As an industry, we have developed a sense of comfort in our approaches to data integrity validation.  The evolution in data types, data sources, data volumes, data velocity, and trial conduct outside of physical sites create a new challenge beyond the traditional chain of custody.  Data sources and metadata that are outside the realm of traditional checks and balances require interrogation and have the potential to not only expose errors but also more nefarious data concerns.

Elevating data-driven organizations to new heights involves constructing a robust data infrastructure, harnessing cutting-edge technologies, adhering to privacy and data security regulations, and enforcing data governance principles. Enabling data innovation can transcend internal and external barriers, effectively overcoming silos.

Bringing key roles together in a next-generation data review platform that facilitates near real-time access to clinical trial data allows for early decision-making and cross-functional risk-based data review methodologies (medical review, clinical data review, central monitoring). Building on a platform that can further develop machine learning capabilities prepares us for the future evolution of data becoming bigger and increasingly complex.

Digitizing clinical protocol information can streamline system configuration during study start-up, enable analytics-assisted study design and feasibility, and support more routine use of point-of-care data for research. TransCelerate’s DDF initiative and CDISC have delivered a foundational data model, exchange standard, and open-source reference implementation aimed at supporting widespread use of digital protocols across industry. This presentation will highlight current capabilities, demonstrated use cases, and a future roadmap for multi-stakeholder collabs to achieve interoperability.

This talk will explore the application of Artificial Intelligence (AI) and Machine Learning (ML) in informing the entire clinical development lifecycle of therapeutics, from early stages of drug discovery to late-stage clinical trials leveraging a variety of digital measurements. AI/ML technologies, including the latest Generative AI capabilities, play a transformative role in accelerating drug candidate identification, predicting safety and efficacy, optimizing patient recruitment, and enabling personalized medicine approaches. These technologies leverage clinical trials, RWE, wearables, and molecular data to inform drug safety and efficacy as well as to help meet patients where they are. The talk will briefly touch on the challenges and collaborations required for the ethical and responsible use of AI/ML in drug development, highlighting the immense potential of these technologies to revolutionize the pharmaceutical industry and deliver safer and more effective treatments to patients.

The biopharmaceutical industry strives to ensure patient wellness and safety by producing new drug candidates that have undergone rigorous testing and approval by global health authorities. However, R&D inefficiencies around approaches and processes for drug development have caused roadblocks for successfully starting, recruiting, executing and completing a clinical research study, ultimately delaying development of needed medications for patients.

Historically data validation for ECOA modules focused on "within form/within visit" checks that were skewed to a focus on Data Quality - but allowed very easily identifiable data integrity matters to be overlooked - such as implausible changes from visit to visit within an assessment, or cross-assessment dis-correlations. This presentation will provide approaches to ensure DQ and DI and unleash the true value that CDS brings to ensuring both.

Advances in clinical research over the last decade have given rise to an intricate patient data ecosystem, presenting both challenges and opportunities for clinical research professionals. This session will explore the convergence of three key innovation themes that are shaping the future of the clinical research ecosystem. We'll first explore how roles ensuring data quality and patient safety are evolving and collaborating differently in this data-rich landscape. We'll then highlight the transformative potential of AI and automation technologies to optimize clinical trial efficiencies and unlock deeper insights for all applicable roles. Third, we'll speak to the need to make sites more active participants in clinical oversight by providing them access to capabilities that ensure more timely safety oversight and ultimately improve the patient experience.

This presentation focuses on the rapid advancements in Generative AI, particularly highlighting GPT-4's unprecedented capabilities. As the latest iteration in AI technology, GPT-4 emerges as a pivotal tool in clinical research, adept at diverse tasks including data generation, summarization, translation, extraction, and comprehensive analysis. This session underscores the significance of accessible, varied data sources - from clinical protocols to statistical analysis plans - as the backbone for successful Generative AI applications in life sciences. The session aims to offer an in-depth insight into GPT-4's role in reshaping clinical research and development, marking a new era of innovation and efficiency in the field

This session will introduce synthetic data generation for health data. Case studies will be presented to show synthetic data as a privacy enhancing technology that facilitates data sharing and as a means of enhancing clinical trial data.

This presentation will share several case studies in various therapeutic areas of challenges and solutions of AI and ML implementation in clinical trial design.

Using artificial patients for drug development or medical device development is a promising field, only if the models can reach the required complexity and can be truly representative of the human population.