Kholoud Bastaki

Background: Real-world evidence on pediatric psychotropic prescribing and polypharmacy in Gulf health systems remains limited. Characterizing medication-class patterns and multi-class prescribing is essential for medication safety surveillance in children and adolescents. This study examined psychotropic prescribing, potential polypharmacy, and recorded medication combinations among children and adolescents using public-sector electronic health records in Qatar. Methods: A retrospective observational study used electronic health record data from Hamad Medical Corporation and the Primary Health Care Corporation, Qatar’s principal public healthcare providers, from January 2019 to December 2022. Children and adolescents aged 0–17 years with at least one psychotropic prescription were included. Medications were classified as antidepressants, antipsychotics, anxiolytics, mood stabilizers, or attention-deficit/hyperactivity disorder (ADHD) medications. Potential cross-class polypharmacy was assessed as the recording of two or more medication classes per patient during the study period; available duration information did not permit confirmation of concurrent exposure. Logistic regression examined patient-level associations with this outcome. Results: The dataset included 2,941 eligible prescription records corresponding to 2,221 unique patients. The proportion of prescriptions for ADHD medications decreased from 28.7% in 2019 to 13.7% in 2022, whereas the proportion assigned to anxiolytics increased from 26.0% to 37.5%. ADHD medications predominated among prescriptions for children aged 5–11 years, while antidepressants and antipsychotics predominated among those for adolescents aged 12–17 years. The potential-polypharmacy criterion was met by 322 patients (14.5%), reaching 21.6% among adolescents. In a gender-adjusted model restricted to patients aged 1–17 years, each additional year of age was associated with higher odds of meeting the criterion (adjusted OR 1.13, 95% CI 1.10-1.16). After excluding patients with prescription-linked seizure disorder or cerebral palsy, the corresponding proportion was 16.96% (259/1,527). Among patients meeting the criterion, antidepressant-antipsychotic and ADHD medication-antipsychotic pairs were most frequent; methylphenidate-risperidone was the most common recorded individual-drug pair. Conclusions: This study characterizes pediatric psychotropic prescribing and potential polypharmacy in Qatar’s public healthcare records and helps address the limited regional evidence from Gulf health systems. The observed patterns identify potential medication-safety surveillance priorities, particularly among adolescents and patients with antipsychotic-containing medication histories. Further assessment incorporating exposure duration and clinical outcomes is needed to establish concurrency and clinical significance.

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.

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.

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.

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.

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.