Prospective multicenter evaluation of fecal tumor pyruvate kinase type M2 (M2-PK) as a screening biomarker for colorectal neoplasia

Proliferating cells, particularly the tumor cells, express a dimeric isoenzyme of pyruvate kinase, termed M2-PK. It's a direct target of several oncoproteins; the determination of fecal tumor pyruvate kinase type M2 (M2-PK) might be another promising tool for colorectal cancer (CRC) screening. In this study, we have evaluated fecal M2-PK as a screening biomarker for colorectal neoplasia. It was compared against fecal occult blood (FOB) and colonoscopy. Three hundred and seventeen consecutive subjects from 4 different centers were included. Stool specimens were collected before purgation, processed appropriately and were tested for FOB and quantitatively analyzed for M2-PK. Colonoscopies were performed by experienced endoscopists who were unaware of fecal assay results. At cutoff value of 4 U/ml, fecal M2-PK assay had a sensitivity, specificity, PPV and NPV of 81.1, 86.7, 71.1 and 61.9% respectively for diagnosing CRC whereas FOBT showed a sensitivity of 36.5%, specificity of 92.2%, PPV of 72.9% and NPV of 71.5% for CRC. Such low specificity of fecal M2-PK will lead to unacceptably high number of false positives if it is used for mass CRC screening, leading to unindicated colonoscopies with its associated inconveniences, risks and costs. CRC screening test must have high specificity; a high sensitivity is not as vital. To conclude, M2-PK was found to be a poor screening biomarker for CR neoplasia in a subject population at above average risk based on its prospective comparison with colonoscopy. These marginal performance characteristics do not permit its use as a screening tool for CR neoplasia in present clinical settings.     

Adenosine regulation of canine cardiac adenylate cyclase

Adenosine has a biphasic, [Mg²⁺]-dependent effect on the catalytic activity of dog heart adenylate cyclase. In the presence of 0.5 mM Mg²⁺, adenosine stimulated cyclic AMP formation, but when the cyclase was activated with 4 mM Mg²⁺ plus 0.5 mM Mn²⁺, adenosine inhibited catalytic activity in a dose-dependent fashion. Adenine, 3'-deoxyadenosine and selected purine-modified adenosine analogs stimulated the enzyme, whereas 2'-deoxyadenosine, 5'-deoxyadenosine and adenine-α-l-lyxofuranoside mimicked the inhibitory effect of adenosine on the Mg²⁺ plus Mn²⁺ stimulated enzyme. These results are consistent with the ‘two receptor’ model of Londos and Wolff [C. Londos and J. Wolff, Proc. natn. Acad. Sci. U.S.A.74, 5482 (1978)], but they raise the possibility of subtle organ and species differences in the chemical determinants of adenosine binding. Adenosine in both intracellular and extracellular compartments may modulate adenylate cyclase activity in the beating heart, in addition to its putative role in the regulation of coronary vascular resistance.     

Non-Linear Mixed Effects Modeling – From Methodology and Software Development to Driving Implementation in Drug Development Science

Few scientific contributions have made significant impact unless there was a champion who had the vision to see the potential for its use in seemingly disparate areas—and who then drove active implementation. In this paper, we present a historical summary of the development of non-linear mixed effects (NLME) modeling up to the more recent extensions of this statistical methodology. The paper places strong emphasis on the pivotal role played by Lewis B. Sheiner (1940–2004), who used this statistical methodology to elucidate solutions to real problems identified in clinical practice and in medical research and on how he drove implementation of the proposed solutions. A succinct overview of the evolution of the NLME modeling methodology is presented as well as ideas on how its expansion helped to provide guidance for a more scientific view of (model-based) drug development that reduces empiricism in favor of critical quantitative thinking and decision making     

Pharmacokinetics of low-dose doxorubicin and metabolites in patients with AIDS-related Kaposi sarcoma

