Faquetti Maria L, Slappendel Laura, Bigonne Hélène, Grisoni Francesca, Schneider Petra, Aichinger Georg, Schneider Gisbert, Sturla Shana J, Burden Andrea M
Department of Chemistry and Applied Biosciences Institute of Pharmaceutical Sciences ETH Zurich Zurich Switzerland.
Department of Health Sciences and Technology Institute of Food, Nutrition and Health, ETH Zurich Zurich Switzerland.
Alzheimers Dement (N Y). 2024 Jan 26;10(1):e12445. doi: 10.1002/trc2.12445. eCollection 2024 Jan-Mar.
Janus kinase (JAK) inhibitors were recently identified as promising drug candidates for repurposing in Alzheimer's disease (AD) due to their capacity to suppress inflammation via modulation of JAK/STAT signaling pathways. Besides interaction with primary therapeutic targets, JAK inhibitor drugs frequently interact with unintended, often unknown, biological off-targets, leading to associated effects. Nevertheless, the relevance of JAK inhibitors' off-target interactions in the context of AD remains unclear.
Putative off-targets of baricitinib and tofacitinib were predicted using a machine learning (ML) approach. After screening scientific literature, off-targets were filtered based on their relevance to AD. Targets that had not been previously identified as off-targets of baricitinib or tofacitinib were subsequently tested using biochemical or cell-based assays. From those, active concentrations were compared to bioavailable concentrations in the brain predicted by physiologically based pharmacokinetic (PBPK) modeling.
With the aid of ML and in vitro activity assays, we identified two enzymes previously unknown to be inhibited by baricitinib, namely casein kinase 2 subunit alpha 2 (CK2-α2) and dual leucine zipper kinase (MAP3K12), both with binding constant ( ) values of 5.8 μM. Predicted maximum concentrations of baricitinib in brain tissue using PBPK modeling range from 1.3 to 23 nM, which is two to three orders of magnitude below the corresponding binding constant.
In this study, we extended the list of baricitinib off-targets that are potentially relevant for AD progression and predicted drug distribution in the brain. The results suggest a low likelihood of successful repurposing in AD due to low brain permeability, even at the maximum recommended daily dose. While additional research is needed to evaluate the potential impact of the off-target interaction on AD, the combined approach of ML-based target prediction, in vitro confirmation, and PBPK modeling may help prioritize drugs with a high likelihood of being effectively repurposed for AD.
This study explored JAK inhibitors' off-targets in AD using a multidisciplinary approach.We combined machine learning, in vitro tests, and PBPK modelling to predict and validate new off-target interactions of tofacitinib and baricitinib in AD.Previously unknown inhibition of two enzymes (CK2-a2 and MAP3K12) by baricitinib were confirmed using in vitro experiments.Our PBPK model indicates that baricitinib low brain permeability limits AD repurposing.The proposed multidisciplinary approach optimizes drug repurposing efforts in AD research.
由于Janus激酶（JAK）抑制剂能够通过调节JAK/STAT信号通路抑制炎症，最近被确定为有望用于阿尔茨海默病（AD）治疗的药物。除了与主要治疗靶点相互作用外，JAK抑制剂药物还经常与意想不到的、通常未知的生物学脱靶相互作用，从而产生相关效应。然而，JAK抑制剂的脱靶相互作用在AD背景下的相关性仍不清楚。
使用机器学习（ML）方法预测巴瑞替尼和托法替布的潜在脱靶。在筛选科学文献后，根据与AD的相关性对脱靶进行筛选。随后，使用生化或基于细胞的试验对以前未被确定为巴瑞替尼或托法替布脱靶的靶点进行测试。从中将活性浓度与基于生理药代动力学（PBPK）模型预测的大脑中的生物利用度浓度进行比较。
借助ML和体外活性试验，我们确定了两种以前未知被巴瑞替尼抑制的酶，即酪蛋白激酶2亚基α2（CK2-α2）和双亮氨酸拉链激酶（MAP3K12），两者的结合常数（ ）值均为5.8 μM。使用PBPK模型预测的脑组织中巴瑞替尼的最大浓度范围为1.3至23 nM，比相应的结合常数低两到三个数量级。
在本研究中，我们扩展了与AD进展可能相关的巴瑞替尼脱靶列表，并预测了其在大脑中的药物分布。结果表明，即使在最大推荐日剂量下，由于脑通透性低，在AD中成功重新利用的可能性也很低。虽然需要进一步研究来评估脱靶相互作用对AD的潜在影响，但基于ML的靶点预测、体外确认和PBPK建模的联合方法可能有助于对有高可能性有效重新用于AD的药物进行优先排序。
本研究采用多学科方法探索了JAK抑制剂在AD中的脱靶。我们结合机器学习、体外试验和PBPK建模来预测和验证托法替布和巴瑞替尼在AD中的新脱靶相互作用。使用体外实验证实了巴瑞替尼以前未知对两种酶（CK2-α2和MAP3K12）的抑制作用。我们的PBPK模型表明巴瑞替尼的低脑通透性限制了其在AD中的重新利用。所提出的多学科方法优化了AD研究中的药物重新利用工作。
