Background: Pharmacogenomics (PGx) testing aims to identify the most appropriate drug and dose for individual patients based on their genetic profiles. In Jordan, patients with genetic disorders often use multiple medications, some of which have clinical guidelines recommending PGx testing. Aims: This study aimed to screen the frequency of clinically relevant PGx biomarkers among a sample of Jordanian patients with genetic disorders. Methods: A total of 76 patients (average age 13 ± 14 years; 71% under 13 years old) attending Innovia Biobank in Amman between January 2023 and January 2024 participated. Buccal swabs were collected, and DNA was extracted for whole-genome sequencing using Illumina technology. Variant calling and annotation were performed using DRAGEN, Geneyx, and ANNOVAR tools. A PGx panel based on PharmCAT v2.8.3 and Clinical Pharmacogenetics Implementation Consortium v1.30.0, covering 20 pharmacogenes, was applied.

Results: In Phase I enzymes, CYP2D6*10 (25%) and CYP2C19*1/*17 (18.4%) were most common, while CYP2C9 and CYP3A4 variants were less frequent. In Phase II enzymes, UGT1A180+2B appeared in 7.9% and multiple DPYD variants found in heterozygous forms 925%). Among toxicity-related markers, G6PD and HLA-B*57:01 were detected in 3.9% and 2.6%, respectively. Transporter gene variants in SLCO1B1 (15%) and ABCB1 (21.1%) were relatively frequent. For pharmacodynamic genes, VKORC1 -1639G>A (52.6%) and CYP4F2 V433M (40.8%) were most prevalent. Accordingly, over half of the patients had genetic variants affecting warfarin response, with additional impacts seen on antidepressants (45%), clopidogrel (35%), and anticancers (30%).

Conclusions: This study demonstrates the presence of key PGx biomarkers among Jordanian patients with genetic diseases and supports the integration of PGx testing to optimize the use of drugs like antidepressants, clopidogrel, and warfarin.

Introduction: Colorectal cancer (CRC) ranks third as a leading cause of cancer-related death globally. The urgent development of accurate diagnostic and therapeutic strategies is extensively needed. The drug discovery process in oncology remains a critical bottleneck in modern medicine due to its high cost, long timelines, and frequent clinical trial failures. In contrast, Drug Repurposing, a subset of Drug Discovery, identifies new therapeutic uses for existing drugs and has emerged as a powerful strategy to accelerate treatment development. This study aims to establish a computational framework integrating transcriptomic profiling, network pharmacology, and machine learning to identify prognostic biomarkers and prioritize repurposable drugs for colorectal cancer (CRC).

Methods: Publicly available high-throughput RNA-seq data were analyzed to identify differentially expressed genes. A protein–protein interaction (PPI) network was constructed to isolate hub genes, of which three genes were significantly overexpressed in tumors and strongly correlated with poor patient survival. This process proved them to be key prognostic biomarkers. A drug–gene interaction network was built using curated databases. An unsupervised machine learning pipeline combining principal component analysis (PCA) and K-means clustering was developed to integrate gene expression data, survival scores, and interaction profiles for drug ranking.

Result: Among 34 candidate drugs, Palbociclib, Vorinostat, and Methotrexate were identified as high-potential multi-target drugs linked to all three identified potential biomarkers. These findings highlight multi-target repurposing opportunities in CRC.

Conclusion: This interdisciplinary approach demonstrates how omics data with AI-driven analytics can accelerate the discovery of personalized, multi-target therapies. The proposed framework offers a scalable, data-driven approach to rapidly identify drug candidates in colorectal cancer and other complex diseases.

Background: In the evolving landscape of precision oncology for non-small cell lung cancer (NSCLC), timely and comprehensive molecular profiling is essential for identifying biomarker-matched therapies that improve outcomes in advanced stages. However, tissue biopsy, the current gold standard, presents challenges including invasiveness, insufficient tissue (in 5–16% of cases), inadequate DNA, and long turnaround times (for example, median 36.5 days in some Ontario centres), all of which may delay or compromise treatment. Liquid biopsy, a minimally invasive technique analyzing circulating tumour DNA (ctDNA) and RNA (cfRNA), offers a promising alternative.

Objective: This review evaluates Ontario’s experience with liquid biopsy implementation in NSCLC as a case study to inform adoption strategies in New Brunswick, where such programs are emerging.

Methods: A targeted policy and literature synthesis was conducted, drawing on publicly available health technology assessments (HTAs), multidisciplinary working group reports, peer-reviewed publications, and international guidelines (ASCO, ESMO, IASLC, and NCCN). Key focus areas included eligibility criteria, diagnostic performance, turnaround time, cost-effectiveness, and integration into public healthcare.

Results: Ontario proposes liquid biopsy as a complementary, not replacement, tool for tissue biopsy. Funding eligibility includes (1) insufficient or failed tissue biopsy; (2) clinically suspected advanced NSCLC where biopsy is not feasible; and (3) high-risk patients likely to deteriorate before tissue results are available. Liquid biopsy offers faster results (7–10 days) compared to tissue (up to 36.5 days), facilitating earlier treatment. Although sensitivity ranges from 63% to 81% (lower for fusions and CNVs), economic modeling suggests that it may reduce downstream costs and improve patient outcomes when used appropriately.

