“亲和力”被定义为“自然或自然的喜欢或同情某人或某事”。这一概念也适用于我们帮助发展的生物制剂（大分子）。单克隆抗体（mAb）或双特异性抗体等药物是理想的药物候选物，因为它们具有非常高的亲和力，与其靶物质或位点结合。鉴于目标，安全性曲线和治疗窗的可变性，了解目标的亲和力的特征以及如何转化靶介导的药物处理（TMDD）的特征。
What is TMDD?
TMDD is the phenomenon in which a drug binds with high affinity to its pharmacological target site (such as a receptor) to such an extent that this affects its pharmacokinetic (PK) characteristics. The concept was first formulated by Gerhard Levy in 1994 and has been the focus of extensive research to improve the understanding and application of TMDD(1)。The target binding and subsequent elimination of the drug-target complexes could affect both drug distribution and elimination, and result in nonlinearity of PK in a dose-dependent manner. This is most commonly observed as linear PK at high dose levels or high concentrations and nonlinear PK at low dose levels or low concentrations.
TMDD PK Models & Characteristics
Successful development of biologics requires accurate prediction of human exposure. This is more challenging for biologics demonstrating TMDD. Due to the nonlinearity of exposure, simple allometric scaling models cannot be applied. Mechanistic-based models that quantitatively describe the drug–targeting interactions are needed for more accurate simulation and prediction. Over time various TMDD model structures have been proposed and tested against observed data, and / or used for prediction. A representative model structure is shown below.
The structure of these model is commonly determined by factors such as drug dosing route (intravenous or not), drug distribution (number of compartments), location of the target (tissue or blood), and binding kinetics (fast or slow, high or low affinity, reversible binding or not, single target or not, elimination mechanisms, and others).
A key characteristic of TMDD is the dose-dependent pharmacokinetic behavior. The figure below shows representative concentration–time profiles at different dose levels after bolus intravenous administration and application of the model structure from the above figure. The concentration–time profiles can be divided into four phases according to the concentrations. The first phase shows quick decrease in concentration, corresponding to initial binding to target and also distribution into the peripheral compartment. The second phase shows linear elimination, where the target is saturated with drug. At this phase, elimination is mainly by non-target related routes, together with fixed rate of target mediated elimination, which is negligible. The PK is largely linear. At the third phase, the concentration becomes lower so the targets are not all saturated, and both non-target mediated and target mediated elimination routes are important. Nonlinear PK is observed at this phase. At the last phase, the concentration is so low that targets are not saturated, the target mediated elimination becomes the mainly route of elimination, and the PK becomes linear again.
给定的马药物的浓度时间配置文件y not show all the phases, depending on the model structure and other factors including binding kinetics, target turnover rates, elimination of drug, and drug-target complex. Another common factor is the bioanalysis detection sensitivity. High detection limits may limit detection at the lower end of the profiles. Anti-drug antibodies (ADA) are also common for biologics and can impact the shapes of the concentration–time profiles. Understanding the characteristics of TMDD PK profiles can be helpful in identifying whether the nonlinearity is due to TMDD or ADA.
When to use a TMDD model and model structure determination
A TMDD model may not be needed in all cases. Determining when to use a TMDD model is based on factors such as the type of molecule, the shape of the concentration–time profiles, and the noncompartmental pharmacokinetic analysis (NCA) results. Biologics are more likely to show TMDD in their PK profiles because they are designed to bind to their target with high affinity. However, small molecules can also have TMDD kinetics. The shape of the concentration – time profiles and NCA analysis results can indicate if non-linear clearance (CL) and volume of distribution (V) are indicators of TMDD kinetics. TMDD model structure should be determined based on information on the drugs and the pharmacological mechanisms. These could include the properties of the targets (soluble or not, locations, concentrations, and binding kinetics with the drugs). Such information or parameters may be measuredin vitro, or estimated by data-fitting if the concentrations can be measured where targets are not saturated.
应用TMDD模型为人类PK预测
Multiple studies have shown that, in general, PK data from nonhuman primates (NHP) are more preferable and recommended for predicting human mAb PK(2)。这是基于观察到，由于NHP和人类之间观察到的序列同源性更大，大多数治疗性mAb更常见于啮齿动物抗原的抗原。PK参数（CL和V）可以全缩放到人，或者通过使用物种不变时间方法将浓度 - 时间谱转换为人类来估计。目标相关参数通常在猴子和人类之间保持相同，或者使用实验确定的值（如果有）。可以执行灵敏度分析以估计各个参数的影响。在预测患者的PK时，应注意小心，因为疾病状态的参数可能与在健康状态或NHP中观察到的参数显着不同，因此需要根据可用的信息进行评估或证明。
每个生物药物候选人在机制和性能方面是独一无二的，而且还具有一些类似的性质和追求普遍的发展策略。它拥有经验和仔细调查，以发展成功的TMDD模型和对专业科学的高亲和力。Visit our site for more information.
参考
- DUA，P.等人，靶介导的药物处理（TMDD）模型的教程。CPT Pharmacometrics Syst Pharmacol.2015;4(6):324-337.
- Aman, P., et al., Quantitative prediction of human pharmacokinetics for mAbs exhibiting target-mediated disposition. AAPSJ, 2015; 17(2), 389-399.
