The landscape of psychiatric care is poised for transformation through the integration of pharmaco-multiomics, encompassing genomics, proteomics, metabolomics, transcriptomics, epigenomics, and microbiomics. This review discusses how these approaches can revolutionize personalized treatment strategies in psychiatry by providing a nuanced understanding of the molecular bases of psychiatric disorders and individual pharmacotherapy responses. With nearly one billion affected individuals globally, the shortcomings of traditional treatments, characterized by inconsistent efficacy and frequent adverse effects, are increasingly evident. Advanced computational technologies such as artificial intelligence (AI) and machine learning (ML) play crucial roles in processing and integrating complex omics data, enhancing predictive accuracy, and creating tailored therapeutic strategies. To effectively harness the potential of pharmaco-multiomics approaches in psychiatry, it is crucial to address challenges such as high costs, technological demands, and disparate healthcare systems. Additionally, navigating stringent ethical considerations, including data security, potential discrimination, and ensuring equitable access, is essential for the full realization of this approach. This process requires ongoing validation and comprehensive integration efforts. By analyzing recent advances and elucidating how different omic dimensions contribute to therapeutic customization, this review aims to highlight the promising role of pharmaco-multiomics in enhancing patient outcomes and shifting psychiatric treatments from a one-size-fits-all approach towards a more precise and patient-centered model of care.

Type 2 diabetes (T2D) is the fastest-growing non-communicable disease worldwide, accounting for around 90% of all diabetes cases and imposing a significant health burden globally. Due to its phenotypic heterogeneity and composite genetic underpinnings, T2D requires a precision medicine approach personalized to individual molecular profiles, thereby shifting away from the traditional “one-size-fits-all” medical methods. This review advocates for a thorough pharmacomultiomics approach to enhance precision medicine for T2D. It emphasizes personalized treatment strategies that enhance treatment efficacy while minimizing adverse effects by integrating data from genomics, proteomics, metabolomics, transcriptomics, microbiomics, and epigenomics. We summarize key findings on candidate genes impacting diabetic medication responses and explore the potential of pharmacometabolomics in predicting drug efficacy. The role of pharmacoproteomics in prognosis and discovering new therapeutic targets is discussed, along with transcriptomics’ contribution to understanding T2D pathophysiology. Additionally, pharmacomicrobiomics is explored to understand gut microbiota interactions with antidiabetic drugs. Emerging evidence on utilizing epigenomic profiles in improving drug efficacy and personalized treatment is also reviewed, illustrating their implications in personalized medicine. In this paper, we discuss the integration of these layers of omics data, examining recently developed paradigms that leverage complex data to deepen our understanding of diabetes. Such integrative approaches advance precision medicine strategies to tackle the disease by better understanding its complex biology.

Pharmacogenomics (PGx) is an important component of precision medicine that promises tailored treatment approaches based on an individual’s genetic information. Exploring the initiatives in research that help to integrate PGx test into clinical setting, identifying the potential barriers and challenges as well as planning the future directions, are all important for fruitful PGx implementation in any population. Qatar serves as an exemplar case study for the Middle East, having a small native population compared to a diverse immigrant population, advanced healthcare system, national genome program, and several educational initiatives on PGx and precision medicine. This paper attempts to outline the current state of PGx research and implementation in Qatar within the global context, emphasizing ongoing initiatives and educational efforts. The inclusion of PGx in university curricula and healthcare provider training, alongside precision medicine conferences, showcase Qatar’s commitment to advancing this field. However, challenges persist, including the requirement for population specific implementation strategies, complex genetic data interpretation, lack of standardization, and limited awareness. The review suggests policy development for future directions in continued research investment, conducting clinical trials for the feasibility of PGx implementation, ethical considerations, technological advancements, and global collaborations to overcome these barriers.

