- Complexity of Clinical Trials - Clinical trials have been growing increasingly complex for years. Study teams are particularly concerned with meeting the ‘challenge of focussing on the best study design’ for ‘modern clinical trials’. Such concerns are borne out in the ‘high rate of failure to meet primary endpoints due to poor or complex design’.
- Tight Timelines & Spiralling cost - Increasing complexity and tight timelines is putting more pressure on the need for resources to implement and control every step and the cost of trials is at an all-time high.
- Handling Multiple Documents - It’s a challenge to handle multiple versions of specification (Requirements, UT & UAT) documents and track the difference from one version to the other eventually leading to lot of rework. Start-up phase is time-consuming, labour-intensive, and expensive, so it helps to establish realistic expectations especially while drafting the User requirements and performing various testing’s (UT & UAT). Pricewaterhouse (PWC) Coopers estimates that the industry will need to reduce the cost of drug development by between 20 and 40 percent in order to sustain profitability. To avoid such difficulties a Study Trial management system that reduces trial design and build time by 50% compared with paper-based designs can be used.
- TxDesigner A solution for the Visual Specification creation. Provides visual platform to create/update Trial specifications & progress of study build with version history’s & audit trails.
Journey to Intelligent Clinical Data Services: How will RPAs (Robotic Process Automations), Machine Learning, Artificial Intelligence, Natural Language Processing transform the current ways of working
Chairperson: Gaurav Bhatnagar
There is a burgeoning increase in the volume, velocity, and variety of real world data being submitted to regulatory agencies which has brought opportunities to leverage- deep-learning techniques on multimodal data sources such as combining genomic, clinical, payer, operational data to detect new predictive and prescriptive analytical models for use cases such as site selection, patient identification, protocol design, clinical and data management planning among them. The speakers of the session would be thought-leaders and practitioners designing and implementing solutions to transform the current ways of working.
The focus of this session would be to embellish the transformation using ML/AI and RPA in the clinical development industry. Discussing the strategy and vision of leveraging these newer paradigms as well as nuancing the limitations and challenges of change management.
Weaving the three critical attributes - Data Integrity, Data Quality and Data Security (OP)
Chairperson: Ullas Arabhavi
Data Integrity, Data Quality and Data Security are the three most important areas of consideration for clinical development these days where the use of technology support is being increasingly adopted. FDA investigators continue to cite a significant number of data integrity, data quality and security observations during inspections. There has also been an increase in regulators taking action against data fraud or false information. They are keeping pressure on those with data integrity and data quality shortcomings, issuing a growing number of warning letters and FDA-483s every year for the past few years. With a great change in the digital landscape for clinical development, it would be ideal for the Clinical Data Management community to discuss and understand more about the opportunities and challenges of fundamental Data integrity, Data Quality and Data Security principles being implemented in the new digital world of affairs.
Clinical Data Management: a Biostatistician's perspective (OP)
Chairperson: Mahesh Iyer
Clinical data management (CDM) function has always been an important enabler to the Biostatisticians – after all, they are the real custodians of the data. Biostatisticians rely on CDM to get high quality, timely data which helps them analyse the data more effectively. However, of late, as the world of clinical trials undergoes transformative changes, the requirements of the Biostatisticians in terms of quality and timeliness of data is also changing. For e.g., given the various “v’s” of big data – especially volume and variety, what are the new expectations of the Biostatisticians from the CDM function? Given the need to run models almost in real time, how could CDM provide data in a timelier manner to the Biostatisticians? What, if anything, has changed from a quality perspective? And what continues to remain the same? These are some of the questions that will be explored as part of this session
Inspection Readiness: Case Studies based on learnings of past Audits & Inspections in Biometrics (OP)
Chairperson: Prabhat Kumar
With ever evolving regulatory land scape and technological transformation of clinical trial methodologies including adaptation of e-diaries, patient wearables, robotic automation, virtual trials. It is important to have a right strategy in place to prepare, coordinate and host regulatory inspection at any time. Consequently, Inspection readiness (IR) approach helps organization towards consistent culture of preparedness for regulatory inspection. This session will focus on Case Studies based learning approach for global regulatory inspection readiness in Biometrics. Furthermore, may include inspection readiness tools, collaborative approach with internal and external stake holders, good practices, essential documents, lessen leaned etc.
