Molecular modeling and 3D-QSAR studies of indolomorphinan derivatives as kappa opioid antagonists

Novel selective κ agonists SLL-039 and SLL-1206 produce potent antinociception with fewer sedation and aversion

SLL-039 (N-cyclopropylmethyl-7α-4'-(N'-benzoyl) amino-phenyl-6,14-endoethano-tetrahydronorthebaine) and SLL-1206 (N-cyclopropylmethyl-7α-3'-(p-methoxybenzyl) amino-phenyl-6,14-endoethano-tetrahydronorthebaine) are two 4,5-epoxymorphinan-based high selective κ receptor agonists that we recently discovered. In the present study we characterized their pharmacological properties in comparison with arylacetamide-based typical κ agonist U50,488H. We showed that both SLL-039 and SLL-1206 produced potent and long-lasting antinociceptive actions in three different rodent models of pain via activation of κ opioid receptor. In hot-plate assay, the antinociceptive potency of SLL-039 and SLL-1206 increased about 11-and 17.3-fold compared to U50,488H and morphine, respectively, with ED₅₀ values of 0.4 mg/kg. Following repeated administration, SLL-1206, SLL-039, and U50,488H all developed analgesic tolerance tested in hot-plate assay. U50,488H and SLL-039 produced antipruritic effects in a dose-dependent manner, whereas SLL-1206 displayed some antipruritic effects only at very low doses. In addition, SLL-1206 was capable of decreasing morphine-induced physical dependence. More importantly, SLL-039 and SLL-1206 at effective analgesic doses did not cause sedation and conditioned place aversion (CPA), whereas U50,488H did. In comparison with SLL-039, SLL-1206 caused similar antinociceptive responses, but fewer sedation and CPA. In conclusion, our results suggest that SLL-039 and SLL-1206 have potential to be developed as novel analgesic agents, and 4,5-expoxymorphinan scaffold is an attractive structure for the development of selective κ agonists with fewer side effects.

Determination of structural factors affecting binding to mu, kappa and delta opioid receptors

Addiction is a complex behavioral phenomenon in which naturally occurring or synthetic chemicals modulate the response of the reward system through their binding to a variety of neuroreceptors, resulting in compulsive substance-seeking and use despite harmful consequences to the individual. Among these, the opioid receptor (OR) family and more specifically, the mu-opioid receptor (MOR) subtype plays a critical role in the addiction to powerful prescription and illicit drugs such as hydrocodone, oxycodone, fentanyl, cocaine, and methamphetamine (Contet et al. in Curr Opin Neurobiol 14(3):370-378, 2004). Conversely, agonists binding to kappa (KOR) and antagonists binding to delta opioid receptors (DOR) have been reported to induce negative reinforcing effects. As more than 700 new psychoactive substances were illegally sold between 2009 and 2016 (DEA-DCT-DIR-032-18), most of them lacking basic toxicological and pharmacological profiles, molecular modeling approaches that could quickly and reliably fill the gaps in our knowledge would be highly desirable tools for determining the effects of these synthetics. Here, we report accurate 3D-spectrometric data-activity relationship classification models for large and diverse datasets of MOR, KOR and DOR binders with areas under the receiver operating characteristic curve for the "blind" prediction sets exceeding 0.88. Structural features associated with (selective) binding to MOR, KOR and/or DOR were identified. These models could assist regulatory agencies in evaluating the health risks associated with the use of unprofiled substances as well as to help the pharmaceutical industry in its search for new drugs to combat addiction.

