Investigating the effects of bus numbering in a radial transmission network using load-flow study.

This paper explains the effects of bus numbering using a load-flow study to investigate the Nigerian 330 kV radial transmission networks. The objectives of this research include the Newton-Raphson-based load-flow analysis and verification of power losses. The simulation of the load-flow analysis is carried out using the software of Power World Simulator and MATLAB, while verification of power losses is simulated with only MATLAB software. The Nigerian 330-kV transmission lines used in this study are radial and are overloaded; thus, it has been subjected to numerous studies covering many areas as to how improvements can be made. All these studies aimed at increasing the efficiency of the network and reduce real and reactive power losses. In this study, analysis is carried out on the failure to convergence; the result of load-flow as obtained for the 28-bus power system of the Nigerian 330-kV network using two different bus identification numbering sequence types. The results of the Newton-Raphson load-flow solution in Power World Simulator and MATLAB platform obtained for each of the two bus identification types revealed the convergence failure in one identification model numbering type. This result's inconsistency further necessitated the study of load-flow analysis on the Nigerian 330-kV network for other different bus identification numbering types as reviewed from past work for case studies. The same bus data and transmission line data obtained from PHCN are used for all the bus numbering model types generated in the study. The results revealed variations in the real and reactive power losses and the number of iterations in solving each case. Besides, the study discovered that the failure in convergence comes from the power solution method's failure (software) used, hence, a code-based platform should be used for verification.

Effectiveness of community-based DOTS strategy on tuberculosis treatment success rates in Namibia.

DOTS is a key pillar of the global strategy to end tuberculosis (TB). To assess the effectiveness of community-based compared with facility-based DOTS on TB treatment success rates in Namibia. Annual TB treatment success, cure, completion and case notification rates were compared between 1996 and 2015 using interrupted time series analysis. The intervention was the upgrading by the Namibian government of the TB treatment strategy from facility-based to community-based DOTS in 2005. The mean annual treatment success rate during the pre-intervention period was 58.9% (range 46-66) and increased significantly to 81.3% (range 69-87) during the post-intervention period. Before the intervention, there was a non-significant increase (0.3%/year) in the annual treatment success rate. After the intervention, the annual treatment success rate increased abruptly by 12.9% (P < 0.001) and continued to increase by 1.1%/year thereafter. The treatment success rate seemed to have stagnated at ∼85% at the end of the observation period. Expanding facility-based DOTS to community-based DOTS increased annual treatment success rates significantly. However, the treatment success rate at the end of the observation period had stagnated below the targeted 95% success rate. .

The process of knowledge discovery from large pharmacokinetic data sets.

The advent of statistical software with powerful graphical and modeling capabilities has revolutionized the manner in which pharmacokinetic and pharmacodynamic analyses are performed. Knowledge discovery from a large (population) pharmacokinetic data set incorporates all steps taken from data assembly to the development of a population pharmacokinetic model and the communication of the results thereof. The process can be formalized into a number of steps: (1) creation of a data set for pharmacokinetic knowledge discovery, (2) data quality analysis, (3) data structure analysis (exploratory examination of raw data), (4) determination of the basic pharmacokinetic model that best describes the data and generating post hoc empiric individual Bayesian parameter estimates, (5) the search for patterns and relationships between parameters and parameters and covariates by visualization, (6) the use of modern statistical modeling techniques for data structure revelation and covariate selection, (7) consolidation of the discovered knowledge into irreducible form (i.e., developing a population pharmacokinetic model), (8) the determination of model robustness (determination of the reliability of model parameter estimates), and (9) the communication and integration of the discovered pharmacokinetic knowledge. This process is discussed, and a motivating example is presented. The use of modern graphical, modeling, and statistical techniques for knowledge discovery from large pharmacokinetic data sets has given the data analyst the freedom to choose statistical methodology appropriate to the problem at hand with the maximization of information extraction, rather than on the basis of mathematical/statistical tractability.

The frog and the scorpion: sado-masochistic injury.