Power of BIG Data - Impact on Clinical Data Management (OP)
Chairperson: Srinivasa Subramani
According to Dr. Eric Schadt of the Icahn Institute of Genomics and Multiscale Biology, in an interview with Sastry Chilukuri of McKinsey and Company says that one of the biggest limitations with medicine and pharmaceutical industry is that we still do not understand the biology of disease(s). Further continuing, Big Data will help in aggregating large amounts of data across multiple scales of what constitutes a disease, namely, like what kind DNA makeup of humans causes certain diseases, metabolites cells, tissues, organs, organisms and ecosystems. When we model these scales of biology and integrate big data, allow it to evolve over a period of time, it becomes highly predictive at an individual level
(people interested to read the full interview can go to https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/the-role-of-big-data-in-medicine It is not going to be too far when Individualized Medicine is going to be the norm and would be driven by Big Data.While Big Data in Pharma/Clinical research is fast unleashing many possibilities, it would be interesting to see what does it hold in store for professionals in Clinical Data Management – after all we need to be prepared and evolve as professionals to be relevant. In this context, the session “Power of Big data – Impact of Clinical Data Management” is a going to be a great opportunity for clinical data management professionals, on one hand to share what they envision the impact of Big Data is going to be and on the other an opportunity to listen to how Technology, read Big Data, is impacting Medicine, in general, and Clinical data management in particular.
eSource: Optimization of Clinical Trial Costs, Reducing Timelines and Improving Data Integrity (OP)
Chairperson: Dr Anitha Kumari A.
There is huge demand for the new innovations that streamline processes and help us all achieve our operational goals. The regulators have urged increased use of eSource, application of the use of electronic sources of data for clinical trials has been slow to be adopted across the industry, particularly for registration trials, due in part to difficulties in operationalization. Research indicates there are numerous obstacles and challenges behind this delay.
Clinical trials become less about collecting protocol-defined data points, and more about using data from patient records and other electronic sources, electronic interoperability and consistent use of data standards becomes vital.
eSources that’s considered to be operationised and proven are:
Direct Data Capture:
The purpose is to share learnings to enable more efficient, effective use, and/or adoption of eSource. There are many initiatives and technologies are in place in creating the processes, and also recommendations for the standards. We would like to explore more into this field and get nuances from service providers and academia to gather knowledge and find solutions to the current challenges.
1.Reimagining Clinical Database Build in the future with so much of disruption happening on technology front (ST/IS)
Chairperson: Raghuram Thata
With cost pressures and need to bring drugs faster to market, pharma companies are looking at disruptive innovation.
One of them being master protocol, to address multidimensional questions (drug, dose, comparators, combinations) in one protocol. Databases will be flexible to include umbrella/basket/platform trials for adaptive trial designs.
Wearable/connected devices are growing at a fast pace. 60-75% data today are captured this way, interoperable systems to integrate data real time with CTDB/EDC are evolving.
Reimagining sensors, circuits to provide richer data efficiently is providing an exciting outlook for the future. Novel Database Designs for efficient data collection are evolving with analytics. There is a paradigm shift to use Descriptive, Predictive and Patient Journey Analytics to derive meaningful patterns and insights to improve study build.
Clinical Database Build practices are evolving rapidly. How are you reimagining?
2. Collaboration amongst Clinical Operations, Data Management & Biostatistics for effective database build & delivering of quality data (ST/IS)
Clinical trials are burdened with many challenges such as subject retention, protocol adherence, documentation at sites… to name a few. It requires bandwidth to address these challenges proactively. We can manage these to a large extent by building effective databases by collaborating early with Clinical Operations, Data Management and Biostatistics.
