EPPTB

Dravet Syndrome is a severe childhood drug-resistant epilepsy. The predominant etiology of this condition is related to de novo mutations within the SCN1A gene, which codes for the alpha subunit of the NaV1.1 sodium channels. This dysfunction leads to hypoexcitability of GABAergic interneurons. In turn, the loss of electrical excitability in GABAergic interneurons leads to an imbalance of excitation over inhibition in many neural circuits. Notably, exacerbation of symptoms is observed when non-selective sodium channel blockers are administered to patients with Dravet. Recent studies in animal models of Dravet have highlighted the potential of highly specific sodium channel blockers capable of blocking other sodium channel subtypes without inhibiting NaV1.1 current and selective activators of NaV1.1 as viable therapeutic strategies for alleviating Dravet Syndrome symptoms. Here, we describe the development and validation of ligand-based machine learning models to identify ligands with inhibitory effects on sodium channel isoforms NaV1.1 and NaV1.2. These models were built based on in-house open-source routines and Mordred molecular descriptors. First, linear classifiers were inferred using a combination of feature-bagging and Forward Stepwise selection. Secondly, ensemble learning was applied to build meta-classifiers with improved predictive ability, whose performance was tested in retrospective screening experiments. After in silico validation, the models were applied to screen for drug repurposing opportunities in the DrugBank and Drug Repurposing Hub databases, to identify selective blocking agents of NaV1.2 devoid of NaV1.1 blocking activity, as potential compounds for the treatment of Dravet Syndrome. Forty in silico hits were later identified in a prospective screening experiment. Four of them were acquired and submitted to experimental confirmation via patch clamp: three of these candidates, Eltrombopag, Sufugolix, and Glesatinib, showed blocking effects on NaV1.2 currents, although no subtype selectivity was observed. The different predictive abilities of the NaV1.1 and NaV1.2 models may be attributed to the different sizes of the datasets used to train and validate the respective models.

Dexmedetomidine (DMED) is widely used in sedative anesthetics in clinical settings. Currently, there are no therapeutic interventions available for the lethal toxicity induced by DMED. Administered at doses ranging from 25 to 100 mg/kg intraperitoneally (i.p.), DMED exhibited a dose-dependent fatality in mice. The 50 % lethal dose (LD50) was determined to be 48.5 mg/kg within 24 h and 44.8 mg/kg within 7 days. Alpha2-adrenergic receptor antagonists, namely yohimbine and atipamezole, demonstrated no mitigating effect on the lethal toxicity induced by DMED at the dose of 44.8 mg/kg. Conversely, the administration of atipamezole and yohimbine increased the mortality associated with DMED. By contrast, the imidazoline receptor antagonist idazoxan and the opioid receptor antagonist naloxone significantly attenuated the mortality induced by DMED, thereby prolonging the median survival time following administration of DMED at 44.8 mg/kg (i.p.). Furthermore, the immune modulator imiquimod, calcium sensitizer levosimendan, and TAAR1 antagonist RO5212773 decreased acute mortality without impacting chronic mortality induced by DMED. Histopathological analysis revealed characteristic lung alterations, including capillary hemorrhage, widened alveolar septa, fusion of pulmonary alveoli, and inflammatory cell infiltrate upon DMED exposure. Pretreatment with idazoxan or naloxone inhibited these pathological changes induced by DMED, while atipamezole or yohimbine pretreatment exacerbated lung damage. Elevated levels of serum creatine kinase and myoglobin were noted following DMED administration. However, pretreatment with idazoxan or naloxone decreased the rise of serum creatine kinase and myoglobin. Collectively, these results highlighted the involvement of imidazoline receptors and opioid receptor in DMED mortality.