This is a phase 2 study, designed as a European multicentre 6-month double blind random-ized controlled trial (RCT) of AZD0530 versus matched placebo, followed by a 12 month trial comparing open-label extended AZD0530 treatment with historical control data.
Study population: Male and female adult patients aged 18 years and older with a diagnosis of FOP who meet the inclusion (active disease) and exclusion criteria will be eligible for participation in this study. The total number of enrolled patients will be 20.
Intervention: Patients will be randomized to receive either AZD0530 100mg once daily or matched placebo, taken orally for the first 6 months, immediately followed by an open-label extension in which all patients will receive AZD0530 100mg once daily oral dose for a further 12 months.
Endpoints: Endpoints include objective change in heterotopic bone volume measured by low-dose whole-body computer tomography (CT) , [18F] NaF Positron Emission Tomography (PET) activity and patient reported outcome measures.

开始日期2020-08-05

申办/合作机构

Stichting Onderzoek Cancer Center Amsterdam

[+6]

NCT03661125

/ Unknown status早期临床1期

A Randomised, Balanced, Double-blind Two-way Crossover Design Study to Evaluate the Effects of SRC Kinase Inhibitor, Saracatinib, on Brain Activity Associated With Visual Processing in Patients With Parkinson's Disease Psychosis.

Parkinson's disease is often characterised by movement symptoms such as rigidity and bradykinesia, however, there are a number of non-motor symptoms that can have a significant impact on quality of life. One of the most common non-motor symptoms of Parkinson's disease is visual hallucinations (where someone sees things that don't exist outside their mind). . Recent findings led to the approval of a drug called Pimavanserin as a treatment for PD psychosis in the USA. Based on other recent studies, we believe that Saracatinib, a drug that interacts within the same system as Pimavanserin, is a potential treatment for PD psychosis. Saracatinib has shown to reduce the intensity of the psychedelic effect induced by psilocybin (a naturally occurring psychedelic found in psilocybe mushrooms) and attenuate social cognition and brain changes in healthy volunteers. The aim of this study is to test the effects of 14 days dosing of saracatinib or placebo on 30 volunteers with PD psychosis. We aim to to use neuroimaging combined with psychopharmacology to provide evidence that a putative new treatment approach can modulate abnormal visual cortex activation in patients with PD psychosis. If positive, this proof of mechanism study would provide a strong platform to pursue symptom modification studies with Saracatinib.

开始日期2019-04-11

申办/合作机构

King's College London

[+2]

NCT02955186

/ Completed临床2期IIT

Fyn Kinase Inhibitors and Alcohol Drinking

The purpose of this study is to evaluate the effects of the study medication, saracatinib/AZD0530 (placebo or 125 mg/day) on alcohol drinking behavior in a laboratory setting in which participants are given an initial drink of alcohol followed by the choice to drink up to 12 more drinks over a three-hour period. The investigators hypothesize that saracatinib will reduce craving and number of drinks consumed prior to and after exposure to the initial drink of alcohol and during the three hour drinking period.

开始日期2017-05-09

申办/合作机构

Yale University

[+1]

100 项与塞卡替尼相关的临床结果

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100 项与塞卡替尼相关的转化医学

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100 项与塞卡替尼相关的专利（医药）

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项与塞卡替尼相关的文献（医药）

2025-11-01·COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING

A Novel Prognostic Risk Model Based on Oxidative Stress to Predict Survival and Improve Treatment Strategies in Stomach Adenocarcinoma

Article

作者: Li, Xuezhi ; Qu, Xiaodong ; Zhang, Jie ; Yao, Nuo ; Li, Songbo ; Shi, Yongquan ; Zhao, Xingyu ; Lin, Kexin

Background::

Stomach adenocarcinoma (STAD) is the fifth most common tumor worldwide, imposing a significant disease burden on populations, particularly in Asia. Oxidative stress is well-known to play an essential role in the occurrence and progression of malignancies. Our study aimed to construct a prediction model by exploring the correlation between oxidative stress-related genes and the prognosis of patients with STAD.

Method::

STAD data from TCGA were used to identify the differentially expressed oxidative stress-related genes (OSGs), with data from GEO serving as the validation cohort. Univariate Cox and LASSO regression analyses were performed to select prognosis-related genes for the risk model, which was then integrated with clinical features into a nomogram. The physiological functions and pathways of these identified genes were explored using GO and KEGG analyses. After evaluating the prediction value of the prognostic model in the GEO cohort, drug sensitivity and immune infiltration were comprehensively analyzed using R. Expression levels of the prognostic genes were verified by quantitative real-time PCR in gastric cancer and paired normal tissues.

Results::

Cox regression and LASSO regression analysis identified SERPINE1, VHL, CD36, NOS3, ANXA5, ADCYAP1, POLRMT and GPX3 as the signature genes from 160 differentially expressed OSGs. Both Kaplan–Meier survival analysis and ROC curve at 5 years in the TCGA and the GEO cohort exhibited great predictive ability of the prognostic model, with the AUC >0.7 in TCGA. Validated as an independent risk factor, the model was integrated with clinicopathological variables (including age, stage, and gender) to build a nomogram for more accurate risk stratification. Moreover, therapy sensitivity analysis between the low- and high-risk categories showed that those who scored higher would benefit more from BEZ235, Dasatinib, Pazopanib, and Saracatinib. Meanwhile, differences in the tumor environment, immune infiltration and response to immunotherapy between the two groups were noted. Finally, qRT-PCR validated the differential expression of these genes in STAD and paired normal tissues.

Conclusion::

Our study has effectively established an oxidative stress-related prognostic model, providing a promising tool for personalized clinical strategies and improved STAD patient outcomes.

2025-07-01·British Journal of Pharmacology

High‐throughput screening identifies bazedoxifene as a potential therapeutic for dysferlin‐deficient limb girdle muscular dystrophy

Article

作者: Benabides, Manon ; Grossi, Noella ; Tournois, Johana ; Nissan, Xavier ; Bigot, Anne ; Polentes, Jerome ; Richard, Isabelle ; Bourg, Nathalie ; Brureau, Anthony ; Pellier, Emilie ; Bruge, Celine

Abstract:

Background and Purpose:

Limb‐girdle muscular dystrophy R2 (LGMD R2) is a rare genetic disorder characterised by progressive weakness and wasting of proximal muscles. LGMD R2 is caused by the loss of function of dysferlin, a transmembrane protein crucial for plasma membrane repair in skeletal muscles. This study aimed to identify drugs that could improve the localisation and restore the function of an aggregated mutant form of dysferlin (DYSFL1341P).