Main facets of study startup from a Database Build perspective are alignment on:
1. Trial and protocol design
3. Alignment on Database Design, Clinical data flow
4. CRF completion guidelines
These help in early finalization of Data handling, review and lock plans for a predictable and smooth study conduct. This greatly reduces rework and creates bandwidth for additional scope – improving end user experience, investigators & site monitors burden, early insights, adaptive trials, Risk Based Monitoring, Targeted Data Review…
What are your strategies to improve collaboration amongst Clinical trial team to build effective databases.
1. Is the industry ready for Virtual trials: Understanding the challenges and the future roadmap (ST/IS)>
Chairperson: Dr.Naveen K.K.
Last one decade has seen a major transformation of data moving from paper based to digital, this transformation is acting as foundation for another cycle of disruption through potential virtualization of clinical trial roles & activities. Recent innovations in the area of mobile technology, connected devices, centralized monitoring, ePRO, eCOA, eConsent, cloud based solutions and mature logistic solutions to connect subjects/patients with investigators, have been a great enabler for opening up potential opportunities of Virtual trials across the regions.
Traditional trials have been facing inherent challenges, these challenges have been continuously pushing the Clinical research fraternity & technology world to come out with transformative solution.
There are many advantages that virtual trials bring on to the table compared to traditional model, but it also comes with its own challenges – lack of consistent technology availability across the regions, efforts in change management/training at the level of investigators & patients, validating the data quality, integration of data from wide range of disparate devices, complex trial designs and regulatory clarity.
2. Case studies: GxP and its impact on Clinical Data Management (ST/IS)
Clinical Studies Arena is changing at a Rapid phase; Transformation is the buzz word in the Clinical space today especially in the areas of Clinical Data Management, a few examples can be conventional clinical trial to virtual trials, routine monitoring to Risk based and centralized monitoring, eConsent, mobile technology and many more.
With all the transformations the bottom line stay’s unchanged: Patient Safety - Providing Safe and Effective Medicines in a timely manner to the Patient Population for a Better tomorrow. Key player to confirm this bottom line is the “GxP” and has taken the Center Stage.
How is GxP maintained and demonstrated with all the changing and complex scenarios is a big question that the Manufacturers, CRO, and even Regulators’ are trying to solve. There is going to be a shift in the definitions such as Source data/meta data. This is indeed a challenging time and hence interesting time to redefine the definitions and look at the demonstration of GxP compliance with a new pair of lens.
The Journey to Intelligent Clinical Data Management: Opportunities, Benefits and Challenges in the Application of Automation and Artificial Intelligence
Chairperson: Kevin Julian
Clinical trials have become increasingly complex in recent years with more sophisticated clinical trial designs, more focused target markets and complex outsourcing models. Despite this complexity, they require real-time access to up-to-date, standardized data that upholds data quality and regulatory requirements. With this in mind, automation and artificial intelligence have the potential to dramatically improve the efficiency and quality of one of the most critical functions within global biopharmaceutical organizations – Clinical Data Management. Automation can assist the data manager with the execution of transactional activities, thereby increasing efficiency, reducing rework and improving quality. Artificial intelligence and machine learning further equips the data manager to perform judgment-based data cleaning activities in an effective way. In order to make the most of these opportunities, sponsors and service providers must understand and address the challenges of applying leading-edge digital capabilities to clinical data management.
A Ringside View of Innovation & its Impact on Advancing Clinical Research
Chairperson: Sanjay Vyas
Cost for pharma R&D have been rising in the past few years and are expected to continue to increase. Rare and orphan indications are moving faster. We’re moving toward personalized medicine. The old methodologies for developing drugs will not succeed in the new paradigm where patient populations are much smaller and where moving drugs through development, ensuring they are safe and efficacious, and making them available to physicians and patients downstream becomes more complex. Recent advances in science require that the industry re-examines its approach to clinical development and put the patient first. They are the key stakeholder in everything we do.
During this presentation we will focus on a few real life examples & cases around such innovations that are taking place within the clinical industry and are having a positive impact on patients. We will focus at innovations on patient centric trial designs, around medical imaging, supply chain and also the potentials of blockchain benefits within clinical studies.
