Dataset Information

Do We Build Similar Molecules for Comorbid Diseases? Tevarud in Drug Design, an Analysis for Depression and Inflammation.

ABSTRACT: Tevarud designates two poets coincidently writing a same verse in the Ottoman Divan literature. This study aims to analyze the structural similarity of molecules independently designed for inflammation and depression to determine if coincidentally we are building similar molecules for comorbid diseases. For this purpose, a molecule library was first constituted with structures that were developed as anti-inflammatory (AI) and antidepressant (AD) agents these last decades. Then, the similarity of the structures was determined by calculating the Tanimoto and Cosine similarity coefficients for each AD/AI pair. The highest scores were obtained for two theophylline derivatives: AD17 (for which some AI activity was found to be mentioned) and AI42. The study also pointed out the similarity of some AD coumarins with some AI flavonoids interestingly found to be highly similar to some AI coumarins and AD flavonoids, respectively. Thus, our investigation demonstrated that structures independently developed as AD and AI derivatives can present extremely high structural similarity, a finding that can suggest mechanistic interconnection for these comorbid diseases and also guide for the design of novel bioactive compounds.

SUBMITTER: Onen Bayram FE

PROVIDER: S-EPMC7025378 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Publications

Do We Build Similar Molecules for Comorbid Diseases? <i>Tevarud</i> in Drug Design, an Analysis for Depression and Inflammation.

Önen Bayram F Esra FE Alradhwani Sarah A A SAA Tugcu Gulcin G Sipahi Hande H

ACS medicinal chemistry letters 20200116 2

<i>Tevarud</i> designates two poets coincidently writing a same verse in the Ottoman Divan literature. This study aims to analyze the structural similarity of molecules independently designed for inflammation and depression to determine if coincidentally we are building similar molecules for comorbid diseases. For this purpose, a molecule library was first constituted with structures that were developed as anti-inflammatory (AI) and antidepressant (AD) agents these last decades. Then, the simila ...[more]

PMID: 32071681

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Project description:ImportanceEvery third to sixth patient with medical diseases receives antidepressants, but regulatory trials typically exclude comorbid medical diseases. Meta-analyses of antidepressants have shown small to medium effect sizes, but generalizability to clinical settings is unclear, where medical comorbidity is highly prevalent.ObjectiveTo perform an umbrella systematic review of the meta-analytic evidence and meta-analysis of the efficacy and safety of antidepressant use in populations with medical diseases and comorbid depression.Data sourcesPubMed and EMBASE were searched from inception until March 31, 2023, for systematic reviews with or without meta-analyses of randomized clinical trials (RCTs) examining the efficacy and safety of antidepressants for treatment or prevention of comorbid depression in any medical disease.Study selectionMeta-analyses of placebo- or active-controlled RCTs studying antidepressants for depression in individuals with medical diseases.Data extraction and synthesisData extraction and quality assessment using A Measurement Tool for the Assessment of Multiple Systematic Reviews (AMSTAR-2 and AMSTAR-Content) were performed by pairs of independent reviewers following PRISMA guidelines. When several meta-analyses studied the same medical disease, the largest meta-analysis was included. Random-effects meta-analyses pooled data on the primary outcome (efficacy), key secondary outcomes (acceptability and tolerability), and additional secondary outcomes (response and remission).Main outcomes and measuresAntidepressant efficacy presented as standardized mean differences (SMDs) and tolerability (discontinuation for adverse effects) and acceptability (all-cause discontinuation) presented as risk ratios (RRs).ResultsOf 6587 references, 176 systematic reviews were identified in 43 medical diseases. Altogether, 52 meta-analyses in 27 medical diseases were included in the evidence synthesis (mean [SD] AMSTAR-2 quality score, 9.3 [3.1], with a maximum possible of 16; mean [SD] AMSTAR-Content score, 2.4 [1.9], with a maximum possible of 9). Across medical diseases (23 meta-analyses), antidepressants improved depression vs placebo (SMD, 0.42 [95% CI, 0.30-0.54]; I2 = 76.5%), with the largest SMDs for myocardial infarction (SMD, 1.38 [95% CI, 0.82-1.93]), functional chest pain (SMD, 0.87 [95% CI, 0.08-1.67]), and coronary artery disease (SMD, 0.83 [95% CI, 0.32-1.33]) and the smallest for low back pain (SMD, 0.06 [95% CI, 0.17-0.39]) and traumatic brain injury (SMD, 0.08 [95% CI, -0.28 to 0.45]). Antidepressants showed worse acceptability (24 meta-analyses; RR, 1.17 [95% CI, 1.02-1.32]) and tolerability (18 meta-analyses; RR, 1.39 [95% CI, 1.13-1.64]) compared with placebo. Antidepressants led to higher rates of response (8 meta-analyses; RR, 1.54 [95% CI, 1.14-1.94]) and remission (6 meta-analyses; RR, 1.43 [95% CI, 1.25-1.61]) than placebo. Antidepressants more likely prevented depression than placebo (7 meta-analyses; RR, 0.43 [95% CI, 0.33-0.53]).Conclusions and relevanceThe results of this umbrella systematic review of meta-analyses found that antidepressants are effective and safe in treating and preventing depression in patients with comorbid medical disease. However, few large, high-quality RCTs exist in most medical diseases.

Dataset Information

Do We Build Similar Molecules for Comorbid Diseases? Tevarud in Drug Design, an Analysis for Depression and Inflammation.

Publications

Do We Build Similar Molecules for Comorbid Diseases? <i>Tevarud</i> in Drug Design, an Analysis for Depression and Inflammation.

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