Pathways to new drug discovery in neuropsychiatry

There is currently a crisis in drug discovery for neuropsychiatric disorders, with a profound, yet unexpected drought in new drug development across the spectrum. In this commentary, the sources of this dilemma and potential avenues to redress the issue are explored. These include a critical review of diagnostic issues and of selection of participants for clinical trials, and the mechanisms for identifying new drugs and new drug targets. Historically, the vast majority of agents have been discovered serendipitously or have been modifications of existing agents. Serendipitous discoveries, based on astute clinical observation or data mining, remain a valid option, as is illustrated by the suggestion in the paper by Wahlqvist and colleagues that treatment with sulfonylurea and metformin reduces the risk of affective disorder. However, the identification of agents targeting disorder-related biomarkers is currently proving particularly fruitful. There is considerable hope for genetics as a purist, pathophysiologically valid pathway to drug discovery; however, it is unclear whether the science is ready to meet this promise. Fruitful paradigms will require a break from the orthodoxy, and creativity and risk may well be the fingerprints of success

PRELIMINARY REPORT ON THE PUTATIVE ASSOCIATION OF IL10 -3575 T/A GENETIC POLYMORPHISM WITH MALARIA SYMPTOMS

Only a small percentage of individuals living in endemic areas develop severe malaria suggesting that host genetic factors may play a key role. This study has determined the frequency of single nucleotide polymorphisms (SNPs) in some pro and anti-inflammatory cytokine gene sequences: IL6 (-174; rs1800795), IL12p40 (+1188; rs3212227), IL4 (+33; rs2070874), IL10 (-3575; rs1800890) and TGFb1 (+869; rs1800470), by means of PCR-RFLP. Blood samples were collected from 104 symptomatic and 37 asymptomatic subjects. Laboratory diagnosis was assessed by the thick blood smear test and nested-PCR. No association was found between IL6 (-174), IL12p40 (+1188), IL4 (+33), IL10 (- 3575), TGFb1 (+869) SNPs and malaria symptoms. However, regarding the IL10 -3575 T/A SNP, there were significantly more AA and AT subjects, carrying the polymorphic allele A, in the symptomatic group (c2 = 4.54, p = 0.01, OR = 0.40 [95% CI - 0.17- 0.94]). When the analysis was performed by allele, the frequency of the polymorphic allele A was also significantly higher in the symptomatic group (c2 = 4.50, p = 0.01, OR = 0.45 [95% CI - 0.21-0.95]). In conclusion, this study has suggested the possibility that the IL10 - 3575 T/A SNP might be associated with the presence and maintenance of malaria symptoms in individuals living in endemic areas. Taking into account that this polymorphism is related to decreased IL10 production, a possible role of this SNP in the pathophysiology of malaria is also suggested, but replication studies with a higher number of patients and evaluation of IL10 levels are needed for confirmation

The NRAMP1, VDR, TNF-α, ICAM1, TLR2 and TLR4 gene polymorphisms in Iranian patients with pulmonary tuberculosis: A case-control study

The innate immune response drives early events in Mycobacterium tuberculosis infection. Since human genetic variation is an important determinant in the outcome of infection with M. tuberculosis, we typed polymorphisms in the innate immune molecules, such as natural-resistance-associated macrophage protein 1 (NRAMP1), Vitamin D receptor (VDR), Tumor necrosis factor alpha (TNF-α), intercellular adhesion molecule1 (ICAM-1), Toll-like receptor 2 (TLR2) and Toll-like receptor 4 (TLR4) in a case-control study of pulmonary tuberculosis in Iranian population. We conducted an association study and included 96 patients and 122 matched healthy individuals. We used single ARMS-PCR technique to simultaneously genotype fourteen polymorphisms in this survey. Among all fourteen polymorphisms that were examined, three polymorphisms were significantly different between case and control groups. The TNF - 308A polymorphism showed significant increase in allele and genotype frequencies among patients compared to control individuals - 308A allele: 19.3 vs. 9.4%, GA genotype: 28.1 vs. 17.2%, AA genotype: 5.2 vs. 0.8%; Corrected P (Pc) <. 0.05, and the TLR4 variant allele and genotypes prevalence (D299G and T399I) were significantly higher among patients compared to controls DG genotype: 14.6 vs. 5.7%, Pc< 0.05 and I399 allele: 4.2 vs. 0.8%, TI genotype: 8.3 vs. 1.6%; Pc< 0.05, respectively. In conclusion, our data suggest that TLR4 (D299G and T399I) and TNF (- 308G/A) genetic polymorphisms may influence the risk of developing tuberculosis after exposure to Mycobacterium. © 2016 Elsevier B.V

Epigenetic differences in monozygotic twins discordant for major depressive disorder

Although monozygotic (MZ) twins share the majority of their genetic makeup, they can be phenotypically discordant on several traits and diseases. DNA methylation is an epigenetic mechanism that can be influenced by genetic, environmental and stochastic events and may have an important impact on individual variability. In this study we explored epigenetic differences in peripheral blood samples in three MZ twin studies on major depressive disorder (MDD). Epigenetic data for twin pairs were collected as part of a previous study using 8.1-K-CpG microarrays tagging DNA modification in white blood cells from MZ twins discordant for MDD. Data originated from three geographical regions: UK, Australia and the Netherlands. Ninety-seven MZ pairs (194 individuals) discordant for MDD were included. Different methods to address non independently-and-identically distributed (non-i.i.d.) data were evaluated. Machine-learning methods with feature selection centered on support vector machine and random forest were used to build a classifier to predict cases and controls based on epivariations. The most informative variants were mapped to genes and carried forward for network analysis. A mixture approach using principal component analysis (PCA) and Bayes methods allowed to combine the three studies and to leverage the increased predictive power provided by the larger sample. A machine-learning algorithm with feature reduction classified affected from non-affected twins above chance levels in an independent training-testing design. Network analysis revealed gene networks centered on the PPAR-γ (NR1C3) and C-MYC gene hubs interacting through the AP-1 (c-Jun) transcription factor. PPAR-γ (NR1C3) is a drug target for pioglitazone, which has been shown to reduce depression symptoms in patients with MDD. Using a data-driven approach we were able to overcome challenges of non-i.i.d. data when combining epigenetic studies from MZ twins discordant for MDD. Individually, the studies yielded negative results but when combined classification of the disease state from blood epigenome alone was possible. Network analysis revealed genes and gene networks that support the inflammation hypothesis of MDD