Experimental Approach:

We developed an in vitro high‐throughput assay to monitor the expression and reallocation of aggregated mutant dysferlin (DYSFL1341P) in immortalised myoblasts. After screening 2239 clinically approved drugs and bioactive compounds, the ability of the more promising candidates to improve cell survival following hypo‐osmotic shock was assessed. Their protective effects were evaluated on immortalised myoblasts carrying other dysferlin mutations and on dysferlin‐deficient muscle fibres from Bla/J mice.

Key Results:

We identified two compounds, saracatinib and bazedoxifene, that increase dysferlin content in cells carrying the DYSFL1341P mutation. Both drugs improved cell survival and plasma membrane resistance following osmotic shock. Whereas saracatinib acts specifically on misfolded L1341P dysferlin, bazedoxifene shows an additional protective effect on dysferlin KO immortalised myoblasts and mice muscle fibres. Further analysis revealed that bazedoxifene induces autophagy flux, which may enhance the survival of LGMD R2 myofibres.

Conclusion and Implications:

Our drug screening identified saracatinib and bazedoxifene as potential treatments for LGMD R2, especially for patients with the L1341P mutation. The widespread protective effect of bazedoxifene reveals a new avenue toward genotype‐independent treatment of LGMD R2 patients.

2025-06-19·Nature

Selective inhibition of stromal mechanosensing suppresses cardiac fibrosis

Article

作者: Wang, Xi ; Mahajan, Abhay ; Mukherjee, Souhrid ; Thomas, Dilip ; Wang, Mingqiang ; Mullen, McKay ; Leitz, Jeremy ; Thai, Phung N ; Rhee, Siyeon ; Baker, Natalie M ; Wu, Joseph C ; Blau, Helen M ; Paik, David T ; Woo, Y Joseph ; Jahng, James W ; Shin, Hye Sook ; Madl, Christopher M ; Kim, Hyeonyu ; Caudal, Arianne ; Cho, Sangkyun ; Kojic, Ana ; Winn, Virginia D

Matrix-derived biophysical cues are known to regulate the activation of fibroblasts and their subsequent transdifferentiation into myofibroblasts^1-6, but whether modulation of these signals can suppress fibrosis in intact tissues remains unclear, particularly in the cardiovascular system^7-10. Here we demonstrate across multiple scales that inhibition of matrix mechanosensing in persistently activated cardiac fibroblasts potentiates-in concert with soluble regulators of the TGFβ pathway-a robust transcriptomic, morphological and metabolic shift towards quiescence. By conducting a meta-analysis of public human and mouse single-cell sequencing datasets, we identify the focal-adhesion-associated tyrosine kinase SRC as a fibroblast-enriched mechanosensor that can be targeted selectively in stromal cells to mimic the effects of matrix softening in vivo. Pharmacological inhibition of SRC by saracatinib, coupled with TGFβ suppression, induces synergistic repression of key profibrotic gene programs in fibroblasts, characterized by a marked inhibition of the MRTF-SRF pathway, which is not seen after treatment with either drug alone. Importantly, the dual treatment alleviates contractile dysfunction in fibrotic engineered heart tissues and in a mouse model of heart failure. Our findings point to joint inhibition of SRC-mediated stromal mechanosensing and TGFβ signalling as a potential mechanotherapeutic strategy for treating cardiovascular fibrosis.

