Concentration/dose addition (CA) is widely used for compounds that act by similar mechanisms. But, CA cannot make predictions for mixtures of full and partial agonists for effect levels above that of the least efficacious component. As partial agonists are common, we developed Generalized Concentration Addition (GCA), which has been successfully applied to systems in which ligands compete for a single binding site. Here, we applied a pharmacodynamic model for a system with two binding sites, the androgen receptor (AR). AR acts according to the classic homodimer activation model: each cytoplasmic AR protein binds ligand, undergoes a conformational change that relieves inhibition of dimerization, and binds to DNA response elements as a dimer. We generated individual dose-response data for full (dihydroxytestosterone, BMS564929) and partial (TFM-4AS-1) agonists and a competitive antagonist (MDV3100) using reporter data generated in the MDA-kb2 cell line. We used the Schild method to estimate the binding affinity of AR for MDV3100. Data for individual compounds fit the AR pharmacodynamic model well. The partial agonist had agonistic effects at low effect levels and antagonistic effects at high levels, as predicted by pharmacological theory. The GCA model fit the empirical mixtures data—full/full agonist, full/partial agonist and full agonist/antagonist—as well or better than relative potency factors (a special case of CA) or effect summation. The ability of generalized concentration addition to predict the activity of mixtures of different types of androgen receptor ligands is important as a number of environmental compounds act as partial AR agonists or antagonists.

中文翻译：

---

使用广义浓度加法预测配体混合物对雄激素受体的激活

浓度/剂量添加（CA）被广泛用于通过相似机理起作用的化合物。但是，CA无法预测完全或部分激动剂的混合物，其作用水平高于最不起作用的组分。由于部分激动剂很常见，因此我们开发了通用浓度加成（GCA），该化合物已成功应用于配体竞争单个结合位点的系统。在这里，我们为具有两个结合位点雄激素受体（AR）的系统应用了药效学模型。AR按照经典的同型二聚体激活模型起作用：每个细胞质AR蛋白都结合配体，经历构象变化，从而减轻了对二聚化的抑制作用，并以二聚体的形式结合到DNA响应元件上。我们生成了完整的（二羟基睾丸激素，BMS564929）和部分（TFM-4AS-1）激动剂和竞争性拮抗剂（MDV3100），使用在MDA-kb2细胞系中生成的报告基因数据。我们使用Schild方法估计AR对MDV3100的结合亲和力。单个化合物的数据非常适合AR药效学模型。如药理学理论所预测的，部分激动剂在低水平的激动作用和高水平的拮抗作用。GCA模型适合经验混合物数据（完全/完全激动剂，完全/部分激动剂和完全激动剂/拮抗剂），甚至优于相对效价因子（CA的特殊情况）或效果求和。