Mathematical Models in Pharmacodynamics

描述(申请人提供)：决定药物反应的主要因素是控制药代动力学的输入和处置速率、药物在作用部位(生物相)的分布、药物在改变介体或受体水平时的作用机制以及周转和转导过程。在量化药理反应方面的一个重大进展来自于我们认识到，可以使用四个基本(和扩展的)间接反应模型来表征不同的药效作用。这些(和大多数)模型需要使用微分方程组进行分析，而这通常不能完全解析求解。该项目致力于表征和量化作用于周转过程的药物的一般性质，这些过程对许多身体功能、结构或生物标记物是重要的。我们的具体目标包括继续开发与大多数激素和内源性物质相关的前体池产生的反应的扩展间接模型，探索基于多池寿命的间接反应模型，说明来自造血系统的大多数细胞的寿命的可变性，开发具有靶向介导处置的药物的高级模型，说明几种可能的控制因素和与越来越多的生物技术产品相关的反应位点的周转，并扩展间接反应模型以处理几种类型的药物-药物相互作用，这些模型描述了自然协同作用，并适用于许多药物的联合作用，如免疫抑制剂和化疗药物。先进的微积分和模拟方法将被用来寻找这些模型的精确或近似的解或行为，以确定如何控制响应的起始、范围、返回、持续时间、响应的积分和响应的稳态，更容易地从实验数据中恢复参数，并区分可用于描述典型数据的不同模型。这项研究解决了药物开发中对更好地利用生物标记物以及量化和预测药物效果的策略的相当大的需求。这些努力将产生更好的洞察力和方法，以了解和表征与主要生理和药理作用机制有关的药物反应的时间过程。

DESCRIPTION (provided by applicant): The major factors determining drug responses are the input and disposition rates controlling pharmacokinetics, drug distribution to the site of action (biophase), the mechanism of drug action in altering mediator or receptor levels, and turnover and transduction processes. A major advance in quantitating pharmacologic responses came from our recognition that diverse pharmacodynamic effects can be characterized using a family of four basic (and extended) indirect response models. These (and most) models require analysis using differential equations which usually cannot be fully solved analytically. This project seeks to characterize and quantify the general properties of drugs acting on turnover processes which are important for numerous body functions, structures, or biomarkers. Our specific aims include continued development of extended indirect models for responses generated from a precursor pool as relevant for most hormones and endogenous substances, explore multi-pool lifespan based indirect response models accounting for variability of lifespans of most cells derived from the hematopoietic system, develop advanced models for drugs with target-mediated disposition accounting for several possible control factors and turnover of reactive sites as relevant for a growing array of biotechnology products, and extend indirect response models to handle several types of drug-drug interactions which describe natural synergy and apply to many joint effects of drugs such as immunosuppressants and chemotherapeutic agents. Advanced methods of calculus and simulations will be employed to seek exact or approximate solutions or behaviors for these models, to identify how the onset, extent, return, duration, integrals of response, and steady-state of responses are controlled, to recover parameters more easily from experimental data, and to discriminate among diverse models available to describe typical data. This research addresses the considerable need in drug development for better strategies for utilizing biomarkers and for quantitating and predicting drug effects. These efforts will yield improved insights and methods for understanding and characterizing the time-course of drug responses as related to major mechanisms of physiology and pharmacologic action.