Patient-centric thinking: will machines be able to do what patients really want?
Chairperson: Suresh Ramu
At the core of all drug development and research, it is about improving clinical outcomes for patients. Newer technologies have been at play for a few years now in pushing the boundaries of how we design, conduct, analyse and report the research being done. And yet, now like never before we believe that the very playing field is being changed with the rise of the new machines! It is imperative that we explore how technology has the potential for disruption, while patient-centred thinking will still remain central to all innovation. How will machines deliver to the promise that patients are seeking out of all research? Will machines learn to use humans better than we ever could do so? Or will it be the other way around?
Ugly Duckling to Elegant Swan
Chairperson: Alan Morgan
Thirty years ago, Clinical Data Management (CDM) was the ugly duckling of clinical research. Today CDM has galvanized technology to transform clinical trials. Readily available clinical data for review anywhere in the world is expected now, and new challenges of effective analytics have emerged. I will review the historical pace of change as a foundation for making some predictions…
What will the next thirty years bring? Will sensors automatically transmit all required patients’ clinical information in real time? Will we even need patients to register drugs or will Artificial Intelligence enable the creation of simulated humans, their diseases and responses to drugs? We already have ‘organs-on-chips’ , it is a small leap to interconnect these to create a whole ‘body-on-a-chip’ ideal for drug discovery and drug testing.
What will these changes mean for a data science organisation in 2049? What will be the ideal competencies of individuals capable of leading and developing these organisations over the next 30 years?
Good, Fast and Cheap; a use case where we do not have to pick just two
Chairperson: Demetris Zambas
The fundamentals of Data Sciences have never served our industry in a more critical and significant way before. Evolving technologies as with a strong thoroughbred require ever-stronger and smarter jockeys. The application of machine learning capabilities is the Clinical Data domain is very recent, although the capabilities have been leveraged in other industries and other areas within the healthcare sector.
Can we leverage these technologies to eliminate or minimize entire categories of data interrogation? Do we develop these capabilities in-house or select partners to collaborate with? How can we select a partner with confidence in a space so new? And what does this all mean to our discipline and how do we not only ride but actively steer this thoroughbred? A first hand approach to answering these questions and leveraging crowdsourcing to solve specific challenges will be summarized.
Reimagining Clinical Data Operations - 5 Big changes that are coming down the pike.
Chairperson: Jagadeesh Rudraswamymath
The world of clinical trials is changing rapidly as sponsors pivot towards more complex / higher cost therapeutics; Global regulators increase their level of scrutiny and big data brings with it significant new challenges, not least around data integrity and data privacy. This presentation will explore the changes we are seeing today and will look forward at how Clinical Data Operations teams will need to adapt in the future in order to support Pharma’s need to increase the speed of drug development.
The Future of Clinical Trials: A Data Management Perspective
Chairperson: Jennifer Duff
The Life Sciences industry is currently facing significant change driven by new science, digital transformation and patient-centricity. The industry requires new ways of working and to move towards intelligent operations. The industry needs to continue to bring therapies to market in a more cost-effective and efficient way, all while developing highly-effective, innovative, and personalized treatments. The clinical trial of the future will be very different to that of the past. With this change in the clinical development landscape, Clinical Data Management is no longer a relevant term. The focus has moved to data science as a culmination of data discovery, collection, cleaning, management, analytics, insights, quality, integrity and governance.
Data quality, integrity and timely availability remain imperative to a trial’s success. Which means development of digital, automated, intelligent technologies to strengthen and enhance the role of the data manager and their mission to ensure that accurate, complete, and standardized data is available for trial analysis. As companies look to apply these technologies, Human + machine will have to work in symphony to shape the workforce of future. Accenture and Merck, along with other sponsors, are partnering to enable Artificial Intelligence and Machine Learning solutions to detect clinical data anomalies, thus disrupting the traditional person-heavy solutions that exist in the market today.
