Laboratory for Therapeutic and Diagnostic Antibodies, Department of Pharmaceutical and Pharmacological Sciences, Faculty of Pharmaceutical Sciences, Catholic University of Leuven, Leuven, Belgium.
Translational Research in GastroIntestinal Disorders (TARGID), Department of Clinical and Experimental Medicine, Faculty of Medicine, Catholic University of Leuven, University Hospitals Leuven, Leuven, Belgium.
Curr Drug Targets. 2018;19(7):757-776. doi: 10.2174/1389450117666160307144329.
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.
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.
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.
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.
抗肿瘤坏死因子-α和抗整合素单克隆抗体在诱导和维持缓解、愈合黏膜以及恢复炎症性肠病患者生活质量方面显示出巨大的益处。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。一些患者从这些治疗中没有获益，而另一些患者随着时间的推移失去了反应。治疗药物监测（TDM）是进一步提高治疗效果的有前途的工具，这一发现得到了证实，即高度可变的临床反应与药代动力学（PK）变异性相关。测量药物的血清谷底浓度（TC）并调整剂量方案，以达到与有益治疗结果相关的目标 TC。TC 概念相对简单，但仅提供 PK 的部分见解。应根据解释变异性的患者特定影响（即协变量）来解释 PK 曲线。
因此，TDM 的目的必须是通过这种方式给生物制剂进行给药，从而达到个人最佳 PK 曲线。此外，目前使用的“靶向治疗”算法已被证明增加了药物的治疗潜力，但剂量方案的调整仍然是大胆的猜测。
一个用于准确预测药物暴露的临床决策支持工具将对 TDM 产生重大影响，并建议促进个体化预测性治疗在临床实践中的成功实施。
本文综述了个体化预测性治疗的最新技术、现有知识的差距以及未来的潜力，为临床医生提供了一个概述。
