利用反向药理图谱的新型假设生成计算工作流程--从药物再利用的角度看重度抑郁障碍的异曲非林。

IF 6.8 2区 医学 Q1 PHARMACOLOGY & PHARMACY
Mietha Magdalena van der Walt, Arnold Petrus Smith
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引用次数: 0

摘要

背景和目的:药物再利用(DR)为传统药物发现的漫长、资源密集型过程提供了一个令人信服的替代方案。药物再利用是一种低风险、低成本的策略,是为已获批准的药物确定替代临床应用的过程。在 DR 的早期假设生成阶段,计算方法至关重要。然而,"大规模 "数据检索仍然是一项重大挑战。解决这些局限性的计算工作流程可能会改善假设的生成,最终造福患者并推动 DR 研究:实验方法:我们介绍了一种新颖的计算工作流程(结合了可免费访问的计算平台),为预先批准的药物是否适合再利用提供 "概念证明"。其中包括三个关键阶段:靶点钓取(通过反向药效图谱)、靶点识别(通过疾病和药物靶点通路识别)以及文献和类药物回顾性分析(通过硅学 ADMET 特性测定)。伊斯替菲林是一种已获批准的帕金森病药物,但文献归因的抗抑郁特性尚不明确。在实际应用中,我们在重度抑郁症(MDD)的背景下评估了伊曲替菲林的抗抑郁活性:主要结果:通过靶点识别进行数据挖掘,发现异曲非林可能是一种新型抗抑郁药物。检索到的药物靶点(KYNU、MAO-B、ALOX12和PLCB2)与选定的MDD通路(色氨酸代谢和5-羟色胺能突触)相关,由此产生了一个假设:异曲飞林能提高细胞外5-羟色胺水平(抑制MAO-B)并减轻炎症(抑制KYNU、ALOX12和PLCB2):实际应用的工作流程生成的假设与已知实验数据一致,验证了这一新型计算工作流程的有效性。它是一种低风险、低成本的 DR 计算工具,可为探索已批准药物的其他临床应用提供鸟瞰图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel hypothesis-generating computational workflow utilizing reverse pharmacophore mapping-A drug repurposing perspective of istradefylline towards major depressive disorder.

Background and purpose: Drug repurposing (DR) offers a compelling alternative to traditional drug discovery's lengthy, resource-intensive process. DR is the process of identifying alternative clinical applications for pre-approved drugs as a low-risk and low-cost strategy. Computational approaches are crucial during the early hypothesis-generating stage of DR. However, 'large-scale' data retrieval remains a significant challenge. A computational workflow addressing such limitations might improve hypothesis generation, ultimately benefit patients and advance DR research.

Experimental approach: We introduce a novel computational workflow (combining free-accessible computational platforms) to provide 'proof-of-concept' of the pre-approved drug's suitability for repurposing. Three key phases are included: target fishing (via reverse pharmacophore mapping), target identification (via disease- and drug-target pathway identification) and retrospective literature and drug-like analysis (via in silico ADMET properties determination). Istradefylline is a Parkinson's disease-approved drug with literature-attributed antidepressant properties remaining unclear. Practically applied, istradefylline's antidepressant activity was assessed in the context of major depressive disorder (MDD).

Key results: Data mining aided by target identification resulted in istradefylline potentially representing a novel antidepressant drug class. Retrieved drug targets (KYNU, MAO-B, ALOX12 and PLCB2) associated with selected MDD pathways (tryptophan metabolism and serotonergic synapse) generated a hypothesis that istradefylline increased extracellular 5-HT levels (MAO-B inhibition) and reduced inflammation (KYNU, ALOX12 and PLCB2 inhibition).

Conclusion and implications: The practically applied workflow's generated hypothesis aligns with known experimental data, validating the effectiveness of this novel computational workflow. It is a low-risk and low-cost DR computational tool providing a bird's-eye view for exploring alternative clinical applications of pre-approved drugs.

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来源期刊
CiteScore
15.40
自引率
12.30%
发文量
270
审稿时长
2.0 months
期刊介绍: The British Journal of Pharmacology (BJP) is a biomedical science journal offering comprehensive international coverage of experimental and translational pharmacology. It publishes original research, authoritative reviews, mini reviews, systematic reviews, meta-analyses, databases, letters to the Editor, and commentaries. Review articles, databases, systematic reviews, and meta-analyses are typically commissioned, but unsolicited contributions are also considered, either as standalone papers or part of themed issues. In addition to basic science research, BJP features translational pharmacology research, including proof-of-concept and early mechanistic studies in humans. While it generally does not publish first-in-man phase I studies or phase IIb, III, or IV studies, exceptions may be made under certain circumstances, particularly if results are combined with preclinical studies.
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