A Novel Small Molecule GDNF Receptor RET Agonist, BT13, Promotes Neurite Growth from Sensory Neurons in Vitro and Attenuates Experimental Neuropathy in the Rat.

Neuropathic pain caused by nerve damage is a common and severe class of chronic pain. Disease-modifying clinical therapies are needed as current treatments typically provide only symptomatic relief; show varying clinical efficacy; and most have significant adverse effects. One approach is targeting either neurotrophic factors or their receptors that normalize sensory neuron function and stimulate regeneration after nerve damage. Two candidate targets are glial cell line-derived neurotrophic factor (GDNF) and artemin (ARTN), as these GDNF family ligands (GFLs) show efficacy in animal models of neuropathic pain (Boucher et al., 2000; Gardell et al., 2003; Wang et al., 2008, 2014). As these protein ligands have poor drug-like properties and are expensive to produce for clinical use, we screened 18,400 drug-like compounds to develop small molecules that act similarly to GFLs (GDNF mimetics). This screening identified BT13 as a compound that selectively targeted GFL receptor RET to activate downstream signaling cascades. BT13 was similar to NGF and ARTN in selectively promoting neurite outgrowth from the peptidergic class of adult sensory neurons in culture, but was opposite to ARTN in causing neurite elongation without affecting initiation. When administered after spinal nerve ligation in a rat model of neuropathic pain, 20 and 25 mg/kg of BT13 decreased mechanical hypersensitivity and normalized expression of sensory neuron markers in dorsal root ganglia. In control rats, BT13 had no effect on baseline mechanical or thermal sensitivity, motor coordination, or weight gain. Thus, small molecule BT13 selectively activates RET and offers opportunities for developing novel disease-modifying medications to treat neuropathic pain.

Subchronic Oral and Inhalation Toxicities: a Challenging Attempt for Modeling and Prediction.

The article deals with a challenging attempt to model and predict "difficult" properties as long-term subchronic oral and inhalation toxicities (90 days) using nonlinear QSAR approach. This investigation is one of the first to tackle such multicomplex properties where we have employed nonlinear models based on artificial neural network for the prediction of NOAEL (no observable adverse effect level). Despite the complex nature of the NOAEL property based on in vivo rat experiments, the successful models can be used as alternative tools to non-animal tests for the initial assessment of these chronic toxicities. The model for oral subchronic toxicity is able to describe 88 %, and the inhalation model 87 % of the statistical variance. For the sake of future predictions, we have also defined in a quantitative way the applicability domain of all neural network models.

Fragment-based development of HCV protease inhibitors for the treatment of hepatitis C.

A novel computational technology based on fragmentation of the chemical compounds has been used for the fast and efficient prediction of activities of prospective protease inhibitors of the hepatitis C virus. This study spans over a discovery cycle from the theoretical prediction of new HCV NS3 protease inhibitors to the first cytotoxicity experimental tests of the best candidates. The measured cytotoxicity of the compounds indicated that at least two candidates would be suitable further development of drugs.

Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks.

An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration. Deeper analysis of the PCA results also showed clear clusterization of the peptides according to their molecular features.

Feeding versus infusion: a novel approach to study the NAA metabolism in rat brain.

Using in vivo (13)C-NMR spectroscopy, the energy metabolism in rat brain has commonly been studied via infusion of (13)C-labeled substrates on a minute to hour time scale. In the present study, as a novel approach, (13)C-enriched animal chow was administered over several days and compared with a 2 h infusion of [U-(13)C(6)]-glucose. Rats received chow containing either [U-(13)C(6)]-glucose or [U-(13)C]-biomass (a mixture of proteins, lipids, DNA, and carbohydrates) during 3 to 5 days. During feeding with (13)C-labeled glucose and biomass, in vivo (13)C-NMR spectroscopy was carried out daily and revealed slow but successive label incorporation into a large number of metabolites. Lipids and proteins were not significantly (13)C-enriched during a 2 h infusion of (13)C-labeled glucose, but became the most prominent resonances in the (13)C feeding experiment. Likewise, feeding with (13)C-enriched biomass led to additional (13)C-label incorporation into creatine, urea carbons and glycogen. Finally, only the acetyl moiety of N-acetyl-aspartate (NAA) became significantly enriched during the 2 h infusion experiment, whereas the aspartyl moiety remained at natural abundance levels. In the feeding experiments, however, label incorporation into all carbons of NAA could be observed. Moreover, isotopomer analysis of brain extracts revealed that the acetyl moiety of NAA in feeding experiments was always more strongly (13)C-enriched than its aspartyl moiety, suggesting that the turnover of the acetyl moiety is faster than that of the aspartyl moiety. The different enrichment kinetics of acetyl and aspartyl moiety could be explained by the existence of two different metabolic pathways reflecting the compartmentalised synthesis of NAA.