Population pharmacokinetics (Pop PK) analysis is an essential component of the drug development process, providing insights into drug behavior across diverse patient populations, guiding dosing decisions, and informing regulatory submissions. Unlike traditional pharmacokinetic studies, which rely on data from a small number of individuals, Pop PK analysis incorporates data from a larger and more diverse population, facilitating a comprehensive understanding of drug behavior and its application in tailoring personalized drug dosing.

This analysis had majorly been led by sophisticated tools such as NONMEM (NONlinear Mixed Effects Modeling), necessitating tool-specific input datasets and a high degree of technical expertise. As clinical data scientists and programmers, we understand the significance of Pop PK analyses within the clinical trial process and the need for standardization to improve efficiency and consistency. The introduction of ADPPK dataset as CDISC's new standard for Pop PK reporting on 6th October 2023 marks a shift in the conduct of this analysis, showing a promising future in this area by minimizing variability, expediting regulatory review, and enabling automation.

Embracing the new standard within Novo Nordisk, we've initiated the alignment of our in-house analysis with the new Pop PK ADAM dataset creation. This presentation will provide insights into our ongoing efforts, including a trial run using past submitted studies to generate ADPPK datasets and conduct modeling analysis. We'll explore the challenges, successes, and the potential impact of this alignment, shedding light on our journey towards enhancing efficiency and standardization in our Pop PK analysis.

In a recent submission, our team used a pooled SDTM approach, deriving ADaM datasets from pooled SDTM for ISS/ISE/ISI activities. This method enhances traceability and prepares for potential regulatory requests but presents challenges. Effective collaboration with the SDTM integration and study teams is crucial to align expectations, address ADaM derivation changes, and manage TDW domain alignment, codelist harmonization, and aCRF handling for define. A key hurdle is the lack of robust tools to detect inconsistencies within standard SDTM domains. Additionally, conducting analyses alongside individual studies complicates consistency efforts, emphasizing the need for careful planning. This presentation will explore these challenges and our solutions, including macro development for harmonization and collaborative efforts with the integrated SDTM team.

This presentation will provide insights of overall updates and improvisation in the Define.XML Version 2.1, exploring the experience, challenges encountered, impacts to Validation, highlighting the reasons for the update and the beneficial outcome, advancing the Clinical field. The presentation will also highlight good practices for Users, while up versioning their existing version. This also highlights the enhanced validation capabilities that facilitate data integrity, thus quick debugging, and issue resolution. With the latest version, it is effective in handling larger and more complex datasets. Improved Metadata definitions clarifying the Data structure and Data relationships, Origin enhancements, Versioning of CDISC standards and Controlled Terminology and expanded variable attributes are essential for easier review process and offering a shared understanding of the data elements among various stake holders.  The up versioning of Define.XML Version 2.1 supports more efficient workflows , elevates the Overall data quality and positions CDISC as a Forward-looking solution.

Real-world evidence (RWE) data, derived from diverse sources like electronic health records (EHRs) and claims databases, holds immense potential for advancing healthcare research and decision-making. However, the heterogeneity and quality issues inherent in RWE data pose significant challenges for standardization and effective implementation of Clinical Data Interchange Standards Consortium (CDISC) standards. This presentation will delve into the key challenges encountered in standardizing RWE data, including data heterogeneity, quality concerns, privacy and regulatory complexities, and the emergence of new data types like wearable device data and genomic data. Furthermore, it will explore strategies for effective CDISC implementation, such as data harmonization, data quality assessment, data mapping, and adherence to regulatory guidelines. By addressing these challenges with real life examples and leveraging CDISC standards, healthcare organizations can unlock the full potential of RWE data to improve patient outcomes and inform evidence-based practices.

Pharmaceutical investigators must adhere to a specific set of procedures and standards for clinical trial submissions. CDISC is an organization which sets specific standards for clinical data collection and analysis which includes SDTM (Study Data Tabulation model) and ADaM (Analysis Data Model). ADaM.jl is a Julia-based package that works with Pumas ecosystem of tools that facilitates the creation, validation, and visualization of ADaM datasets from SDTM datasets. Julia is chosen for the development of this tool as it offers metaprogramming and multiple dispatch, which enhances the speed and efficiency of data processing, making it ideal for handling large datasets. In addition, ADaM.jl is designed to integrate with other statistical and modelling packages such as Statsbase.jl, HypothesisTests.jl, Pumas.jl, ensuring that it can be incorporated into existing clinical data analysis workflows. Due to abstraction of complex functionalities and adherence to a derivation structure, it helps to reduce complex data preparation steps and minimize errors.