Purpose Systemic chemotherapy is the treatment of choice for AIDS-related advanced Kaposi sarcoma. One principal schedule combines adriamycin (doxorubicin), bleomycin, and vincristine (ABV). We analysed the plasma concentrations of low-dose doxorubicin (Dx) and its metabolites doxorubicinol, 7-deoxydoxorubicinone, doxorubicinone, doxorubicinolone, and 7-deoxydoxorubicinolone in AIDS-patients to define patient-group and dose-specific pharmacokinetic parameters.Materials and methods A previously described high-performance liquid chromatographic (HPLC) method and a population approach with non-linear mixed effects modelling (NONMEM) were used for analysis and subsequent modelling of the time–concentration data of low-dose Dx and metabolites in seven patients with AIDS-related advanced Kaposi sarcoma. Patients received Dx 20 mg m^–2, bleomycin 15 U m^–2 and vincristine 2 mg as a 30-min intravenous infusion each. Blood samples were collected up to 72 h after the start of Dx treatment. WinNonlin software version 4.1 was used for non-compartmental analysis and NONMEM software version V for compartmental analysis. Covariate analysis was performed for various clinical and laboratory parameters.Results Non-compartmental analysis yielded an area under the plasma concentration–time curve (AUC) for Dx of 566 g h L^–1, a maximum plasma concentration (c_max) of 599 g L^–1 and an elimination half-life (t_1/2) of 30.8 h. Compartmental analysis resulted in a two-compartment model with first-order elimination, which best fitted the concentration–time data. Model estimate for Dx clearance was 61.8 L h^–1, for intercompartmental clearance (Q) 112 L h^–1, for the volume of the central compartment (V₁) 23.3 L, and for the volume of the peripheral compartment (V₂) 1,130 L. Metabolite data could adequately be estimated by NONMEM using single-compartment models. Graphical plots of residuals versus time for all metabolites yielded no evidence of non-linear pharmacokinetic behaviour. Laboratory parameters of liver and renal function were all in the normal range and their inclusion in the pharmacokinetic model did not improve data fit. A final jack-knife analysis was performed.Conclusions Concentration–time data for low-dose Dx and metabolites in the ABV-regimen are best described by a two-compartment model with first-order elimination. The results confirm that the aglycones doxorubicinone, 7-deoxydoxorubicinone, and doxorubicinolone can be reliably detected in the studied patient group and implemented into a common metabolic model. Model estimates suggest that pharmacokinetic parameters are similar for low-dose Dx and higher-dosed Dx. As the role of the aglycones is still poorly understood, despite their potential clinical relevance, their analysis should be implemented in future pharmacokinetic and pharmacodynamic studies of Dx.     

Stochastic Differential Equations in NONMEM®: Implementation,Application, and Comparison with Ordinary Differential Equations

Purpose The objective of the present analysis was to explore the use of stochastic differential equations (SDEs) in population pharmacokinetic/pharmacodynamic (PK/PD) modeling.Methods The intra-individual variability in nonlinear mixed-effects models based on SDEs is decomposed into two types of noise: a measurement and a system noise term. The measurement noise represents uncorrelated error due to, for example, assay error while the system noise accounts for structural misspecifications, approximations of the dynamical model, and true random physiological fluctuations. Since the system noise accounts for model misspecifications, the SDEs provide a diagnostic tool for model appropriateness. The focus of the article is on the implementation of the Extended Kalman Filter (EKF) in NONMEM^® for parameter estimation in SDE models.Results Various applications of SDEs in population PK/PD modeling are illustrated through a systematic model development example using clinical PK data of the gonadotropin releasing hormone (GnRH) antagonist degarelix. The dynamic noise estimates were used to track variations in model parameters and systematically build an absorption model for subcutaneously administered degarelix.Conclusions The EKF-based algorithm was successfully implemented in NONMEM for parameter estimation in population PK/PD models described by systems of SDEs. The example indicated that it was possible to pinpoint structural model deficiencies, and that valuable information may be obtained by tracking unexplained variations in parameters.     

