Journal of Pharmacotherapy and Clinical Translational Research (JPCTR) is proud to present the latest issue featuring cutting-edge research and reviews in pharmacotherapy and clinical translational research. Below, you will find the table of contents for the current issue:
Featured Articles in the Latest Issue
- Volume 3 (Issue 1) JANUARY- JUNE 2026
Research Articles
Pharmacokinetic Optimization of Novel Oral Anticoagulants in Elderly Populations: A Translational Cohort Study
Vol.3(1); Pages:1-9. Published on March 2026
Abstract
The anticoagulant therapy optimization in elderly groups is a serious issue because of age-associated physiological alterations influencing the tablet drugs metabolism and clearance. This paper explores pharmacokinetic variability of new oral anticoagulants (NOACs) among older adults aged 70 years and above. An analysis was conducted on a potential group (180 participants) with the emphasis on plasma drug concentration, renal functioning, and markers of bleeding risks. The use of advanced population pharmacokinetic modeling was used to determine important covariates that affect the exposure of drugs. The outcomes suggested a pronounced inter-individual variability, which was mainly caused by renal impairment and changes in body composition. Pharmacokinetic profiling-based dose adjustments showed better therapeutic results and fewer adverse events. Translational implications point to the need to implement a system of dosing on an individual basis, where clinical and biochemical variables are incorporated. The present work will help develop accurate-based pharmacotherapy in geriatrics and the importance of translational research in particular that will help in the translation of laboratory results to clinical practice.
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Engineering Tumor-Directed Nanocarriers to Improve Chemotherapy Distribution: A Translational Perspective
Vol.3(1); Pages:10-17. Published on March 2026
Abstract
It is now possible to talk of nanocarrier-based drug delivery systems as a potential approach to improve the efficacy and safety of chemotherapeutic agents. The present Phase II clinical trial is an evaluation of a liposomal nanocarrier formulation, which is used to deliver doxorubicin to patients with advanced solid tumors, in a targeted manner. There were 96 enrolled and randomized patients to undergo the standard chemotherapy or nanocarrier-mediated therapy. Tumor response rate and systemic toxicity were to be considered as primary endpoints. Secondary analyses determined the pharmacodynamic markers and tumor tissue drug accumulation. Nanocarrier group showed much greater efficiency in tumor targeting and cardiotoxicity decreased as opposed to conventional therapy. Pharmacokinetic profiling was that of increased circulation time and enhanced bioavailability. These results highlight the translational capability of nanotechnology in oncology, which is a potential avenue of cancer treatment that will be more effective and safer. These results should be confirmed by further large-scale trials to confirm their validity and allow their clinical adoption.
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Decoding Antidepressant Variability Using Patient-Specific Pharmacogenomic Profiles
Vol.3(1); Pages:18-27. Published on April 2026
Abstract
Variability in antidepressant response presents a significant challenge in psychiatric pharmacotherapy. This study explores the association between genetic polymorphisms and treatment outcomes in patients with major depressive disorder. A cohort of 210 patients undergoing selective serotonin reuptake inhibitor (SSRI) therapy was genotyped for key variants in CYP450 enzymes and serotonin transporter genes. Clinical response was measured using standardized depression rating scales over a 12-week period. The analysis revealed strong correlations between specific genotypes and both therapeutic efficacy and adverse effects. Patients with certain CYP2C19 polymorphisms exhibited altered drug metabolism, leading to suboptimal responses or increased side effects. These findings demonstrate the clinical utility of pharmacogenomic profiling in guiding antidepressant selection and dosing. The study highlights the importance of integrating genetic data into routine clinical practice to enhance treatment precision and patient outcomes.
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Patient-Centered Outcomes of Biologic Treatments in Autoimmune Conditions in Everyday Care
Vol.3(1); Pages:28-38. Published on April-2026
Abstract
Biologic therapies have revolutionized the treatment of autoimmune diseases but there is little real-life evidence of their efficacy and safety. This is a multicenter retrospective study that determines the clinical outcome of biologic agents in patients with rheumatoid arthritis and psoriasis on five international centers. The treatment response, adverse events and changes in biomarkers of 340 patients were analyzed. Outcomes showed a long term clinical remission in a substantial percentage of patients, whose variability was dependent on the severity of the disease at baseline and biomarkers. It is important to note that patients with high inflammatory markers responded better to individual biologics. The profile of adverse events was manageable and was similar to clinical trial data as safety was analyzed. The research highlights the importance of translational studies in the validation of therapeutic treatment strategies in clinical practice and justifies the application of biomarker based strategies in the optimization of biologic therapy.
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Artificial Intelligence–Enabled Detection of Hepatotoxic Risk in Pharmacotherapy
Vol.3(1); Pages:39-48. Published on May 2026
Abstract
Drug withdrawal is a major complication of drugs and a major problem of drug development is drug induced liver injury (DILI). This paper proposes a machine learning model of DILI prediction with a combination of both clinical and molecular data. A collection of 1200 compounds with tested hepatotoxic profiles were used to train and test a variety of predictive models, such as random forest and neural networks. Significant ones were the chemical structure descriptors, the gene expression data, and the clinical biomarkers. The optimized model was very predictive and it also was robust at external validation data. Notably, the framework revealed new risk factors related to hepatotoxicity, which provided information on the mechanisms. This translational methodology fills the gap between computational prediction and clinical relevance and is a useful instrument in drug development early risk assessment. The adoption of artificial intelligence in pharmacotherapy is promising to enhance the safety of drugs and to expedite research in the translational context.
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