Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Pharmacy Building (A15), Sydney, NSW, 2006, Australia.
Westmead Hospital, Sydney, NSW, Australia.
Clin Pharmacokinet. 2024 Aug;63(8):1067-1087. doi: 10.1007/s40262-024-01398-9. Epub 2024 Jul 15.
Saliva is a patient-friendly matrix for therapeutic drug monitoring (TDM) but is infrequently used in routine care. This is due to the uncertainty of saliva-based TDM results to inform dosing. This study aimed to retrieve data on saliva-plasma concentration and subsequently determine the physicochemical properties that influence the excretion of drugs into saliva to increase the foundational knowledge underpinning saliva-based TDM.
Medline, Web of Science and Embase (1974-2023) were searched for human clinical studies, which determined drug pharmacokinetics in both saliva and plasma. Studies with at least ten subjects and five paired saliva-plasma concentrations per subject were included. For each study, the ratio of the area under the concentration-time curve between saliva and plasma was determined to assess excretion into saliva. Physicochemical properties of each drug (e.g. pKa, lipophilicity, molecular weight, polar surface area, rotatable bonds and fraction of drug unbound to plasma proteins) were obtained from PubChem and Drugbank. Drugs were categorised by their ionisability, after which saliva-to-plasma ratios were predicted with adjustment for protein binding and physiological pH via the Henderson-Hasselbalch equation. Spearman correlation analyses were performed for each drug category to identify factors predicting saliva excretion (α = 5%). Study quality was assessed by the risk of bias in non-randomised studies of interventions tool.
Overall, 42 studies including 40 drugs (anti-psychotics, anti-microbials, immunosuppressants, anti-thrombotic, anti-cancer and cardiac drugs) were included. The median saliva-to-plasma ratios were similar for drugs in the amphoteric (0.59), basic (0.43) and acidic (0.41) groups and lowest for drugs in the neutral group (0.21). Higher excretion of acidic drugs (n = 5) into saliva was associated with lower ionisation and protein binding (correlation between predicted versus observed saliva-to-plasma ratios: R = 0.85, p = 0.02). For basic drugs (n = 21), pKa predicted saliva excretion (Spearman correlation coefficient: R = 0.53, p = 0.02). For amphoteric drugs (n = 10), hydrogen bond donor (R = - 0.76, p = 0.01) and polar surface area (R = - 0.69, p = 0.02) were predictors. For neutral drugs (n = 10), protein binding (R = 0.84, p = 0.004), lipophilicity (R = - 0.65, p = 0.04) and hydrogen bond donor count (R = - 0.68, p = 0.03) were predictors. Drugs considered potentially suitable for saliva-based TDM are phenytoin, tacrolimus, voriconazole and lamotrigine. The studies had a low-to-moderate risk of bias.
Many commonly used drugs are excreted into saliva, which can be partly predicted by a drug's ionisation state, protein binding, lipophilicity, hydrogen bond donor count and polar surface area. The contribution of drug transporters and physiological factors to the excretion needs to be evaluated. Continued research on drugs potentially suitable for saliva-based TDM will aid in adopting this person-centred TDM approach to improve patient outcomes.
唾液是一种适合患者的治疗药物监测(TDM)基质,但在常规护理中很少使用。这是由于唾液 TDM 结果不确定,无法告知剂量。本研究旨在检索有关唾液-血浆浓度的数据,并随后确定影响药物排泄到唾液中的理化性质,以增加基于唾液的 TDM 的基础知识。
在 Medline、Web of Science 和 Embase(1974-2023)中搜索了确定唾液和血浆中药物药代动力学的人类临床研究。包括至少有 10 名受试者且每名受试者有 5 对唾液-血浆浓度的研究。对于每项研究,均确定了唾液与血浆之间浓度-时间曲线下面积的比值,以评估药物排泄到唾液中的情况。从 PubChem 和 Drugbank 获得了每种药物的理化性质(例如 pKa、亲脂性、分子量、极性表面积、旋转键和未与血浆蛋白结合的药物分数)。根据药物的离解度对药物进行分类,然后通过 Henderson-Hasselbalch 方程调整蛋白结合和生理 pH 值,预测唾液与血浆的比值。对每个药物类别进行 Spearman 相关性分析,以确定预测唾液排泄的因素(α = 5%)。通过非随机干预研究工具评估研究质量的偏倚风险。
总体而言,纳入了 42 项研究,共涉及 40 种药物(抗精神病药、抗生素、免疫抑制剂、抗血栓药、抗癌药和心脏药物)。两性离子(0.59)、碱性(0.43)和酸性(0.41)组的药物唾液与血浆比值相似,中性组(0.21)的药物比值最低。酸性药物(n = 5)在唾液中的排泄量较高与较低的离解度和蛋白结合有关(预测与观察到的唾液与血浆比值之间的相关性:R = 0.85,p = 0.02)。对于碱性药物(n = 21),pKa 预测了唾液排泄(Spearman 相关系数:R = 0.53,p = 0.02)。对于两性离子药物(n = 10),氢键供体(R = -0.76,p = 0.01)和极性表面积(R = -0.69,p = 0.02)是预测因素。对于中性药物(n = 10),蛋白结合(R = 0.84,p = 0.004)、亲脂性(R = -0.65,p = 0.04)和氢键供体计数(R = -0.68,p = 0.03)是预测因素。被认为可能适用于基于唾液的 TDM 的药物有苯妥英、他克莫司、伏立康唑和拉莫三嗪。这些研究的偏倚风险低至中度。
许多常用药物都排泄到唾液中,药物的离解状态、蛋白结合、亲脂性、氢键供体计数和极性表面积在一定程度上可以预测药物的排泄。需要评估药物转运体和生理因素对排泄的贡献。对可能适用于基于唾液的 TDM 的药物进行持续研究将有助于采用这种以患者为中心的 TDM 方法来改善患者的治疗效果。