Interpreting the lesions of victims of sexual violence is one of the most difficult and most controversial areas of forensic medicine. The case we report involves the care of a victim of sado-masochism. It identifies the difficulties of both the forensic and legal management of sexual violence in an unusual context.

The role of population pharmacokinetics in drug development in light of the Food and Drug Administration's 'Guidance for Industry: population pharmacokinetics'.

Population pharmacokinetics (PPK) has evolved from a discipline primarily applied to therapeutic drug monitoring to one that plays a significant role in clinical pharmacology in general and drug development in particular. In February 1999 the US Food and Drug Administration issued a 'Guidance for Industry: Population Pharmacokinetics' that sets out the mechanisms and philosophy of PPK and outlines its role in drug development. The application of PPK to the drug development process plays an important role in the efficient development of safe and effective drugs. PPK knowledge is essential for mapping the response surface, explaining subgroup differences, developing and evaluating competing dose administration strategies, and as an aid in designing future studies. The mapping of the response surface is done to maximise the benefit-risk ratio, so that the impact of the input profile and dose magnitude on beneficial and harmful pharmacological effects can be understood and applied to individual patients. PPK combined with simulation methods provides a tool for estimating the expected range of concentrations from competing dose administration strategies. Once extracted, this knowledge can be applied to labelling or used to assess various future study designs. PPK should be implemented across all phases of drug development. For preclinical studies, PPK can be applied to allometric scaling and toxicokinetic analyses, and is useful for determining 'first time in man' doses and explaining toxicological results. Phase I studies provide initial understanding of the structural model and the effect of possible covariates, and may later be used to evaluate PPK differences between patients and healthy individuals. Phase II studies provide the greatest opportunity to map the response surface. With these PPK models it is possible to gain an improved understanding of the role of the dose on the response surface and of the range of expected responses. In phase III and IV studies, PPK is implemented to further refine the PPK model and to explain unexpected responses. Planning for the implementation of PPK across all phases of drug development is necessary, as well as planning for individual PPK studies. Planning should include: defining important questions, identifying covariates and drug-drug interactions that need to be investigated, and identifying the applications and intended use of the model(s). The plan for each project must have a strategy for data management, data collection, data quality assurance, staff training for data collection, data analysis and model validation.

Designing population pharmacokinetic studies: performance of mixed designs.

The interplay of the following factors: population design (PDN), the cost function in terms of maximum cost (Max. C) (i.e., maximum number of samples/sample size), sample size, and intersubject variability [restricted (30%) to moderate (60%)] on the estimation of pharmacokinetic parameters from population pharmacokinetic data sets obtained using mixed designs was investigated in a simulation study. A two compartment model with multiple bolus intravenous inputs was assumed, and the residual variability was set at 15%. The sample size (N) investigated ranged from 30 to 200 with the associated cost function varying accordingly with the five individual and sixteen population designs studied. Accurate and precise estimates of structural model parameters were obtained for N > or = 50 (Max. C > or = 150) irrespective of the intersubject variability (ITV) and PDN investigated. When ITV was 30%, all structural model parameters were well estimated irrespective of the PDN. Robust estimates of clearance and its variability were obtained for all N at all levels of ITV with Max. C > or = 90 (PDN > or = 4). Imprecise estimates of ITV in V1, V2, and Q were obtained at 60% ITV irrespective of N, PDN, or Max. C. Positive bias was associated with the estimation of variability in V1, V2, and Q with PDN < or = 4 (Max. C < or = 150). This was due in part to a greater proportion of subjects sampled only once. Correspondingly, residual variability was underestimated. It is of utmost importance to avoid this artifact by ensuring that at least a moderate subset of subjects contributing data to a population pharmacokinetic study contribute data more than once. Given a sample size and ITV, the cost function must be considered in designing a population pharmacokinetic study using mixed designs.

Population pharmacokinetics. A regulatory perspective.

