Lantern-type G-quadruplex fluorescent sensors for detecting divalent metal ions.

Three thioflavin T (ThT) derivatives, namely ThT/ethylenediaminetetraacetic acid conjugates (E1T, E2T, and E1T1P), were designed and synthesized as sensing components for divalent metal ion detection. Furthermore, these ThT derivatives were used to design lantern-type G-quadruplex (G4) fluorescent sensors. The fluorescence intensities of the ThT derivatives decreased by 1.2- to 5.6-folds in the presence of Ni2+ and Cu2+, respectively, regardless of the topology of the utilized G4. Conversely, when Mn2+ and Zn2+ coexisted in antiparallel G4, the fluorescence intensities of E2T increased to approximately 3.3- and 2.3-folds, respectively, depending on the concentration of the divalent metal ion, allowing for quantitative analyses. The Job plot analysis revealed that the binding ratio of G4 and E2T changed from 2:1 to 1:2 with the increasing concentration of the divalent metal ions. These results indicated that the basic principle of such a lantern-type G4 sensor can be applied to the detection of divalent metal ions and other types of targets, such as proteins, and small molecules via ThT derivatization.

Idiopathic pulmonary fibrosis-specific Bayesian network integrating extracellular vesicle proteome and clinical information.

Idiopathic pulmonary fibrosis (IPF) is a progressive disease characterized by severe lung fibrosis and a poor prognosis. Although the biomolecules related to IPF have been extensively studied, molecular mechanisms of the pathogenesis and their association with serum biomarkers and clinical findings have not been fully elucidated. We constructed a Bayesian network using multimodal data consisting of a proteome dataset from serum extracellular vesicles, laboratory examinations, and clinical findings from 206 patients with IPF and 36 controls. Differential protein expression analysis was also performed by edgeR and incorporated into the constructed network. We have successfully visualized the relationship between biomolecules and clinical findings with this approach. The IPF-specific network included modules associated with TGF-ß signaling (TGFB1 and LRC32), fibrosis-related (A2MG and PZP), myofibroblast and inflammation (LRP1 and ITIH4), complement-related (SAA1 and SAA2), as well as serum markers, and clinical symptoms (KL-6, SP-D and fine crackles). Notably, it identified SAA2 associated with lymphocyte counts and PSPB connected with the serum markers KL-6 and SP-D, along with fine crackles as clinical manifestations. These results contribute to the elucidation of the pathogenesis of IPF and potential therapeutic targets.

Latent inter-organ mechanism of idiopathic pulmonary fibrosis unveiled by a generative computational approach.

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease characterized by complex lung pathogenesis affecting approximately three million people worldwide. While the molecular and cellular details of the IPF mechanism is emerging, our current understanding is centered around the lung itself. On the other hand, many human diseases are the products of complex multi-organ interactions. Hence, we postulate that a dysfunctional crosstalk of the lung with other organs plays a causative role in the onset, progression and/or complications of IPF. In this study, we employed a generative computational approach to identify such inter-organ mechanism of IPF. This approach found unexpected molecular relatedness of IPF to neoplasm, diabetes, Alzheimer's disease, obesity, atherosclerosis, and arteriosclerosis. Furthermore, as a potential mechanism underlying this relatedness, we uncovered a putative molecular crosstalk system across the lung and the liver. In this inter-organ system, a secreted protein, kininogen 1, from hepatocytes in the liver interacts with its receptor, bradykinin receptor B1 in the lung. This ligand-receptor interaction across the liver and the lung leads to the activation of calmodulin pathways in the lung, leading to the activation of interleukin 6 and phosphoenolpyruvate carboxykinase 1 pathway across these organs. Importantly, we retrospectively identified several pre-clinical and clinical evidence supporting this inter-organ mechanism of IPF. In conclusion, such feedforward and feedback loop system across the lung and the liver provides a unique opportunity for the development of the treatment and/or diagnosis of IPF. Furthermore, the result illustrates a generative computational framework for machine-mediated synthesis of mechanisms that facilitates and complements the traditional experimental approaches in biomedical sciences.

DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform.

