Sigma-1 Receptor Signaling: In Search of New Therapeutic Alternatives for Cardiovascular and Renal Diseases.

Cardiovascular and renal diseases are among the leading causes of death worldwide, and regardless of current efforts, there is a demanding need for therapeutic alternatives to reduce their progression to advanced stages. The stress caused by diseases leads to the activation of protective mechanisms in the cell, including chaperone proteins. The Sigma-1 receptor (Sig-1R) is a ligand-operated chaperone protein that modulates signal transduction during cellular stress processes. Sig-1R interacts with various ligands and proteins to elicit distinct cellular responses, thus, making it a potential target for pharmacological modulation. Furthermore, Sig-1R ligands activate signaling pathways that promote cardioprotection, ameliorate ischemic injury, and drive myofibroblast activation and fibrosis. The role of Sig-1R in diseases has also made it a point of interest in developing clinical trials for pain, neurodegeneration, ischemic stroke, depression in patients with heart failure, and COVID-19. Sig-1R ligands in preclinical models have significantly beneficial effects associated with improved cardiac function, ventricular remodeling, hypertrophy reduction, and, in the kidney, reduced ischemic damage. These basic discoveries could inform clinical trials for heart failure (HF), myocardial hypertrophy, acute kidney injury (AKI), and chronic kidney disease (CKD). Here, we review Sig-1R signaling pathways and the evidence of Sig-1R modulation in preclinical cardiac and renal injury models to support the potential therapeutic use of Sig-1R agonists and antagonists in these diseases.

Sigma-1 receptor antagonist (BD-1063) potentiates the antinociceptive effect of quercetin in neuropathic pain induced by chronic constriction injury.

Neuropathic pain is characterized by the presence of hyperalgesia and allodynia. Pharmacological treatments include the use of antiepileptics such as pregabalin or gabapentin, as well as antidepressants; however, given the role of the sigma-1 receptor in the generation and maintenance of pain, it has been suggested that sigma-1 receptor antagonists may be effective. There are also other alternatives that have been explored, such as the use of flavonoids such as quercetin. Due to the relevance of drug combinations in therapeutics, the objective of this work was to evaluate the effect of the combination of BD-1063 with quercetin in a chronic sciatic nerve constriction model using the "Surface of Synergistic Interaction" analysis method. The combination had preferable additive or synergistic effects, with BD-1063 (17.8 mg/kg) + QUER (5.6 mg/kg) showing the best antinociceptive effects. The required doses were also lower than those used individually to obtain the same level of effect. Our results provide the first evidence that the combination of a sigma-1 receptor antagonist and the flavonoid quercetin may be useful in the treatment of nociceptive behaviors associated with neuropathic pain, suggesting a new therapeutic alternative for this type of pain.

The antihyperalgesic effect of docosahexaenoic acid in streptozotocin-induced neuropathic pain in the rat involves the opioidergic system.

Docosahexaenoic acid (DHA) is a polyunsaturated fatty acid that has shown an antinociceptive effect in multiple pain models, such as inflammatory and neuropathic pain by chronic constriction injury in rats; however, its mechanism of action is still not well-understood. Reports suggest that DHA activates opioid signaling, but there is no information on this from a model of neuropathic pain. As a result, the aims of this study were (1) to determine the antihyperalgesic and antiallodynic effect of peripheral DHA administration, and (2) to evaluate the participation of the opioid receptors in the antihyperalgesic effect of DHA on streptozotocin-induced neuropathic pain in the rat. Female Wistar rats were injected with streptozotocin (50 mg/kg, i.p.) to induce hyperglycemia. The formalin, Hargreaves, and von Frey filaments tests were used to assess the nociceptive activity. Intraplantar administration of DHA (100-1000 µg/paw) or gabapentin (562-1778 µg/paw) decreased formalin-evoked hyperalgesia in diabetic rats, in a dose-dependent manner. Furthermore, DHA (562 µg/paw) and gabapentin (1000 µg/paw) reduced thermal hyperalgesia and allodynia. Local peripheral administration of naloxone (non-selective opioid receptor antagonist; 100 µg/paw), naltrindole (selective δ receptor antagonist; 1 µg/paw), and CTOP (D-Phe-Cys-Tyr-D-Trp-Orn-Thr-Pen-Thr-NH2, µ receptor antagonist; 20 µg/paw) prevented formalin-evoked hyperalgesia in diabetic rats but not by GNTI (guanidinonaltrindole, κ receptor antagonist;1 µg/paw). It is suggested that peripheral DHA shows an antihyperalgesic effect in neuropathic pain in the rat. Furthermore, δ and µ receptors are involved in the antihyperalgesic peripheral effect of DHA in diabetic rats.