Case Studies of Effective & Efficient Monitoring of Data Quality using RBM
Chairperson: Shobhit Shrotriya
Risk-based monitoring (RBM) is now considered as a pro-active & adaptive approach that directs clinical trial monitoring focus and associated activities. RBM as an approach promises to improve clinical trial efficiency while ensuring data quality. As industry adoption of RBM increases manifolds, it is imperative to reflect on lessons learned to further refine the process as well as focus on leveraging RBM data for clinical operations. This conference offers case studies and practical solutions from across global biopharma and CROs on effectively implementing RBM as well as looking into the future of RBM.
Data Management in the world of Virtual Trials
Chairperson: Suraj Ravindran
Technology is emerging and evolving by the second. This is to re-assure that the human intelligence is by far racing towards the advancements in the clinical research techniques, strategies and of course basic practices to bridge the gap between patient & time taken to release the drugs required for patient care. In the event of narrowing the gap, companies, manufacturers and scientists are brainstorming in various ways to iron out the complexities that arise between patient and release of the patented product. The professionals invited to speak on this topic have multitudes of scenarios and world-wide practice examples that’ll open your mind to newer ideas, provide food for thought to either implement in your current processes or improvise on the current research practices that the world is following today. Be there to know more!
Descriptive, Predictive, Prescriptive Analytics & Visualizations for better decision making - Case Studies
Chairperson: Arghya Chattopadhya
The large volume of operational and scientific data collected from clinical trials and healthcare systems may contains hidden knowledge and insights that needs to be considered in decision making. However, the insights cannot just be gathered from the data itself, we need to curate, summarize and analyze the data in order to bring new insights. Visual analytics combines information visualization and data analysis to allow simultaneous exploration of human perceptual power and machines' processing power. In this session, we would discuss how we use the science and technology to ingest, curate and analyze and visualize the big volume of data to bring new insights that may help us making better decision.
CDISC Standards in the Age of Aritifical Intelligence & Machine Learning
Chairperson: Sarvesh Singh
We are in era of Industry 4.0 and its having an impact on all sectors including Pharmaceutical Clinical Research. CDISC standards which started in 1990’s have evolved a lot in past two decades and are going to evolve further in years to come with introduction of artificial intelligence and machine learning. Consumers and providers are more readily adopting technologies that leverage AI because they are becoming increasingly familiar with them in other parts of their lives. Just as any other standard and other industry, Clinical industry is also trying to take advantage of Artificial Intelligence and machine learning to identify hidden patterns in the data and identify novel solutions. In this session; you will get to know more about the evolution of CDISC standards until now, current status of CDISC standards and also to have a brief look at what’s in store for future. We will also cover the specific challenges with CDISC standards in terms of machine readability and on-going efforts towards optimizing machine learning of CDISC standards.
Leadership Forum (By Invitation Only)
Chair Person Name : Arshad Mohammed
Clinical Research has come a long way since using paper in the 1990s. Most studies have adopted electronic solutions with rapid improvements in connectivity and wide availability of clinical technology solutions. With the growth of the clinical research industry in established geographies and expansion of research in emerging countries, organizations have been using electronic data capture to improve accuracy and speeds of patient data collection methods, which has helped to increase compliance with regulations as well as reduce costs. With improving technology, mobile and handheld device proliferation, innovative study designs and evolving regulations including those for global data security and privacy, we are witnessing trends in clinical development that are looking to better utilize the latest technology and appropriate data collection devices. Clinical research is expanding use of automated and advanced approaches to day-to-day tasks, adoption of more sophisticated methods of mining study data and metadata, identifying and cleaning anomalous data, and working with a wider variety of data sources. In this special session, we will discuss such latest developments, benefits and challenges of such technological and process advances.
