Mirtazapine has a therapeutic potency in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced mice model of Parkinson's disease.

BACKGROUND: Mirtazapine, a noradrenergic and specific serotonergic antidepressant (NaSSA), shows multiple pharmacological actions such as inhibiting presynaptic α2 noradrenaline receptor (NAR) and selectively activating 5-hydroxytriptamine (5-HT) 1A receptor (5-HT1AR). Mirtazapine was also reported to increase dopamine release in the cortical neurons with 5-HT dependent manner. To examine whether mirtazapine has a therapeutic potency in Parkinson's disease (PD), we examined this compound in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated mice model of PD. RESULTS: Male C57BL/6 mice were subjected to MPTP treatment to establish a PD model. Mirtazapine was administered once a day for 3 days after MPTP treatment. MPTP-induced motor dysfunction, assessed by beam-walking and rota-rod tests, was significantly improved by administration of mirtazapine. Biochemical examinations by high performance liquid chromatography and western blot analysis suggested mirtazapine facilitated utilization of dopamine by increasing turnover and protein expression of transporters, without affecting on neurodegenerative process by MPTP. These therapeutic effects of mirtazapine were reduced by administration of WAY100635, an inhibitor for 5HT1AR, or of clonidine, a selective agonist for α2-NAR, or of prazosin, an inhibitor for α1-NAR, respectively. CONCLUSION: Our results showed mirtazapine had a therapeutic potency against PD in a mouse model. Because PD patients sometimes show depression together, it will be a useful drug for a future PD treatment.

A Collaborative Study on the Classification of Silicone Oil Droplets and Protein Particles Using Flow Imaging Method.

In this study, we conducted a collaborative study on the classification between silicone oil droplets and protein particles detected using the flow imaging (FI) method toward proposing a standardized classifier/model. We compared four approaches, including a classification filter composed of particle characteristic parameters, principal component analysis, decision tree, and convolutional neural network in the performance of the developed classifier/model. Finally, the points to be considered were summarized for measurement using the FI method, and for establishing the classifier/model using machine learning to differentiate silicone oil droplets and protein particles.

Collaborative Study for Analysis of Subvisible Particles Using Flow Imaging and Light Obscuration: Experiences in Japanese Biopharmaceutical Consortium.

The evaluation of subvisible particles, including protein aggregates, in therapeutic protein products has been of great interest for both pharmaceutical manufacturers and regulatory agencies. To date, the flow imaging (FI) method has emerged as a powerful tool instead of light obscuration (LO) due to the fact that (1) protein aggregates contain highly transparent particles and thereby escape detection by LO and (2) FI provides detailed morphological characteristics of subvisible particles. However, the FI method has not yet been standardized nor listed in any compendium. In an attempt to assess the applicability of the standardization of the FI method, we conducted a collaborative study using FI and LO instruments in a Japanese biopharmaceutical consortium. Three types of subvisible particle preparations were shared across 12 laboratories and analyzed for their sizes and counts. The results were compared between the methods (FI and LO), inter-laboratories, and inter-instruments (Micro Flow Imaging and FlowCam). We clarified the marked difference between the detectability of FI and LO when counting highly transparent protein aggregates in the preparations. Although FlowCam provided a relatively higher number of particles compared with MFI, consistent results were obtained using the instrument from the same manufacturer in all 3 samples.

Recognition of a flipped base in a hairpinloop DNA by a small peptide.

Two tiny hairpin DNAs, CORE (dAGGCTTCGGCCT) and AP2 (dAGGCTXCGGCCT; X: abasic nucleotide), fold into almost the same tetraloop hairpin structure with one exception, that is, the sixth thymine (T6) of CORE is exposed to the solvent water (Kawakami, J. et al., Chem. Lett. 2001, 258-259). In the present study, we selected small peptides that bind to CORE or AP2 from a combinatorial pentapeptide library with 2.5 x 10(6) variants. On the basis of the structural information, the selected peptide sequences should indicate the essential qualifications for recognition of the hairpin loop DNA with and without a flipped base. In the selected DNA binding peptides, aromatic amino acids such as histidine for CORE and glutamine/aspartic acid for AP2 were found to be abundant amino acids. This amino acid preference suggests that CORE-binding peptides use pi-pi stacking to recognize the target while hydrogen bonding is dominant for AP2-binding peptides. To investigate the binding properties of the selected peptide to the target, surface plasmon resonance was used. The binding constant of the interaction between CORE and a CORE-binding peptide (HWHHE) was about 1.1 x 10(6) M(-1) at 25 degrees C and the resulting binding free energy change at 25 degrees C (DeltaG degrees (25)) was -8.2 kcal mol(-1). The binding of the peptide to AP2 was also analyzed and the resulting binding constant and DeltaG degrees (25) were about 4.2 x 10(4) M(-1) and -6.3 kcal mol(-1), respectively. The difference in the binding free energy changes (DeltaDeltaG degrees (25)) of 1.9 kcal mol(-1) was comparable to the values reported in other systems and was considered a consequence of the loss of pi-pi stacking. Moreover, the stabilization effect by stacking affected the dissociation step as well as the association step. Our results suggest that the existence of an aromatic ring (T6 base) produces new dominant interactions between peptides and nucleic acids, although hydrogen bonding is the preferable mode of interaction in the absence of the flipping base. These findings regarding CORE and AP2 recognition are expected to give useful information in the design of novel artificial DNA binding peptides.