Dataset Information

SAEgnal: A Predictive Assessment Framework for Optimizing Safety Profiles in Immuno-Oncology Combination Trials.

ABSTRACT: Combination therapies are an emerging drug development strategy in cancer, particularly in the immunooncology (IO) space. Many combination studies do not meet their safety objectives due to serious adverse events (SAEs). Prediction of SAEs based on evidence from single and combination studies would be highly beneficial. To address the emerging challenge of optimizing the safety and efficacy of combination studies, we have assembled a novel oncology clinical trial data set with 329 trials, 685 arms (279 unique treatment arms), including 200 combinations, 79 mono arms, and 59 curated adverse event categories in the setting of non-small cell lung cancer (NSCLC). We integrated the database with an analytical framework: SAEgnal. Using SAEgnal, we have investigated the difference in the risk of 39 adverse event types between combination and monotherapy arms across a subset of 34 combination trials. We observed different risk profiles between combination and monotherapies; interestingly, while the risk of elevated AST/ALT is lower in combination arms (in 1/8 trials, p-value < 0.05), it is higher for bleeding (7/8 trials, p-value < 0.05). We envisage that the SAEgnal framework would enable rapid predictive analytics of SAEs in oncology and accelerate drug development in oncology.

SUBMITTER: Prokop A

PROVIDER: S-EPMC8378624 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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SAEgnal: A Predictive Assessment Framework for Optimizing Safety Profiles in Immuno-Oncology Combination Trials.

Prokop Andrzej A Zhang Youyi Y Mukhopadhyay Pralay P Khan Faisal F Shameer Khader K

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science 20210517

Combination therapies are an emerging drug development strategy in cancer, particularly in the immunooncology (IO) space. Many combination studies do not meet their safety objectives due to serious adverse events (SAEs). Prediction of SAEs based on evidence from single and combination studies would be highly beneficial. To address the emerging challenge of optimizing the safety and efficacy of combination studies, we have assembled a novel oncology clinical trial data set with 329 trials, 685 ar ...[more]

PMID: 34457169

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Project description:Host immunity recognizes and eliminates most early tumor cells, yet immunological checkpoints, exemplified by CTLA-4, PD-1, and PD-L1, pose a significant obstacle to effective antitumor immune responses. T-lymphocyte co-inhibitory pathways influence intensity, inflammation and duration of antitumor immunity. However, tumors and their immunosuppressive microenvironments exploit them to evade immune destruction. Recent PD-1 checkpoint inhibitors yielded unprecedented efficacies and durable responses across advanced-stage melanoma, showcasing potential to replace conventional radiotherapy regimens. Neverthless, many clinical problems remain in terms of efficacy, patient-to-patient variability, and undesirable outcomes and side effects. In this review, we evaluate recent advances in the immuno-oncology field and discuss ways forward. First, we give an overview of current immunotherapy modalities, involving mainy single agents, including inhibitor monoclonal antibodies (mAbs) targeting T-cell checkpoints of PD-1 and CTLA-4. However, neoantigen recognition alone cannot eliminate tumors effectively in vivo given their inherent complex micro-environment, heterogeneous nature and stemness. Then, based mainly upon CTLA-4 and PD-1 checkpoint inhibitors as a "backbone," we cover a range of emerging ("second-generation") therapies incorporating other immunotherapies or non-immune based strategies in synergistic combination. These include targeted therapies such as tyrosine kinase inhibitors, co-stimulatory mAbs, bifunctional agents, epigenetic modulators (such as inhibitors of histone deacetylases or DNA methyltransferase), vaccines, adoptive-T-cell therapy, nanoparticles, oncolytic viruses, and even synthetic "gene circuits." A number of novel immunotherapy co-targets in pre-clinical development are also introduced. The latter include metabolic components, exosomes and ion channels. We discuss in some detail of the personalization of immunotherapy essential for ultimate maximization of clinical outcomes. Finally, we outline possible future technical and conceptual developments including realistic in vitro and in vivo models and inputs from physics, engineering, and artificial intelligence. We conclude that the breadth and quality of immunotherapeutic approaches and the types of cancers that can be treated will increase significantly in the foreseeable future.

Dataset Information

SAEgnal: A Predictive Assessment Framework for Optimizing Safety Profiles in Immuno-Oncology Combination Trials.

Publications

SAEgnal: A Predictive Assessment Framework for Optimizing Safety Profiles in Immuno-Oncology Combination Trials.

Similar Datasets

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