Functional selectivity is a phenomenon observed in G protein-coupled receptors in which intermediate active-state conformations are stabilized by mutations or ligand binding, resulting in different sets of signaling pathways. Peptides capable of selectively activating β-arrestin, known as biased agonists, have already been characterized in vivo and could correspond to a new therapeutic approach for treatment of cardiovascular diseases. Despite the potential of biased agonism, the mechanism involved in selective signaling remains unclear. In this work, molecular dynamics simulations were employed to compare the conformational profile of the angiotensin II type 1 receptor (AT1R) crystal bound to angiotensin II, bound to the biased ligand TRV027, and in the apo form. Our results show that both ligands induce changes near the NPxxY motif in transmembrane domain 7 that are related to receptor activation. However, the biased ligand does not cause the rotamer toggle alternative positioning and displays an exclusive hydrogen-bonding pattern. Our work sheds light on the biased agonism mechanism and will help in the future design of novel biased agonists for AT1R.

Coarse-grained molecular dynamics simulations are used to calculate the free energies of transfer of miltefosine, an alkylphosphocholine anticancer agent, from water to lipid bilayers to study its mechanism of interaction with biological membranes. We consider bilayers containing lipids with different degrees of unsaturation: dipalmitoylphosphatidylcholine (DPPC, saturated, containing 0%, 10%, and 30% cholesterol), dioleoylphosphatidylcholine (DOPC, diunsaturated), palmitoyloleoylphosphatidylcholine (POPC, monounsaturated), diarachidonoylphosphatidylcholine (DAPC, polyunsaturated), and dilinoleylphosphatidylcholine (DUPC, polyunsaturated). These free energies, calculated using umbrella sampling, were used to compute the partition coefficients (K) of miltefosine between water and the lipid bilayers. The K values for the bilayers relative to that of pure DPPC were found to be 5.3 (DOPC), 7.0 (POPC), 1.0 (DAPC), 2.2 (DUPC), 14.9 (10% cholesterol), and 76.2 (30% cholesterol). Additionally, we calculated the free energy of formation of miltefosine-cholesterol complexes by pulling the surfactant laterally in the DPPC + 30% cholesterol system. The free energy profile that we obtained provides further evidence that miltefosine tends to associate with cholesterol and has a propensity to partition into lipid rafts. We also quantified the kinetics of the transport of miltefosine through the various bilayers by computing permeance values. The highest permeance was observed in DUPC bilayers (2.28 × 10(-2) m/s) and the lowest permeance in the DPPC bilayer with 30% cholesterol (1.10 × 10(-7) m/s). Our simulation results show that miltefosine does indeed interact with lipid rafts, has a higher permeability in polyunsaturated, loosely organized bilayers, and has higher flip-flop rates in specific regions of cellular membranes.

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The dopamine hypothesis states that decreased dopaminergic neurotransmission reduces schizophrenia symptoms. Neurokinin-3 receptor (NK 3 ) antagonists reduce dopamine release and have shown positive effects in pre-clinical and clinical trials. We employed 2D and 3D-QSAR analysis on a series of 40 non-peptide NK 3 antagonists. Multivariate statistical analysis, PCA and HCA, were performed to rational training/test set splitting and PLS regression was employed to construct all QSAR models. We constructed one highly predictive CoMFA model (q 2 ¼ 0.810 and r 2 ¼ 0.929) and acceptable HQSAR and CoMSIA models (HQSAR q 2 ¼ 0.644 and r 2 ¼ 0.910; CoMSIA q 2 ¼ 0.691, r 2 ¼ 0.911). The three different techniques provided convergent physicochemical results. All models indicate cyclopropane, piperidine and di-chloro-phenyl ring attached to cyclopropane ring and also the amide group attached to the piperidine ring could play an important role in ligand-receptor interactions. These findings may contribute to develop potential NK 3 receptor antagonists for schizophrenia.

AT1 is a G protein‐coupled receptor (GPCR), and the interaction with angiotensin II (AngII) causes vasoconstriction by Gq protein signaling. AT1 activation leads to receptor internalization by β‐arrestin, which can also act as a second messenger. Biased agonists are pharmacologically advantageous because they can antagonize the effects of Gq without interrupting β‐arrestin beneficial effects (cellular growth and survival), however, their binding mode remains unknown. Here, we investigated how biased agonists bind to AT1 using molecular docking and molecular dynamics simulations. AT1 homology model was built based on CXCR4 crystal structure and inserted in a lipid bilayer. The binding mode of five biased agonists (TRV027, TRV023, SI, SII and DVG) and 2 full agonists (AngII and SVdF) was compared. The analysis of interatomic distances showed that biased agonists interact with Trp84, Tyr87, Glu173, Asn174, Cys180 and Tyr292, while SVdF interacts with His183 and Gln187. Ser105, Arg167, Tyr184, Glu185 and Lys199 help positioning both biased and full agonists inside the receptor pocket, not affecting their activation mechanism. Because of the different binding mode, biased agonists cause conformational changes in AT1 that block Gq actions, still keeping β‐arrestin activated.

