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.

CRC remains a significant public health challenge due to its high prevalence and mortality rates. Emerging evidence highlights the critical role of the gut microbiota in both the pathogenesis of CRC and the efficacy of treatment strategies, including chemotherapy and immunotherapy. Dysbiosis, characterized by imbalances in microbial communities, has been implicated in CRC progression and therapeutic outcomes. This review examines the intricate relationship between gut microbiota composition and CRC, emphasizing the potential for microbial profiles to serve as biomarkers for early detection and prognosis. Various interventions, such as prebiotics, probiotics, postbiotics, fecal microbiota transplantation, and dietary modifications, aim to restore microbiota balance and shift dysbiosis toward eubiosis, thereby improving health outcomes. Additionally, the integration of microbial profiling into clinical practice could enhance diagnostic capabilities and personalize treatment strategies, advancing the field of oncology. The study of intratumoral microbiota offers new diagnostic and prognostic tools that, combined with artificial intelligence algorithms, could predict treatment responses and assess the risk of adverse effects. Given the growing understanding of the gut microbiome–cancer axis, developing microbiota-oriented strategies for CRC prevention and treatment holds promise for improving patient care and clinical outcomes.

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.

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.

Thyroid cancer comprises a biologically diverse group of tumors initiated by a limited number of recurrent driver alterations, with additional molecular events promoting dedifferentiation, therapeutic resistance, and aggressive clinical behavior. Advances in tumor sequencing have clarified the molecular architecture of papillary, follicular, oncocytic, poorly differentiated, anaplastic, and medullary thyroid carcinomas, and have already changed management in selected settings. Molecular testing improves diagnostic refinement and risk assessment in cytologically indeterminate thyroid nodules, while alterations involving BRAF, RET, and NTRK can guide targeted therapy in advanced disease. Beyond DNA, transcriptomic, proteomic, epigenetic, metabolomic, immune, spatial, and liquid-biopsy approaches offer functional insight into differentiation state, treatment sensitivity, and resistance, although most remain investigational. Their clinical value depends not only on biological plausibility, but on reproducibility, incremental value beyond established clinicopathological variables, and the ability to alter patient management. Computational tools may further support integration of molecular and clinical data, but their usefulness likewise depends on calibration, external validation, interpretability, and demonstration of decision impact. This review synthesizes the genomic and multi-omics determinants of thyroid cancer management across diagnosis, risk stratification, treatment selection, resistance monitoring, and follow-up, and discusses the practical barriers that continue to limit routine implementation, including assay standardization, cost, access, and real-world feasibility. Progress in precision thyroid oncology will depend on robust validation of emerging biomarkers and clear evidence that they improve patient outcomes.