such as informed consent, Trial File, patient-reported outcomes into digital sources that expand the continuum of digital monitoring. Innovations in wearables, mobile diagnostics, and home healthcare interventions are propelling patient centricity and decentralized clinical trials. Amidst these changing paradigms and tectonic shifts in data computation using AI & ML, significant changes to clinical trial monitoring are warranted to align with these positive disruptions in the industry. This session offers to share insights and experiences into the evolution of monitoring from traditional to data science enabled monitoring and share perspectives on the path towards more de-centralized clinical monitoring that is reliable and become the new normal.|
|Session 1 - Track 3|
|SDTM & CDASH- Do we need both data standards?
Session Chair: Shrishaila Patil-Vice President, Navitas life sciences
|Some think CDISC's CDASH data capture standard is unnecessary as it is very similar to SDTM, and few differences create confusion and extra work.
CDASH is very similar to SDTM, but they solve different problems. Used together they positively impact Data Capture, Quality, Usability, repurposing and traceability.
• CDASH is optimized for data capture, investigator site activities & Data Quality.
• SDTM is optimized for tabulation, analysis dataset creation & data submission.
CDASH establishes a standard way to collect data consistently across studies and sponsors so that data collection formats and structures provide clear traceability of submission data into the Study Data Tabulation Model (SDTM), delivering more transparency to regulators and others who conduct data review.
SDTM provides a standard for organizing and formatting data to streamline processes in collection, management, analysis and reporting. Implementing SDTM supports data aggregation and warehousing; fosters mining and reuse; facilitates sharing; helps perform due diligence and other important data review activities; and improves the regulatory review and approval process.
In this session, we will explore differences between CDASH and SDTM and why both standards are critical?
|Session 2: Track 1|
|Technology & Big Data Impacting Clinical Development during New normal
Session Chair: Raghuram Thata (IQVIA)
|Over years, clinical trials industry has been discussing and evaluating technology driven landscape that could be exploited. However adoption has been slow due to fail-safe approaches which at times are translated as deacceleration of tech-adoption. It took a pandemic like COVID-19 for us to realize and has forced us to change to adopt to new technologies as we entered into this new normal where speed of adoption became a compulsion while keeping patient safety at the center.
In this panel discussion, emphasis will be on a solution-based discussion on the available massive amount of data generated from disparate means and how tech enablers have helped telemedicine, remote patient monitoring, decentralized trials, virtual trials, EHRs, and others during this unprecedented time. The use of predictive analytics and sensors gaining popularity, adding up humongous amount of data in the process. Pharma companies have been working persistently to implement the most ideal patient-centric framework during this time. Challenges with site visits and steps in removing logistic barriers and patient engagement were key to keep up with the pace of the trials. The successful were the ones who looked up to innovative technological adoption, like for e.g. using sensors to collect data during virtual visits and analyzing the results real-time. Another aspect is on data visualization and analytics. Data collected through different data sources coupled with visualization techniques have helped the companies take informed decisions during this pandemic. One example is how few companies used publicly available COVID data plus study data to determine which sites were the most impacted, showing trends of recovery, and estimating when they can reopen again.
Industry needs solutions to make use of the data and also must look at the other side of the coin – which advocates on need for a robust mechanism to understand if technology and digitalization is adding any burden on patients or is it really bringing them closer to the industry to sustain during this challenging phase of new normal!
|Session 2: Track 2|
|Embracing Risk-Based Monitoring during New Normal
Session Chair: Bhavesh Acharya-Chief Operating Officer,Cytespace Research Private Limited
|Risk-based monitoring (RBM) is now considered as a new normal that only reduces monitoring efforts and related cost but also provides further opportunity for data management and clinical operations team to collaborate towards common goal of protecting subject's safety and producing good data quality.
