Chair: Jian Zhu, PhD (Servier)
Abstract: The use of external and literature data has greatly contributed to clinical development, and has been expanding to cover areas other than trial analysis. In this session the speakers will particularly focus on these new areas, sharing the use of AI/ML in enhancing clinical trial operations such as supply chain management, individual patient level data synthesis based on literature and signal detection using large language models for drug safety surveillance, in addition to recent advancement in Bayesian methods and tools for using external data in hybrid control clinical trials. The scope of the session will provide a broader understanding of these new trends to the audience and highlight the implementation of AI/ML in these areas.
Speaker: Brian Hobbs, PhD (Telperian Inc.)
Title: PD-L1 Subpopulations of Metastatic Urothelial Carcinoma Demonstrate Heterogeneity to Chemotherapy: A Systematic Review and Meta-Analysis of Synthetic Trial Data
Abstract: Efforts to translate advances in immunology into anti-cancer immunotherapies have progressed rapidly in recent years. Six antibodies acting on programmed death ligand 1 or programmed death 1 pathways were approved in 75 cancer indications between 2015 and 2021. Several of these therapies were granted accelerated approval for specific cancer indications on the basis of evidence acquired in single-arm phase II clinical trials. In the absence of randomization, however, patient prognosis for progression-free and overall survival may not have been studied under standard of care chemotherapies for PD-1 and PD-L1 biomarker subpopulations. In 2021, two immunotherapies were withdrawn from accelerated approval applications for treatment of metastatic urothelial carcinoma after randomized phase III trials failed to demonstrate evidence for survival advantage when compared to standard of care. The findings of the IMvigor210 (NCT02108652) and IMvigor211 (NCT02302807) trials of atezolizumab are reviewed for the purpose of elucidating the statistical implications of PD-L1 subpopulation heterogeneity. To place the findings into the context of external evidence, a systematic review was conducted to identify trials that assigned metastatic urothelial carcinoma patients to the same chemotherapy agents administered as a part of the control arm in IMvigor211. The systematic review searched PubMed for phase II/III clinical trials on the second-line treatment of urothelial carcinoma with single-agent chemotherapy drugs including paclitaxel, docetaxel and vinflunine, published in English between 1993 and 2016. Eleven trials including 742 patients met the inclusion and exclusion criteria. Of these, 11 were used to evaluate overall response rate (ORR) and 10 were used to evaluate overall survival (OS). This article defines the extent to which PD-L1 IC2/3 subpopulations outperformed their expectations in the IMvigor211study based on external trial evidence. The results found that the IC2/3 subgroup in IMvigor211 demonstrated statistically significantly higher ORR and prolonged OS as compared to prior mUC studies. Given the extent of PD-L1 heterogeneity identified by results, trial simulation was applied to define the probability that IMvigor211 would have resulted in a positive trial based on its actual design as well as alternative designs that enrolled a higher proportion of IC2/3 patients or had longer durations. The results indicated that as designed the IMvigor 211 trial had a <24% probability of success and would have required at least 559 patients in the PD-L1 high subgroup, as compared to the actual 234, or an additional 20 months of follow-up.
Speaker: Jinfeng Zhang, PhD (Insilicom LLC)
Title: iKraph: a comprehensive large-scale biomedical knowledge graph for AI-powered data-driven biomedical research
Abstract: The explosion of biomedical literature poses a major challenge for researchers trying to stay current and extract actionable insights. In this talk, I will present iKraph, a large-scale biomedical knowledge graph (KG) we built using a state-of-the-art information extraction pipeline that won first place in the LitCoin NLP Challenge. iKraph was constructed from the entirety of PubMed abstracts and integrates data from 40 public databases, as well as high-throughput genomics data, resulting in a KG that not only matches expert-level annotations but also vastly exceeds the coverage of existing curated resources.
I’ll also introduce a novel, interpretable probabilistic inference method we developed to uncover indirect causal relationships, and demonstrate its real-world impact through a retrospective analysis of COVID-19 drug repurposing efforts. Remarkably, our system identified over 1,200 candidate drugs in the early months of the pandemic, many of which were later validated by clinical trials or publications.
To support the broader research community, we’ve made this resource available via a cloud-based platform, enabling academic users to explore, query, and build upon the structured knowledge in iKraph.
Speaker: Zoe Hua, PhD (Servier)
Title: Application of Artificial Intelligence in Clinical Trial Supply Chain Management
Abstract: This Emerging pivotal challenges from the intricate landscape of drug supply chain management can potentially offset the benefit in applying innovative adaptive designs in clinical trials. The challenges include the uncertainty of maximum drug supply needed, shifting of supply requirement, cost control, and high-dimension factors impacting the decision of drug resupply. To address these issues, we designed an optimization digital tool tailored for the efficient management of drug supply in clinical trials. The tool optimizes drug supply strategies in a sequential manner throughout the trial, with leveraging real-time data and statistical simulations to make informed decisions. Real world scenarios are integrated into the framework as pragmatic assumptions and setup. Statistical simulations are applied to optimize drug supply strategy in pre-study planning stage as well as during study monitoring stage. Drug supply is optimized via minimizing the total cost using real-time data from the study over time. An artificial intelligence model particle swarm optimization algorithm is applied to perform optimization, where feature extraction is implemented to reduce dimensionality and computational cost.
Speaker: Min Lin (University of Connecticut)
Title: Tool and Methods for Bayesian Divide-and-Conquer External Data Borrowing in Clinical Trials
Abstract: This talk introduces a Bayesian divide-and-conquer methodology for leveraging real-world data (RWD) in single-arm trials and randomized controlled trials (RCTs). Utilizing propensity-score stratification with tailored borrowing-by-parts power priors, this approach enables robust borrowing of external data while addressing challenges such as population heterogeneity and temporal effects. The first part provides an overview of the methodology’s application in both single-arm and RCT settings, highlighting key advances and simulation results. The second part demonstrates an R Shiny application that operationalizes these methods, offering an intuitive tool for trial design and analysis. Attendees will also preview features planned for an accompanying R package.
