Bayesian Population Pharmacokinetic Modeling of Eltrombopag in Chronic Hepatitis C Patients.

BACKGROUND AND OBJECTIVES: Eltrombopag is a thrombopoietic growth factor that is approved for the treatment of thrombocytopenia in chronic hepatitis C virus (HCV) patients. We aimed to describe eltrombopag population pharmacokinetics in hepatitis C patients. Bayesian statistical approach will be applied to screen for patients' characteristics associated with eltrombopag pharmacokinetic parameters. METHODS: A population pharmacokinetic analysis was conducted using WinBUGS version 1.4.3. Data from 483 individuals with chronic HCV infection were analyzed. This analysis is a secondary analysis of two clinical studies (ENABLE1 and ENABLE2) sponsored by GlaxoSmithKline. Several patients' characteristics were examined as possible covariates of the population pharmacokinetic model. Prior information from previous studies was incorporated in the bayesian model as prior distribution to estimate pharmacokinetic parameters. RESULTS: A two-compartment pharmacokinetic model with first-order absorption with exponential error model best fit the data. We identified East Asian race and total bilirubin level as predictors of eltrombopag clearance. Typical value for distributional clearance was 0.762 L/h (95% Bayesian credible set, 0.703-0.826), for volume of distribution of the central and peripheral compartments were 12 L (10.9-13.4) and 10.9 L (10.4-11.5), and for absorption lag time was 0.947 h (0.918-0.977). Assuming an average total bilirubin of 21.7 µmol/L, the typical elimination clearance value for an East Asian patient was 0.14 L/h and for other races was 0.20 L/h. CONCLUSIONS: Eltrombopag pharmacokinetic behavior was described using population bayesian approach. This model can be applied to optimize eltrombopag dosing in order to reduce the incidence of thrombocytopenia in HCV-infected patient receiving interferon-based therapy.

Predictors of Severe Thrombocytopenia Secondary to Peginterferon Alfa-2a Treatment in Subjects With Hepatitis C Virus Infection.

In this study, we aim to identify patient characteristics that predict severe thrombocytopenia induced by peginterferon alfa-2a in hepatitis C virus-infected patients. Demographic, clinical, and genetic data collected from patients with chronic hepatitis C virus infection (n = 232; age ≥18 years) who received peginterferon alfa-2a following eltrombopag treatment. Predictors of severe thrombocytopenia (platelet count below 50 GI/L) were identified using a 2-step approach: First, univariate analysis, using χ test for categorical variables and t test for continuous variables, was performed to identify possible predictors of severe thrombocytopenia (P < 0.05). Second, a logistic regression with backward stepwise selection was then performed using predictors identified in univariate analysis step to produce final model containing independent predictors at P < 0.05. Logistic model identified several predictors of severe thrombocytopenia. Increased spleen length and increased alkaline phosphatase levels increases the likelihood of severe thrombocytopenia. However, being Central/South Asian, increased neutrophils count and increased platelet baseline count decreases the probability of developing severe thrombocytopenia. In summary, we identified several patient characteristics that predict severe thrombocytopenia induced by peginterferon alfa-2a. Early selection of individuals with high risk of developing interferon-associated severe thrombocytopenia allows early intervention (such as eltrombopag treatment). Early intervention in turn minimizes the odds of developing severe thrombocytopenia and allows the continual of antiviral therapy before patient progress into liver decompensation.

Pharmacogenetic-guided Warfarin Dosing Algorithm in African-Americans.

We aim to develop warfarin dosing algorithm for African-Americans. We explored demographic, clinical, and genetic data from a previously collected cohort of 163 African-American patients with a stable warfarin dose. We explored 2 approaches to develop the algorithm: multiple linear regression and artificial neural network (ANN). The clinical significance of the 2 dosing algorithms was evaluated by calculating the percentage of patients whose predicted dose of warfarin was within 20% of the actual dose. Linear regression model and ANN model predicted the ideal dose in 52% and 48% of the patients, respectively. The mean absolute error using linear regression model was estimated to be 10.8 mg compared with 10.9 mg using ANN. Linear regression and ANN models identified several predictors of warfarin dose including age, weight, CYP2C9 genotype *1/*1, VKORC1 genotype, rs12777823 genotype, rs2108622 genotype, congestive heart failure, and amiodarone use. In conclusion, we developed a warfarin dosing algorithm for African-Americans. The proposed dosing algorithm has the potential to recommend warfarin doses that are close to the appropriate doses. The use of more sophisticated ANN approach did not result in improved predictive performance of the dosing algorithm except for patients of a dose of ≥49 mg/wk.

Adverse drug reactions experience in a teaching hospital in Jordan.