项与塞卡替尼相关的新闻（医药）

2025-06-15

·DrugAI

Nat. Commun. | 利用大语言模型打造统一框架，破解药物协同效应预测难题

DRUGAI今天为大家介绍的是来自耶鲁大学生物统计学系的赵宏宇教授团队的一篇论文。药物协同效应的预测在癌症等复杂疾病的治疗中具有重要意义，但这一任务仍存在诸多挑战。本文提出了一个名为BAITSAO的统一模型，旨在通过标准化流程处理不同数据集中的协同效应预测任务。研究团队首先基于大语言模型（Large Language Models）生成的上下文嵌入信息，构建了药物和细胞系的初始表示，并据此形成训练数据集。在验证这些嵌入表示具有实际价值后，研究者采用多任务学习（multi-task learning）框架对BAITSAO进行大规模预训练，训练任务经过精心筛选。通过一系列基准测试，BAITSAO在模型结构和预训练策略上均表现出优于现有方法的性能。此外，研究还探讨了模型对输入变化的敏感性，并展示了其在药物发现、药物与基因相互作用预测以及多药组合效果预测等方面的广阔应用潜力。联合用药已成为治疗HIV和癌症等复杂疾病的重要手段，其中关键在于实现药物的协同效应——即联合使用多种药物所产生的治疗效果超过它们单独使用的总和。这种策略不仅能增强疗效，还能降低单药所需剂量，减少耐药性。传统上，研究人员依赖高通量筛选实验来寻找有效的药物组合，但面对成千上万的可能组合，实验成本和时间代价高昂，特别是涉及三种或更多药物时更为困难。因此，发展高效的计算预测方法，结合大规模实验数据和生物学先验知识，成为提升协同效应识别效率的关键。针对这一需求，许多基于深度学习的算法相继提出，如DeepSynergy、TreeComb、MatchMarker等。这些方法主要分为两类：一类直接预测协同得分，另一类则将连续得分转换为二元分类任务。然而，大部分方法未能充分利用公开数据库中的丰富信息，且在处理药物同分异构体或不完整数据集时泛化能力有限。此外，随着数据库持续更新，如何追踪和管理训练数据版本也成为影响模型可信度的重要因素。近年来，大语言模型（Large Language Models, LLM）作为基础模型的代表，已在自然语言处理领域取得显著突破。部分研究尝试将药物协同预测问题转化为问答任务，通过LLM获取药物和细胞系的先验知识来预测未知组合的协同效果。虽然这种方法在一定程度上提高了可解释性，但依赖预设阈值的分类方式可能引入偏差，而且所使用的模型未开源，不利于学术界的进一步验证和拓展。为此，研究团队提出了BAITSAO，一个统一且可扩展的药物协同预测模型。该模型充分利用LLM提供的上下文嵌入信息，作为药物与细胞系的输入表示，在大规模协同效应数据库上通过多任务学习（Multi-Task Learning）进行预训练，兼顾分类与回归任务。更重要的是，BAITSAO支持零样本学习与模型微调，能够对从未见过的药物组合进行有效预测，同时支持多药组合分析及元信息的引入，展现出强大的扩展性和应用潜力。BAITSAO总览BAITSAO模型有两项关键创新。首先，研究团队构建了一套统一的数据处理流程，将药物和细胞系的信息转换为适用于机器学习的表格数据形式，并据此生成可用于多种任务的训练数据集。实验证明，这些由大语言模型生成的嵌入表示中包含了有助于模型预测的生物功能信息。其次，BAITSAO在多任务学习框架下，对不同类型的协同效应预测任务进行统一建模和训练，通过系统实验验证了其模型架构和预训练策略在准确性和泛化能力上的显著优势。图 1此外，BAITSAO支持灵活迁移，能够广泛应用于各类药物协同相关的下游分析任务。图1a 和1b展示了 BAITSAO的整体架构与运行流程，图1c则对比总结了BAITSAO与其他药物协同预测方法之间的主要差异，而图1d概览了BAITSAO支持的核心功能，包括任务扩展、多药组合分析等实际应用场景。来自LLM的药物嵌入反映了功能相似性与细胞级反应本研究利用大语言模型（LLM）生成药物与细胞系的嵌入信息，揭示其在功能识别与细胞响应预测中的潜力。研究团队基于GPT-3.5提取药物和细胞系描述的嵌入向量，并验证其在表达功能信息上的有效性。与GPT-4和Claude 3.5相比，GPT-3.5嵌入在效率与准确性之间取得平衡，且显著优于Gemini。UMAP可视化和相似度分析显示，同一药物在不同描述下的嵌入相似度远高于不同药物之间，说明其具备稳定性与辨别力。图 2进一步比较GPT-3.5嵌入与DrugBank的原始描述，发现三类说明（指征、摘要、背景）的相似度均高于0.87，皮尔森相关系数也显著偏高，验证了嵌入的功能保真性。图2b展示了如何通过嵌入推测未知药物（如MK-4541）的功能。此外，研究将该嵌入用于单细胞转录组（scRNA-seq）中药物扰动的基因表达预测。与传统基于SMILES的方法相比，GPT-3.5嵌入及其与SMILES的联合使用显著提升了预测性能（p < 0.05）。结果表明，LLM嵌入不仅能表征药物功能，还可连接药物与细胞响应，有助于构建更强的药物协同效应预测模型。通过无需预训练的评估来展示强大的嵌入和架构为了验证大语言模型（LLM）嵌入表示与BAITSAO架构本身的有效性，研究团队分别在药物协同预测的回归与分类任务中进行了无预训练评估。结果表明，GPT-3.5默认生成的药物嵌入无需依赖额外的提示工程（如MetaPrompt或Chain-of-Thought），已具备充分功能信息。此外，在使用相同嵌入作为输入的条件下，BAITSAO架构相较DeepSynergy表现更优（p = 0.002），说明模型结构优化对性能提升具有显著贡献。图 3在回归任务中，团队选取D1和D2数据集，分别用皮尔森相关系数（PCC）与均方误差（MSE）作为评估指标；在分类任务中，评估D1和D3数据集，使用ROCAUC和准确率（ACC）指标。图3显示，BAITSAO在四项评估中有三项排名第一，且在深度学习方法中表现最为稳定。相比之下，BERT方法整体表现不如BAITSAO，验证了使用嵌入向量作为输入的优势。实验结果表明，将高质量嵌入与合理架构结合，能够构建出性能更优、泛化能力更强的药物协同预测框架。