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 1 (Issue 2) JULY– DECEMBER 2025
Research Articles
Leveraging Biological Samples for Customized Smoking Cessation: Insights from Genomic, Metabolomic, and Epigenomic Research
Vol.1(2); Pages:1-8. Published on July-2025
Abstract
Major progress in human genetics has revealed variations that may cause a person to become addicted to nicotine or use high amounts of tobacco. Based on what we have learned, researchers are now trying to spot genetic and biological signs that can tell us who will benefit the most from quitting smoking or from certain treatments to avoid it part of today’s precision medicine. The study discusses the main findings from this field and pays attention to genetic factors, CHRNA5, CYP2A6, the nicotine metabolite ratio (NMR), and changes in the genes caused by smoking. All in all, these biomarkers, which we detect in biospecimens, confirm that doctors may tailor cessation therapies more effectively based on an individual’s genetics. According to the findings, collecting biosamples in clinical trials helps scientists cooperate on research leading to new ways of treating tobacco dependence.
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Pharmacogenomic Profiling of CYP2C19 Variants as well as Their Clinical Implications to Clopidogrel Sensitivity in Asian Individuals
Vol.1(2); Pages:9-15. Published on August-2025
Abstract
Clopidogrel is an antiplatelet agent of widespread use, one with a variable response to it according to individuals, which can be attributed to the difference in genetic makeup of the cytochrome P450 enzyme, namely CYP2C19. This paper is an investigation of the prevalence of CYP2C19 loss-of-function alleles (2 and 3) and the influence on the responsiveness to clopidogrel in cardiovascular patients between two populations that are Asian. Two hundred and twenty patients proceeded to genotype by real-time PCR as they performed the percutaneous coronary intervention (PCI). VerifyNow P2Y12 was utilized to measure platelet aggregation. CYP2C192 allele was observed in 38 percent patients whereas 11 percent were detected to have CYP2C19*3. Individuals who are carriers for such alleles exhibited highly decreased inhibition of platelets (p < 0.001) and 44 percent of them were found to be poor metabolizers. In addition, there occurred an increased rate of negative cardiovascular outcomes in this population during clinical follow up at 6 months. The research backs the therapeutical worth of the regular pharmacogenomic testing before clopidogrel treatment in Asian patients, and proposes genotype directional treatment of non-responders with other anti-platelet drugs like ticagrelor.
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Clinical Implementation Study of Bayesian forecasting of precision dosed Vancomycin in renal impaired patients
Vol.1(2); Pages:16-25. Published on September-2025
Abstract
Dose adjustments of the antibiotic Vancomycin in patients with renal impairment have not been easy; it has a very narrow therapeutic index and it is interindividually variable. The goal of the current research was to introduce and test a Bayesian-based precision dosing algorithm of vancomycin to the existing nephrology inpatient scenario. A total of 80 patients with different stages of chronic kidney disease (CKD) (stage 2-5) were used to dose vancomycin using a population pharmacokinetic model on a Bayesian software platform. The levels were measured on day 3 in initial trough and the doses were modified. The Bayesian dosing group compared to a historical control attained therapeutic levels (15-20 mg/L) in 81.3 percent of patients compared to 56.7 percent of patients in the historical dose group (p < 0.01). Also, the prevalence of nephrotoxicity was less and the frequency of dosage reduction reduced. The clinicians say the software gave them greater confidence and efficiency in their flow of work. The results are on the use of Bayesian forecasting as a clinical utility to determine individualized dosing of vancomycin especially on the patient with compromised renal capacity where fixed dose regimens have failed.
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Genetic Variants In the Slco1b1 Gene and Their Effect on Statin Associated Myopathy Risk: A Population-Based Overview
Vol.1(2); Pages:26-37. Published on October-2025
Abstract
There is an association of SLCO1B1 gene polymorphism, especially the c.521T>C variant, with abnormal hepatic uptake of statins, which is associated with an elevated risk of statin-induced myopathy (SIM). This paper set out to assess the frequency of the SLCO1B1 c.521T>C variant use and its relationship to SIM amongst 300 hyperlipidemic people on statin treatment. PCR-RFLP techniques were used to conduct genotyping and clinical data such as levels of creatine kinase, perceptive muscle symptoms, and type of statin was obtained. The allele SLCO1B1 c.521T>C was present in 23.7 percent of the entire population, and the risk of myopathy occurrence in carriers of homozygous variants was increased 4.6 times (p < 0.001). Such association was stronger in patients receiving simvastatin, as carriers of a variant had higher chances of lowering the dose or discontinuing the drug. It was found that, on the basis of them, it is possible to state that such a strategy as clinical recommendation algorithm can be offered that selective applications of the genotype based guidelines in terms of statin choice and dose adjustments are acceptable in high-risk patients. These findings highlight the need to screen SLCO1B1 to reduce adverse drug reaction and the need to support the personalization of statin treatment in a population at risk.
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Machine Learning-Driven Warfarin Dose Prediction across Multi-Ethnic Populations: A Comparative Model Validation Study
Vol.1(2); Pages:38-49. Published on November-2025
Abstract
Warfarin dosing is a difficult procedure because the drug has a narrow therapeutic index and large individuality, which are both genetic and demographic conditions. The research was based on evaluating the predictive ability of machine learning (ML)-derived models to predict optimal warfarin maintenance doses using a multi-ethnic cohort. The data consisted of the information of 500 patients (of Russian, European, and Asian origins) including the clinical information and the genetic ones. A comparison of several ML algorithms including random forest, support vector machine (SVM) and gradient boosting against traditional clinical algorithms (including Gage and IWPC) was performed to predict the doses of warfarin. An optimized model, a gradient boosting regressor, could predict to a confidence of R 2 of 0.78 and mean absolute error (MAE) of 4.2 mg/week which is 18 percent better than using conventional tools. Analysis of the importance of features has shown that the most significant factors are VKORC1, CYP2C9, body weight, and ethnicity.
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