Department of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, An der Immenburg 4, 53121, Bonn, Germany.
Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Hospital, Bonn, Germany.
Cancer Chemother Pharmacol. 2020 Sep;86(3):435-444. doi: 10.1007/s00280-020-04128-7. Epub 2020 Aug 27.
The inclusion of the patient's perspective has become increasingly important when reporting adverse events and may assist in management of toxicity. The relationship between drug exposure and toxicity can be quantified by combining Markov elements with pharmacometric models. A minimal continuous-time Markov model (mCTMM) was applied to patient-reported outcomes using hand-foot syndrome (HFS) induced by capecitabine anti-cancer therapy as an example.
Patient-reported HFS grades over time of 150 patients from two observational studies treated with oral capecitabine were analyzed using a mCTMM approach. Grading of HFS severity was based on the Common Terminology Criteria for Adverse Events. The model was evaluated by visual predictive checks (VPC). Furthermore, a simulation study of the probability of HFS severity over time was performed in which the standard dosing regimen and dose adjustments according to HFS severity were investigated.
The VPC of the developed dose-toxicity model indicated an accurate description of HFS severity over time. Individual absolute daily dose was found to be a predictor for HFS. The simulation study demonstrated a reduction of severe HFS using the recommended dose adjustment strategy.
A minimal continuous-time Markov model was developed based on patient-reported severity of hand-foot syndrome under capecitabine. Thus, a modeling framework for patient-reported outcomes was created which may assist in the optimization of dosage regimens and adjustment strategies aiming at minimizing symptom burden during anti-cancer drug therapy.
在报告不良反应时，纳入患者观点变得越来越重要，这有助于毒性管理。可以通过将马尔可夫元素与药代动力学模型相结合来量化药物暴露与毒性之间的关系。本文以卡培他滨抗肿瘤治疗引起的手足综合征（HFS）为例，应用最小连续时间马尔可夫模型（mCTMM）来分析患者报告的结局。
采用 mCTMM 方法分析了来自两项接受口服卡培他滨治疗的 150 例患者的 2 项观察性研究中随时间变化的患者报告 HFS 分级。HFS 严重程度分级基于不良事件通用术语标准。通过可视化预测检查（VPC）对模型进行了评估。此外，还进行了 HFS 严重程度随时间变化的概率模拟研究，研究了标准给药方案和根据 HFS 严重程度的剂量调整。
开发的剂量-毒性模型的 VPC 表明，该模型可以准确描述 HFS 随时间的严重程度。个体绝对日剂量被认为是 HFS 的预测因子。模拟研究表明，使用推荐的剂量调整策略可以减少严重 HFS 的发生。
基于卡培他滨下患者报告的 HFS 严重程度，建立了最小连续时间马尔可夫模型。因此，创建了一种患者报告结局的建模框架，可能有助于优化旨在减轻抗肿瘤药物治疗期间症状负担的剂量方案和调整策略。