The lack of standardized structures for output datasets has created challenges in the pharmaceutical industry, especially with growing automation demands. To address this, Novo Nordisk is experimenting with the Analysis Results Standard (ARS), a unified output dataset format for future Biostatistics projects. The ARS aims to streamline data handling, improve automation, and ensure better integration among projects and stakeholders, contributing to a comprehensive Biostatistics database solution. Currently, ARS is implemented as a macro in SAS and a function in R, which can restructure any output dataset to adhere to ARS guidelines. For ongoing projects, aligning output programs with ARS from the beginning will eliminate the need for later restructuring. The ARS implementation document outlines the steps for integrating ARS into each output program and details the required input values. By standardizing datasets, the ARS will enhance efficiency, consistency, and reliability across Biostatistics projects.

sdtm.oak is an EDC- (Electronic Data Capture) and data standard–agnostic solution designed to support the pharmaceutical programming community in developing CDISC (Clinical Data Interchange Standards Consortium) SDTM (Study Data Tabulation Model) datasets in R. By leveraging reusable algorithms, sdtm.oakprovides a framework for modular programming and can automate SDTM creation based on standard SDTM specifications. To address challenges in SDTM dataset development, sdtm.oak offers an open-source, EDC- and standards-agnostic framework for modular SDTM programming in R.

The Trial Design Model (TDM), a subset of SDTM, provides a standardized framework for capturing and describing clinical trial designs. TDM Automation employs advanced techniques like web scraping, document extraction, and NLP to enhance data quality and ensure regulatory compliance. This automation minimizes manual errors, reduces effort, and shortens the time needed to develop. By streamlining the creation process, accelerating setup, and improving trial outcomes, this significantly enhances efficiency in clinical trials.

In the complex landscape of global regulatory submissions, organizations face the challenge of adapting to evolving requirements from various health authorities such as the FDA, PMDA, and NMPA. This paper explores the strategies and lessons learned in navigating these regulatory changes, emphasizing insights gained from initiatives like the EMA Clinical Trials Raw Data proof-of-concept pilot.

In a significant collaboration, seven leading vaccine companies — AstraZeneca, GlaxoSmithKline, Johnson & Johnson, Merck, Moderna, Pfizer, and Sanofi — have formed the Vaccines Industry Standards Group (VISG). Over the past two years, this initiative has focused on harmonizing interpretations of regulatory submission guidance and recurrent feedback, as well as CDISC data standards. The group recognizes that aligning the understanding of requirements — such as participant diary data collection and the submission of reactogenicity and efficacy data — accelerates time to market and benefits global health. This unified approach could facilitate future collaboration with Health Authorities and CDISC, aiming to update the CDISC Vaccines TAUG to meet current Health Authorities' expectations, thereby ensuring clarity and consistency in submission standards. Our collaborative model can serve as a blueprint for other therapeutic areas within the pharmaceutical industry, demonstrating how organizations can work together to streamline regulatory processes while maintaining a competitive edge in product innovation.

The regulatory landscape for clinical data submission is complex and continuously evolving. In the process of approvals for NDA/BLAs, statistical programmers need to prepare a variety of packages consisting of multiple files (e.g., Define XML) by aligning with CDISC standards and Technical Conformance Guide (TCG) of FDA or PMDA expectations. Sometimes, ambiguity or contradictions in validation rules across these might make this task cumbersome. The FDA TCG provides detailed instructions for the submission of standardized study data, emphasizing the use of specific controlled terminologies and validation rules. CDISC IGs offer a broader framework for data standardization, focusing on the structure and content of datasets to ensure consistency and interoperability across studies. The P21E Validation Report, on the other hand, serves as a critical tool for assessing the compliance of submitted data with regulatory requirements, highlighting any discrepancies that need to be addressed. This presentation aims to elucidate few key differences between these and possible ways to bridge this gap.