摘要
背景:抗肿瘤坏死因子-α和抗整联蛋白单克隆抗体对于诱导和维持缓解,愈合粘膜和恢复炎症性肠病患者的生活质量显示出极大的益处。然而,这些内在强大的生物制剂的治疗潜力因反应的高度可变性而减弱。一些患者没有从这些治疗中获益,而其他患者随着时间的推移失去了应答。治疗性药物监测(TDM)是进一步改善治疗结果的有希望的工具,通过发现高度可变的临床反应与药代动力学(PK)变异性相关的证据证实。测量药物的血清谷浓度(TC)并调整剂量方案以实现与有益治疗结果相关的目标TC。 TC概念相对简单,但只给出PK的部分见解。 PK曲线应根据患者特异性影响(即协变量)解释可变性。 目标:因此,TDM的目标必须是以实现个人最佳PK概况的方式给予生物体剂量。此外,目前使用的“治疗靶点”算法已被证明可以增加药物的治疗潜力,但剂量方案的适应仍然是可靠的猜测。 结果:用于准确预测药物暴露的临床决策支持工具将显着影响TDM,并建议在临床实践中促进成功实施个体化预测治疗。 结论:本综述提供了一个面向临床医师的综述,介绍了当前的知识水平和个体化预测治疗的未来潜力。
关键词: 抗整合素,炎性肠病,免疫原性,药代动力学,谷浓度,治疗药物监测,模型,肿瘤坏死因子-α.
图形摘要
Current Drug Targets
Title:Pharmacokinetic Modeling and Simulation of Biologicals in Inflammatory Bowel Disease: The Dawning of a New Era for Personalized Treatment
Volume: 19 Issue: 7
关键词: 抗整合素,炎性肠病,免疫原性,药代动力学,谷浓度,治疗药物监测,模型,肿瘤坏死因子-α.
摘要: Background: Anti-tumor necrosis factor-alpha and anti-integrin monoclonal antibodies show great benefits for inducing and maintaining remission, healing the mucosa and restoring the quality of life of patients with inflammatory bowel disease. However, the therapeutic potential of these intrinsically powerful biologicals is abated by a high variability in response. Some patients experience no benefit from these treatments, while others lose response over time. Therapeutic Drug Monitoring (TDM) is a promising tool to further improve therapeutic outcome, substantiated by the finding that highly variable clinical response is correlated with pharmacokinetic (PK) variability. Serum Trough Concentrations (TCs) of the drug are measured and dosage regimens are adapted in order to achieve target TCs that correlate with beneficial therapeutic outcomes. The TC concept is relatively simple but gives only a partial insight in PK. PK profiles should be interpreted in the light of patient specific influences (i.e., covariates) that explain variability.
Objective: Therefore, the aim of TDM must be to dose the biological in such a way that a personal optimal PK profile is achieved. Furthermore, currently used “treat-to-target” algorithms have proven to increase the therapeutic potential of the drugs, but dosage regimen adaptations are still robust guesswork.
Results: A clinical decision support tool for accurately forecasting drug exposure would significantly impact TDM and is suggested to promote successful implementation of individualized predictive treatment in clinical practice.
Conclusion: This review provides a clinician-oriented overview of the state-of-the-art, the gaps in current knowledge and future potential of individualized predictive treatment.
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Cite this article as:
Pharmacokinetic Modeling and Simulation of Biologicals in Inflammatory Bowel Disease: The Dawning of a New Era for Personalized Treatment, Current Drug Targets 2018; 19 (7) . https://dx.doi.org/10.2174/1389450117666160307144329
DOI https://dx.doi.org/10.2174/1389450117666160307144329 |
Print ISSN 1389-4501 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-5592 |
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