Deák Katalin
Semmelweis Egyetem, Gyógyszerészi Kémiai Intézet, 1092 Budapest, Hogyes Endre u. 9.
Acta Pharm Hung. 2008;78(3):110-20.
Physico-chemical profiling is a fundamental tool at the early stage of drug discovery in screening drug-like candidates. Complex physico-chemical profiling, including molecular properties such as solubility, ionization, lipophilicity and permeability, has been found to be of predictive power in ADME (absorption, distribution, metabolism, elimination). In the present thesis work, the physico-chemical properties of centrally acting compounds were investigated. We determined the protonation constants (K), the partition coeffitient in octanol/water (Poct) and cyclohexane/water (Pch) systems of antidepressive sertraline and 15 antipsychotic piperidine and piperazine derivatives and calculated the delta logP (logPoct-logPch) values of the molecules. Due to the poor water solubility of the compounds potentiometry using the "co-solvent" technique was applied for the determination of the protonation constants. The logP values were measured by the dual-phase potentiometric titration in octanol/water system and the traditional shake-flask method was used in cyclohexane/water system. Highly precise physico-chemical data were obtained by these validated methods. The relationship between the structure of the molecules and the physico-chemical data was investigated. The pharmacokinetic properties of the compounds were predicted by the physico-chemical parameters. Linear relationship has been found between the brain penetration characterized by the logBB values and the delta logP values. The validity of the equation was controlled by the delta logP and the logBB values of sertraline.
物理化学性质分析是药物发现早期筛选类药物候选物的一项基本工具。复杂的物理化学性质分析,包括诸如溶解度、离子化、亲脂性和渗透性等分子性质,已被发现对药物的吸收、分布、代谢和排泄(ADME)具有预测能力。在本论文工作中,对中枢作用化合物的物理化学性质进行了研究。我们测定了抗抑郁药舍曲林以及15种抗精神病哌啶和哌嗪衍生物在辛醇/水(Poct)和环己烷/水(Pch)体系中的质子化常数(K)、分配系数,并计算了这些分子的δlogP(logPoct - logPch)值。由于这些化合物水溶性较差,采用“共溶剂”技术的电位滴定法来测定质子化常数。logP值通过辛醇/水体系中的双相电位滴定法测量,而环己烷/水体系则采用传统的摇瓶法。通过这些经过验证的方法获得了高精度的物理化学数据。研究了分子结构与物理化学数据之间的关系。通过物理化学参数预测了这些化合物的药代动力学性质。已发现以logBB值表征的脑渗透性与δlogP值之间存在线性关系。通过舍曲林的δlogP和logBB值对该方程的有效性进行了验证。