Exploring real-world evidence to uncover unknown drug benefits and support the discovery of new treatment targets for depressive and bipolar disorders

Foundational model aided automatic high-throughput drug screening using self-controlled cohort study

Background

Developing medicine from scratch to governmental authorization and detecting adverse drug reactions (ADR) have barely been economical, expeditious, and risk-averse investments. The availability of large-scale observational healthcare databases and the popularity of large language models offer an unparalleled opportunity to enable automatic high-throughput drug screening for both repurposing and pharmacovigilance.

Objectives

To demonstrate a general workflow for automatic high-throughput drug screening with the following advantages: (i) the association of various exposure on diseases can be estimated; (ii) both repurposing and pharmacovigilance are integrated; (iii) accurate exposure length for each prescription is parsed from clinical texts; (iv) intrinsic relationship between drugs and diseases are removed jointly by bioinformatic mapping and large language model - ChatGPT; (v) causal-wise interpretations for incidence rate contrasts are provided.

Methods

Using a self-controlled cohort study design where subjects serve as their own control group, we tested the intention-to-treat association between medications on the incidence of diseases. Exposure length for each prescription is determined by parsing common dosages in English free text into a structured format. Exposure period starts from initial prescription to treatment discontinuation. A same exposure length preceding initial treatment is the control period. Clinical outcomes and categories are identified using existing phenotyping algorithms. Incident rate ratios (IRR) are tested using uniformly most powerful (UMP) unbiased tests.

Results

We assessed 3,444 medications on 276 diseases on 6,613,198 patients from the Clinical Practice Research Datalink (CPRD), an UK primary care electronic health records (EHR) spanning from 1987 to 2018. Due to the built-in selection bias of self-controlled cohort studies, ingredients-disease pairs confounded by deterministic medical relationships are removed by existing map from RxNorm and nonexistent maps by calling ChatGPT. A total of 16,901 drug-disease pairs reveals significant risk reduction, which can be considered as candidates for repurposing, while a total of 11,089 pairs showed significant risk increase, where drug safety might be of a concern instead.

Conclusions

This work developed a data-driven, nonparametric, hypothesis generating, and automatic high-throughput workflow, which reveals the potential of natural language processing in pharmacoepidemiology. We demonstrate the paradigm to a large observational health dataset to help discover potential novel therapies and adverse drug effects. The framework of this study can be extended to other observational medical databases.

Incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease in patients with multiple sclerosis initiating disease-modifying therapies: Retrospective cohort study using a frequentist model averaging statistical framework

Researchers are increasingly using insights derived from large-scale, electronic healthcare data to inform drug development and provide human validation of novel treatment pathways and aid in drug repurposing/repositioning. The objective of this study was to determine whether treatment of patients with multiple sclerosis with dimethyl fumarate, an activator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, results in a change in incidence of type 2 diabetes and its complications. This retrospective cohort study used administrative claims data to derive four cohorts of adults with multiple sclerosis initiating dimethyl fumarate, teriflunomide, glatiramer acetate or fingolimod between January 2013 and December 2018. A causal inference frequentist model averaging framework based on machine learning was used to compare the time to first occurrence of a composite endpoint of type 2 diabetes, cardiovascular disease or chronic kidney disease, as well as each individual outcome, across the four treatment cohorts. There was a statistically significantly lower risk of incidence for dimethyl fumarate versus teriflunomide for the composite endpoint (restricted hazard ratio [95% confidence interval] 0.70 [0.55, 0.90]) and type 2 diabetes (0.65 [0.49, 0.98]), myocardial infarction (0.59 [0.35, 0.97]) and chronic kidney disease (0.52 [0.28, 0.86]). No differences for other individual outcomes or for dimethyl fumarate versus the other two cohorts were observed. This study effectively demonstrated the use of an innovative statistical methodology to test a clinical hypothesis using real-world data to perform early target validation for drug discovery. Although there was a trend among patients treated with dimethyl fumarate towards a decreased incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease relative to other disease-modifying therapies-which was statistically significant for the comparison with teriflunomide-this study did not definitively support the hypothesis that Nrf2 activation provided additional metabolic disease benefit in patients with multiple sclerosis.

