Therapeutic Drug Monitoring of Vancomycin in Jordanian Patients. Development of Physiologically-Based Pharmacokinetic (PBPK) Model and Validation of Class II Drugs of Salivary Excretion Classification System (SECS).

Vancomycin is a commonly used antibiotic for multi-drug resistant gram-positive infections treatment, especially methicillin-resistant Staphylococcus aureus (MRSA). Despite that, it has wide individual pharmacokinetic variability and nephrotoxic effect. Vancomycin trough concentrations for 57 Jordanian patients were measured in plasma and saliva through immunoassay and liquid chromatography-mass spectrometry (LC-MS/MS), respectively. Plasma levels were within accepted normal range, with exception of 6 patients who showed trough levels of more than 20 µg/ml and vancomycin was discontinued. Bayesian dose-optimizing software was used for patient-specific pharmacokinetics prediction and AUC/MIC calculation. Physiological-based pharmacokinetic (PBPK) vancomycin model was built and validated through GastroPlus™ 9.8 using in-house plasma data. A weak correlation coefficient of 0.2478 (P=0.1049) was found between plasma and saliva concentrations. The suggested normal saliva trough range of vancomycin is 0.01906 to 0.028589 (µg/ml). Analysis of variance showed significant statistical effects of creatinine clearance and albumin concentration on dose-normalized Cmin plasma and saliva levels respectively, which is in agreement with PBPKmodeling. It can be concluded that saliva is not a suitable matrix for TDM of vancomycin. Trough levels in plasma matrix should always be monitored for the safety of patients.

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques.

Breast cancer must be addressed by a multidisciplinary team aiming at the patient's comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.