The application of population approaches to drug development is recommended in several US Food and Drug Administration (FDA) guidance documents. Population pharmacokinetic (and pharmacodynamic) techniques enable identification of the sources of inter- and intra-individual variability that impinge upon drug safety and efficacy. This article briefly discusses the 2-stage approach to the estimation of population pharmacokinetic parameters, which requires serial multiple measurements on each participant, and comprehensively reviews the nonlinear mixed-effects modelling approach, which can be applied in situations where extensive sampling is not done on all or any of the participants. Certain preliminary information, such as the compartment model used in describing the pharmacokinetics of the drug, is required for a population pharmacokinetic study. The practical design considerations of the location of sampling times, number of samples/participants and the need to sample an individual more than once should be borne in mind. Simulation may be useful for choosing the study design that will best meet study objectives. The objectives of the population pharmacokinetic study can be secondary to the objectives of the primary clinical study (in which case an add-on population pharmacokinetic protocol may be needed) or primary (when a stand-alone protocol is required). Having protocols for population pharmacokinetic studies is an integral part of 'good pharmacometric practice'. Real-time data assembly and analysis permit an ongoing evaluation of site compliance with the study protocol and provide the opportunity to correct violations of study procedures. Adequate policies and procedures should be in place for study blind maintenance. Real-time data assembly creates the opportunity for detecting and correcting errors in concentration-time data, drug administration history and covariate data. Population pharmacokinetic analyses may be undertaken in 3 interwoven steps: exploratory data analysis, model development and model validation (i.e. predictive performance). Documentation for regulatory purposes should include a complete inventory of key runs in the analyses undertaken (with flow diagrams if possible), accompanied by articulation of objectives, assumptions and hypotheses. Use of diagnostic analyses of goodness of fit as evidence of reliability of results is advised. Finally, the use of stability testing or model validation may be warranted to support label claims. The opinions expressed in this article were revised by incorporating comments from various sources and published by the FDA as 'Guidance for Industry: Population Pharmacokinetics' (see the FDA home page http:/(/)www.fda.gov for further information).

The effects of rifampin and rifabutin on the pharmacokinetics and pharmacodynamics of a combination oral contraceptive.

BACKGROUND: Rifampin (INN, rifampicin), a CYP34A inducer, results in significant interactions when coadministered with combination oral contraceptives that contain norethindrone (INN, norethisterone) and ethinyl estradiol (INN, ethinylestradiol). Little is known about the effects of rifabutin, a related rifamycin. OBJECTIVES AND METHODS: The relative effects of rifampin and rifabutin on the pharmacokinetics and pharmacodynamics of ethinyl estradiol and norethindrone were evaluated in a prospective, randomized, double-blinded crossover study in 12 premenopausal women who were on a stable oral contraceptive regimen that contained 35 microg ethinyl estradiol/1 mg norethindrone. Subjects were randomized to receive 14 days of rifampin or rifabutin from days 7 through 21 of their menstrual cycle. After a 1-month washout period (only the oral contraceptives were taken), subjects were crossed over to the other rifamycin. RESULTS: Rifampin significantly decreased the mean area under the plasma concentration-time curve from time 0 to 24 hours [AUC(0-24)] of ethinyl estradiol and the mean AUC(0-24) of norethindrone. Rifabutin significantly decreased the mean AUC(0-24) of ethinyl estradiol and the mean AUC(0-24) of norethindrone. The effect of rifampin was significantly greater than rifabutin on each AUC(0-24). Despite these changes, subjects did not ovulate (as determined by progesterone concentrations) during the cycle in which either rifamycin was administered. Levels of mean follicle-stimulating hormone increased 69% after rifampin. CONCLUSION: In this study, rifampin (600 mg daily) was a more significant inducer of ethinyl estradiol and norethindrone clearance than rifabutin (300 mg daily), but neither agent reversed the suppression of ovulation caused by oral contraceptives. The carefully monitored oral contraceptive administration and the limited exposure to rifamycins may restrict the application of this study to clinical situations.