Discovery of a Highly Selective and Potent κ Opioid Receptor Agonist from N-Cyclopropylmethyl-7α-phenyl-6,14-endoethanotetrahydronorthebaines with Reduced Central Nervous System (CNS) Side Effects Navigated by the Message-Address Concept

Effective and safe analgesics represent an unmet medical need for the treatment of acute and chronic pain. A series of N-cyclopropylmethyl-7α-phenyl-6,14-endoethanotetrahydronorthebaines were designed, synthesized, and assayed, leading to the discovery of a benzylamine derivative (compound 4, SLL-039) as a highly selective and potent κ opioid agonist (κ, K_i = 0.47 nM, κ/μ = 682, κ/δ = 283), which was confirmed by functional assays in vitro and antinociceptive assays in vivo. The in vivo effect could be blocked by pretreatment with the selective κ antagonist nor-BNI. Moreover, this compound did not induce sedation, a common dose limiting effect of κ opioid receptor agonists, at its analgesic dose compared to U50,488H. The dissociation of sedation/antinociception found in SLL-039 was assumed to be correlated with the occupation of its benzamide motif in a unique subsite involving V118^2.63, W124^EL1, and E209^EL2.

Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

INTRODUCTION

Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery.

AREAS COVERED

In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field.

EXPERT OPINION

The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

Comparison data sets for benchmarking QSAR methodologies in lead optimization

2D and 3D QSAR techniques are widely used in lead optimization-like processes. A compilation of 40 diverse data sets is described. It is proposed that these can be used as a common benchmark sample for comparisons of QSAR methodologies, primarily in terms of predictive ability. Use of this benchmark set will be useful for both assessment of new methods and for optimization of existing methods.

Partial charge calculation method affects CoMFA QSAR prediction accuracy

The 3D-QSAR method comparative molecular field analysis (CoMFA) involves the estimation of atomic partial charges as part of the process of calculating molecular electrostatic fields. Using 30 data sets from the literature the effect of using different common partial charge calculation methods on the predictivity (cross-validated R2) of CoMFA was studied. The partial charge methods ranged from the popular Gasteiger and the newer MMFF94 electronegativity equalization methods, to the more complex and computationally expensive semiempirical charges AM1, MNDO, and PM3. The MMFF94 and semiempirical MNDO, AM1, and PM3 methods for computing charges were found to result in statistically significantly more predictive CoMFA models than the Gasteiger charges. Although there was a trend toward the semiempirical charges performing better than the MMFF94 charges, the difference was not statistically significant. Thus, semiempirical partial charge calculation methods are suggested for the most predictive CoMFA models, but the MMFF94 charge calculation method is a very good alternative if semiempirical methods are not available or faster calculation speed is important.

Effect of steric molecular field settings on CoMFA predictivity

Steric molecular field can be represented in a number of ways in comparative molecular field analysis (CoMFA). This study aimed to investigate whether the choice of steric molecular field settings significantly influences the predictive performance of CoMFA and, if so, which is the best. The three-dimensional quantitative structure activity relationship (3D-QSAR) models based on Lennard-Jones, indicator, parabolic and Gaussian steric fields were compared using 28 datasets taken from the literature. The analysis of the predictive ability of these models (cross validated R(2)) indicates that steric fields in which the value drops off quickly with distance (i.e. Lennard-Jones and indicator fields) tend to perform better than the Gaussian version, which has a slower and smoother decrease. Furthermore, depending on the steric field type used, the field sampling density (i.e. grid spacing) has a variable influence on the predictive ability of the models generated.

QSAR Study on the Antinociceptive Activity of Some Morphinans

Quantitative structure-activity relationship studies were performed to describe and predict the antinociceptive activity of 31 morphinan derivatives reported by the US Drug Evaluation Committee in 2005 and 2006. From these, three data sets were constructed and several models were calculated following the multiple linear regression and Leave-One-Out Cross-Validation (LOO-CV) tests. In general, these models achieved good descriptive power (approximately 92%) as well as predictive power (approximately 76%), but were unable to predict an external validation set of morphinan derivatives. When artificial neural networks were applied to these models, an improvement of the predictive and external validation values was obtained. It was observed that the results of the NN models are significantly better that those obtained by multiple linear regression. In spite that the problem under investigation can be handled adequately by a linear model, a neural network does bring slight improvements in the predictive power.