Sigma-1 Receptor Antagonist (BD1047) Decreases Cathepsin B Secretion in HIV-Infected Macrophages Exposed to Cocaine.

Pathogenesis of HIV-associated neurocognitive disorders (HAND) is mediated through the infiltration of perivascular macrophages into the brain with the secretion of viral, neurotoxic and inflammatory proteins. One of these proteins is cathepsin B (CATB), a lysosomal cysteine protease that induces neuronal apoptosis, and increases in plasma and cerebrospinal fluid from HIV-1 infected patients (Cantres-Rosario et al. AIDS 27(3):347-356, 2013). Cocaine further potentiates CATB neurotoxicity in vitro and in vivo (Zenón et al. J NeuroImmune Pharmacol 9(5):703-715, 2014). Modulation of sigma-1 (Sig1R) by cocaine increases oxidative species, cytokines and other factors that promote lysosomal disruption. However, the role of Sig1R in CATB secretion and HIV-1 replication in macrophages exposed to cocaine is unknown. We hypothesized that pharmacological modulation of Sig1R would alter CATB secretion from HIV-1 infected macrophages in vitro and in vivo. To test our hypothesis, monocyte derived-macrophages (MDM) from HIV-1 seronegative donors were isolated, infected with HIV-1ADA, and pretreated with Sig1R antagonist (BD1047) or Sig1R agonist (PRE-084) prior to cocaine exposure and followed for 3,6,9 and 11 days post-infection (dpi). Experiments in vivo were conducted using the HIV encephalitis mouse model (HIVE) with BD1047 treatments prior to cocaine for 14 days. Results demonstrate that in presence of cocaine, BD1047 decreases CATB secretion at 11 dpi, while PRE-084 did not have an effect. In the mouse model, BD1047 treatment prior to cocaine decreased CATB expression, cleaved caspase-3 an p24 antigen levels, reduced astrocytosis, but did not increase MAP-2 or synaptophysin. Results demonstrate that Sig1R plays a role in the modulation of CATB levels in HIV-1 infected MDM exposed to cocaine in vitro and in vivo. Graphical Abstract ᅟ.

New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists.

The treatment of neuropathic pain is very complex and there are few drugs approved for this purpose. Among the studied compounds in the literature, sigma-1 receptor antagonists have shown to be promising. In order to develop QSAR studies applied to the compounds of 1-arylpyrazole derivatives, multivariate analyses have been performed in this work using partial least square (PLS) and artificial neural network (ANN) methods. A PLS model has been obtained and validated with 45 compounds in the training set and 13 compounds in the test set (r2training = 0.761, q2 = 0.656, r2test = 0.746, MSEtest = 0.132 and MAEtest = 0.258). Additionally, multi-layer perceptron ANNs (MLP-ANNs) were employed in order to propose non-linear models trained by gradient descent with momentum backpropagation function. Based on MSEtest values, the best MLP-ANN models were combined in a MLP-ANN consensus model (MLP-ANN-CM; r2test = 0.824, MSEtest = 0.088 and MAEtest = 0.197). In the end, a general consensus model (GCM) has been obtained using PLS and MLP-ANN-CM models (r2test = 0.811, MSEtest = 0.100 and MAEtest = 0.218). Besides, the selected descriptors (GGI6, Mor23m, SRW06, H7m, MLOGP, and µ) revealed important features that should be considered when one is planning new compounds of the 1-arylpyrazole class. The multivariate models proposed in this work are definitely a powerful tool for the rational drug design of new compounds for neuropathic pain treatment. Graphical abstract Main scaffold of the 1-arylpyrazole derivatives and the selected descriptors.

Haloperidol Decreases Hyperalgesia and Allodynia Induced by Chronic Constriction Injury.