通过多模态学习，展现BAITSAO对药物基因相互作用和药物细胞系相互作用的可解释性本研究展示了BAITSAO如何通过整合基因表达数据与SHAP解释方法，实现药物与基因、药物与细胞系之间交互机制的可解释性分析。在D3数据集中，作者将细胞系的基因表达信息作为额外模态输入，并通过SHAP评估不同基因对药物协同预测的贡献。以DEXAMETHASONE–DINACICLIB药物组合为例，分析结果显示VIM基因在多个细胞系中具有最高重要性，且该基因已知与多种癌症相关。进一步分析发现，部分高SHAP值基因（如BMP4）在协同与非协同样本间呈显著表达差异（p = 0.0062），并验证为药物靶点。同时，这些基因在表达水平和方差上表现出明显异质性，说明其反映了细胞对药物响应的多样性。GO和MsigDB富集分析亦表明，这些基因涉及多个癌症相关通路。在药物组合层面，图3c显示BAITSAO可区分不同细胞系中药物组合的协同潜力（p < 2.22e-308）。图3d进一步证实预测标签数量与真实标签高度相关，表明模型具备准确识别高协同覆盖药物组合的能力。例如，L778123–MK-8669被预测在最多细胞系中具有协同效应，展现了BAITSAO在药物筛选中的应用潜力。预训练数据的统计特征研究团队使用DrugComb数据库构建了BAITSAO的预训练数据集。DrugComb是当前规模最大的药物协同数据库，包含多个细胞系与药物组合的协同效应信息。经清洗后，数据集中共收录超过70万条药物-细胞系组合。图4a和图4b分别显示这些组合及细胞系在不同组织类型中的分布，以皮肤、淋巴和肺组织最为常见，代表了多种重要的生理组织。图 4图4c展示协同评分的分布，整体偏向低分，说明协同效应具有稀疏性。图4d显示药物的IC₅₀分布，也呈非对称形态。图4e揭示IC₅₀与协同评分之间存在非线性关系，进一步验证了采用神经网络等非线性模型的合理性。图4f分析了不同组织间组合的重叠情况，显示组织来源较为多样，有助于模型在多组织背景下泛化。此外，研究对药物和细胞系的嵌入向量进行了Leiden聚类分析，同一聚类内对象具有功能相似性，进一步验证嵌入在生物学属性上的表示能力。由于DrugComb发布于2021年6月，而GPT-3.5的训练数据截至同年9月，因此可以合理推断本研究使用的数据未出现在GPT-3.5训练集中，确保了模型分析的独立性与可靠性。预训练的BAITSAO有助于多任务条件下的药物协同效应预测研究团队进一步优化了BAITSAO的结构，并在多任务学习框架下进行大规模预训练，显著提升模型在药物协同预测方面的表现。模型设计包括四项任务：协同效应的回归与分类预测，以及两种单药抑制效应的回归预测。最终选定其中三项用于训练，并采用不确定性加权策略平衡各任务损失，提升稳定性。图5a所示的Help-Harm矩阵帮助判断各任务之间的协同关系，发现分类任务能提升其他任务表现，而某些冗余信息（如单药 RI_col）反而削弱分类效果，因此在训练中被剔除。图 5通过图5b与图5c的对比实验证明，多任务预训练下的BAITSAO（BAITSAO-FT）在多个数据集上均优于零样本版本（BAITSAO-ZS）。即使在预训练组合未包含的测试样本上，BAITSAO-ZS依然表现出超过随机水平的分类能力（ACC > 0.5），显示良好的泛化性与效率。此外，研究还展示了BAITSAO在预测三药组合中的应用潜力（图5d, e）。利用Monte Carlo Dropout方法生成置信区间，评估不同第三药物对协同效应的影响。例如，I-BET151相比I-BET显著提高协同得分，PF562271相比Saracatinib也具有更强的抑制效果。这些预测与实验结果高度一致，表明BAITSAO可为复杂药物组合优化和临床设计提供科学支持。灵敏度分析图 6研究通过系统性敏感性分析，深入评估了BAITSAO模型在不同设置下的性能变化与资源适应性。首先，图6a显示，在多种药物与细胞系嵌入组合策略中，将药物嵌入取平均并与细胞嵌入按行堆叠，可在所有任务中取得最优表现，兼顾效率与效果。图6b表明，无需预训练的BAITSAO比传统方法如DeepSynergy和SVM更高效，而经预训练并微调后的模型运行速度更快，且全流程可在单个GPU上完成，具备良好部署潜力。在多任务策略方面，辅助图14和15的消融实验显示，BAITSAO采用的修正版不确定性加权（UW）在分类任务中表现最稳健，相较其他梯度匹配方法更具优势。最终选择每个任务配置一个专属层，达到最佳性能与资源平衡。图6c–d进一步揭示，训练数据占比越高，模型性能越好，分类任务即使在仅使用0.1%数据时仍能获得较高准确率（ROCAUC）。图6e–f表明，模型参数宽度越大，表现越优，符合深度学习中“扩展规律（Scaling Law）”。因此，若计算资源有限，也可选择宽度较小的模型（如 4096）进行部署，在维持性能的同时控制资源消耗，为实际应用提供灵活的配置参考。讨论BAITSAO是一种面向药物协同效应预测的统一模型，结合大语言模型（LLM）嵌入表示与多任务学习（MTL）框架，能够同时完成药物组合的协同预测与单药抑制效果分析。通过统一的数据构建流程，BAITSAO有效解决了跨数据集药物与细胞系名称不一致的问题，并在多项基准测试中表现出领先的分类与回归预测能力。此外，预训练策略显著提升了模型的泛化能力，使其在零样本分类任务中亦能维持良好性能。研究还通过敏感性分析优化了模型结构和参数设置，验证了在小规模数据条件下模型依然具备稳定表现，并展示了模型在参数扩展下的性能提升趋势。尽管如此，BAITSAO在缺乏功能或结构描述的新药物上预测效果有限，未来有望通过整合单细胞数据与全基因组关联研究（GWAS）进一步增强模型在早期药物开发场景中的适用性。编译|于洲审稿|王梓旭参考资料Liu, T., Chu, T., Luo, X. et al. Building a unified model for drug synergy analysis powered by large language models. Nat Commun 16, 4537 (2025). https://doi.org/10.1038/s41467-025-59822-y

