Determination of a Human Hepatic Microsomal Scaling Factor for Predicting in Vivo Drug Clearance

Purpose To determine a microsomal scaling factor for human liver suitable for prediction of in vivo drug clearance from in vitro data and to explore the role of inter-liver variability in this factor on the reported underprediction from microsomal parameters. Methods Cytochrome P450 (henceforth P450) content in whole homogenates and microsomes from 38 donor livers was used to determine a microsomal scaling factor. In a subset (n = 20) of these preparations, individual P450 enzymes were examined by Western blotting and selective probe activities were determined. Results The scaling factor from 38 livers averaged 40 mg microsomal protein per gram liver with a coefficient of variation of 31%. Western blotting experiments indicated that there was no P450 enzyme-specific trend in the distribution of individual P450 enzymes in liver microsomes relative to whole homogenate. Predictions based on an average scaling factor resulted in a satisfactory prediction of intrinsic clearance of three benzodiazepines similar to that obtained using individual factors for the same livers. Conclusion A value for human liver microsomal scaling of 40 mg microsomal protein per gram liver has been established. The reason for underprediction previously reported for 52 different drug substrates was not the use of an incorrect value for the scaling factor.     

Retinitis pigmentosa associated with rhodopsin mutations: Correlation between phenotypic variability and molecular effects

Similar retinitis pigmentosa (RP) phenotypes can result from mutations affecting different rhodopsin regions, and distinct amino acid substitutions can cause different RP severity and progression rates. Specifically, both the R135L and R135W mutations (cytoplasmic end of H3) result in diffuse, severe disease (class A), but R135W causes more severe and more rapidly progressive RP than R135L. The P180A and G188R mutations (second intradiscal loop) exhibit a mild phenotype with regional variability (class B1) and diffuse disease of moderate severity (class B2), respectively. Computational and in vitro studies of these mutants provide molecular insights into this phenotypic variability.     

Are prediction equations for glomerular filtration rate useful for the long-term monitoring of type 2 diabetic patients?

Background. The aim of this study was to compare the accuracyof prediction equations [modification of diet in renal disease(MDRD), simplified MDRD, Cockcroft–Gault (CG), reciprocalof creatinine and creatinine clearance] in a cohort of patientswith type 2 diabetes. Methods. A total of 525 glomerular filtration rates (GFRs) using¹²⁵I-iothalamate were carried out over 10 years in 87 type 2diabetic patients. Accuracy was evaluated at three levels ofrenal function according to the baseline values obtained withthe isotopic method: hyperfiltration (GFR: >140 ml/min/1.73m²; 140 isotopic determinations in 27 patients), normal renalfunction (GFR: 140–90 ml/min/1.73 m²; 294 isotopic determinationsin 47 patients) and chronic kidney disease (CKD) stages 2–3(GFR: 30–89 ml/min/1.73 m²; 87 isotopic determinationsin 13 patients). The annual slope for GFR (change in GFR expressedas ml/min/year) was considered to ascertain the variabilityin the equations compared with the isotopic method during follow-up.Student's t-test was used to determine the existence of significantdifferences between prediction equations and the isotopic method(P < 0.05 with Bonferroni adjusted for five contrast tests). Results. In the subgroup of patients with hyperfiltration, aGFR slope calculated with ¹²⁵I-iothalamate –4.8 ±4.7 ml/min/year was obtained. GFR slope in patients with normalrenal function was –3.0 ± 2.3 ml/min/year. In bothsituations, all equations presented a significant underestimationcompared with the isotopic GFR (P < 0.01; P < 0.05). Inthe subgroup of CKD stages 2–3, the slope for GFR with¹²⁵I-iothalamate was –1.4 ± 1.8 ml/min/year. Thebest prediction equation compared with the isotopic method provedto be MDRD with a slope for GFR of –1.4 ± 1.3 ml/min/year(P: NS) compared with the CG formula –1.0 ± 0.9ml/min/year (P: NS). Creatinine clearance presented the greatestvariability in estimation (P < 0.001). Conclusions. In the normal renal function and hyperfiltrationgroups, none of the prediction equations demonstrated acceptableaccuracy owing to excessive underestimation of renal function.In CKD stages 2–3, with mean serum creatinine 133 µmol/l(1.5 mg/dl), the MDRD equation can be used to estimate GFR duringthe monitoring and follow-up of patients with type 2 diabetesreceiving insulin, anti-diabetic drugs or both.     

