Mathematical Models in Pharmacodynamics

描述（由申请方提供）：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、药物分布至作用部位（生物相）、改变介质或受体水平的药物作用机制以及转换和转导过程。定量药理学反应的一个主要进展来自我们认识到，不同的药效学效应可以使用一个家庭的四个基本（和扩展）间接反应模型的特点。这些（以及大多数）模型需要使用微分方程进行分析，而微分方程通常无法完全解析求解。该项目旨在表征和量化药物作用于周转过程的一般特性，这些周转过程对许多身体功能，结构或生物标志物非常重要。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库产生的反应的扩展间接模型，探索基于多库寿命的间接反应模型，解释来自造血系统的大多数细胞寿命的变化，开发具有靶向作用的药物的先进模型，介导的处置占几个可能的控制因素和营业额的反应位点有关的一系列日益增长的生物技术产品，并扩展了间接反应模型以处理几种类型的药物-药物相互作用，其描述了自然协同作用并适用于诸如免疫抑制剂和化疗剂的药物的许多联合作用。微积分和模拟的先进方法将被用来寻求这些模型的精确或近似的解决方案或行为，以确定如何控制响应的开始，程度，回报，持续时间，积分和响应的稳态，更容易从实验数据中恢复参数，并区分不同的模型来描述典型的数据。这项研究解决了药物开发中对利用生物标志物以及定量和预测药物作用的更好策略的巨大需求。这些努力将产生更好的见解和方法，用于理解和表征与生理学和药理学作用的主要机制相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.