Biodistribution and metabolism of immunostimulatory oligodeoxynucleotide CPG 7909 in mouse and rat tissues following subcutaneous administration

To evaluate pharmacokinetics (PK) and biodistribution, CPG 7909, a 24-mer immunostimulatory fully phosphorothioated oligodeoxynucleotide (PS-ODN), was administered by subcutaneous injection at 2, 5 and 12.5mg/kg to mice and at 9mg/kg to rats. Parent compound and metabolites were isolated from plasma and tissues and quantified by capillary gel electrophoresis with UV detection (CGE-UV) and molecular masses were determined by matrix-assisted-laser-desorption-ionization time of flight detection (MALDI-TOF). An established method for PS-ODN isolation from plasma and tissue was modified to prevent oxidation of the phosphorothioate bonds during the extraction process, significantly increasing sensitivity in the subsequent MALDI-TOF analysis. Concentrations of CPG 7909 and metabolites were highest at the injection site (>600mg/kg at 4h). Maximal concentrations in local (draining) lymph nodes (LLN), kidney and liver were 10-15% of that at the injection site. The highest total amount of PS-ODN (percentage of administered dose) was found in the liver (32% at 4h), followed closely by the injection site (23% at 4h). Only very low levels of CPG 7909 and metabolites were found in plasma and only during the first hours. Metabolites identified by MALDI-TOF were similar for both species and all analyzed tissues, although the relative amounts of the different metabolites varied with tissue and over time. Degradation of CPG 7909 in vivo occurred predominantly by 3'exonucleases with additional cleavage by endonucleases.     

Prediction of headache response in migraine treatment

Triptans are efficacious for the acute treatment of migraine attacks. Yet, defining a concentration-effect relation for these compounds is difficult as the dynamics of the migraine attack are not thoroughly understood. The objective of this investigation was to develop a disease model to predict measures of headache in randomized placebo-controlled clinical trials investigating oral sumatriptan as a paradigm compound. A hidden Markov model based on the states of response (no relief, relief, and pain free) and headache scores (observed variable) was used in conjunction with population pharmacokinetics. Model parameters were capable of predicting the course of headache relief, pain-free status and headache recurrence. It was shown that sumatriptan shortens mean transit times between states by up to 5 h. The potency of sumatriptan (EC(50)) was 9 ng/ml. These findings demonstrate the value of combining pharmacokinetic and efficacy information to model disease and characterize time-independent drug properties in a population of migraineurs.     

我院ICU病房主要革兰阴性菌感染情况及抗菌药物治疗方案优化

目的:了解ICU病房主要革兰阴性菌对常用抗菌药物的敏感性,以便合理使用抗菌药物。方法:收集我院ICU病房2004～2008年分离出的病原菌菌株,采用纸片扩散法进行药物敏感性试验。结果:2004～2007年革兰阴性菌的菌株数有上升趋势,在2008年全院控制药品比例的前提下,2008年革兰阴性菌的总菌数有所下降。分离数占前3位的细菌分别是大肠埃希菌、铜绿假单胞菌和鲍曼不动杆菌,除碳青霉烯类、酶抑制剂类、第4代头孢菌素类保持不同程度敏感性外,其余大多数抗菌药物均已耐药。结论:做好细菌敏感性监测,根据药动学/药效学(PK/PD)理论优化抗菌药物对于临床治疗有重要意义。     

Drug interactions evaluation: An integrated part of risk assessment of therapeutics

Pharmacokinetic drug interactions can lead to serious adverse events or decreased drug efficacy. The evaluation of a new molecular entity's (NME's) drug-drug interaction potential is an integral part of risk assessment during drug development and regulatory review. Alteration of activities of enzymes or transporters involved in the absorption, distribution, metabolism, or excretion of a new molecular entity by concomitant drugs may alter drug exposure, which can impact response (safety or efficacy). The recent Food and Drug Administration (FDA) draft drug interaction guidance (http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm072101.pdf) highlights the methodologies and criteria that may be used to guide drug interaction evaluation by industry and regulatory agencies and to construct informative labeling for health practitioner and patients. In addition, the Food and Drug Administration established a “Drug Development and Drug Interactions” website to provide up-to-date information regarding evaluation of drug interactions (http://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/DrugInteractionsLabeling/ucm080499.htm). This review summarizes key elements in the FDA drug interaction guidance and new scientific developments that can guide the evaluation of drug-drug interactions during the drug development process.