Search for serum biomarkers in patients with bipolar disorder and major depressive disorder using metabolome analysis

Objective

Bipolar disorder (BD) and major depressive disorder (MDD) are two common psychiatric disorders. Due to the overlapping clinical symptoms and the lack of objective diagnostic biomarkers, bipolar disorder (BD) is easily misdiagnosed as major depressive disorder (MDD), which in turn affects treatment decisions and prognosis. This study aimed to investigate biomarkers that could be used to differentiate BD from MDD.

Methods

Nuclear magnetic resonance (NMR) spectroscopy was performed to assess serum metabolic profiles in depressed patients with BD (n = 59), patients with MDD (n = 14), and healthy controls (n = 10). Data was analyzed using partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis and t-tests. Different metabolites (VIP > 1 and p < 0.05) were identified and further analyzed using Metabo Analyst 5.0 to identify relevant metabolic pathways.

Results

The metabolic phenotypes of the BD and MDD groups were significantly different from those of the healthy controls, and there were different metabolite differences between them. In the BD group, the levels of 3-hydroxybutyric acid, n-acetyl glycoprotein, β-glucose, pantothenic acid, mannose, glycerol, and lipids were significantly higher than those in the healthy control group, and the levels of lactate and acetoacetate were significantly lower than those in the healthy control group. In the MDD group, the levels of 3-hydroxybutyric acid, n-acetyl glycoprotein, pyruvate, choline, acetoacetic acid, and lipids were significantly higher than those of healthy controls, and the levels of acetic acid and glycerol were significantly lower than those of healthy controls.

Conclusion

Glycerolipid metabolism is significantly involved in BD and MDD. Pyruvate metabolism is significantly involved in MDD. Pyruvate, choline, and acetate may be potential biomarkers for MDD to distinguish from BD, and pantothenic acid may be a potential biomarker for BD to distinguish from MDD.

Neuropharmacological interventions of quercetin and its derivatives in neurological and psychological disorders

Quercetin is a naturally occurring bioactive flavonoid abundant in many plants and fruits. Quercetin and its derivatives have shown an array of pharmacological activities in preclinical tests against various illnesses and ailments. Owing to its protective role against oxidative stress and neuroinflammation, quercetin is a possible therapeutic choice for the treatment of neurological disorders. Quercetin and its derivatives can modulate a variety of signal transductions, including neuroreceptor, neuroinflammatory receptor, and redox signaling events. The research on quercetin and its derivatives in neurology-related illnesses mainly focused on the targets, such as redox stress, neuroinflammation, and signaling pathways; however, the function of quercetin and its derivatives on specific molecular targets, such as nuclear receptors and proinflammatory mediators are yet to be explored. Findings showed that various molecular targets of quercetin and its derivatives have therapeutic potential against psychological and neurodegenerative disorders.

Antidepressant Potential of Quercetin and its Glycoside Derivatives: A Comprehensive Review and Update

Depression is a global health problem with growing prevalence rates and serious impacts on the daily life of patients. However, the side effects of currently used antidepressants greatly reduce the compliance of patients. Quercetin is a flavonol present in fruits, vegetables, and Traditional Chinese medicine (TCM) that has been proved to have various pharmacological effects such as anti-depressant, anti-cancer, antibacterial, antioxidant, anti-inflammatory, and neuroprotective. This review summarizes the evidence for the pharmacological application of quercetin to treat depression. We clarified the mechanisms of quercetin regulating the levels of neurotransmitters, promoting the regeneration of hippocampal neurons, improving hypothalamic-pituitary-adrenal (HPA) axis dysfunction, and reducing inflammatory states and anti-oxidative stress. We also summarized the antidepressant effects of some quercetin glycoside derivatives to provide a reference for further research and clinical application.