核酸药物

2024-11-28

·精准药物

共价药物体外时间依赖性抑制（TDI）数据分析和解读

前言共价药物（Covalent drug）是指在结构中包含反应性的官能团，能与靶蛋白形成共价键，实现与靶蛋白长期结合，从而提供独特的药效学特征。从偶然发现到目标性设计，共价药物在多个疾病领域蓬勃发展，尤其是抗肿瘤领域。自KRAS靶点在癌症中的作用被发现以来，近30年间研究者使用传统药物发现方法一直没有突破这一靶点的不可成药性，而共价抑制剂Sotorasib（Lumakras）和Adagrasib（Krazati）打破了KRAS“不可成药靶点”的魔咒。近年来，多款共价药物成功上市，包括针对EGFR、BTK、KRAS等靶点的抑制剂。细胞色素P450酶（以下简称CYP酶）是体内参与药物代谢最广泛的I相代谢酶，主要位于肝脏，参与约75%的小分子药物代谢。药物对CYP酶的抑制会引起联用药物的暴露量增加或活性代谢产物暴露量减少，造成潜在毒性或影响疗效。抑制评价分为可逆性抑制（Reversible inhibition）和时间依赖性抑制（Time-dependent inhibition, TDI）评价，其中TDI会造成更为严重的药物相互作用（Drug interaction, DDI）。药物本身或其代谢后生成的反应性中间体与CYP酶的共价结合是TDI发生的机理之一，那么共价药物是否一定会发生TDI呢？共价药物的TDI风险比非共价抑制剂更高吗？如何评估TDI风险和解读TDI数据呢？本文分析了29个已上市的共价药物对CYP酶的TDI研究结果，并对体外TDI风险评估的方法和数据解读提供相关建议。上市共价药物对CYP450酶的抑制结果分析据报道，自1947年以来共117种共价药物获批[1]，已上市的共价药物主要涉及抗感染（青霉素、磷霉素）、抗病毒（奈马特韦）和抗肿瘤（奥西替尼、达克替尼）等治疗领域。本文检索了自2000年以来上市的共价药物对CYP酶的体外TDI结果，共整理出29个共价药物的数据，详见文末附表1。其中19个共价药物对至少一种CYP酶表现出TDI，约占65.5%。TDI在7种常见CYP酶的分布汇总如图1，有15个药物对CYP3A存在TDI，约占79%；其次是2C8，5个共价药物对其存在TDI，约占26%；对1A2和2B6存在TDI的共价药物都为3个，占比约16%；有1个共价药物对2C19存在TDI，占比约5%。19个共价药物对2C9和2D6不存在TDI或目前未见报道。图1. 自2000年之后上市的具有TDI的共价药物在常见CYP酶的分布情况一项体外[22]研究评估了14个蛋白激酶抑制剂对CYP2C8和CYP3A的TDI，结果表明经过与NADPH预孵育以后，11个激酶抑制剂对CYP3A的抑制效果增强，3个对CYP2C8的抑制效果增强，（如图2）。其中有8个已被文献报道为CYP3A的TDI抑制剂，另外3个在随后的研究中证实抑制CYP3A的IC50偏移率大于2倍。这14个药物并非共价药物，由此看来，非共价药物尤其是蛋白激酶抑制剂的TDI风险依然不容忽视。对于共价药物和非共价药物，TDI都是需要重点关注的内容。图2. 蛋白激酶抑制剂与NADPH预孵育30min后抑制阿莫地喹N-去乙基化（CYP2C8）和咪达唑仑1'-羟化（CYP3A）的结果。BOSU, bosutinib; BOSU iso, bosutinib isomer 1; CRIZ, crizotinib; DASA, dasatinib; ERLO, erlotinib; GEFI, gefitinib; LEST, lestaurtinib; NILO, nilotinib; PAZO, pazopanib; SARA, saracatinib; SORA, sorafenib; SUNI, sunitinib. [22] 体外TDI风险评估的方法建议监管机构和制药公司已经认识到评估TDI风险的重要性，人用药品技术要求国际协调理事会（ICH）发布的指导原则M12《Drug Interaction Studies》[23]于今年5月21日最终采纳。结合指南推荐，体外评估药物TDI风险的方法总结如下： “ 1 酶活性损失百分比、假一级速率常数kobs和IC50偏移早期药物发现阶段，需要高通量、快速筛选确定CYP酶的TDI潜力，可以通过单一化合物浓度下含和不含NADPH预孵育后损失百分比（%Activity loss），CYP酶活性降低（kobs）或多浓度的化合物含和不含NADPH预孵育后IC50值的偏移(IC50 shift)来判断化合物是否有TDI潜力。 “ 2 TDI抑制的动力学常数测定（kinact、KI）评估CYP酶TDI的重要动力学常数包括最大失活速率常数（kinact）、导致半数最大失活的抑制剂浓度（KI）。采用不同浓度的化合物（至少6个）与酶源（即肝微粒体、肝细胞、重组酶系统）及NADPH进行第1次孵育（至少6个时间点），也称失活孵育；在特定的孵育时间后，将失活孵育混合物加入含有CYP同工酶探针底物的二次孵育当中，反应一定时间后，终止反应，并测量探针底物的代谢产物生成。计算失活后相对于没有抑制剂时所测得的活性的剩余百分比，剩余百分比与失活孵育的时间进行线性拟合作图，得到一系列一级衰减曲线，如图3a，直线斜率的负值即为一个浓度点的表观抑制速率（kobs）。用不同浓度下的表观抑制速率与化合物的浓度进行非线性拟合即可计算得到kinact和KI，如图3b。图3. Verapamil的体外TDI动力学常数测定（kinact和KI）测量kinact和KI的体外实验方法通常不适合早期药物发现项目中的高通量筛选。但从药物发现的角度，较大的kinact值和较小的KI值通常会增加CYP酶失活导致DDI的可能性。因此在临床前药物发现中，也可以通过kinact/KI比值衡量化合物CYP酶失活的效率，对同系列化合物进行排序。案例分享与TDI数据的解读建议 Ritlecitinib是一种共价JAK3酪氨酸激酶抑制剂，主要用于治疗脱发症，尤其是斑秃和非节段型白癜风（NSV）。其共价抑制机制主要通过与JAK3催化域中的半胱氨酸残基（Cys-909）发生不可逆的共价结合来实现。这一残基在其他JAK家族成员中被丝氨酸残基所取代，这使得Ritlecitinib对JAK3具有高度的选择性[24, 25]。 Ritlecitinib在体外肝微粒体TDI评价体系中，被评估为CYP1A2和CYP3A的TDI抑制剂。其中，Ritlecitinib对CYP1A2（以咖啡因底物）代谢活性抑制常数分别是KI为327 μmol/L，kinact为0.0917 min-1。由于在测试的浓度范围内（3-200 μmol/L）未达到抑制饱和，因此无法确定对CYP3A（以咪达唑仑底物）代谢活性的KI或kinact，但kinact/KI估计为0.871 mL/min/μmol[20]。在斑秃患者中，每日一次口服50 mg的Ritlecitinib后最大稳态游离药物浓度（Cmax,u）估计值1.1 μmol/L[20]，结合上述体外TDI参数，根据ICH M12指导原则[23]的基本方法初步评价TDI风险如表1。计算得到Ritlecitinib对CYP1A2的表观一级失活速率常数(Kobs)为0.001517 min-1。Kdeg是酶的表观一级降解速率常数，引用自指导原则，计算（kobs+kdeg）/kdeg的值为6.06，大于1.25，说明需关注Ritlecitinib潜在临床抑制风险。表1. 基本模型预测Ritlecitinib对CYP1A2的TDI风险注：kobs=(kinact*5*Cmax,u)/(KI+5*Cmax,u)，此处计算所使用数据引用自文献[20] 该药物开展了相应的临床DDI研究。Liu[26]等人在健康志愿者中开展的临床研究中发现，咖啡因（CYP1A2底物）与Ritlecitinib联合使用时，咖啡因的AUC和Cmax分别增加了约165%和10%，表明Ritlecitinib是CYP1A2的中等抑制剂，可以增加CYP1A2底物的暴露量。Ritlecitinib与CYP酶底物合用的临床DDI研究结果汇总如表2，Ritlecitinib与咪达唑仑（CYP3A底物）合用导致咪达唑仑的AUC和Cmax分别增加了约169%和81%，而其与Efavirenz（CYP2B6底物）和Tolbutamide（CYP2C底物）合用后，底物的暴露量变化不具有临床意义[27]。在Ritlecitinib药品说明书中，提示与CYP3A和CYP1A2的底物合用时，可能增加不良反应风险，需考虑额外的监测或调整剂量。表2. Ritlecitinib临床与CYP酶底物合用引起的暴露量变化 c: 合用Ritlecitinib相比于单独给药的Cmax和AUCinf的比值。 d: 报告了Efavirenz的AUC0-72。以上结果整理自药品说明书。 TDI数据解读建议药物研发早期可通过单一浓度下的酶活性降低(酶活性损失率%Activity loss或假一级速率常数kobs或多浓度点下IC50偏移(≥1.5)，检测化合物的TDI潜力；如药物具有TDI潜力，可通过体外研究获得TDI的动力学参数kinact、KI，结合人体PK数据，应用模型排除或预测临床TDI风险。结语本文整理的数据表明65%以上的共价药物表现出对CYP酶的TDI，而文献显示非共价药物如蛋白激酶抑制剂的TDI风险同样不容忽视，因此，不论是否为共价药物，均建议在研发早期考察TDI风险。 TDI引起的临床不良反应往往是极其严重的，如米贝拉地尔的撤市，其作为CYP3A的强TDI抑制剂，与其他3A的底物合用会引起严重的毒性反应，严重时导致死亡。对于存在TDI风险的药物，必要时通过体外实验获取抑制动力学参数kinact和KI，结合临床PK数据进行临床DDI风险的评估，如果体外数据和模型无法排除临床抑制风险，则应使用敏感探针底物进行临床DDI研究。药明康德DMPK已经建立了一套专门针对共价药物的药代动力学研究体系，其中包括早期的高通量筛选、共价药物多样化的体内外代谢模型与方法、丰富的体外ADME试验类型、以及14C标记化合物的合成和相应的ADME技术平台，可以支持客户加速共价药物的研发和申报。