PIP: The relative effects of rifampin and rifabutin (a related rifamycin) on the pharmacokinetics and pharmacodynamics of ethinyl estradiol (EE) and norethindrone were evaluated in a prospective, randomized, double-blinded crossover study in 12 premenopausal women who were on a stable oral contraceptive regimen that contained 35 mcg EE and 1 mg norethindrone. Subjects were randomized to receive 14 days of rifampin or rifabutin from days 7 through 21 of their menstrual cycle. After a 1-month washout period (only the oral contraceptives were taken), subjects were crossed over to the other rifamycin. Findings showed that rifampin significantly decreased the mean area under the plasma concentration-time curve from time 0 to 24 hours [AUC (0-24)] of EE and the mean AUC (0-24) of norethindrone. Rifabutin significantly decreased the mean AUC (0-24) of EE and the mean AUC (0-24) of norethindrone. The effect of rifampin was significantly greater than rifabutin on each AUC (0-24). Despite these changes, subjects did not ovulate (as determined by progesterone concentrations) during the cycle in which either rifamycin was administered. Levels of mean follicle-stimulating hormone increased 69% after rifampin. This study suggests that rifampin (600 mg daily) was a more important inducer of EE and norethindrone clearance than rifabutin, but none of these agents were able to reverse the suppression of ovulation done by oral contraceptives.

Statistical graphics in pharmacokinetics and pharmacodynamics: a tutorial.

OBJECTIVE: To discuss the use of statistical graphics in the analysis of pharmacokinetics and pharmacodynamics data. METHODS: Information on graphic techniques and their application was retrieved from a MEDLINE search (January 1980-March 1997) of the English-language literature and bibliographic reviews of review articles and books. Data used to generate plots were extracted from some new drug applications submitted to the Food and Drug Administration and by simulation. DATA SYNTHESIS: In carrying out data analysis, we should look at data in several ways, construct a number of plots, and do several analyses, letting the results of each step suggest the next. The information from a plot should be relevant to the goals of the analysis. Thus, in choosing a graphic method, it is necessary to match the capabilities of the method to the need in the context of the application. For example, if linear relationships among variables in a set of multidimensional data are relevant, scatter plots such as the pairs plot with smoothing is likely to be more informative than other graphic methods. It is necessary to recognize what kinds of perceived structure are attributable to the data, and what kinds are artifacts of the display technique itself when using graphs for data analysis. CONCLUSIONS: Graphic techniques enable the data analyst to explore data thoroughly, look for patterns and relationships, confirm or disprove the expected, and discover new phenomena. An important element of statistical graphic techniques is flexibility, both in tailoring the analysis to the structure of the data and in responding to patterns that successive steps of analysis uncover. Statistical graphics can and should be used to enhance numeric statistical analyses.

Balanced designs in longitudinal population pharmacokinetic studies.

A simulation study was performed using a balanced design to determine the sample size required for accurate and precise estimation of a parameter at a given level of intersubject variability in a longitudinal population pharmacokinetic study. A two-compartment model parameterized in terms of clearance (Cl), volumes of the central (V1) and peripheral (V2) compartments, and intercompartmental clearance (Q) with multiple intravenous bolus inputs was assumed. Six samples were obtained from each subject using the informative profile (block) randomized design. Variability (in terms of coefficient of variation, CV) in model parameters was varied between 30% and 100%, and residual variability was fixed at 15%. Sample sizes ranging from 30 to 1,000 subjects were studied, and a hundred replicate data sets were generated and analyzed with NONMEM for each sample size at each CV. A sample size of 30 was required for accurate and precise estimation of structural model parameters when CV < or = 75%, except for Cl where it is adequate for CV < or = 100%. A sample size of 80 was required for intersubject variability estimation with CV < or = 60%. Robust estimates of variability in Cl were obtained with sample sizes of 30 (CV < or = 45%), 60 (CV 60-75%), and 100 (CV > or = 75%). Positively biased estimates of residual variability were obtained irrespective of sample size at > or = 60% CV. This indicates that estimates of residual variability obtained in study situations where CV > or = 60% should be interpreted with caution. In such situations model misspecification may not be the issue, because in this simulation study concentration-time profiles were generated and analyzed with the same model. Although these results should be interpreted within the context of the study, they provide a framework for addressing the issue of sample size in longitudinal population pharmacokinetic study with a balanced sampling design. The result of a population pharmacokinetic study can be anticipated by comparing the results of several simulations in which the various input factors have been varied.