BACKGROUND: Adverse drug reactions (ADRs) represent a major health care problem. OBJECTIVE: To identify the most common ADRs, drugs implicated in ADRs, and to assess their causality, severity, preventability and risk factors predisposing to reported ADRs in Jordan. SETTING: Al-Karak teaching hospital, southern of Jordan. Method A cross sectional observational study was carried out for 11 months from January to November 2013. Suspected ADRs were recorded in ADRs report forms and analyzed for causality, severity, and preventability. MAIN OUTCOME MEASURE: Most common ADRs, drugs involved in these ADRs, causality, severity, and preventability of suspected ADRs. RESULTS: A total of 64 reports were received. Some patients suffered more than one ADR. The total number of ADRs identified was 108. Forty one drugs were involved in causing these ADRs. About 2/3 of adverse reactions (73.4 %) did not cause admission to the hospital, whereas 26.6 % of the ADRs resulted in admission. Majority of the ADRs were type A (62.5 %). Most of ADRs (92.2 %) were assessed as probable. Nearly, 65.6 % of ADRs were categorized as mild. Majority of ADRs were assessed as "not preventable" (75 %). The most common classes of drugs involved in ADRs were antibiotics, analgesics, vaccines and antiepileptics. The most commonly identified ADRs were abdominal pain, skin rash, shortness of breath, fever, upper gastrointestinal bleeding and vomiting. Risk factors contributed to ADRs were age and polypharmacy. CONCLUSION: Jordanian healthcare providers should be aware of the importance of detecting and reporting ADRs, in order to prevent and reduce the incidence of ADRs. Awareness of risk factors predisposing to ADRs may help in identifying patients with higher risk and therefore reducing the risk of these ADRs and improving patient outcome.

Dosage individualization of warfarin using artificial neural networks.

BACKGROUND AND OBJECTIVES: Our objective was to explore artificial neural networks (ANNs) as a possible tool for dosage individualization of warfarin. METHODS: Demographic, clinical, and genetic data were gathered from a previously collected cohort of patients with a stable warfarin dosage who were able to achieve an observed international normalized ratio of 2-3. Data from a cohort of 3,415 patients were used to develop an ANN dosing algorithm. Data from another cohort of 856 were used to validate the algorithm. The clinical significance of the ANN dosing algorithm was evaluated by calculating the percentage of patients whose predicted dosage of warfarin was within 20 % of the actual stable therapeutic dose. The clinical significance was also compared with a previously published dosing algorithm. RESULTS: A feed-forward neural network with three layers was able to successfully predict the ideal warfarin dosage in 48 % of the patients. The neural network model explained 48 % and 43 % of the dosage variability observed among patients in the derivation and validation cohorts, respectively. ANN analysis identified several predictors of warfarin dosage including race; age; height; weight; cytochrome P450 (CYP)2C9 genotype; VKORC1 genotype; sulfonamide, azole antifungals, or macrolide administration; carbamazepine, phenytoin, or rifampicin administration; and amiodarone administration. CONCLUSION: An ANN was applied to develop a warfarin dosing algorithm. The proposed dosing algorithm has the potential to recommend warfarin dosages that are close to the appropriate dosages.

UDP-glucuronosyltransferase 1A4 (UGT1A4) polymorphisms in a Jordanian population.

Glucuronidation is one of the most important phase II metabolic pathways. It is catalyzed by a family of UDP-glucuronosyltransferase enzymes (UGTs). One of the subfamilies is UGT1A. Allele frequencies in UGT1A4 differ among ethnic groups. The aim of this study was to determine the allelic frequency of two most common defective alleles: UGT1A4*2 and UGT1A4*3 in a Jordanian population. A total of 216 healthy Jordanian Volunteers (165 males and 51 females) were included in this study. Genotyping for UGT1A4*1, UGT1A4*2 and UGT1A4*3 was done using a well established polymerase chain reaction-restriction fragment length polymorphism test. Among 216 random individuals studied for UGT1A4*2 mutation there were 26 individuals who were heterozygous, giving a prevalence of 12% and an allele frequency of 6.5%. Only one individual was homozygous for UGT1A4*2. The UGT1A4*3 mutation was detected as heterozygous in 9 of 216 individuals indicating a prevalence of 4.2% and allele frequency of 3.5%. Three individuals were homozygous for the UGT1A4*3 indicating a prevalence of 1.4%. The prevalence of UGT1A4*2 is similar to the Caucasians but different from other populations whilst the UGT1A4*3 prevalence in the Jordanian population is distinct from other populations. Our results provide useful information for the Jordanian population and for future genotyping of Arab populations in general.