We developed a novel drug metabolism and pharmacokinetics (DMPK) analysis platform named DruMAP. This platform consists of a database for DMPK parameters and programs that can predict many DMPK parameters based on the chemical structure of a compound. The DruMAP database includes curated DMPK parameters from public sources and in-house experimental data obtained under standardized conditions; it also stores predicted DMPK parameters produced by our prediction programs. Users can predict several DMPK parameters simultaneously for novel compounds not found in the database. Furthermore, the highly flexible search system enables users to search for compounds as they desire. The current version of DruMAP comprises more than 30,000 chemical compounds, about 40,000 activity values (collected from public databases and in-house data), and about 600,000 predicted values. Our platform provides a simple tool for searching and predicting DMPK parameters and is expected to contribute to the acceleration of new drug development. DruMAP can be freely accessed at: https://drumap.nibiohn.go.jp/.

Structural dynamics of the N-terminal SH2 domain of PI3K in its free and CD28-bound states.

Protein conformational changes with fluctuations are fundamental aspects of protein-protein interactions (PPIs); understanding these motions is required for the rational design of PPI-regulating compounds. Src homology 2 (SH2) domains are commonly found in adapter proteins involved in signal transduction and specifically bind to consensus motifs of proteins containing phosphorylated tyrosine (pY). Here, we analysed the interaction between the N-terminal SH2 domain (nSH2) of the regulatory subunit in phosphoinositide 3-kinase (PI3K) and the cytoplasmic region of the T-cell co-receptor, CD28, using NMR and molecular dynamics (MD) simulations. First, we assigned the backbone signals of nSH2 on 1 H-15 N heteronuclear single quantum coherence spectra in the absence or presence of the CD28 phosphopeptide, SDpYMNMTPRRPG. Chemical shift perturbation experiments revealed allosteric changes at the BC loop and the C-terminal region of nSH2 upon CD28 binding. NMR relaxation experiments showed a conformational exchange associated with CD28 binding in these regions. The conformational stabilisation of the C-terminal region correlated with the regulation of PI3K catalytic function. Further, using 19 F- and 31 P-labelled CD28 phosphopeptide, we analysed the structural dynamics of CD28 and demonstrated that the aromatic ring of the pY residue fluctuated between multiple conformations upon nSH2 binding. Our MD simulations largely explained the NMR results and the structural dynamics of nSH2 and CD28 in both bound and unbound states. Notably, in addition to its major conformation, we detected a minor conformation of nSH2 in the CD28 bound state that may explain the allosteric conformational change in the BC loop.

Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters.

One solution to compensate for the shortage of publicly available data is to collect more quality-controlled data from the private sector through public-private partnerships. However, several issues must be resolved before implementing such a system. Here, we review the technical aspects of public-private partnerships using our initiative in Japan as an example. In particular, we focus on the procedure for collecting data from multiple private sector companies and building prediction models and discuss how merging public and private sector datasets will help to improve the chemical space coverage and prediction performance. Teaser: Japan's first public-private consortium in pharmacokinetics has incorporated data from multiple pharmaceutical companies to create useful predictive models.

Structure-Activity Relationships of Anti-microRNA Oligonucleotides Containing Cationic Guanidine-Modified Nucleic Acids.

Anti-microRNA oligonucleotides (AMOs) are valuable tools for the treatment of diseases caused by the dysregulation of microRNA expression. However, the correlation between chemical modifications in AMO sequences and the microRNA-inhibitory activity has not been fully elucidated. In this study, we synthesized a series of AMOs containing cationic guanidine-bridged nucleic acids (GuNA) and evaluated their activities using a dual luciferase assay. We also optimized the site of GuNA substitution and found an effective design for the inhibition of microRNA-21, which was partially different from that of conventional nucleic acid derivatives. This study showed that GuNA-substituted AMOs are effective in inhibiting the function of microRNA.

Development of an In Silico Prediction Model for P-glycoprotein Efflux Potential in Brain Capillary Endothelial Cells toward the Prediction of Brain Penetration.

Developing in silico models to predict the brain penetration of drugs remains a challenge owing to the intricate involvement of multiple transport systems in the blood brain barrier, and the necessity to consider a combination of multiple pharmacokinetic parameters. P-glycoprotein (P-gp) is one of the most important transporters affecting the brain penetration of drugs. Here, we developed an in silico prediction model for P-gp efflux potential in brain capillary endothelial cells (BCEC). Using the representative values of P-gp net efflux ratio in BCEC, we proposed a novel prediction system for brain-to-plasma concentration ratio (Kp,brain) and unbound brain-to-plasma concentration ratio (Kp,uu,brain) of P-gp substrates. We validated the proposed prediction system using newly acquired experimental brain penetration data of 28 P-gp substrates. Our system improved the predictive accuracy of brain penetration of drugs using only chemical structure information compared with that of previous studies.