Neuropathic pain has proven to be a difficult condition to treat, so investigational therapy has been sought that may prove useful, such as the use of sigma-1 antagonists. Haloperidol (HAL) is a compound that shows a high affinity with these receptors, acting as an antagonist. Therefore, the objective of this study was to demonstrate its effect in an experimental model of neuropathic pain and corroborate its antagonistic action of the sigma-1 receptors under these conditions. BD-1063 was used as a sigma-1 antagonist control, and gabapentin (Gbp) was used as a positive control. The antihyperalgesic and anti-allodynic effects of the drugs were determined after single-dose trials. In every case, the effects increased in a dose-dependent manner. HAL had the same efficacy as both BD-1063 and Gbp. In the analysis of pharmacological potency, in which the ED50 were compared, HAL was the most potent drug of all. The effect of HAL on chronic constriction injury (CCI) rats was reversed by the sigma-1 agonist (PRE-084). HAL reversed the hyperalgesic and allodynic effects of PRE-084 in naïve rats. The dopamine antagonist, (-)-sulpiride, showed no effect in CCl rats. These results suggest that HAL presents an antinociceptive effect via sigma-1 receptor antagonism at the spinal level in the CCl model.

Bond-based 3D-chiral linear indices: theory and QSAR applications to central chirality codification.

The recently introduced non-stochastic and stochastic bond-based linear indices are been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. These improved modified descriptors are applied to several well-known data sets to validate each one of them. Particularly, Cramer's steroid data set has become a benchmark for the assessment of novel quantitative structure activity relationship methods. This data set has been used by several researchers using 3D-QSAR approaches such as Comparative Molecular Field Analysis, Molecular Quantum Similarity Measures, Comparative Molecular Moment Analysis, E-state, Mapping Property Distributions of Molecular Surfaces, and so on. For that reason, it is selected by us for the sake of comparability. In addition, to evaluate the effectiveness of this novel approach in drug design we model the angiotensin-converting enzyme inhibitory activity of perindoprilate's sigma-stereoisomers combinatorial library, as well as codify information related to a pharmacological property highly dependent on the molecular symmetry of a set of seven pairs of chiral N-alkylated 3-(3-hydroxyphenyl)-piperidines that bind sigma-receptors. The validation of this method is achieved by comparison with earlier publications applied to the same data sets. The non-stochastic and stochastic bond-based 3D-chiral linear indices appear to provide a very interesting alternative to other more common 3D-QSAR descriptors.

Atom-based stochastic and non-stochastic 3D-chiral bilinear indices and their applications to central chirality codification.

Non-stochastic and stochastic 2D bilinear indices have been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. In order to evaluate the effectiveness of this novel approach in drug design we have modeled the angiotensin-converting enzyme inhibitory activity of perindoprilate's sigma-stereoisomers combinatorial library. Two linear discriminant analysis models, using non-stochastic and stochastic linear indices, were obtained. The models had shown an accuracy of 95.65% for the training set and 100% for the external prediction set. Next the prediction of the sigma-receptor antagonists of chiral 3-(3-hydroxyphenyl)piperidines by multiple linear regression analysis was carried out. Two statistically significant QSAR models were obtained when non-stochastic (R(2)=0.953 and s=0.238) and stochastic (R(2)=0.961 and s=0.219) 3D-chiral bilinear indices were used. These models showed adequate predictive power (assessed by the leave-one-out cross-validation experiment) yielding values of q(2)=0.935 (s(cv)=0.259) and q(2)=0.946 (s(cv)=0.235), respectively. Finally, the prediction of the corticosteroid-binding globulin binding affinity of steroids set was performed. The obtained results are rather similar to most of the 3D-QSAR approaches reported so far. The validation of this method was achieved by comparison with previous reports applied to the same data set. The non-stochastic and stochastic 3D-chiral linear indices appear to provide a very interesting alternative to other more common 3D-QSAR descriptors.

3D-chiral quadratic indices of the 'molecular pseudograph's atom adjacency matrix' and their application to central chirality codification: classification of ACE inhibitors and prediction of sigma-receptor antagonist activities.

