The COVID-19/SARS-CoV-2 pandemic struck health, social and economic systems worldwide, and represents an open challenge for scientists —coping with the high inter-individual variability of COVID-19, and for policy makers —coping with the responsibility to understand environmental factors affecting its severity across different geographical areas. Air pollution has been warned of as a modifiable factor contributing to differential SARS-CoV-2 spread but the biological mechanisms underlying the phenomenon are still unknown. Air quality and COVID-19 epidemiological data from 110 Italian provinces were studied by correlation analysis, to evaluate the association between particulate matter (PM)_2.5 concentrations and incidence, mortality rate and case fatality risk of COVID-19 in the period 20 February–31 March 2020. Bioinformatic analysis of the DNA sequence encoding the SARS-CoV-2 cell receptor angiotensin-converting enzyme 2 (ACE-2) was performed to identify consensus motifs for transcription factors mediating cellular response to pollutant insult. Positive correlations between PM_2.5 levels and the incidence (r = 0.67, p < 0.0001), the mortality rate (r = 0.65, p < 0.0001) and the case fatality rate (r = 0.7, p < 0.0001) of COVID-19 were found. The bioinformatic analysis of the ACE-2 gene identified nine putative consensus motifs for the aryl hydrocarbon receptor (AHR). Our results confirm the supposed link between air pollution and the rate and outcome of SARS-CoV-2 infection and support the hypothesis that pollution-induced over-expression of ACE-2 on human airways may favor SARS-CoV-2 infectivity.

Discovering that calcitonin-related peptide (CGRP) plays a key role in the complex pathophysiology of migraine has allowed us to make great strides in the development of new approaches for acute and preventive treatment. This evidence has led to the development of small molecules antagonist molecules of the CGRP receptor (“gepants”) and of a new class of medications called “Ditans”. This review presents the data from clinical trials reporting the efficacy, safety, and tolerability of the new drugs used in the treatment of migraines. Evidences show that therapeutic approaches targeted to CGRP have the potential to transform the clinical management of migraine, even though its appropriate place has yet to be determined with accuracy.

Deficiency of dihydropyrimidine dehydrogenase (DPD), encoded by the DPYD gene, is associated with severe toxicity induced by the anti-cancer drug 5-Fluorouracil (5-FU). DPYD genotyping of four recommended polymorphisms is widely used to predict toxicity, yet their prediction power is limited. Increasing availability of next generation sequencing (NGS) will allow us to screen rare variants, predicting a larger fraction of DPD deficiencies. Genotype–phenotype correlations were investigated by performing DPYD exon sequencing in 94 patients assessed for DPD deficiency by the 5-FU degradation rate (5-FUDR) assay. Association of common variants with 5-FUDR was analyzed with the SNPStats software. Functional interpretation of rare variants was performed by in-silico analysis (using the HSF system and PredictSNP) and literature review. A total of 23 rare variants and 8 common variants were detected. Among common variants, a significant association was found between homozygosity for the rs72728438 (c.1974+75A>G) and decreased 5-FUDR. Haplotype analysis did not detect significant associations with 5-FUDR. Overall, in our sample cohort, NGS exon sequencing allowed us to explain 42.5% of the total DPD deficiencies. NGS sharply improves prediction of DPD deficiencies, yet a broader collection of genotype–phenotype association data is needed to enable the clinical use of sequencing data.

Patients affected by mental disorders smoke more than the general population. The reasons behind this habit are genetic, environmental, etc. This study aims to investigate the correlations between some polymorphisms and the smoking habits and nicotine dependence in patients with psychiatric disorders. We recruited 88 patients with treatment-resistant mental disorders, including 35 with major depressive disorder, 43 with bipolar spectrum disorder, and 10 with schizophrenia spectrum disorder. We carried out a clinical and psychometric assessment on current smoking habits, years of smoking, number of daily cigarettes, and level of nicotine addiction. The patients performed a peripheral blood sample for DNA analyses of different polymorphisms. We searched for correlations between the measures of nicotine addiction and analysed genotypes. The expression of the T allele of the DRD2 rs1800497 and DRD3 rs6280 polymorphisms significantly correlated with a lower level of nicotine dependence and lower use of cigarettes. We did not find significant correlations between nicotine dependence and OPRM1 rs1799971, COMT rs4680 and rs4633 polymorphisms, CYP2A6 rs1801272 and rs28399433, or 5-HTTLPR genotype. Concluding, DRD2 rs1800497 and DRD3 rs6280 polymorphisms are involved in nicotine dependence and cigarette smoking habits in patients with treatment-resistant mental disorders

Background: The objective of this study was to investigate the DRD2 rs1800497, rs1799732, rs1801028, DRD3 rs6280, and HTR2A rs6314, rs7997012, and rs6311 single-nucleotide polymorphism (SNP) correlations with resistance to second-generation antipsychotics (SGAs) in a real-world sample of patients with treatment-resistant mental disorders. Methods: We divided 129 participants into a high treatment resistance (HTR) group (current treatment with two SGAs, or clozapine, or classic neuroleptics for a failure of previous SGAs trials) and a low treatment resistance (LTR) group (current treatment with one atypical antipsychotic). We used Next-Generation Sequencing on DNA isolated from peripheral blood samples to analyze the polymorphisms. We performed logistic regression to search for predictors of HTR membership. Results: A diagnosis of schizophrenia significantly predicted the HTR membership compared to other diagnoses. Other predictors were the DRD3 rs6280 C|T (OR = 22.195) and T|T (OR = 18.47) vs. C|C, HTR2A rs7997012 A|G vs. A|A (OR = 6.859) and vs. G|G (OR = 2.879), and DRD2 rs1799732 I|I vs. D|I (OR = 12.079) genotypes. Conclusions: A diagnosis of schizophrenia and the DRD2 rs1799732, DRD3 rs6280, and HTR2A rs7997012 genotypes can predict high treatment resistance to SGAs.

Treatment-resistant depression (TRD) reduces affected patients’ quality of life and leads to important social health care costs. Pharmacogenomics-guided treatment (PGT) may be effective in the cure of TRD. The main aim of this study was to evaluate the clinical changes after PGT in patients with TRD (two or more recent failed psychopharmacological trials) affected by bipolar disorder (BD) or major depressive disorder (MDD) compared to a control group with treatment as usual (TAU). We based the PGT on assessing different gene polymorphisms involved in the pharmacodynamics and pharmacokinetics of drugs. We analyzed, with a repeated-measure ANOVA, the changes between the baseline and a 6 month follow-up of the efficacy index assessed through the Clinical Global Impression (CGI) scale, and depressive symptoms through the Hamilton Depression Rating Scale (HDRS). The PGT sample included 53 patients (26 BD and 27 MDD), and the TAU group included 52 patients (31 BD and 21 MDD). We found a significant within-subject effect of treatment time on symptoms and efficacy index for the whole sample, with significant improvements in the efficacy index (F = 8.544; partial η² = 0.077, p < 0.004) and clinical global impression of severity of illness (F = 6.818; partial η² = 0.062, p < 0.01) in the PGT vs. the TAU group. We also found a significantly better follow-up response (χ² = 5.479; p = 0.019) and remission (χ² = 10.351; p = 0.001) rates in the PGT vs. the TAU group. PGT may be an important option for the long-term treatment of patients with TRD affected by mood disorders, providing information that can better define drug treatment strategies and increase therapeutic improvement.