Conclusion: Ontario’s model provides a structured framework for New Brunswick’s implementation. Prioritizing high-need patients, investing in diagnostic infrastructure, and ensuring cost-efficiency can enable broader access to precision therapies while minimizing diagnostic delays.

Introduction: Cardiovascular disease (CVD) remains a leading cause of mortality globally. Although tools like the Pooled Cohort Equation (PCE) are widely used, genetic testing remains rare and typically targets high-penetrance monogenic variants. Using UK Biobank data (18,686 CVD cases and 12,100 controls), this study evaluates the combined use of clinical risk, polygenic risk scores (PRSs), and monogenic variants to improve CVD prediction.

Methods: Clinical risk is evaluated using the PCE, polygenic risk through a PRS derived from 1.2 million SNPs, and monogenic risk by pathogenic variants in LDLR, APOB, and PCSK9. Combined models are evaluated using categorical rules, logistic regression, and XGBoost to capture non-linearities. We also enhance prediction by adding 26 clinical variables from five established risk models.

Results: Cross-validation shows that combined risk models outperform individual predictors in terms of the AUC, particularly among younger individuals. The clinical and PRS models alone yield AUCs of 0.677 and 0.621, while the categorical, logistic regression, and XGBoost models achieve 0.669, 0.699, and 0.701, respectively. The categorical model offers the highest net reclassification index (NRI = 0.058) compared to logistic regression (0.007) and XGBoost (0.010). Incorporating an expanded set of clinical variables further boosts performance, with the AUC reaching 0.796 and the NRI increasing to 0.099.

Conclusions: Our findings underscore the trade-offs among modeling approaches for cardiovascular risk prediction. Combined models outperformed individual predictors, with logistic regression and XGBoost providing better discrimination, especially when enhanced with additional clinical variables, while categorical methods yielded a higher NRI.

Introduction: Breast cancer is the most prevalent malignancy among women worldwide, and the adverse effects of conventional chemotherapy highlight the need for more selective and less toxic therapeutic alternatives. In this context, bioinformatics approaches have emerged as promising tools for preclinical screening of new chemotherapeutics based on natural compounds. Methodology: The cytotoxic potential and molecular mechanisms of action of the phenylpropanoids verbascoside and isoverbascoside, isolated from Pyrostegia venusta, were evaluated using in silico methods. Cytotoxicity predictions (pIC₅₀) were performed with CLC-Pred 2.0 for the ER-positive tumor cell lines MCF7 and T47D and the non-tumorigenic MCF-10A cell line. Estrogen receptor interactions were assessed using ADMETLab 2.0, and nuclear receptor binding was further evaluated by molecular docking using CB-DOCK2, with tamoxifen used as a reference. Differentially expressed microRNAs were analyzed using CancerMIRNome, including through the creation of ROC curves and Kaplan–Meier survival analyses. Protein–protein interaction networks were constructed to investigate potential molecular mechanisms. Results: Verbascoside and isoverbascoside exhibited higher safety profiles in MCF-10A cells compared to tamoxifen. Tamoxifen showed greater cytotoxicity (pIC₅₀ > 5.3), whereas the natural compounds had lower pIC₅₀ values. Verbascoside stood out with higher predicted growth inhibition (pIG₅₀ = 6.0363), indicating potential antiproliferative effects. Only tamoxifen and isoverbascoside interacted with the nuclear estrogen receptor. The microRNA hsa-miR-21-5p demonstrated high accuracy in tissue discrimination (AUC = 0.97) but no significant prognostic value (HR = 0.99; p = 0.97). Network analysis suggested involvement of PTEN as a molecular target, with isoverbascoside showing a greater binding affinity to the receptor (-8.4 kcal.mol^-1), comparable to that of tamoxifen (-7.5 kcal.mol^-1). Conclusion: The findings indicate that these phenylpropanoids, particularly isoverbascoside, hold promising potential for use as natural antitumor agents for treating breast cancer, exhibiting favorable cellular selectivity and relevant interactions with hormonal receptors and molecular biomarkers.

The increasing incidence of tumors underscores the urgent need for precise diagnostic tools to enhance treatment outcomes and improve patient prognosis. Traditional diagnostic methods, often limited by subjectivity and variability, struggle to meet the demands of modern oncology. This study aims to construct an artificial intelligence (AI)-assisted tumor precision diagnosis model and explore its clinical application value. We collected comprehensive multicenter tumor imaging and clinical data, including histopathological features and patient demographics. Using advanced deep learning algorithms, we developed a diagnostic model capable of distinguishing various tumor types with high accuracy. The model was rigorously validated on an independent dataset, demonstrating superior performance compared to traditional diagnostic methods in terms of diagnostic accuracy, sensitivity, and specificity. For example, in ultrasonographic detection of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), the AI model showed a significant improvement in diagnostic sensitivity. Additionally, the model exhibited good generalizability across different tumor types and clinical settings, indicating its potential for widespread application. The conclusion indicates that the AI-assisted diagnostic model can significantly enhance the precision of tumor diagnosis, providing strong support for clinical decision-making and holding important application prospects. Future research will focus on further optimizing the model architecture and expanding its clinical applications to cover a broader range of tumor types and clinical scenarios. The integration of AI into clinical practice holds promise for improving diagnostic efficiency and patient outcomes in oncology, ultimately contributing to the development of precision medicine.