附表1. 29个共价药物对CYP酶的TDI结果汇总（下滑查看更多） a: 结果引用自药品说明书。 b: 由于测试浓度（3-200 μM）下抑制未被饱和，无法计算准确KI和kinact值。参考文献： [1] Journal of Medicinal Chemistry 2022 65 (8),5886-5901 DOI: 10.1021/acs.jmedchem.1c02134 [2] Suchy D, Łabuzek K, Stadnicki A, Okopień B. Ezetimibe--a new approach in hypercholesterolemia management. Pharmacol Rep. 2011;63(6):1335-48. doi: 10.1016/s1734-1140(11)70698-3. PMID: 22358082. [3] Investigation of drug-drug interaction potential of bortezomib in vivo in female Sprague-Dawley rats and in vitro in human liver microsomes；Drug Metab Dispos. 2006 Apr;34(4):702-8. [4] M. F Moghaddam, et al. Drug Metabolism Letters, 2014, 8, 19-30. [5] NDA 22-134: https://www.fda.gov/files/drugs/published/22134-Alcaftadine-ClinPharm-PREA.pdf [6] Chapron B, Risler L, Phillips B, Collins C, Thummel K, Shen D. Reversible, time-dependent inhibition of CYP3A-mediated metabolism of midazolam and tacrolimus by telaprevir in human liver microsomes. J Pharm Pharm Sci. 2015;18(1):101-11. doi: 10.18433/j3288c. PMID: 25877445. [7] Wang, Z., Yang, J., Kirk, C., Fang, Y., Alsina, M., Badros, A., Infante, J. R. (2012). Clinical Pharmacokinetics, Metabolism, and Drug-Drug Interaction of Carfilzomib. Drug Metabolism and Disposition, 41(1), 230–237. doi:10.1124/dmd.112.047662 [8] Jordi Aubets et,al; No evidence for interactions of dimethylfumarate (DMF) and its main metabolite monomethylfumarate (MMF) with human cytochrome P450 (CYP) enzymes and the P‐glycoprotein (P‐gp) drug transporter;Pharmacol Res Perspect. 2019 Dec; 7(6): e00540. [9] NDA 206829: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2014/206829orig1s000clinpharmr.pdf [10] DMD Fast Forward. Published on March 10, 2014 as DOI: 10.1124/dmd.113.055335 [11] Evaluation of the Drug–Drug Interaction Potential of Acalabrutinib and Its Active Metabolite, ACP‐5862, Using a Physiologically‐Based Pharmacokinetic Modeling Approach; CPT Pharmacometrics Syst Pharmacol. 2019 Jul; 8(7): 489–499. [12] NDA 209776: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2017/209776Orig1s000PharmR.pdf [13] Report on the Deliberation Results: https://www.pmda.go.jp/files/000240418.pdf [14] Zhang H, Ou YC, Su D, Wang F, Wang L, Sahasranaman S, Tang Z. In vitro investigations into the roles of CYP450 enzymes and drug transporters in the drug interactions of zanubrutinib, a covalent Bruton's tyrosine kinase inhibitor. Pharmacol Res Perspect. 2021 Dec;9(6):e00870. doi: 10.1002/prp2.870. PMID: 34664792; PMCID: PMC8524670. [15] Report on the Deliberation Results: https://www.pmda.go.jp/files/000237315.pdf [16] NDA 214665: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2021/214665Orig1s000MultidisciplineR.pdf [17] NDA 215301: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2021/215310Orig1s000MultidisciplineR.pdf [18] KRAZATI 200 mg film-coated tablets: ANNEX I [19] Heather Eng et al, Disposition of Nirmatrelvir, an Orally Bioavailable Inhibitor of SARS-CoV-2 3C-Like Protease, across Animals and Humans; Drug Metab Dispos. 2022 May;50(5):576-590. [20] Litfulo review report; https://www.pmda.go.jp/files/000266925.pdf [21] https://www.ema.europa.eu/en/documents/rmp-summary/skyclarys-epar-risk-management-plan_en.pdf [22] Drug Metab Dispos 42:1202–1209, July 2014, http://dx.doi.org/10.1124/dmd.114.057695 [23] ICH M12《Drug Interaction Studies》 [24] Xu H, Jesson MI, Seneviratne UI, et al. PF-06651600, a dual JAK3/TEC family kinase inhibitor. ACS Chem Biol. 2019;14(6):1235–42. [25] Telliez JB, Dowty ME, Wang L, et al. Discovery of a JAK3- selective inhibitor: functional diferentiation of JAK3-selective inhibition over pan-JAK or JAK1-selective inhibition. ACS Chem Biol. 2016;11(12):3442–51. [26] Liu J, Solan R, Wolk R, et all. Evaluation of the effect of ritlecitinib on the pharmacokinetics of caffeine in healthy participants. Br J Clin Pharmacol. 2023;89:2208–2215. [27] Pfzer Inc. LITFULO™ (ritlecitinib) capsules, for oral use: US prescribing information. 2023. https://www.accessdata.fda.gov/ drugsatfda_docs/label/2023/215830s000lbl.pdf. Accessed 26 Jul 2023. （下滑查看更多）作者：吴文萍，马虹莹，马利萍，周莹，金晶编辑：姜利芳，钱卉娟，陈根富设计：倪德伟，张莹莹声明：发表/转载本文仅仅是出于传播信息的需要，并不意味着代表本公众号观点或证实其内容的真实性。据此内容作出的任何判断，后果自负。若有侵权，告知必删！长按关注本公众号粉丝群/投稿/授权/广告等请联系公众号助手觉得本文好看，请点这里↓

