Pyrrolidinones as orally bioavailable antagonists of the human melanocortin-4 receptor with anti-cachectic activity

Small Molecule Neuropeptide S and Melanocortin 4 Receptor Ligands as Potential Treatments for Substance Use Disorders

There is a vital need for novel approaches and biological targets for drug discovery and development. Treatment strategies for substance use disorders (SUDs) to date have been mostly ineffective other than substitution-like therapeutics. Two such targets are the peptide G-protein-coupled receptors neuropeptide S (NPS) and melanocortin 4 (MC4). Preclinical evidence suggests that antagonists, inverse agonists, or negative allosteric modulators of these receptors might be novel therapeutics for SUDs. NPS is a relatively unexplored receptor with high potential for treating SUD. MC4 has a strong link to early-onset obesity, and emerging evidence suggests significant overlap between food-maintained and drug-maintained behaviors making MC4 an intriguing target for SUD. This chapter provides an overview of the literature in relation to the roles of NPS and MC4 in drug-seeking behaviors and then provides a medicinal chemistry-based survey of the small molecule ligands for each receptor.

Rank order entropy: why one metric is not enough

The use of Quantitative Structure-Activity Relationship models to address problems in drug discovery has a mixed history, generally resulting from the misapplication of QSAR models that were either poorly constructed or used outside of their domains of applicability. This situation has motivated the development of a variety of model performance metrics (r(2), PRESS r(2), F-tests, etc.) designed to increase user confidence in the validity of QSAR predictions. In a typical workflow scenario, QSAR models are created and validated on training sets of molecules using metrics such as Leave-One-Out or many-fold cross-validation methods that attempt to assess their internal consistency. However, few current validation methods are designed to directly address the stability of QSAR predictions in response to changes in the information content of the training set. Since the main purpose of QSAR is to quickly and accurately estimate a property of interest for an untested set of molecules, it makes sense to have a means at hand to correctly set user expectations of model performance. In fact, the numerical value of a molecular prediction is often less important to the end user than knowing the rank order of that set of molecules according to their predicted end point values. Consequently, a means for characterizing the stability of predicted rank order is an important component of predictive QSAR. Unfortunately, none of the many validation metrics currently available directly measure the stability of rank order prediction, making the development of an additional metric that can quantify model stability a high priority. To address this need, this work examines the stabilities of QSAR rank order models created from representative data sets, descriptor sets, and modeling methods that were then assessed using Kendall Tau as a rank order metric, upon which the Shannon entropy was evaluated as a means of quantifying rank-order stability. Random removal of data from the training set, also known as Data Truncation Analysis (DTA), was used as a means for systematically reducing the information content of each training set while examining both rank order performance and rank order stability in the face of training set data loss. The premise for DTA ROE model evaluation is that the response of a model to incremental loss of training information will be indicative of the quality and sufficiency of its training set, learning method, and descriptor types to cover a particular domain of applicability. This process is termed a "rank order entropy" evaluation or ROE. By analogy with information theory, an unstable rank order model displays a high level of implicit entropy, while a QSAR rank order model which remains nearly unchanged during training set reductions would show low entropy. In this work, the ROE metric was applied to 71 data sets of different sizes and was found to reveal more information about the behavior of the models than traditional metrics alone. Stable, or consistently performing models, did not necessarily predict rank order well. Models that performed well in rank order did not necessarily perform well in traditional metrics. In the end, it was shown that ROE metrics suggested that some QSAR models that are typically used should be discarded. ROE evaluation helps to discern which combinations of data set, descriptor set, and modeling methods lead to usable models in prioritization schemes and provides confidence in the use of a particular model within a specific domain of applicability.

The ups and downs of structure-activity landscapes

In this chapter we discuss the landscape view of structure-activity relationships (SARs). The motivation for such a view is that SARs come in a variety of forms, such as those where small changes in structure lead to small changes in activity or where small structural lead to significant changes in activity (also termed activity cliffs). Thus, an SAR dataset is viewed as a landscape comprised of smooth plains, rolling hills, and jagged gorges. We review the history of this view and early quantitative approaches that attempted to encode the landscape. We then discuss some recent developments that directly characterize structure-activity landscapes, in one case with the goal of highlighting activity cliffs while the other allows one to resolve different types of SAR that may be present in a dataset. We highlight some applications of these approaches, such as predictive model development and SAR elucidation, to SAR datasets obtained from the literature. Finally, we conclude with a summary of the landscape approach and why it provides an intuitive and rigorous alternative to standard views of structure-activity data.

Assessing how well a modeling protocol captures a structure-activity landscape

We introduce the notion of structure-activity landscape index (SALI) curves as a way to assess a model and a modeling protocol, applied to structure-activity relationships. We start from our earlier work [ J. Chem. Inf. Model., 2008, 48, 646-658], where we show how to study a structure-activity relationship pairwise, based on the notion of "activity cliffs"--pairs of molecules that are structurally similar but have large differences in activity. There, we also introduced the SALI parameter, which allows one to identify cliffs easily, and which allows one to represent a structure-activity relationship as a graph. This graph orders every pair of molecules by their activity. Here, we introduce the new idea of a SALI curve, which tallies how many of these orderings a model is able to predict. Empirically, testing these SALI curves against a variety of models, ranging over two-dimensional quantitative structure-activity relationship (2D-QSAR), three-dimensional quantitative structure-activity relationship (3D-QSAR), and structure-based design models, the utility of a model seems to correspond to characteristics of these curves. In particular, the integral of these curves, denoted as SCI and being a number ranging from -1.0 to 1.0, approaches a value of 1.0 for two literature models, which are both known to be prospectively useful.

