NEW DECISION SUPPORTS AND DATABASES FOR DRUG DOSAGE

精确的药物治疗需要剂量方案来实现和维持 尽可能精确地选择治疗目标，最大限度地减少井- 患者反应的已知可变性。 在过去的三年里，我们开发了 为大多数人建立群体药代动力学行为模型的实用方法 单独使用的药物，确定整个概率分布的 参数值，不作传统的假设， 这些分布，能够发现快速和缓慢代谢者， 例如，当他们没有预料到的时候。 NPEM2计划是 操作。 它提供了描述药物行为的有用数据库。 它还没有加上我们的新的'多模式'（MM）剂量 药物剂量决策支持设计师， 最大限度地减少患者对所选治疗药物的反应变异性 目标. 这些都是突破。 临床研究正在进行中。 我们现在建议在这些成功的基础上实施临床 MM剂量设计器的图形用户界面， 社区医院的临床医生，并扩大这些应用 从三室线性模型到大型线性和非线性模型的方法 药代动力学和药效学（效应）模型，以更好地控制 毒副作用，例如，在治疗感染，艾滋病， 和癌症 这将优化多种药物的协调治疗， 将其常见的毒性作用保持在规定的可容忍限度内 （例如，血小板不低于35，000）。 我们还实现了作为数据库的参数， 记录每位患者接受这些药物治疗的质量 以及每个中心给出的（环境噪声参数）。 我们将 也可以将其扩展到大型线性模型。 此外，我们将根据MM剂量的成功实施 设计师，一个新的'主动'剂量设计师，使用剂量和 血清数据作为学习过程中的积极伙伴，利用 我们知道我们会在给药后进行监测。 这位新设计师不仅从过去的错误中学习， 剂量和预期的血清数据一起发挥作用， 先进，许多治疗方案，以优化，第一次， 在治疗的同时必须了解病人的过程 他/她同时 我们还将实施这些人口 将建模和控制方法转化为临床应用的实用软件， 社区医院，继续改善患者护理，优化 病人护理和进一步缩短住院时间。

Precise drug therapy demands dosage regimens to achieve and maintain selected therapeutic goals as precisely as possible, minimizing the well- known variability in patient response. In the past 3 years we developed practical methods to model population pharmacokinetic behavior for most drugs used singly, determining the entire probability distribution of the parameter values, making no conventional assumptions as to the shape of those distributions, capable of discovering fast and slow metabolizers, for example, when they were not anticipated. This NPEM2 program is operational. It provides useful databases to describe drug behavior. It has not been coupled with our new 'multiple model' (MM) dosage designer for decision support in drug dosage, which specifically minimizes patient variability in response about the selected therapeutic goals. These are breakthroughs. Clinical studies are in progress. We now propose to build on these successes to implement a clinical graphical user interface for the MM dosage designer to make it usable by clinicians in community hospitals, and to extend the application of these methods from 3 compartment linear models to large linear and nonlinear pharmacokinetic and pharmacodynamic (effect) models, to better control toxic side effects, for example, seen in treatment of infections, AIDS, and cancer. This will optimize coordinated therapy with multiple drugs, holding their common toxic effects within stated tolerable limits (platelets not below 35,000, for example). We have also implemented parameters which serve as database for documenting the quality of care each patient receives with these drugs and which each center gives (environmental noise parameters). We will extend these to large linear models as well. Further, we will implement, based on the success of the MM dosage designer, a new 'Active' dosage designer which uses both the dose and the serum data as active partners in the learning process, taking advantage of the fact that we know we will monitor the regimen after it is given. Instead of learning only from past mistakes, this new designer used both the dosage and the anticipated serum data together to play out, in advance, many therapeutic scenarios, to optimize, for the first time, the process of having to learn about the patient while having to treat him/her at the same time. We will also implement these population modeling and control methods into practical software for clinical use in community hospitals, to continue to improve patient care, to optimize patient care and further shorten hospital stay.