Revealing Unknown Benefits of Existing Medications to Aid the Discovery of New Treatments for Post‐Traumatic Stress Disorder

Objective

To systematically identify novel pharmacological strategies for preventing or treating post‐traumatic stress disorder (PTSD) by leveraging large‐scale analysis of real‐world observational data.

Methods

Using a self‐controlled study design, the association between 1399 medications and the incidence of PTSD across four US insurance claims databases covering commercially insured, Medicare eligible, and Medicaid patients was examined. A validated algorithm for identifying PTSD in claims data was used, and medications were identified by their RxNorm ingredient. Medications used to treat PTSD or its symptoms (e.g., antidepressants, antipsychotics) were excluded. Medications associated with ≥30% reduction in risk of PTSD in ≥2 databases were identified.

Results

A total of 137,182,179 individuals were included in the analysis. Fifteen medications met the threshold criteria for a potential protective effect on PTSD; six were categorized as “primary signals” while the remaining nine were considered “potential signals”. The primary signals include a beta blocker that has been previously studied for PTSD, and five medications used to treat attention‐deficit/hyperactivity disorder. The potential signals include four medications used to treat substance use disorders and five medications used to treat sleep disorders.

Discussion

The medications identified in this analysis provide targets for further research in studies that are designed to examine specific hypotheses regarding these medications and the incidence of PTSD. This work may aid in discovering novel therapeutic approaches to treat PTSD, wherein new and effective treatments are badly needed.

Four large US‐based administrative claims databases were used to analyze the association between all marketed prescription medications and the outcome of incident post‐traumatic stress disorder (PTSD)

Of the 1399 medications examined, there were 15 that met the strict filtering criteria for showing consistent, moderate‐to‐strong, protective effects against the outcome

Medications fell into four main classes: (1) a beta blocker (propranolol), (2) five medications used to treat attention‐deficit/hyperactivity disorder (ADHD), (3) four medications used to treat substance use disorders and (4) five medications used to treat sleep disorders

These findings identify rational starting points for future hypothesis‐driven research to explore these associations in greater detail

Alterations in the Plasma Lipidome of Adult Women With Bipolar Disorder: A Mass Spectrometry-Based Lipidomics Research

Lipidomics has become a pivotal tool in biomarker discovery for the diagnosis of psychiatric illnesses. However, the composition and quantitative analysis of peripheral lipids in female patients with bipolar disorder (BD) have been poorly addressed. In this study, plasma samples from 24 female patients with BD and 30 healthy controls (HCs) were analyzed by comprehensive lipid profiling and quantitative validation based on liquid chromatography-mass spectrometry. Clinical characteristics and a correlation between the level of lipid molecules and clinical symptoms were also observed. We found that the quantitative alterations in several lipid classes, including acylcarnitine, lysophosphatidylethanolamine, GM2, sphingomyelin, GD2, triglyceride, monogalactosyldiacylglycerol, phosphatidylinositol phosphate, phosphatidylinositol 4,5-bisphosphate, phosphatidylethanolamine, phosphatidylserine, and lysophosphatidylinositol, were remarkably upregulated or downregulated in patients with BD and were positively or negatively correlated with the severity of psychotic, affective, or mania symptoms. Meanwhile, the composition of different carbon chain lengths and degrees of fatty acid saturation for these lipid classes in BD were also different from those of HCs. Moreover, 55 lipid molecules with significant differences and correlations with the clinical parameters were observed. Finally, a plasma biomarker set comprising nine lipids was identified, and an area under the curve of 0.994 was obtained between patients with BD and the HCs. In conclusion, this study provides a further understanding of abnormal lipid metabolism in the plasma and suggests that specific lipid species can be used as complementary biomarkers for the diagnosis of BD in women.