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采用定量系统药理学方法模拟常见药物治疗和基因型对阿尔茨海默病认知轨迹的影响。

Simulating the Effects of Common Comedications and Genotypes on Alzheimer's Cognitive Trajectory Using a Quantitative Systems Pharmacology Approach.

机构信息

Certara-SimCyp - UK, Berwyn, PA, USA.

In Silico Biosciences, Lexington, MA, USA.

出版信息

J Alzheimers Dis. 2020;78(1):413-424. doi: 10.3233/JAD-200688.

Abstract

BACKGROUND

Many Alzheimer's disease patients in clinical practice are on polypharmacy for treatment of comorbidities.

OBJECTIVE

While pharmacokinetic interactions between drugs have been relatively well established with corresponding treatment guidelines, many medications and common genotype variants also affect central brain circuits involved in cognitive trajectory, leading to complex pharmacodynamic interactions and a large variability in clinical trials.

METHODS

We applied a mechanism-based and ADAS-Cog calibrated Quantitative Systems Pharmacology biophysical model of neuronal circuits relevant for cognition in Alzheimer's disease, to standard-of-care cholinergic therapy with COMTVal158Met, 5-HTTLPR rs25531, and APOE genotypes and with benzodiazepines, antidepressants, and antipsychotics, all together 9,585 combinations.

RESULTS

The model predicts a variability of up to 14 points on ADAS-Cog at baseline (COMTVV 5-HTTLPRss APOE 4/4 combination is worst) and a four-fold range for the rate of progression. The progression rate is inversely proportional to baseline ADAS-Cog. Antidepressants, benzodiazepines, first-generation more than second generation, and most antipsychotics with the exception of aripiprazole worsen the outcome when added to standard-of-care in mild cases. Low dose second-generation benzodiazepines revert the negative effects of risperidone and olanzapine, but only in mild stages. Non APOE4 carriers with a COMTMM and 5HTTLPRLL are predicted to have the best cognitive performance at baseline but deteriorate somewhat faster over time. However, this effect is significantly modulated by comedications.

CONCLUSION

Once these simulations are validated, the platform can in principle provide optimal treatment guidance in clinical practice at an individual patient level, identify negative pharmacodynamic interactions with novel targets and address protocol amendments in clinical trials.

摘要

背景

在临床实践中,许多阿尔茨海默病患者采用多种药物治疗合并症。

目的

虽然药物之间的药代动力学相互作用已经得到了相对较好的确立,并制定了相应的治疗指南,但许多药物和常见的基因型变异也会影响认知轨迹相关的中枢脑回路,导致复杂的药效学相互作用和临床试验中的很大变异性。

方法

我们应用了一种基于机制和 ADAS-Cog 校准的定量系统药理学脑神经元回路模型,用于治疗阿尔茨海默病的标准胆碱能治疗,包括 COMTVal158Met、5-HTTLPR rs25531 和 APOE 基因型,以及苯二氮䓬类药物、抗抑郁药和抗精神病药,共 9585 种组合。

结果

该模型预测在基线时 ADAS-Cog 可达到 14 个点的变异性(COMTVV5-HTTLPRssAPOE4/4 组合最差),且进展率的范围为四倍。进展率与基线 ADAS-Cog 呈反比。在轻度病例中,抗抑郁药、苯二氮䓬类药物、第一代药物多于第二代药物,以及除阿立哌唑以外的大多数抗精神病药与标准治疗联合使用时会使病情恶化。低剂量第二代苯二氮䓬类药物可逆转利培酮和奥氮平的负面作用,但仅在轻度阶段。非 APOE4 携带者的 COMTMM 和 5HTTLPRLL 基因型在基线时预测具有最佳认知表现,但随时间推移会稍微更快地恶化。然而,这种影响会被共用药显著调节。

结论

一旦这些模拟得到验证,该平台原则上可以在个体患者层面为临床实践提供最佳治疗指导,识别与新靶点的负性药效学相互作用,并解决临床试验中的方案修订。

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