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Antibodies Predict Pegaspargase Allergic Reactions and Failure of Rechallenge.

ABSTRACT: PURPOSE:Pegaspargase (PEG-ASP) has largely replaced native Escherichia coli asparaginase (L-ASP) in the treatment of acute lymphoblastic leukemia because of its longer half-life and lower immunogenicity. Risk factors for allergic reactions to PEG-ASP remain unclear. Here, we identify risk factors for reactions in a front-line acute lymphoblastic leukemia trial and assess the usefulness of serum antibodies for diagnosing allergy and predicting rechallenge outcome. PATIENTS AND METHODS:PEG-ASP was administered to 598 patients in St Jude's Total XVI study. Results were compared with Total XV study (ClinicalTrials.gov identifiers: NCT00549848 and NCT00137111), which used native L-ASP. Serum samples (n = 5,369) were analyzed for anti-PEG-ASP immunoglobulin G by enzyme-linked immunosorbent assay. Positive samples were tested for anti-polyethylene glycol (PEG) and anti-L-ASP. We analyzed potential risk factors for reactions and associations between antibodies and reactions, rechallenge outcomes, and PEG-ASP pharmacokinetics. RESULTS:Grade 2 to 4 reactions were less common in the Total XVI study with PEG-ASP (81 [13.5%] of 598) than in the Total XV study with L-ASP (169 [41.2%] of 410; P = 1.4 × 10-23). For Total XVI, anti-PEG, not anti-L-ASP, was the predominant component of anti-PEG-ASP antibodies (96%). In a multivariable analysis, more intrathecal therapy (IT) predicted fewer reactions (P = 2.4 × 10-5), which is consistent with an immunosuppressant contribution of IT. Anti-PEG-ASP was associated with accelerated drug clearance (P = 5.0 × 10-6). Failure of rechallenge after initial reactions was associated with anti-PEG-ASP (P = .0078) and was predicted by the occurrence of angioedema with first reaction (P = .01). CONCLUSION:Less IT therapy was the only independent clinical risk factor for reactions to PEG-ASP. PEG, and not L-ASP, is the major antigen that causes allergic reactions. Anti-PEG-ASP has utility in predicting and confirming clinical reactions to PEG-ASP as well as in identifying patients who are most likely to experience failure with rechallenge.

SUBMITTER: Liu Y

PROVIDER: S-EPMC6804844 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Antibodies Predict Pegaspargase Allergic Reactions and Failure of Rechallenge.

Liu Yiwei Y Smith Colton A CA Panetta John C JC Yang Wenjian W Thompson Lauren E LE Counts Jacob P JP Molinelli Alejandro R AR Pei Deqing D Kornegay Nancy M NM Crews Kristine R KR Swanson Hope H Cheng Cheng C Karol Seth E SE Evans William E WE Inaba Hiroto H Pui Ching-Hon CH Jeha Sima S Relling Mary V MV

Journal of clinical oncology : official journal of the American Society of Clinical Oncology 20190612 23

<h4>Purpose</h4>Pegaspargase (PEG-ASP) has largely replaced native <i>Escherichia</i> <i>coli</i> asparaginase (L-ASP) in the treatment of acute lymphoblastic leukemia because of its longer half-life and lower immunogenicity. Risk factors for allergic reactions to PEG-ASP remain unclear. Here, we identify risk factors for reactions in a front-line acute lymphoblastic leukemia trial and assess the usefulness of serum antibodies for diagnosing allergy and predicting rechallenge outcome.<h4>Patient ...[more]

PMID: 31188727

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Project description:Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles, respectively, are not high throughput, are not generalizable or scalable, and lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry data set consisting of 1630 full multistep reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top-ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of nonproductive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal ( http://cdb.ics.uci.edu) under the Toolkits section.

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Antibodies Predict Pegaspargase Allergic Reactions and Failure of Rechallenge.

Publications

Antibodies Predict Pegaspargase Allergic Reactions and Failure of Rechallenge.

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