Alzheimer's disease (AD) is considered to be the most common cause of dementia and is an incurable, progressive neurodegenerative disorder. Current treatment of the disease, essentially symptomatic, is based on three cholinesterase inhibitors and memantine, affecting the glutamatergic system. Since 2003, no new drugs have been approved for treatment of AD. This article presents current directions in the search for novel, potentially effective agents for the treatment of AD, as well as selected promising treatment strategies. These include agents acting upon the beta-amyloid, such as vaccines, antibodies and inhibitors or modulators of γ- and β-secretase; agents directed against the tau protein as well as compounds acting as antagonists of neurotransmitter systems (serotoninergic 5-HT6 and histaminergic H3). Ongoing clinical trials with Aβ antibodies (solanezumab, gantenerumab, crenezumab) seem to be promising, while vaccines against the tau protein (AADvac1 and ACI-35) are now in early-stage trials. Interesting results have also been achieved in trials involving small molecules such as inhibitors of β-secretase (MK-8931, E2609), a combination of 5-HT6 antagonist (idalopirdine) with donepezil, inhibition of advanced glycation end product receptors by azeliragon or modulation of the acetylcholine response of α-7 nicotinic acetylcholine receptors by encenicline. Development of new effective drugs acting upon the central nervous system is usually a difficult and time-consuming process, and in the case of AD to-date clinical trials have had a very high failure rate. Most phase II clinical trials ending with a positive outcome do not succeed in phase III, often due to serious adverse effects or lack of therapeutic efficacy.
The multitarget approach is a promising paradigm in drug discovery, potentially leading to new treatment options for complex disorders, such as Alzheimer's disease. Herein, we present the discovery of a unique series of 1-benzylamino-2-hydroxyalkyl derivatives combining inhibitory activity against butyrylcholinesterase, β-secretase, β-amyloid, and tau protein aggregation, all related to mechanisms which underpin Alzheimer's disease. Notably, diphenylpropylamine derivative 10 showed balanced activity against both disease-modifying targets, inhibition of β-secretase (IC = 41.60 μM), inhibition of amyloid β aggregation (IC = 3.09 μM), inhibition of tau aggregation (55% at 10 μM); as well as against symptomatic targets, butyrylcholinesterase inhibition (IC = 7.22 μM). It might represent an encouraging starting point for development of multifunctional disease-modifying anti-Alzheimer's agents.
The crucial role of G-protein coupled receptors and the significant achievements associated with a better understanding of the spatial structure of known receptors in this family encouraged us to undertake a study on the histamine H3 receptor, whose crystal structure is still unresolved. The latest literature data and availability of different software enabled us to build homology models of higher accuracy than previously published ones. The new models are expected to be closer to crystal structures; and therefore, they are much more helpful in the design of potential ligands. In this article, we describe the generation of homology models with the use of diverse tools and a hybrid assessment. Our study incorporates a hybrid assessment connecting knowledge-based scoring algorithms with a two-step ligand-based docking procedure. Knowledge-based scoring employs probability theory for global energy minimum determination based on information about native amino acid conformation from a dataset of experimentally determined protein structures. For a two-step docking procedure two programs were applied: GOLD was used in the first step and Glide in the second. Hybrid approaches offer advantages by combining various theoretical methods in one modeling algorithm. The biggest advantage of hybrid methods is their intrinsic ability to self-update and self-refine when additional structural data are acquired. Moreover, the diversity of computational methods and structural data used in hybrid approaches for structure prediction limit inaccuracies resulting from theoretical approximations or fuzziness of experimental data. The results of docking to the new H3 receptor model allowed us to analyze ligand—receptor interactions for reference compounds.
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