The COVID-19 pandemic has had a detrimental impact on the mental health of populations. We aimed in this examines the impact of the COVID-19 pandemic on the prevalence and treatment of mental health disorders (MHDs), with a specific focus on patterns of psychotropic medication prescriptions in Qatar. It provides critical insights into the dynamics of mental healthcare during global crises. In this retrospective observational study, we conducted an analysis of data derived from 267,116 outpatients attending Mental Health Services (MHS) in Qatar between 2019 and 2022. The study focused on trends in outpatient visits and psychotropic medication prescriptions. A significant increase of 56.6 % in MHS outpatient visits was observed in 2022 compared to pre-pandemic levels, indicating heightened utilization of mental health services. Depression and anxiety disorders were predominant, reflecting global patterns. Antidepressant prescriptions rose by approximately 40 %, highlighting increased psychological distress. The findings emphasize the necessity for tailored therapeutic protocols and robust mental healthcare infrastructures to address the rising burden of MHDs during crises. Integrating genetic predispositions with prescription trend analyses offers promise for future interventions, contributing to a resilient mental healthcare ecosystem capable of adapting to evolving needs in crisis contexts and beyond.

Psychotropic medications remain central to psychiatric treatment, yet their use is frequently accompanied by a substantial metabolic burden. Metabolic adverse effects, including weight gain, insulin resistance, dysglycemia, dyslipidemia, and hypertension, complicated clinical management and undermine long-term adherence. Although evidence-based monitoring and mitigation approaches exist, the molecular determinants of individual susceptibility and the drivers of interindividual variability in metabolic outcomes remain insufficiently defined. Emerging evidence identifies the gut-brain-metabolic axis as a key mechanistic interface, with psychotropic medications altering gut microbiota and associated metabolic pathways that contribute to metabolic complications. Multi-omics strategies are beginning to illuminate the complex molecular networks underlying these adverse effects; however, most findings still arise from isolated omics layers, limiting mechanistic resolution and translational utility. Integrative analytical frameworks, including artificial intelligence, now enable the synthesis of molecular, clinical, environmental, lifestyle, and dietary factors to support more precise and individualized intervention. In this narrative review, we synthesize current clinical and mechanistic advances in understanding psychotropic-induced metabolic dysfunction, with a focus on insulin resistance and the gut–brain–metabolic axis and highlight how multi-omics, environmental factors and computational strategies may advance future precision approaches in psychiatric care.

Background/Objectives: Tramadol is an opioid frequently prescribed for moderate to severe pain and has seen a global increase in use. This presents numerous challenges in clinical management. This study aims to elucidate metabolic signatures associated with tramadol consumption, enhancing predictive capabilities for therapeutic outcomes and optimizing patient-specific treatment plans. Methods: Data were obtained from the Qatar Biobank (QBB), focusing on pharmacogenomic variants associated with tramadol use and prescription trends. A cohort of 27 individuals who were administered daily tramadol doses between 100 and 400 mg with available metabolomic profiles were selected. The pharmacokinetics of tramadol were evaluated in relation to specific CYP2D6 genetic variants. Comparative pharmacometabolomic profiles were generated for tramadol users versus a control group of 54 non-users. Additionally, prescription data encompassing tramadol formulations were collected from the electronic medical records (EMR) system of the major public hospital network in Qatar (Hamad Medical Corporation) to discern prescribing patterns. Results: From January 2019 to December 2022, tramadol prescriptions varied, with chronic pain as the primary indication, followed by acute pain. Pharmacogenomic analysis indicated that CYP2D6 allele variations significantly impacted tramadol and O-desmethyltramadol glucuronide levels, notably in ‘normal metabolizers’. Metabolomic analysis revealed distinct metabolic profiles in tramadol users, with significant variations in phosphatidylcholine, histidine, and lysine pathways compared to controls, highlighting tramadol’s unique biochemical impacts. Conclusions: This study underscores the importance of integrating genetic and omics-based approaches to enhance tramadol’s efficacy and safety. These findings support personalized pain management strategies, enhancing treatment outcomes for both chronic and acute pain.