PO.BCS01.17 · 生物信息与计算
获批联合疗法中最严重的不良事件发生率与独立作用一致
Most severe adverse events in approved combination therapies occur at rates consistent with independent action
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言:癌症联合疗法应用广泛,但耐受性可能限制其应用。尽管约95%的获批联合疗法在无进展生存期上显示出相加或低于相加的效应(Hwangbo等人(2023)),但严重不良事件(AE)是否遵循类似模式仍不清楚。由于3-4级AE在临床试验中被一致报告且是临床试验结局的关键决定因素,我们评估了3-4级联合AE发生率是遵循各药物的独立作用,还是遵循最大单药发生率(MMI)模型——在该模型中,预期的联合发生率等于两者中较大的单药值。
方法:我们系统识别了29项包含匹配单药治疗组的联合疗法III期试验,并提取了所报告的所有3-4级AE(A对B对A+B)。联合AE发生率在独立作用的零假设下计算:P(A+B) = 1 - (1-P(A))(1-P(B))(Palmer等人(2017))。使用带Benjamini-Hochberg校正的Wald z分数量化对独立作用的偏离。我们还比较了独立作用模型与MMI模型能更好解释的AE数量,以及单药AE发生率是否影响结果,并按癌症类型、药物类别和AE类别进行了亚组分析。
结果:在所有3-4级AE中,6%(12/198)大幅超过预期发生率,23%(45/198)低于预期,71%(141/198)保持在预期的0.5-2.0倍范围内。以均方误差(MSE)衡量的对零假设的偏离在不同药物类别间差异显著:免疫治疗组合显示最小MSE(0.0003),不含免疫治疗的实体瘤居中(0.0028),血液系统癌症偏离最大(0.0121)。独立作用解释的AE数量(130)多于MMI模型(68),且准确度不依赖于单药AE发生率。许多血液系统AE低于独立作用预期,与骨髓抑制等共享机制一致,而免疫治疗AE与独立作用一致,提示存在导致毒性的不同通路。
结论:联合疗法的严重不良事件发生率主要可用独立作用解释,与此前关于联合疗法疗效的发现相似。发生率"高于预期"(6%)或"低于预期"(22%)的AE比例,与"高于预期"疗效(5%)和"低于预期"疗效(27%)的比例有趣地相似(Hwangbo等人(2017))。这些结果表明,毒性与疗效一样,主要由独立药物作用和患者间异质性驱动,协同或拮抗毒性并不常见。联合AE的可预测性支持将毒性建模纳入试验设计,以改善联合方案的选择以及剂量递增或递减策略。
查看英文原文 English abstract
Introduction: Cancer combination therapies are widely used, but tolerability may limit their application. Although ~95% of approved combination therapies show additive or less-than-additive effects on Progression-Free Survival (Hwangbo et al. (2023)), it remains unclear whether severe AEs follow similar patterns. Since grade 3-4 AEs are consistently reported in clinical trials and critical determinants of clinical trial outcomes, we assessed whether grade 3-4 combination AE incidence follows independent action of individual drugs or by a model of maximum-monotherapy-incidence (MMI), in which the expected combination incidence equals the greater monotherapy value.
Methods: We systematically identified 29 phase III trials of combination therapy that included matching monotherapy arms and extracted all grade 3-4 AEs reported (A vs. B vs. A+B). Combination AE incidences were calculated under the null hypothesis of independent action: P(A+B) = 1 - (1-P(A))(1-P(B)) (Palmer et al. (2017)). Deviations from independent action were quantified using Wald z-scores with Benjamini-Hochberg adjustment. We also compared the number of AEs better explained by independent action versus MMI model, and whether monotherapy AE incidence affected the results, and performed subgroup analyses by cancer type, drug class, and AE category.
Results: Across all grade 3-4 AEs, 6% (12/198) largely exceeded expected incidence, 23% (45/198) fell below it, and 71% (141/198) remained within a 0.5-2.0 fold range of expectation. Deviation from the null, measured as mean squared error (MSE), varied markedly by drug class: immunotherapy combinations showed the least MSE (0.0003), solid cancers without immunotherapy were intermediate (0.0028), and hematologic cancers deviated most (0.0121). Independent action explained more AEs (130) than the MMI model (68), and accuracy did not depend on monotherapy AE incidence. Many hematologic AEs were below independence, consistent with shared mechanisms such as bone marrow suppression, whereas immunotherapy AEs aligned with independence, suggesting distinct pathways to toxicity.
Conclusions: The incidences of serious adverse events from combination therapies are predominantly explained by independent action, similar to prior findings on efficacy of combination therapies. The proportion of AEs with ‘more than expected' (6%) or ‘less than expected' (22%) incidence are interestingly similar to rates of ‘more than expected' efficacy (5%) and ‘less than expected' efficacy (27%) (Hwangbo et al. (2017)). These results indicate that toxicity, like efficacy, is driven mainly by independent drug action and inter-patient heterogeneity, with synergistic or antagonistic toxicities being uncommon. The predictability of combination AEs supports integrating toxicity modeling into trial design to improve combination selection and escalation or de-escalation strategies.
利益披露 Disclosure
K. Kim, None..
R. Bollapalli, None.