Molecular interactions of the CTLA-4 cytoplasmic region with the phosphoinositide 3-kinase SH2 domains.

During T-cell regulation, T-cell receptors and CD28 lead to signaling activation, while T-lymphocyte antigen 4 (CTLA-4) is known to lead to downregulation, similar to programmed cell death-1 (PD-1). In the cytoplasmic tails of CD28 and CTLA-4, phosphoinositide 3-kinase (PI3K) binds to the consensus sequence including phosphotyrosine via SH2 domains, N- and C-terminal SH2 domains (nSH2 and cSH2), of its regulatory subunit, p85. In this study, we determined the crystal structure of a CTLA-4-derived phosphopeptide in complex with a Cys-substituted mutant of cSH2, C656S/C659V/C670L, at a 1.1 Å resolution. Phosphotyrosine of the bound peptide is tightly accommodated by the residues Arg631, Arg649, Ser651, and Ser652, similar to the cSH2 wild-type recognition mode of CD28, as reported previously. Upon the Cys mutation, the cSH2 thermal stability increased while the CTLA-4 binding affinity slightly changed. The binding experiments also showed that the binding affinity of CTLA-4 by cSH2 was approximately two orders of magnitude lower than that of CD28. Similar to CD28 binding, the CTLA-4 binding affinity of nSH2 was lower than that of cSH2. The complex structure of nSH2 and CTLA-4 was modeled, and compared with the crystal structure of cSH2 mutant and CTLA-4. The difference in the binding affinity between CD28 and CTLA-4, along with the difference between nSH2 and cSH2, could be explained by the 3D structures, which would be closely correlated with the respective T-cell signaling.

A public-private partnership to enrich the development of in silico predictive models for pharmacokinetic and cardiotoxic properties.

A novel framework for a public-private (PP) partnership was established by a national initiative of the Development of a Drug Discovery Informatics System, supported by the Japan Agency for Medical Research and Development (AMED). This informatics PP partnership consortium comprised private and public sectors. A database of pharmacokinetic (PK) and cardiotoxic properties was developed, with considerable expansion after integrating proprietary data from private-sector members. This database led to robust in silico prediction models with higher performance than those from the original database. This partnership is a unique example worldwide and could substantially strengthen drug discovery capabilities in both sectors.

Prophylactic Effect of Amiodarone Infusion on Reperfusion Ventricular Fibrillation After Release of Aortic Cross-Clamp in Patients with Left Ventricular Hypertrophy Undergoing Aortic Valve Replacement: ARandomized Controlled Trial.

OBJECTIVE: To investigate whether prophylactic amiodarone infusion prevents ventricular fibrillation after aortic cross-clamp release and attenuates cytokine production in patients with left ventricular hypertrophy undergoing cardiac surgery. DESIGN: Prospective, randomized controlled trial. SETTING: A public hospital. PARTICIPANTS: The study comprised 68 patients undergoing aortic valve replacement for severe aortic stenosis. INTERVENTIONS: Patients were randomly assigned to receive a 150mg bolus then 30mg/h continuous infusion of amiodarone (amiodarone group) or a 1 mg/kg bolus then 1 mg/kg/h continuous infusion of lidocaine (lidocaine group). The primary outcome was the ventricular fibrillation incidence rate after aortic cross-clamp release. Secondary outcomes included perioperative serum interleukin-6 and tumor necrosis factor-alpha levels. MEASUREMENTS AND MAIN RESULTS: The ventricular fibrillation incidence rate was significantly lower in the amiodarone than in the lidocaine group (20.6% v 50%, relative risk 0.41; 95% confidence interval [CI] 0.20-0.86; p = 0.021). Interleukin-6 levels 1hour after aortic cross-clamp release and at intensive care unit admission were significantly lower in the amiodarone than in the lidocaine group (geometric mean [95% CI] 117.4pg/mL [87.1-158.4] v 339.5pg/mL [210.6-547.2]; p < 0.01 and 211.1pg/mL [162.8-73.6] v 434.1pg/mL [293.7-641.5]; p < 0.01, respectively). Tumor necrosis factor-alpha levels 1hour after aortic cross-clamp release were significantly lower in the amiodarone than in the lidocaine group (geometric mean [95% CI] 1.624pg/mL [1.359-1.940] v 2.283pg/mL [1.910-2.731]; p = 0.02). CONCLUSIONS: Amiodarone prevented reperfusion ventricular fibrillation in patients with left ventricular hypertrophy undergoing aortic valve replacement to a greater extent than did lidocaine. Furthermore, amiodarone inhibited postoperative interleukin-6 and tumor necrosis factor-alpha production.

