Brand-Name Antidepressants Outperform Their Generic Counterparts in Preventing Hospitalization for Depression: The Real-World Evidence from Taiwan

BackgroundGeneric antidepressants are approved on the market based on evidence of bioequivalence to their brand-name versions. We aimed to assess whether generic antidepressants exert equal effectiveness as their brand-name counterparts for treating patients with depressive disorders.MethodsIn a nationwide, population-based cohort in Taiwan from 1997 through 2013, patients with a diagnosis of a depressive disorder aged between 18 and 65 years who were new users of antidepressant drugs were classified into either the brand-name group or the generic group. All patients were followed up until medication discontinuation or the end of the study period. We assessed the risk for hospitalization as a primary outcome and augmentation therapy, daily dose, medication discontinuation, or switching to another antidepressant as secondary outcomes.ResultsA total of 277 651 brand-name users (35.8% male; mean age: 41.2 years) and 270 583 generic users (35.8% male; mean age: 41.0 years) were divided into 10 different antidepressant groups (fluoxetine, sertraline, paroxetine, escitalopram, citalopram, venlafaxine, mirtazapine, moclobemide, imipramine, and bupropion). We found that patients treated with the generic form of sertraline, paroxetine, escitalopram, venlafaxine, mirtazapine, and bupropion demonstrated significantly higher risks of psychiatric hospitalization (adjusted hazard ratios ranged from 1.20–2.34), compared to their brand-name counterparts. The differences between brand-name antidepressants and their generic counterparts in secondary outcomes varied across different drugs.ConclusionsCompared to most generic antidepressants, brand-name drugs exhibited more protective effects on psychiatric hospitalization for depressive patients. These findings could serve as an important reference for clinicians when encountering patients with depressive disorder.     

Population Genetics Identifies Challenges in Analyzing Rare Variants

Geneticists have, for years, understood the nature of genome‐wide association studies using common genomic variants. Recently, however, focus has shifted to the analysis of rare variants. This presents potential problems for researchers, as rare variants do not always behave in the same way common variants do, sometimes rendering decades of solid intuition moot. In this paper, we present examples of the differences between common and rare variants. We show why one must be significantly more careful about the origin of rare variants, and how failing to do so can lead to highly inflated type I error. We then explain how to best avoid such concerns with careful understanding and study design. Additionally, we demonstrate that a seemingly low error rate in next‐generation sequencing can dramatically impact the false‐positive rate for rare variants. This is due to the fact that rare variants are, by definition, seen infrequently, making it hard to distinguish between errors and real variants. Compounding this problem is the fact that the proportion of errors is likely to get worse, not better, with increasing sample size. One cannot simply scale their way up in order to solve this problem. Understanding these potential pitfalls is a key step in successfully identifying true associations between rare variants and diseases.     

Binding mode of conformations and structure-based pharmacophore development for farnesyltransferase inhibitors

Farnesyltransferase (FTase) is one of the prenyltransferase family enzymes that catalyse the transfer of 15-membered isoprenoid (farnesyl) moiety to the cysteine of CAAX motif-containing proteins including Rho and Ras family of G proteins. Inhibitors of FTase act as drugs for cancer, malaria, progeria and other diseases. In the present investigation, we have developed two structure-based pharmacophore models from protein–ligand complex (3E33 and 3E37) obtained from the protein data bank. Molecular dynamics (MD) simulations were performed on the complexes, and different conformers of the same complex were generated. These conformers were undergone protein–ligand interaction fingerprint (PLIF) analysis, and the fingerprint bits have been used for structure-based pharmacophore model development. The PLIF results showed that Lys164, Tyr166, TrpB106 and TyrB361 are the major interacting residues in both the complexes. The RMSD and RMSF analyses on the MD-simulated systems showed that the absence of FPP in the complex 3E37 has significant effect in the conformational changes of the ligands. During this conformational change, some interactions between the protein and the ligands are lost, but regained after some simulations (after 2 ns). The structure-based pharmacophore models showed that the hydrophobic and acceptor contours are predominantly present in the models. The pharmacophore models were validated using reference compounds, which significantly identified as HITs with smaller RMSD values. The developed structure-based pharmacophore models are significant, and the methodology used in this study is novel from the existing methods (the original X-ray crystallographic coordination of the ligands is used for the model building). In our study, along with the original coordination of the ligand, different conformers of the same complex (protein–ligand) are used. It concluded that the developed methodology is significant for the virtual screening of novel molecules on different targets.     

Intervention for arch obstruction after the Norwood procedure: Prevalence,associated factors,and practice variability

Objective

Arch obstruction after the Norwood procedure is common and contributes to mortality. We determined the prevalence, associated factors, and practice variability of arch reintervention and assessed whether arch reintervention is associated with mortality.