Department of Pharmaceutical Sciences & Drug Research, Punjabi University, Patiala, India.
Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia; Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia.
Comput Biol Med. 2022 Aug;147:105754. doi: 10.1016/j.compbiomed.2022.105754. Epub 2022 Jun 20.
Drug-resistant epilepsy results from multiple mechanisms which are difficult to fully acquire in animal models. Technological advances, that allow transformation of big data into novel therapies, are now assisting in identification a disease targets for animal modeling. Our goal was to transform the available genomic and proteomic data related to drug-resistant epilepsy into ubiquitous disease target using system biology and network pharmacology approaches, followed by animal modeling and assess its validity. We used a dataset of 42 antiseizure drugs, 175 drug targets, and 601 epilepsy-gene associations to create interactome of 543 diseased proteins linked to drug-resistant epilepsy. DIAMOnD algorithm and DAVID web-services were used to identify 35 disease pathways whereby mitochondrial complex-I was selected for animal modeling. Albino mice were treated with specific inhibitor of mitochondrial complex-I (i.e., rotenone 2.5 mg/kg, i.p on daily basis) along with chemical and electric kindling stimulus for 35 days and 15 days, respectively. According to our results, the rotenone kindling model with inhibited complex-I activity showed significant (P < 0.001) resistance to lamotrigine (15 mg/kg), levetiracetam (40 mg/kg), carbamazepine (40 mg/kg), zonisamide (100 mg/kg), gabapentin (224 mg/kg), pregabalin (30 mg/kg), phenytoin (35 mg/kg), topiramate (300 mg/kg), valproate (200 mg/kg), and drug combinations at doses that had significantly (P < 0.001) controlled seizure severity in lamotrigine-pentylenetetrazole and corneal kindling models. In conclusion, lamotrigine kindling model is more advantageous than earlier described lamotrigine and corneal kindling models which respond to drug combinations. As a result, pre-clinical drug screening through rotenone kindling may uncover broad spectrum drugs with novel antiseizure mechanisms which is a pressing issue to deal with drug-resistant epilepsy.
耐药性癫痫是由多种机制引起的，这些机制在动物模型中很难完全获得。现在，技术的进步使得将大数据转化为新疗法成为可能，这有助于为动物模型确定疾病靶点。我们的目标是使用系统生物学和网络药理学方法将与耐药性癫痫相关的现有基因组和蛋白质组学数据转化为普遍的疾病靶点，然后进行动物建模并评估其有效性。我们使用了一个包含 42 种抗癫痫药物、175 个药物靶点和 601 个癫痫基因关联的数据集，创建了与耐药性癫痫相关的 543 种疾病蛋白相互作用网络。DIAMOnD 算法和 DAVID 网络服务用于识别 35 种疾病途径，其中线粒体复合物-I 被选为动物模型。白化病小鼠每天用线粒体复合物-I 的特异性抑制剂（即鱼藤酮 2.5mg/kg，腹腔注射）和化学和电点燃刺激分别处理 35 天和 15 天。根据我们的结果，抑制复合物-I 活性的鱼藤酮点燃模型对拉莫三嗪（15mg/kg）、左乙拉西坦（40mg/kg）、卡马西平（40mg/kg）、唑尼沙胺（100mg/kg）、加巴喷丁（224mg/kg）、普瑞巴林（30mg/kg）、苯妥英钠（35mg/kg）、托吡酯（300mg/kg）、丙戊酸钠（200mg/kg）和药物组合表现出显著的（P<0.001）耐药性，这些药物组合在拉莫三嗪戊四氮和角膜点燃模型中显著（P<0.001）控制了癫痫严重程度。总之，拉莫三嗪点燃模型比以前描述的拉莫三嗪和角膜点燃模型更有优势，这些模型对药物组合有反应。因此，通过鱼藤酮点燃进行临床前药物筛选可能会发现具有新的抗癫痫机制的广谱药物，这是解决耐药性癫痫的紧迫问题。