Data Science - Changing Healthcare Landscape
Chairperson: Prasida Dinesh
One of the major advances happening in digital space is how we can capture the data efficiently at source and analyze both structured and unstructured data to make meaningful inferences to the same. This stream of science is called data science which has been changing the way we live across all walks of life, lot of times even without us realizing this. Healthcare industry is no exception. All of us would have noticed disruptive changes in the last decade or two in our areas of work, making activities more predictable, efficient and ensuring the desired output. In this session, we would hear industry leaders enlightening us on how Data science is expected to drastically change healthcare industry – Be it in the areas of -Public health, Preventive medicine, Drug development, Clinical trials. Looking forward to an interactive session and hope to get insights into what is instore for us in the near future.
Data Lake - For Accelerating Data Analytics & Informed Business Decision
Chairperson: Deven Babre
Data Centricity - with structured and unstructured data constantly being pumped into a data lake – has resulted in a huge unstructured store of data. Mining this data and applying meaningful analytics has amplified various challenges that industry professionals are hard-pressed to address. The industry is enthusiastically embracing new emerging technologies to create innovative approaches and a lucrative habitat that can leverage analytic-ready data for improvements in drug development and clinical trials. Defining Metadata for data has resulted in various data services that allow data lakes to improve data quality, innovate medical treatment procedures, and assist patients in determining care protocols or regimens that offer best value. The use of Monitoring medical devices and analysing fast-moving data in real-time for safety monitoring and adverse event prediction, has enabled payers to monitor adherence to drug and treatment regimens and detect trends that lead to individual and population wellness benefits. With this, the focus is now on improving predictive modelling to lower attrition and produce a leaner, faster, more targeted R&D pipeline for drugs and devices.
Emerging trends in Clinical Data Capture
Chairperson: Dr.Anita Kumar - Associate Vice President : Operational Head - Medical Solutions
1. Trial Initiation and Study Design Visualisation
2. Transforming how technology drives patient-centric clinical research data base build.
3. The clinical trial landscape is ever-changing with technological advancements, changing attitudes to partnerships and new types of drug impacting the industry and Technology is playing a massive role in improving many aspects of trials and is rightly seen as one of the great hopes for future.
4. With technology, so much is happening so fast, the challenge is how to select and use the RIGHT technology, and for that technology to gain the acceptance of Users
Challenges that Study set up faces:
Eliminates Manual Validations and Task allocations.
Reduced timelines & hence lesser costs.
Efficient & Better control of Study.
Highly secure and technologically advanced set up interface which dramatically reduces IT costs.
Workshop - Bridging gap between Data Management & Data Science
Key Note Speaker : Dr Krishna Asvalayan , Amit Upadhyay
Data Analytics and Data Science based solutions have touched and impacted almost all walks of our day to day life. Our cumulative digital footprint is getting bigger with each passing day and a recent study* envisages exponential growth of global datasphere from 16.3 Zettabytes (ZB) in 2016 to humongous 163 ZB by 2025.
Healthcare domain including clinical data management is fast catching up with this trend and new and innovative data science based technologies are being recognized and adopted by enterprises all over the world as need of the hour.
Adoption of these new technologies to analyze patient data and extracting valuable insights has brought with it many challenges. Almost 80 percent of data generated in healthcare is unstructured and thus difficult for organizations to access and integrate this data. Integration of data is kind of prerequisite to adoption of any predictive analysis tools. Considering the fact that more and more real time patient data is being generated at clinical research sites, the orthodox data management methods may become obsolete down few years giving way to new and innovative technologies.
Data Managers, entrusted with clinical trial data stewardship, have an exciting opportunity to grab here in terms of upgrading and arming themselves with new data analysis tools and technologies. Data Managers must think beyond submissions and perceive data as an asset for future.
Data science tools like Python, R, Hadoop, SQL, Java, SAS, Spark, Matlab, Spotfire and Tableau are already here for quite a while now for efficient and real time decision making when it comes to applying statistical models and predictive analysis. Python and R are fast emerging as widely accepted tools of choice.
Time is ripe for Data Manager’s metamorphosis into Data Scientist. All we need is, to step into this completely new realm filled with immense possibilities. The path is wide enough for Data Managers and Data Scientists to walk abreast. Sooner we recognize and adapt to upcoming change, better equipped we will be to retain our relevance and demand in future.