Quadratic indices of the 'molecular pseudograph's atom adjacency matrix' have been generalized to codify chemical structure information for chiral drugs. These 3D-chiral quadratic indices make use of a trigonometric 3D-chirality correction factor. These indices are nonsymmetric and reduced to classical (2D) descriptors when symmetry is not codified. By this reason, it is expected that they will be useful to predict symmetry-dependent properties. 3D-Chirality quadratic indices are real numbers and thus, can be easily calculated in TOMOCOMD-CARDD software. These descriptors circumvent the inability of conventional 2D quadratic indices (Molecules 2003, 8, 687-726. http://www.mdpi.org) and other (chirality insensitive) topological indices to distinguish sigma-stereoisomers. In this paper, we extend our earlier work by applying 3D-chirality quadratic indices to two data sets containing chiral compounds. Consequently, in order to test the potential of this novel approach in drug design we have modelled the angiotesin-converting enzyme inhibitory activity of perindoprilate's sigma-stereoisomers combinatorial library. Two linear discriminant analysis (LDA) models were obtained. The first one model was performed considering all data set as training series and classifies correctly 88.89% of active compounds and 100.00% of nonactive one for a global good classification of 96.87%. The second one LDA-QSAR model classified correctly 83.33% of the active and 100.00% of the inactive compounds in a training set, result that represent a total of 95.65% accuracy in classification. On the other hand, the model classifies 100.00% of these compounds in the test set. Similar predictive behaviour was observed in a leave-one-out cross-validation procedure for both equations. Canonical regression analysis corroborated the statistical quality of these models (R(can) of 0.82 and of 0.76, respectively) and was also used to compute biology activity canonical scores for each compound. Finally, prediction of the biological activities of chiral 3-(3-hydroxyphenyl)piperidines, which are sigma-receptor antagonists, by linear multiple regression analysis was carried out. Two statistically significant QSAR models were obtained (R2=0.940, s=0.270 and R2=0.977, s=0.175). These models showed high stability to data variation in the leave-one-out cross-validation procedure (q2=0.912, scv=0.289 and q2=0.957, scv=0.211). The results of this study compare favourably with those obtained with other chirality descriptors applied to the same data set. The 3D-chiral TOMOCOMD-CARDD approach provides a powerful alternative to 3D-QSAR.

Symmetry considerations in Markovian chemicals 'in silico' design (MARCH-INSIDE) I: central chirality codification, classification of ACE inhibitors and prediction of sigma-receptor antagonist activities.

The MARCH-INSIDE methodology has been generalized, by means of an exponential central symmetry factor, to codify chemical structure information for chiral drugs. In order to test the potential of this novel approach in drug design we have modeled the angiotensin-converting enzyme inhibitory activity of perindoprilate's sigma-stereoisomer combinatorial library. A linear discriminant analysis (LDA) model classifies correctly 83.33% of active compounds and 94.12% of non-active ones in a training set, results that represent a total of 91.3% accuracy in classification. On the other hand, the model classifies 83.33% of these compounds in the predicting series. Only three isomers (those with higher activity) were used in the predicting set and the model classified all three very well. Similar predictive behavior was observed in a leave-1-out cross validation experiment. Canonical regression analysis corroborated the statistical quality of the models (Rcanc=0.79, with a P-level<0.000) and was also used to compute biological activity canonical scores for each compound. Finally, prediction of the biological activities of chiral 3-(3-hydroxyphenyl)piperidines, which are sigma-receptor antagonists, by linear regression analysis was carried out. The model was statistically significant (R=0.963, S=0.29, P<0.00) and can be considered as a preliminary comparative study between MARCH-INSIDE and Chiral Topologic descriptors. Application of the Student test permits the detection of non-symmetric properties within the data set and justified the requirement of non-symmetric (for pairs of enantiomers) molecular descriptors. The MARCH-INSIDE model showed very good stability to data variation in the leave-1-out cross validation experiment (Scv=0.32).

Opioid involvement in behavior modifications of mice infected with the parasitic nematode, Nippostrongylus brasiliensis.

Several studies have documented the opiate effects of parasitic infection on experimental animals. The current study examined the relationships between infection with the intestinal nematode, Nippostrongylus brasiliensis with analgesia and activity levels. Male white mice infected with N. brasiliensis displayed a significant increase in thermal latency thresholds that rose through the duration of infection and subsided with its termination. Analgesia first became apparent on day three-post infection but did not reach statistical significance (p < 0.05) until day 7 post infection. The maximum analgesia was reached on day 8-post infection and gradually declined. By day 15 post infection, there was no significant difference in the latency times between control and infected mice. The initial significant difference in latency roughly corresponded with the onset of egg production by the parasite. The peak difference in latency times and their subsequent decline also parallels peak egg production and the decline in egg production as the infection subsided. Both naloxone and naltrindole significantly reduced the latency times (p < 0.05) of infected mice. There was also a significant difference in total ambulatory activity levels between infected and control mice. Activity levels began to decline on the second day post infection but did not reach a statistically significant difference (p < 0.05) from the controls until 9th day post infection. Infected mice that were injected with either naloxone or naltrindole had a significantly higher activity level than the infected mice injected with saline.