In this session we will look at a) transition from Pre-COVID world to Post-COVID in terms of RBM implementation and change in risks that are to be considered before it's implementation b) how newer way of site functioning and patient visiting sites have become more important than before to assess related risks before RBM Implementation c) How technologies have evolved to take RBM as new normal than being a proactive approach to convey early signals / trends to clinical and to have informed / targeted monitoring and d) how regulatory agencies continue to play pivotal role by getting their feedback on technology to ensure Audit Trail Review (ATR) is ascertain
|Session 2: Track 3|
|“Humans” + “Machines” = Intelligence with Speed: DM Case Studies "
Session Chair: Suresh Sharma- General Manager & Domain Delivery Head, Life Sciences, TCS Limited & Jeba kumar -Statistical Programming & Analysis Programming Standards Lead, Pfizer
|Fact is that shifting of intelligent work between humans and machines will happen. However this will happen with an acute focus on relationship between them, how well these two will collaborate, and how the current workforce along with regulatory framework and the clinical trial business itself will adapt to automation like AI, ML & RPA. Over the years AI has grown into different forms (machine learning, artificial intelligence, data science, deep learning) and adopted in various industries especially in conducting clinical trials. However clinical studies are getting more complex in terms of study designs or remote monitoring technology, different methodology used for data collection. It is pivotal that data generated is used in decision making on the entire life cycle during the course of trial. Powerful ML technologies have the potential to monitor this data as it is generated; identifying issues and inconsistencies as trials are ongoing. In the last few years we have seen many adoption of these technologies to enable efficient data collection and data cleaning, in this session; you will get to know more about the evolution of machines and how human in the loop solutions that enable to increase the machines efficiency and also optimize human efforts.|
|5th December 2020|
|Understanding Analysis of RWD
Session Chair: Roopa Basrur- Senior Director, Data Management (GDO), Parexel International
|An all-student panel will explore the world of Real World Data (RWD) and evidence analysis, from an academic perspective. The students will have a conversation on what RWD is, whether current curriculums in Indian academia cover RWD, the limitations and potential solutions in RWD analysis, and ponder innovations and use cases such as synthetic control arms, etc. The main objective is to provide the audience with insights into what our students (who are the future) current thinking is.|
|Session 3: Track 1|
|Balance in Technology vs Human, Are we there? – Data Science
Session Chair: Tanusha Dutta-Director, Data Management, Covance & Priyadarshini Lobow-Director – Data & Analytics, Eli Lilly
|In data management today, we have a conundrum of novel sources, challenging formats and streaming of data such as sensor data. While data science as a technological discipline is best suited to provide insights from all types of data, ability to interpret and implement these insights requires substantiative expertise. The substantiative expertise refers to a combination of domain knowledge and inherent human ability to apply insights in context of domain. Hence we need a balance of technological prowess and human expertise to effectively leverage the power of data science. Are we there yet? Data science is a relatively young discipline and when it comes to its application in data management, its at best at its infancy stage. Automation and innovation efforts that we are seeing today in data management space is still confined to automating manual efforts through technologies such as RPA. We have a long way to go before we can reach perfect balance of technology and human expertise. This discussion attempts to assess position of data science as a technology in data management landscape , ascertain if we are there in terms of perfect balance , and if we are not there yet, explore ways to strike that perfect balance between technology and human expertise and unravel possibilities.|
|Session 3: Track 2|
|eSource- Current State vs Future State.
Session Chair: Arshad Mohammad-Global Head-CDM Functional Service Provider (FSP), Covance & Sujit Nair-Director Operations, Cenduit
|There has been large scale adoption of digital technologies within main stream clinical trial execution & analysis. The source data that was historically largely paper based, is also moving towards more electronic sources with higher adoption of technology in healthcare delivery set ups - EMR, Digital Imaging, Electronic Lab Systems, ePRO, and wearables among others. Adoption of eSource is helping improve both data flow and quality from source to data capture systems. The session will be focused on major steps and progress made within eSource, non-CRF eSource data, Digitization of primary healthcare, Challenges in adoption, Benefits of using eSource and a future view of eSource.|
|Session 3: Track 3|
|RWE/RWD Is It Beginning To Pay Off
Session Chair: Madhur Garg, Director, Real World Evidence and Market Access, Covance
|RWE/RWD are trending themes in terms of research focus in clinical development globally.
It is safe to say real world studies have received a lot of attention lately and “RWE” & “RWD” have become ‘trending terms”. It’s time to separate hype from reality & signal from noise. We will go back to basics and focus on research methodologies and curated case studies/ use cases. Both retrospective and prospective study designs are important and relevant in the context of RWE & RWD.