Discovery of evocalcet, a next-generation calcium-sensing receptor agonist for the treatment of hyperparathyroidism.

The calcium-sensing receptor (CaSR) plays an important role in sensing extracellular calcium ions and regulating parathyroid hormone secretion by parathyroid gland cells, and the receptor is a suitable target for the treatment of hyperparathyroidism. Cinacalcet hydrochloride is a representative CaSR agonist which widely used for the hyperparathyroidism. However, it has several issues to clinical use, such as nausea/vomiting and strong inhibition of CYP2D6. We tried to improve these issues of cinacalcet for a new pharmaceutical agent as a preferable CaSR agonist. Optimization from cinacalcet resulted in the identification of pyrrolidine compounds and successfully led to the discovery of evocalcet as an oral allosteric CaSR agonist. Evocalcet, which exhibited highly favorable profiles such as CaSR agonistic activity and good DMPK profiles, will provide a novel therapeutic option for secondary hyperparathyroidism.

A novel descriptor based on atom-pair properties.

BACKGROUND: Molecular descriptors have been widely used to predict biological activities and physicochemical properties or to analyze chemical libraries on the basis of similarity. Although fingerprints and properties are generally used as descriptors, neither is perfect for these purposes. A fingerprint can distinguish between molecules, whereas a property may not do the same in certain cases, and vice versa. When the number of the training set is especially small, the construction of good predictive models is difficult. Herein, a novel descriptor integrating mutually compensating fingerprint and property characteristics is described. The format of this descriptor is not conventional. It has two dimensions with variable length in one dimension to represent one molecule. This format is not acceptable for any machine learning methods. Therefore the distance between molecules has been newly defined for application to machine learning techniques. The evaluation of this descriptor, as applied to classification tasks, was performed using a support vector machine after the features of the descriptor had been optimized by a genetic algorithm. RESULTS: Because the optimizing feature is time-intensive due to the complicated calculation of distances between molecules, the optimization was forced to stop before it was completed. As a result, no remarkable improvement was observed in the classification results for the new descriptor compared with those for other descriptors in any evaluation set used in this work. However, extremely low accuracies were also not found for any set. CONCLUSIONS: The novel descriptor proposed in this work can potentially be used to make highly accurate predictive models. This new concept in descriptors is expected to be useful for developing novel predictive methods with quick training and high accuracy.

Association of Preoperative Right and Left Ventricular Diastolic Dysfunction With Postoperative Atrial Fibrillation in Patients Undergoing Lung Surgery: A Prospective Observational Study.

OBJECTIVES: To clarify the relationship between right and left ventricular (RV and LV) diastolic function and postoperative atrial fibrillation (POAF). The early effects of major lung surgery on cardiac function in the intraoperative period during lung surgery were evaluated, using transesophageal echocardiography. DESIGN: Single-center prospective observational study. SETTING: A public hospital. PARTICIPANTS: Patients undergoing elective lobectomy with lymph node dissection for lung cancer (n = 116). INTERVENTIONS: Transesophageal echocardiography examination was performed under general anesthesia before skin incision (preoperative) and after chest closure (postoperative). According to measured echocardiographic variables, ventricular systolic and diastolic functions were classified at each time point. MEASUREMENTS AND MAIN RESULTS: Of the 116 patients, 24 (20.7%) experienced POAF. Preoperative RV and LV diastolic dysfunction were more common in patients with POAF than in those without POAF (58.3 v 28.3%, p = 0.008; 54.2 v 19.6%, p = 0.001, respectively). Among patients without preoperative diastolic dysfunction, a small number developed RV and LV diastolic dysfunction immediately after surgery (9.2% and 16.5%, respectively) and these distributions were comparable between patients with POAF and those without POAF. RV systolic dysfunction was observed in 6.5% of patients immediately after surgery and was not related to the occurrence of POAF. Multivariate analysis revealed older age, chronic obstructive pulmonary disease (COPD), and preoperative biventricular diastolic dysfunction as risk factors for POAF. CONCLUSIONS: Preoperative biventricular diastolic dysfunction, as well as older age and COPD, are associated with POAF in patients undergoing lobectomy. Major lung surgery has minimal early effects on postoperative systolic and diastolic functions.