上市批准一致性评价医药出海

2024-07-30

·Business Wire

New Study Describes a Functional, Aged, Human Cell-Based Platform That Examines the Contribution of Senescence in Alzheimer’s Disease Progression, and Response to Therapeutics

ORLANDO, Fla.--( BUSINESS WIRE )--Researchers from Hesperos and the University of Central Florida have developed a groundbreaking model using human induced pluripotent stem cell (iPSC)-derived cortical neurons to better understand and combat Alzheimer’s disease (AD). Their study, “ A functional aged human iPSC-cortical neuron model recapitulates Alzheimer’s disease, senescence, and the response to therapeutics ," has been published in the prestigious journal Alzheimer's & Dementia ® : The Journal of the Alzheimer's Association. doi.org/10.1002/alz.14044 AD affects an estimated 6.2 million Americans aged 65 and older, with projections suggesting this number could reach 14 million by 2050. Current treatments, such as acetylcholinesterase inhibitors and NMDA receptor antagonists, provide only temporary relief. The high failure rate of AD clinical trials highlights the need for a better understanding of the pathophysiology and drug mechanisms. The study addresses a critical challenge in Alzheimer’s research: the degeneration of cortical layers associated with cognitive decline. Despite substantial efforts to date, few current AD therapies are not disease-modifying. The team accurately reproduced key biological aspects of aging and senescence in a multi-cellular microphysiological system (MPS), also known as a Human-on-a-Chip. This new approach reveals how cellular aging, also known as senescence, contributes to disease progression and therapy resistance, providing researchers with an accurate, human platform to further understand the disease and potential therapeutics in a living context. Further, since other platforms at Hesperos, based on neurodegenerative diseases, have been accepted by the Food and Drug Administration (FDA) for efficacy data, it provides a new method to accelerate development and regulatory submissions of new treatments. This model provides an invaluable tool for real-time measurement of neuronal activity and aging biomarkers, facilitating drug screening and the development of AD therapies. It also reveals complex disease mechanisms, such as the role of inflammatory factors in neuronal senescence and how it differs from non-disease age-related controls, offering new targets for intervention. Researchers cultured human iPSC-derived cortical neurons on patterned microelectrode arrays to measure long-term potentiation (LTP) noninvasively. LTP, a quantifiable metric for learning and memory, is directly impacted by central nervous system neurodegeneration. By treating these neurons with pathogenic amyloid-β (Aβ), a peptide that plays a central role in development of AD, to mimic disease pathophysiology, the team was able to analyze senescence and therapeutic responses. The study demonstrates that this novel human iPSC-cortical neuron model of aging, cultured in serum-free, defined conditions, accurately recapitulates hallmarks of AD showing the following: Pathological Insights : Aβ42 induced synaptic damage, accelerated neuronal senescence, impaired mitochondrial potential, and increased reactive oxygen species (ROS), leading to oxidative stress and inflammation. Therapeutic Responses : Drugs such as memantine, rolipram, saracatinib, and donepezil improved neuronal function and viability, though they did not completely halt Aβ42-driven senescence. The study underscores the potential of biomimetic MPS systems in translational research. By integrating clinically relevant neuronal function with proteome responses, this platform is poised to accelerate the discovery of novel neuroprotective compounds for AD and other neurological disorders. “This marks a significant step forward in Alzheimer's research by providing a deeper understanding of AD pathophysiology in a human aged MPS platform. We are excited about the potential of this model to transform drug discovery in AD, providing hope for patients,” said J. Hickman, PhD, co-founder and Chief Scientist of Hesperos and Professor of Chemistry at UCF. This research was supported by grants from the National Institutes of Health (R44TR001326, R44AG058330). Research Article : doi.org/10.1002/alz.14044 Nanoscience Technology Center (NSTC) at the University of Central Florida (UCF) The mission of the NSTC is to establish a cutting-edge research program in materials and nanotechnology, provide high quality training for students and facilitate the advance of innovations to solve real world technology challenges. https://nanoscience.ucf.edu/ Hesperos, Inc. Hesperos is a global contract research organization (CRO) providing drug development services using its Human-on-a-Chip ® platform - the most advanced, multi-organ microphysiological systems available today. https://hesperosinc.com

100 项与塞卡替尼相关的药物交易

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KEGG	Wiki	ATC	Drug Bank
D09664	塞卡替尼	-	DB11805

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适应症	最高研发状态	国家/地区	公司	日期
输卵管癌	临床3期	英国	Cancer Research UK	2011-03-01
输卵管癌	临床3期	英国	AstraZeneca PLC	2011-03-01
铂耐药性卵巢癌	临床3期	英国	AstraZeneca PLC	2011-03-01
铂耐药性卵巢癌	临床3期	英国	Cancer Research UK	2011-03-01
铂类耐药性原发性腹膜癌	临床3期	英国	AstraZeneca PLC	2011-03-01
铂类耐药性原发性腹膜癌	临床3期	英国	Cancer Research UK	2011-03-01
特发性肺纤维化	临床2期	美国	National Jewish Health, Inc.	2020-10-22
骨化性肌炎	临床2期	德国	AstraZeneca PLC	2020-08-05
骨化性肌炎	临床2期	荷兰	AstraZeneca PLC	2020-08-05
骨化性肌炎	临床2期	英国	AstraZeneca PLC	2020-08-05

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研究	分期	人群特征	评价人数	分组	结果	评价	发布日期