Evolution of RWE & RWD will be driven by the utility derived by various stakeholders in generating and using RWE. These are exciting time with rapid strides in the area of RWE/ RWD globally, but how are things shaping-up in the local Indian context. There are many challenges associated with generating and using RWE, what are those challenges and how can we overcome them. We will shed light on these and many other aspects of RWE & RWD.
|Session 4: Track 1|
|Complex endpoints for Decentralized Trials
Session Chair: Mahesh Iyer-Head - Stats and Programming group, Paraxel
|Over the past few years, patient centricity has been at the core of most Pharma companies’ mission statements. And this year, with COVD-19, this has become even more of a pressing need. One of the commonly accepted ways of making the lives of patients who participate in clinical trials easier is to take the trials closer to them, rather than need them to visit the faraway sites. There is enough data to show that this would help making clinical trials much more easier to recruit, and possibly much more compliant. However, this also throws up many logistical difficulties. What would it mean to have as many clinical trials as patients? What would it mean from a clinical trial supplies perspective? What would it mean from a data management perspective? Would the endpoints remain the same? What about the statistical analyses? These are the types of questions that we would hope to answer in this session.|
|Session 4: Track 2|
|Are We Adequately Addressing Risks and Quality Compliance?
Session Chair: Sanjay Jankar -Glenmark
|There has been significant way the clinical trials are conducted with regulatory guidance in place on monitoring the clinical trials. However current uptake of risk-based approaches to monitoring clinical trials are still lagging as industry sponsors are challenged with working toward transparency and integrating quality into the clinical trial life cycle. In order to handle this, risk-based quality management (RBQM)- a process that focuses on quality by design throughout the clinical trial by viewing it through a life cycle lens. No longer is quality accomplished by way of checklists and box checking, but instead by integrating steps that are specific and measurable. Per ICH E8 R1- “Quality should rely on good design and its execution rather than overreliance on retrospective document checking, monitoring, auditing, and inspection.” Quality needs to be discussed openly, issues such as poor trial design, misconduct, and data collection should be deliberated to avoid future occurrences. Still there are some gaps that RBQM principles are enforced, including: risk characterization processes not applied consistently; issue management among many organizations does not allow for frequent feedback; risk communication and reporting are still mostly manual; tracking/querying is inconsistent; risk review is constrained by incomplete analytics; and risk control key risk indicators (KRIs) are lacking robustness and trial specific capabilities. This Session would talk about how well we are prepared in addressing these risks and compliances|
|Session 4: Track 3|
|‘ML/AI- Challenges and opportunities in implementing analytics in Clinical Trials’
Session Chair - Gunther Christina-European Clinical Domain Lead, Accenture
|We all know how difficult it can be to successfully navigate complexity, regulations and ethical demands that surround clinical trials. Introducing innovative and new technologies such as machine learning and artificial intelligence to such an environment comes with challenges but it also comes with a multitude of opportunities and the possibility to fundamentally change way we look at clinical data.
Before we can successfully implement AI/ML into our research processes we need to fully understand what is technologically possible and what isn’t. Too often, there is an imbalance between reality and our expectations. AI/ML can be an incredibly powerful tool that can enable us to analyze vast amounts of data, reveal connections and relationships between information we had previously not uncovered and help us understand and make sense of an ever-increasing flow of information.
Real world evidence and data is becoming increasingly important and AI/ML may revolutionize how we combine, pool and analyze real life patient information to better understand and inform our basis for decision-making. We can tap into our vast data and information pools to learn from our past experiences and decisions to design and run more robust and impactful trials.
There are still many challenges - some technical, some ethical, but lets look forward at how AI/ML can change the way we work, embed these technologies in our day to day business, and use both to design better trials to ultimately develop better drugs for patients.
|Topic 1 - Regulations how do they Guide and Embrace DDM (Digital Disruption Mutation) - Regulators Lens
Topic 2 - QbD and Quality Risk Management Framework through Regulators Lens
|The collective landscape between pharmaceutical companies, health authorities and patients need to converge to co-create a future state to enable much faster approvals of medicines. This session will outline a potential future state in which cloud submissions are fully connected with data driven content, allowing regulators and patients to have access to our medicines in real time. These paradigm shifts will include a significant reduction of the submission\approval times and incorporation of advance analytical capabilities such as machine learning\AI. The one driving key factor which will be explored in this presentation is our data can supercharge our efforts towards faster submission\approvals and the changes needed to connect our different databases in a seamless manner. The creation of data models in medicines have just begun. For example CDISC data model exists for patient level data, but we do yet have a common data model for RWD across the industry. A key regulatory data model is ISO IDMP which is focused on providing consistency in identification of medicines. There needs to be a move towards a fully connected medical data models rather than defining data by function. The challenges and opportunities are the same for Biometrics and Regulatory data even though the content, the underlining key principles are the same. Externally there are a number of activities with regulators and industry leading organizations such as EFPIA and PhRMA. The session will highlight how these activities are connected and will present a use case of how value is being delivered.|