Targeting melanocortin receptors: an approach to treat weight disorders and sexual dysfunction

The melanocortin system has multifaceted roles in the control of body weight homeostasis, sexual behaviour and autonomic functions, and so targeting this pathway has immense promise for drug discovery across multiple therapeutic areas. In this Review, we first outline the physiological roles of the melanocortin system, then discuss the potential of targeting melanocortin receptors by using MC3 and MC4 agonists for treating weight disorders and sexual dysfunction, and MC4 antagonists to treat anorectic and cachectic conditions. Given the complexity of the melanocortin system, we also highlight the challenges and opportunities for future drug discovery in this area.

Val103Ile polymorphism of the melanocortin-4 receptor gene (MC4R) in cancer cachexia

Background

At present pathogenic mechanisms of cancer cachexia are poorly understood. Previous evidence in animal models implicates the melanocortin-4 receptor gene (MC4R) in the development of cancer cachexia. In humans, MC4R mutations that lead to an impaired receptor function are associated with obesity; in contrast, the most frequent polymorphism (Val103Ile, rs2229616; heterozygote frequency approximately 2%) was shown to be negatively associated with obesity. We tested if cancer patients that are homo-/heterozygous for the Val103Ile polymorphism are more likely to develop cachexia and/or a loss of appetite than non-carriers of the 103Ile-allele.

Methods

BMI (body mass index in kg/m²) of 509 patients (295 males) with malignant neoplasms was determined; additionally patients were asked about premorbid/pretherapeutical changes of appetite and weight loss. Cachexia was defined as a weight loss of at least 5% prior to initiation of therapy; to fulfil this criterion this weight loss had to occur independently of other plausible reasons; in single cases weight loss was the initial reason for seeing a physician. The average age in years (± SD) was 59.0 ± 14.5 (males: 58.8 ± 14.0, females 59.2 ± 14.0). Blood samples were taken for genotyping of the Val103Ile by PCR- RFLP.

Results

Most of the patients suffered from lymphoma, leukaemia and gastrointestinal tumours. 107 of the patients (21%) fulfilled our criteria for cancer cachexia. We did not detect association between the Val103Ile polymorphism and cancer cachexia. However, if we exploratively excluded the patients with early leucaemic stages, we detected a trend towards the opposite effect (p < 0.05); heterozygotes for the 103Ile-allele developed cancer cachexia less frequently in comparison to the rest of the study group. Changes of appetite were not associated with the 103Ile-allele carrier status (p > 0.39).

Conclusion

Heterozygotes for the 103Ile-allele are not more prone to develop cancer cachexia than patients without this allele; possibly, Ile103 carriers might be more resistant to cancer cachexia in patients with solid tumors. Further studies of the melanocortinergic system in cachexia of patients with solid tumors are warranted.

Design and synthesis of 3-arylpyrrolidine-2-carboxamide derivatives as melanocortin-4 receptor ligands

Based on 3-phenylpropionamides, a series of 3-arylpyrrolidine-2-carboxamide derivatives was designed and synthesized to study the effect of cyclizations as melanocortin-4 receptor ligands. It was found that the 2R,3R-pyrrolidine isomer possessed the most potent affinity among the four stereoisomers.

Design, synthesis, in vitro, and in vivo characterization of phenylpiperazines and pyridinylpiperazines as potent and selective antagonists of the melanocortin-4 receptor

Benzylamine and pyridinemethylamine derivatives were synthesized and characterized as potent and selective antagonists of the melanocortin-4 receptor (MC4R). These compounds were also profiled in rodents for their pharmacokinetic properties. Two compounds with diversified profiles in chemical structure, pharmacological activities, and pharmacokinetics, 10 and 12b, showed efficacy in an established murine cachexia model. For example, 12b had a K(i) value of 3.4 nM at MC4R, was more than 200-fold selective over MC3R, and had a good pharmacokinetic profile in mice, including high brain penetration. Moreover, 12b was able to stimulate food intake in the tumor-bearing mice and reverse their lean body mass loss. Our results provided further evidence that a potent and selective MC4R antagonist with appropriate pharmacokinetic properties might potentially be useful for the treatment of cancer cachexia.

Pyrrolidinones as potent functional antagonists of the human melanocortin-4 receptor

A series of pyrrolidinones derived from phenylalaninepiperazines were synthesized and characterized as potent and selective antagonists of the melanocortin-4 receptor. In addition to their high binding affinities, these compounds displayed high functional potencies. 12a had a K(i) of 0.94 nM in binding and IC(50) of 21 nM in functional activity. 12a also demonstrated efficacy in a mouse cachexia model.