ISRCTN23804370 (CRUKE/11/023, Pubmed)	临床2期	晚期乳腺癌 hormone receptor-positive	140	Aromatase inhibitors + Saracatinib	鹹齋糧願憲齋網鏇壓齋(鏇觸網願鑰遞齋鏇願範) = 遞餘廠鏇鏇獵夢顧糧構網製遞構齋醖製廠齋遞 (選獵鹹遞夢憲網製壓鑰 )	不佳	2023-03-02
				Aromatase inhibitors + Placebo	鹹齋糧願憲齋網鏇壓齋(鏇觸網願鑰遞齋鏇願範) = 鏇繭繭鑰糧鹹選糧鹹壓網製遞構齋醖製廠齋遞 (選獵鹹遞夢憲網製壓鑰 )
ISRCTN22566729 (SAPROCAN, ASCO_GU2021)	临床1/2期	前列腺癌	-	Saracatinib 175mg	鏇醖鑰製選襯餘構襯積(壓廠壓醖積製膚窪糧蓋): HR = 1.35 (80% CI, 1.07 ~ 1.7)	不佳	2021-03-02
				Placebo
NCT02955186 (CTgov)	临床2期	-	50	Saracatinib (125 mg Saracatinib)	構憲範廠壓鹽醖觸積範(憲齋簾築廠願積簾鑰顧) = 窪網範構夢衊顧淵襯淵夢遞廠鹹餘鹽遞齋顧窪 (壓選鹽範醖遞鬱憲憲餘, .75) 更多	-	2020-11-13
				Placebos (Placebo)	構憲範廠壓鹽醖觸積範(憲齋簾築廠願積簾鑰顧) = 淵夢醖膚艱繭築廠鹹築夢遞廠鹹餘鹽遞齋顧窪 (壓選鹽範醖遞鬱憲憲餘, .99) 更多
ARISTACAT (SABCS2020)	临床2期	ER+	140	Saracatinib 175mg mg/day	鑰獵鹹製繭觸夢齋糧顧(廠繭艱糧願獵觸襯淵網) = 簾壓範顧壓繭積鑰醖選鏇觸醖衊窪壓憲淵築膚 (積壓範夢網築壓鬱簾選 ) 更多	不佳	2020-02-15
				Placebo	鑰獵鹹製繭觸夢齋糧顧(廠繭艱糧願獵觸襯淵網) = 願遞鹽鑰糧醖範餘獵鬱鏇觸醖衊窪壓憲淵築膚 (積壓範夢網築壓鬱簾選 ) 更多
NCT00752206 (CTgov)	临床2期	复发性骨肉瘤	38	Saracatinib (Saracatinib)	願鏇鏇繭積鑰繭築積壓(窪餘廠廠選窪夢襯鬱鬱) = 繭壓壓膚鬱選壓糧築齋積夢壓醖願構簾膚膚醖 (顧糧顧鏇齋選膚蓋鏇積, 糧願鑰壓製鏇遞遞網糧 ~ 蓋構淵廠蓋網鹹衊構願) 更多	-	2020-01-18
				Placebo (Placebo)	願鏇鏇繭積鑰繭築積壓(窪餘廠廠選窪夢襯鬱鬱) = 蓋衊蓋餘憲繭憲醖顧網積夢壓醖願構簾膚膚醖 (顧糧顧鏇齋選膚蓋鏇積, 齋鏇觸獵簾鬱淵襯淵夢 ~ 顧膚範觸窪獵壓積窪窪) 更多
NCT02732587 (CTgov)	临床1期	-	5	Saracatinib	選糧鹹鏇鏇獵鏇蓋餘築 = 糧鑰艱壓繭廠鏇遞製醖衊艱顧艱顧選艱齋顧餘 (觸壓簾鑰襯網蓋選構膚, 齋簾鬱醖憲積繭鬱構遞 ~ 壓窪製衊願壓襯遞願廠) 更多	-	2020-01-14
NCT02167256 (Pubmed) 人工标引	临床2期	阿尔茨海默症	159	AZD0530	獵襯齋遞網構糧遞鬱構(窪廠鏇窪範繭積蓋膚觸) = 範襯願遞構壓鬱廠壓憲觸壓憲齋廠憲簾遞壓選 (積選選廠壓鏇蓋範夢衊 ) 更多	不佳	2019-10-01
				Placebo	獵襯齋遞網構糧遞鬱構(窪廠鏇窪範繭積蓋膚觸) = 憲壓淵簾艱醖積襯獵積觸壓憲齋廠憲簾遞壓選 (積選選廠壓鏇蓋範夢衊 ) 更多
NCT02167256 (CTgov)	临床2期	阿尔茨海默症	159	Placebo	餘積築鬱膚網壓醖顧網(憲壓選廠網鑰鬱壓窪簾) = 膚膚製製鏇廠鑰顧鏇膚製鬱選構顧窪夢鬱衊膚 (膚鬱壓鬱窪鏇艱艱構選, 0.03) 更多	-	2019-08-14
NCT01216176 (CTgov)	临床1/2期	HR阳性乳腺癌	71	saracatinib+anastrozole (Phase 1 - Cohort A)	選壓簾製齋壓範壓齋艱 = 壓範淵齋獵襯顧憲鏇鏇觸觸壓廠窪齋廠廠淵繭 (艱鏇積齋鬱繭壓糧艱範, 廠壓蓋襯積襯窪衊齋餘 ~ 蓋構簾壓鏇構壓觸鬱鹹) 更多	-	2019-07-31
				saracatinib+anastrozole (Phase 2 - Cohort B [Anastrozole + AZD0530])	簾顧簾觸網夢糧築選簾(築糧鹹蓋齋鏇鹽艱製夢) = 蓋繭憲淵築遞廠遞網鹹廠鹹簾艱餘顧觸膚遞願 (鏇網構範鑰膚鹽糧網齋, 22.5) 更多
COSAK (ASCO2017)	临床2期	肾细胞癌 Haemoglobin \| neutrophil count \| platelet count ... 更多	138	Cediranib	鹽廠壓簾範襯選廠餘積(遞餘鏇網憲夢夢構構顧) = 觸鹹廠齋願獵積範夢顧鹽蓋構築艱餘獵願糧製 (憲糧膚遞糧範醖糧膚鹹 ) 更多	-	2017-05-30
				Cediranib and Saracatinib	鹽觸積觸簾網壓餘糧蓋(鹹積壓範製範蓋遞鏇衊) = 範簾範鑰觸蓋鹽鹽鑰餘艱築網觸淵顧鏇夢鹹顧 (構糧鬱構鏇製築願鬱衊 ) 更多