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Clickable photoaffinity ligands for the human serotonin transporter based on the selective serotonin reuptake inhibitor (S)-citalopram.

ABSTRACT: To date, the development of photoaffinity ligands targeting the human serotonin transporter (hSERT), a key protein involved in disease states such as depression and anxiety, have been radioisotope-based (i.e., ³H or ¹²⁵I). This letter instead highlights three derivatives of the selective serotonin reuptake inhibitor (SSRI) (S)-citalopram that were rationally designed and synthesized to contain a photoreactive benzophenone or an aryl azide for protein target capture via photoaffinity labeling and a terminal alkyne or an aliphatic azide for click chemistry-based proteomics. Specifically, clickable benzophenone-based (S)-citalopram photoprobe 6 (hSERT K_i = 0.16 nM) displayed 11-fold higher binding affinity at hSERT when compared to (S)-citalopram (hSERT K_i = 1.77 nM), and was subsequently shown to successfully undergo tandem photoaffinity labeling-biorthogonal conjugation using purified hSERT. Given clickable photoprobes can be used for various applications depending on which reporter is attached by click chemistry subsequent to photoaffinity labeling, photoprobe 6 is expected to find value in structure-function studies and other research applications involving hSERT (e.g., imaging).

SUBMITTER: Yarravarapu N

PROVIDER: S-EPMC6200329 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Importance:In 2006, the US Food and Drug Administration (FDA) issued an advisory warning on the risk of serotonin syndrome with concomitant use of triptans and selective serotonin reuptake inhibitor (SSRI) or selective norepinephrine reuptake inhibitor (SNRI) antidepressants, but the true risk of serotonin syndrome in these patients remains unknown. Objective:To assess the risk of serotonin syndrome with concomitant use of triptans and SSRI or SNRI antidepressants. Design, Setting, and Participants:This study used electronic health record data from the Partners Research Data Registry (RPDR) to identify patients who had received an International Classification of Diseases, Ninth Revision diagnosis compatible with serotonin syndrome who had been coprescribed triptans and SSRI or SNRI antidepressants in the Greater Boston, Massachusetts, area from January 1, 2001, through December 31, 2014 (14 years). Clinical information was extracted to determine whether the case met formal diagnostic criteria and had coprescription within a calendar year. Both conservative and broad case definitions were used to better characterize the spectrum of risk. Data analysis was performed from November 23, 2016, to July 15, 2017. Main Outcomes and Measures:Incidence of serotonin syndrome. Results:The RPDR search revealed 47?968 (±3) unique patients who were prescribed triptans during the 14-year period of the study. A total of 19?017 (±3) patients were coprescribed triptans and antidepressants during the study, with a total of 30?928 person-years of exposure. Serotonin syndrome was suspected in 17 patients. Only 2 patients were classified as having definite serotonin syndrome (incidence rate, 0.6 cases per 10?000 person-years of exposure; 95% CI, 0.0-1.5). Five patients were classified as having possible serotonin syndrome (incidence rate with these 5 cases added to the 2 definite cases, 2.3 cases per 10?000 person-years of exposure; 95% CI, 0.6-3.9). The proportion of patients with triptan prescriptions who were coprescribed an SSRI or SNRI antidepressant was relatively stable during the study, ranging from 21% to 29%. Conclusions and Relevance:The risk of serotonin syndrome associated with concomitant use of triptans and SSRIs or SNRIs was low. Coprescription of these drugs is common and did not decrease after the 2006 FDA advisory. Our results cast doubt on the validity of the FDA advisory and suggest that it should be reconsidered.

Project description:Selective serotonin reuptake inhibitors (SSRIs) are a standard of care for the pharmacotherapy of patients suffering from Major Depressive Disorder (MDD). However, only one-half to two-thirds of MDD patients respond to SSRI therapy. Recently, a "multiple omics" research strategy was applied to identify genetic differences between patients who did and did not respond to SSRI therapy. As a first step, plasma metabolites were assayed using samples from the 803 patients in the PGRN-AMPS SSRI MDD trial. The metabolomics data were then used to "inform" genomics by performing a genome-wide association study (GWAS) for plasma concentrations of the metabolite most highly associated with clinical response, serotonin (5-HT). Two genome-wide or near genome-wide significant single nucleotide polymorphism (SNP) signals were identified, one that mapped near the TSPAN5 gene and another across the ERICH3 gene, both genes that are highly expressed in the brain. Knocking down TSPAN5 and ERICH3 resulted in decreased 5-HT concentrations in neuroblastoma cell culture media and decreased expression of enzymes involved in 5-HT biosynthesis and metabolism. Functional genomic studies demonstrated that ERICH3 was involved in clathrin-mediated vesicle formation and TSPAN5 was an ethanol-responsive gene that may be a marker for response to acamprosate pharmacotherapy of alcohol use disorder (AUD), a neuropsychiatric disorder highly co-morbid with MDD. In parallel studies, kynurenine was the plasma metabolite most highly associated with MDD symptom severity and application of a metabolomics-informed pharmacogenomics approach identified DEFB1 and AHR as genes associated with variation in plasma kynurenine levels. Both genes also contributed to kynurenine-related inflammatory pathways. Finally, a multiply replicated predictive algorithm for SSRI clinical response with a balanced predictive accuracy of 76% (compared with 56% for clinical data alone) was developed by including the SNPs in TSPAN5, ERICH3, DEFB1 and AHR. In summary, application of a multiple omics research strategy that used metabolomics to inform genomics, followed by functional genomic studies, identified novel genes that influenced monoamine biology and made it possible to develop a predictive algorithm for SSRI clinical outcomes in MDD. A similar pharmaco-omic research strategy might be broadly applicable for the study of other neuropsychiatric diseases and their drug therapy.

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Clickable photoaffinity ligands for the human serotonin transporter based on the selective serotonin reuptake inhibitor (S)-citalopram.

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