Drugs acting on the central nervous system (CNS) have to cross the blood-brain barrier (BBB) in order to perform their pharmacological actions. Passive BBB diffusion can be partially expressed by the blood/ brain partition coefficient (logBB). As the experimental evaluation of logBB is time and cost consuming, theoretical methods such as quantitative structure-property relationships (QSPR) can be useful to predict logBB values. In this study, a 2D-QSPR approach was applied to a set of 28 drugs acting on the CNS, using the logBB property as biological data. The best QSPR model [n = 21, r = 0.94 (r² = 0.88), s = 0.28, and Q² = 0.82] presented three molecular descriptors: calculated n-octanol/water partition coefficient (ClogP), polar surface area (PSA), and polarizability (α). Six out of the seven compounds from the test set were well predicted, which corresponds to good external predictability (85.7%). These findings can be helpful to guide future approaches regarding those molecular descriptors which must be considered for estimating the logBB property, and also for predicting the BBB crossing ability for molecules structurally related to the investigated set.Uniterms: Two-dimensional quantitative structure-property relationships (2D-QSPR). Calculated n-octanol/water partition coefficient (ClogP). Blood-brain barrier. Benzodiazepines.Fármacos que atuam no sistema nervoso central (SNC) devem atravessar a barreira hematoencefálica (BHE) para exercerem suas ações farmacológicas. A difusão passiva através da BHE pode ser parcialmente expressa pelo coeficiente de partição entre os compartimentos encefálico e sanguíneo (logBB, brain/blood partition coefficient). Considerando-se que a avaliação experimental de logBB é dispendiosa e demorada, métodos teóricos como estudos das relações entre estrutura química e propriedade (QSPR, Quantitative Structure-Property Relationships) podem ser utilizados na previsão dos valores de logBB. Neste estudo, uma abordagem de QSPR-2D foi aplicada a um conjunto de 28 moléculas com ação central, usando logBB como propriedade biológica. O melhor modelo de QSPR [n = 21, r = 0,94 (r² = 0,88), s = 0,28 e Q² = 0,82] apresentou três descritores moleculares: o coeficiente calculado de partição n-octanol/água (ClogP), área de superfície polar (PSA) e polarizabilidade (α). Seis dos sete compostos do conjunto de avaliação foram bem previstos pelo modelo, o que corresponde a um bom poder de previsão externa (85,7%). Os resultados obtidos podem auxiliar de forma relevante em estudos futuros, orientando quais descritores moleculares devem ser considerados para estimar logBB e prever a passagem através da BHE de moléculas estruturalmente relacionadas às do conjunto investigado. Unitermos:Relações quantitativas bidimensionais entre estrutura química e propriedade (2D-QSPR). Coeficiente calculado de partição n-octanol/água (ClogP). Barreira hematoencefálica. Benzodiazepínicos.

Alkylphosphocholines (APC) are promising antitumor agents, which have the cellular membrane as primary target; however, red blood cell damage limits their wide therapeutic use. A variety of APC analogs has been synthesized and tested showing less hemolytic effect than the class prototype, Miltefosine (HePC). In this work, chemometric methods were applied to a set of 34 APC derivatives to identify the most relevant structural and molecular features of hemolytic activity. The APC derivatives were divided into three groups: (i) N-methylpiperidine and N-methylmorpholine derivatives with a long alkyl chain or flexible cyclopentadecyl rings, displaying a hemolytic rate of 17 %; (ii) adamantyl and cyclopentadecyl derivatives, showing an average hemolysis of 39 %; and, N,N,N-trimethylammonium, trans-N,N,N-trimethylcyclohexanamine, and trans-N,N,N-trimethylcyclopentanamine derivatives, whose average hemolysis was 41 %. The findings suggested that the presence of either bulky cationic head groups, or rings such as adamantyl and cyclohexyl, primarily increases the hemolysis of compounds with eleven atoms in the alkyl chain. Moreover, the macrocyclic cyclopentadecyl seems to be important to the hemolytic potential especially of compounds with five carbon atoms in the alkyl chain. Regarding linear carbon chain derivatives with no ring substitution, less bulky cationic head groups seem to favor hemolysis. Thus, in order to design more potent and less toxic APC antitumors, the reported structural/molecular patterns should not be included in their structure.

Alkylphosphocholines (APCs) and alkyl-lysophosphocholines (ALPs) are antineoplastic agents that interfere with cellular membranes and signaling proteins. Protein kinase Cα (PKCα) is a signaling protein composed by catalytic (C3, C4) and regulatory domains (C1, C2). The C2 needs calcium (Ca(2+) ) and phosphatidylserine (PS) for activation. Miltefosine inhibits PKCα competitively with regard to PS and non-competitively with regard to Ca(2+) , however, the mechanism of action is unknown. We employed molecular docking, molecular dynamics and chemometric methods to verify how 7 APCs and ALPs derivatives and PS interact with the C2 domain. All ligands except PS were grouped in 2 clusters according to their interactions inside the enzyme. The findings showed that PS's phosphoryl oxygens interact with Ca(2+) , the serine moiety interacts with Asn189, and the carbonyl oxygen of the alkylic chain interacts with Arg249 and Thr251. On the other hand, ligands' phosphoryl oxygens interact with Asn189, Arg249, Thr250, and one water molecule instead of Ca(2+) . Because of the different binding mode, we hypothesize that the ligands cause conformational changes in the calcium binding region. Moreover, the packing mismatch between bilayer-forming lipids and ALP/APC chain impedes the C2 domain from docking to the internal leaflet of cellular membranes, interrupting PKCα activation.