A prospective study of 100 cases of penile cancer managed according to European Association of Urology guidelines

OBJECTIVE: To prospectively assess the outcome of patients treated according to the European Association of Urology (EAU) guidelines on the management of penile cancer, a system originally based on retrospective series. PATIENTS AND METHODS: Between 2002 and 2005, 100 consecutive patients (median age 62 years) with penile cancer were treated at one institution; all were categorized and treated according to EAU guidelines. Data were analysed using the z-test, with significance defined as P < 0.05. RESULTS: Survival curves were limited to those with >12 months of follow-up (mean 29); the survival of the whole group was 92%. Of men with palpable nodes, 72% had lymph node involvement, whereas 18% of those with impalpable nodes who had lymphadenectomy according to the guidelines had lymph node disease. The grade of the primary tumour was more predictive than T stage for lymph node involvement and survival. The 3-year disease-specific survival for N0, N1 and N2 disease was 100%, 100% and 73%, respectively, and survival at 12 months for N3 disease was 67%. The median survival for those with metastases was 3 months. CONCLUSION: The overall survival of men with penile cancer is high, with a clear benefit for early lymphadenectomy in men with positive nodal disease. However, the current EAU guidelines are limited in predicting those patients with micrometastatic disease, with the result that 82% of patients undergo unnecessary prophylactic lymphadenectomy. There is a need to identify more accurate molecular markers for predicting lymph node disease, or the role of novel staging techniques must be assessed.     

Computational Approaches for Predicting Protein–Protein Interactions: A Survey

Discovery of the protein interactions that take place within a cell can provide a starting point for understanding biological regulatory pathways. Global interaction patterns among proteins, for example, can suggest new drug targets and aid the design of new drugs by providing a clearer picture of the biological pathways in the neighborhoods of the drug targets. High-throughput experimental screens have been developed to detect protein–protein interactions, however, they show high rates of errors in terms of false positives and false negatives. Many computational approaches have been proposed to tackle the problem of protein–protein interaction prediction. They range from comparative genomics based methods to data integration based approaches. Challenging properties of protein–protein interaction data have to be addressed appropriately before a higher quality interaction map with better coverage can be achieved. This paper presents a survey of major works in computational prediction of protein–protein interactions, explaining their assumptions, main ideas, and limitations.     

A prospective comparison of severity scores for identifying patients with severe community acquired pneumonia: reconsidering what is meant by severe pneumonia

BACKGROUND: Several severity scores have been proposed to predict patient outcome and to guide initial management of patients with community acquired pneumonia (CAP). Most have been derived as predictors of mortality. A study was undertaken to compare the predictive value of these tools using different clinically meaningful outcomes as constructs for "severe pneumonia". METHODS: A prospective cohort study was performed of all patients presenting to the emergency department with an admission diagnosis of CAP from March 2003 to March 2004. Clinical and laboratory features at presentation were used to calculate severity scores using the pneumonia severity index (PSI), the revised American Thoracic Society score (rATS), and the British Thoracic Society (BTS) severity scores CURB, modified BTS severity score, and CURB-65. The sensitivity, specificity, positive and negative predictive values were compared for four different outcomes (death, need for ICU admission, and combined outcomes of death and/or need for ventilatory or inotropic support). RESULTS: 392 patients were included in the analysis; 37 (9.4%) died and 26 (6.6%) required ventilatory and/or inotropic support. The modified BTS severity score performed best for all four outcomes. The PSI (classes IV+V) and CURB had a very similar performance as predictive tools for each outcome. The rATS identified the need for ICU admission well but not mortality. The CURB-65 score predicted mortality well but performed less well when requirement for ICU was included in the outcome of interest. When the combined outcome was evaluated (excluding patients aged >90 years and those from nursing homes), the best predictors were the modified BTS severity score (sensitivity 94.3%) and the PSI and CURB score (sensitivity 83.3% for both). CONCLUSIONS: Different severity scores have different strengths and weaknesses as prediction tools. Validation should be done in the most relevant clinical setting, using more appropriate constructs of "severe pneumonia" to ensure that these potentially useful tools truly deliver what clinicians expect of them.     

Prediction of small for gestational age by logistic regression in twins

BACKGROUND: Small for gestational age (SGA) is one of the major determinants of perinatal mortality and morbidity, and may relate in adult diseases. Early prediction of SGA could be helpful for health care providers and public health workers in guiding antenatal management and prevention. The reported methods of SGA prediction are not satisfactory because the diagnostic performance is poor and the interval between prediction and delivery is too short. AIMS: To establish a SGA prediction model for twin pregnancies based on variables obtainable in early gestation. METHODS: We used a large twin registry United States data (1995-1997). The study subjects were randomly divided into two groups: group 1 to establish the prediction model by logistic regression and group 2 to validate the prediction model. SGA was defined as birth weight for gestational age z scores less than 10th percentiles. Pair of twin was the unit of analysis. Two sets of multiple logistic regression analyses with different outcome measures - one or both twins SGAs and both twins SGAs - were used to establish the prediction model. RESULTS: The sensitivity, specificity, and positive predictive value were 52.3, 62.5, and 21.5%, respectively, at the cutoff value 0.16 in a SGA prediction model based on maternal race, education, marital status, parity, prenatal care visit initiation, cigarette smoking, and paternal race. CONCLUSIONS: A prediction model based on determinants that can be obtained at early gestation might be useful in the management of pregnancies with high risk of SGA in twins.