Mathematical Models in Pharmacodynamics

药效学数学模型

• 批准号: 7390715
• 负责人: WILLIAM J. JUSKO
• 金额: $ 25.34万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-08-01 至 2010-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7390715
• 关键词: Accounting; Address; Affect; Area; Behavior; Binding; Binding Sites; Biological Markers; Biotechnology; Blood Cells; Bone Marrow; Calculi; Cells; Characteristics; Complex; Computer Simulation; Condition; Data; Data Set; Development; Drug Interactions; Drug Kinetics; Drug Receptors; Drug effect disorder; Equation; Evolution; Exhibits; Family; Feedback; Goals; Hematopoietic; Hematopoietic System; Hormones; Immunosuppressive Agents; Joints; Laboratories; Lead; Literature; Longevity; Mediating; Mediator of activation protein; Methodology; Methods; Modeling; Non-linear Models; Numbers; Pharmaceutical Preparations; Pharmacodynamics; Pharmacologic Actions; Physiological; Physiology; Population; Process; Production; Property; Rate; Research; Site; Solutions; Structure; System; Techniques; Therapeutic; Therapeutic Agents; Time; Toxic effect; base; cell fixing; chemotherapeutic agent; chemotherapy; concept; drug development; drug distribution; expectation; improved; insight; mathematical model; pharmacodynamic model; receptor; response; simulation; size; therapeutic target

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.

描述（由申请人提供）：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率，药物在作用部位的分布（生物期），药物作用改变介质或受体水平的机制，以及转换和转导过程。我们认识到，不同的药效学效应可以用四种基本（和扩展的）间接反应模型来表征，这在定量药理反应方面取得了重大进展。这些（和大多数）模型需要使用微分方程进行分析，而微分方程通常不能完全解析解决。该项目旨在描述和量化作用于代谢过程的药物的一般特性，这对许多身体功能、结构或生物标志物都很重要。我们的具体目标包括继续发展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寿命的可变性。开发具有目标介导处置的药物的先进模型，考虑到与越来越多的生物技术产品相关的几种可能的控制因素和反应位点的转换，并扩展间接反应模型以处理几种描述自然协同作用的药物-药物相互作用，并适用于药物的许多联合效应，如免疫抑制剂和化疗药物。将采用先进的微积分和模拟方法来寻求这些模型的精确或近似解或行为，以确定如何控制响应的开始，范围，返回，持续时间，积分和稳态响应，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各种模型。这项研究解决了药物开发中对更好的利用生物标志物和定量预测药物效应的策略的巨大需求。这些努力将为理解和描述与生理和药理作用主要机制相关的药物反应的时间过程提供更好的见解和方法。