Welcome to the Current Issues section of the Journal of Personalized and Precision Pharmacology (JPPP). This page provides access to the latest research and previously published articles that contribute to the advancement of precision pharmacology, individualized therapy, and pharmacogenomic research.
Featured Articles in the Latest Issue
- Volume 2 (Issue 1) JANUARY- JUNE 2026
Research Articles
Pharmacogenomic-Guided Anticoagulant Dosing in Atrial Fibrillation: A Prospective Cohort Study
Vol.2(1); Pages:1-10. Published on March-2026
Abstract
Exact dosing of the anticoagulants also presents a major challenge in the management of atrial fibrillation as there are inter-individual differences in terms of metabolism and reaction to the anticoagulants. This paper determines the clinical utility of pharmacogenomic-guided oral anticoagulant dosing algorithms in prospective cohort of 312 patients. Genetic polymorphisms of CYP2C9, VKORC1 and CYP4F2 were evaluated with clinical parameters to come up with individualized dosing regimens. The therapeutic efficacy, time in therapeutic range (TTR) and adverse events were compared in patients who went through genotype-guided therapy and patients who went through standard care. Findings showed that TTR and bleeding complications began to improve statistically in the group with the pharmacogenomic group. Furthermore, the time to stable dosage decreased by 28% which was an indicator of better clinical efficacy. The genetic data incorporated in the prescribing process was specifically found to be useful in the comorbid elderly. The results contribute to adopting pharmacogenomic-based dosing model in the standard cardiovascular practice and the significance of precision pharmacology in enhancing the therapeutic effect and reducing the risk of anticoagulant therapy.
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Decoding Treatment Response Variability in Oncology Using Data-Driven Learning Systems
Vol.2(1); Pages:11-22. Published on March-2026
Abstract
Inter-patient differences in drug response are a major challenge in oncology and may result in poor outcomes and excessive toxicity. It is a research on how machine learning algorithms can be applied to measure individualized drug response among cancer patients using retrospective clinical and genomic data. Ensemble learning models, which are random forests and gradient boosting models, were used to analyze data in 1,200 patients undergoing chemotherapy across various types of cancer. Such predictive variables as genomic mutations, transcriptomic profiles, and clinical variables like age and comorbidities were recorded. The resulting models were highly predictive with a higher area under the curve of 0.87 of predicting treatment response. Notably, the models have also identified important biomarkers that affect the drug effect, which could be used to stratify patients into responders and non-responder. Model strength and generality were validated on an independent dataset. The application of machine learning to precision pharmacology proves to show a lot of potential in informing therapeutic decision-making, decreasing trial-and-error prescribing, and enhancing patient outcomes in oncology.
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Impact of Microbiome Variability on Personalized Drug Metabolism: A Controlled Clinical Trial
Vol.2(1); Pages:23-32. Published on April-2026
Abstract
Emerging evidence suggests that the human gut microbiome plays a crucial role in drug metabolism, influencing therapeutic outcomes. This randomized controlled trial assessed the impact of microbiome variability on the metabolism of a commonly prescribed antidiabetic drug in 180 participants. Subjects were stratified based on baseline microbiome composition and randomized into intervention and control groups. Metagenomic sequencing and pharmacokinetic analyses were conducted to evaluate drug metabolism rates and metabolite profiles. Results indicated significant differences in drug bioavailability and metabolic pathways correlated with microbiome diversity and specific bacterial taxa. Participants with higher abundance of certain microbial species exhibited enhanced drug metabolism, leading to reduced plasma drug concentrations. Conversely, altered microbiome profiles were associated with delayed metabolism and increased adverse effects. These findings underscore the importance of incorporating microbiome profiling into personalized pharmacotherapy. The study highlights the need for integrating microbiome data into clinical decision-making frameworks to optimize drug dosing and efficacy.
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Genome Editing–Driven Functional Mapping for Precision Drug Target Confirmation
Vol.2(1); Pages:33-42. Published on April-2026
Abstract
The identification and validation of drug targets remain pivotal in advancing personalized medicine. This study explores the application of CRISPR-Cas9-based functional genomics to validate patient-specific drug targets. Using patient-derived cell lines, gene-editing techniques were employed to selectively knock out candidate genes implicated in disease progression. Functional assays assessed changes in cellular response to targeted therapeutics following gene disruption. Results demonstrated that CRISPR-mediated gene editing effectively identified critical genetic determinants influencing drug sensitivity and resistance. Specific gene knockouts resulted in enhanced drug efficacy, confirming their role as viable therapeutic targets. Additionally, off-target effects were minimized through optimized guide RNA design, ensuring experimental precision. This approach provides a robust framework for validating individualized drug targets and accelerates the development of tailored therapies. The integration of CRISPR technology into precision pharmacology represents a transformative advancement, enabling more accurate and efficient drug development pipelines.
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Real-World Evidence of Personalized Antidepressant Therapy Using Genetic Testing
Vol.2(1); Pages:43-52. Published on May-2026
Abstract
Personalized antidepressant therapy guided by genetic testing has gained increasing attention as a strategy to improve treatment outcomes in major depressive disorder. This observational real-world study analyzed data from 540 patients who underwent pharmacogenetic testing prior to antidepressant initiation. Genetic markers related to drug metabolism, including CYP2D6 and CYP2C19 variants, were evaluated to guide medication selection and dosing. Outcomes were compared with a control cohort receiving standard treatment without genetic guidance. Patients in the personalized therapy group exhibited significantly higher response and remission rates, along with reduced incidence of adverse drug reactions. Treatment adherence was also improved, likely due to fewer side effects and better therapeutic response. The study highlights the practical benefits of integrating pharmacogenetic testing into routine psychiatric care. These findings support broader adoption of precision approaches in neuropsychopharmacology, ultimately enhancing patient-centered care and optimizing therapeutic efficacy in depression management.
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