Blinded Prospective Evaluation of Computer-Based Mechanistic Schizophrenia Disease Model for Predicting Drug Response
Blinded Prospective Evaluation of Computer-Based Mechanistic Schizophrenia Disease Model for Predicting Drug Response
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DOI：
10.1371/journal.pone.0049732
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发表时间：
2012-12-14
期刊：
影响因子：
3.7
通讯作者：
Grace, Anthony A.
中科院分区：
文献类型：
--
作者：
Geerts, Hugo;Spiros, Athan;Grace, Anthony A.
在理解精神分裂症中涉及的神经生物学回路方面取得的巨大进步并没有转化为更有效的治疗方法。另一种策略是使用最近发表的基于临床前生理学、人类病理学和药理学的皮层/皮层下和纹状体回路的计算机机制疾病模型“定量系统药理学”。利用24种不同抗精神病药物的回顾性临床数据，对27个相关的多巴胺、血清素、乙酰胆碱、去甲肾上腺素、γ -氨基丁酸（GABA）和谷氨酸介导的靶点进行了生理学校准。该模型面临着以盲法定量预测两种实验性抗精神病药物临床结果的挑战；JNJ37822681，一种高选择性低亲和力多巴胺D2拮抗剂和奥培酮，一种非常高亲和力的多巴胺D2拮抗剂，仅使用药理学和人类正电子发射断层扫描（PET）成像数据。该模型正确预测了JNJ37822681与奥氮平相比在阳性和阴性综合征量表（PANSS）总分上较低的表现和较高的锥体外症状（EPS）倾斜度，以及奥培酮对奥氮平的相对表现，但没有预测绝对PANSS总分结局和对奥培酮的EPS倾斜度，可能是由于安慰剂反应和EPS评估方法的原因。由于其虚拟性质，这种建模方法可以通过考虑人类药物的独特特性（如人类代谢物、暴露、基因型和脱靶效应）来支持中枢神经系统的研究和开发，并且可以成为药物发现和开发的有用工具。引用本文：Geerts H， Spiros A, Roberts P, Twyman R, Alphs L，等。（2012）基于计算机的机械性精神分裂症疾病模型预测药物反应的盲法前瞻性评价。PLoS ONE 7(12): e49732。doi: 10.1371 / journal.pone.0049732
The tremendous advances in understanding the neurobiological circuits involved in schizophrenia have not translated into more effective treatments. An alternative strategy is to use a recently published 'Quantitative Systems Pharmacology' computer-based mechanistic disease model of cortical/subcortical and striatal circuits based upon preclinical physiology, human pathology and pharmacology. The physiology of 27 relevant dopamine, serotonin, acetylcholine, norepinephrine, gamma-aminobutyric acid (GABA) and glutamate-mediated targets is calibrated using retrospective clinical data on 24 different antipsychotics. The model was challenged to predict quantitatively the clinical outcome in a blinded fashion of two experimental antipsychotic drugs; JNJ37822681, a highly selective low-affinity dopamine D2 antagonist and ocaperidone, a very high affinity dopamine D2 antagonist, using only pharmacology and human positron emission tomography (PET) imaging data. The model correctly predicted the lower performance of JNJ37822681 on the positive and negative syndrome scale (PANSS) total score and the higher extra-pyramidal symptom (EPS) liability compared to olanzapine and the relative performance of ocaperidone against olanzapine, but did not predict the absolute PANSS total score outcome and EPS liability for ocaperidone, possibly due to placebo responses and EPS assessment methods. Because of its virtual nature, this modeling approach can support central nervous system research and development by accounting for unique human drug properties, such as human metabolites, exposure, genotypes and off-target effects and can be a helpful tool for drug discovery and development. Citation: Geerts H, Spiros A, Roberts P, Twyman R, Alphs L, et al. (2012) Blinded Prospective Evaluation of Computer-Based Mechanistic Schizophrenia Disease Model for Predicting Drug Response. PLoS ONE 7(12): e49732. doi:10.1371/journal.pone.0049732