Ignorability and parameter estimation in longitudinal pharmacokinetic studies.

In the analysis of longitudinal pharmacokinetic data, both balanced (equal number of samples per subject) and unbalanced data are used. It is implicitly assumed that the process that caused the missing data can be ignored. A simulation study was performed to determine the effect of ignoring the missing data (i.e., "ignorability") on the accuracy and precision of parameter estimation in longitudinal pharmacokinetic studies. A two-compartment model with multiple intravenous bolus inputs was assumed. Subjects with balanced data sets had six samples, and those with unbalanced data had 1 to 5 samples missing (i.e., supplied in a decreasing order from 5 to 1 samples). The proportion of subjects with 1 to 5 samples missing varied from 25% to 75% in a fixed sample size of 100. The effect of ignorability was studied at intersubject variability ranging from 15% to 60% for a drug assumed to be dosed at its elimination half-life. One hundred replicate data sets of 100 subjects each were simulated for each missing data scenario. The accuracy of parameter estimation was not significantly affected by the amount of ignorable missing data at any given level of variability. However, the precision of parameter estimation was affected by the degree of "missingness."

Stability and performance of a population pharmacokinetic model.

This study aimed to determine the stability (in terms of covariate selection) of a population pharmacokinetic model and evaluate its performance in the absence of a test data set. Data from 88 full-term infants, 11 of whom were human immunodeficiency virus (HIV)-seropositive, taking an antiinfective agent were analyzed using exploratory data analysis methods and the nonlinear mixed-effects modeling (NONMEM) program to obtain the final population pharmacokinetic model. The stability of the population pharmacokinetic model was tested using the nonparametric bootstrap approach in four steps: 1) with the base pharmacokinetic model, 100 bootstrap replicates of the original data were generated by sampling with replacement; 2) ascertainment that each bootstrap data replicate was described by the basic structural model using the NONMEM objective function; 3) generalized additive modeling (GAM) applied to empiric Bayesian estimates for covariate selection at alpha = 0.05 and a frequency (f) cutoff value of 0.50; and 4) NONMEM population model building using covariates selected in the third step with alpha = 0.005. Performance of the population pharmacokinetic model was evaluated using 200 additional bootstrap replicates of the data by fitting the model obtained in step 4 to them. Parameters obtained were compared with those obtained in the model stability step, and improved prediction error, a measure of predictive accuracy as an index of internal validation, was computed. The reciprocal of serum creatinine (RSC; f = 0.73) and HIV (f = 0.70) were selected by GAM as predictors of clearance (Cl). The population pharmacokinetic model obtained without the determination of model stability included RSC as a predictor of Cl, but the final model from the model stability step included both HIV and RSC as predictors of Cl. Final population pharmacokinetic parameters were obtained with this model fitted to the original data; however, the 95% confidence interval on the HIV status regression coefficient included zero, indicating no significance. The mean parameter estimates obtained with the additional 200 bootstrap replicates of data were within 15% of those obtained with the final model at the regression stability step. Bootstrap resampling procedure is useful for evaluating the stability and performance of a population model by repeatedly fitting it to the bootstrap samples when there is no test data set.

On the recording of sample times and parameter estimation from repeated measures pharmacokinetic data.

A pharmacokinetic screen has been advocated for the characterization of the population pharmacokinetics of drugs during Phase 3 clinical trials. A common perception encountered in the collection of such data is that the accuracy of sampling times relative to dose is inadequate. A prospective simulation study was carried out to evaluate the effect of error in the recording of sampling times on the accuracy and precision of population parameter estimates from repeated measures pharmacokinetic data. A two-compartment model with intravenous bolus input(s) (single and multiple doses) was assumed. Random and systematic error in sampling times ranging from 5-50% using profile (block) randomized design were introduced. Sampling times were simulated in EXCEL while concentration data simulation and analysis were done in NONMEM. The effect of error in sampling times was studied at levels of variability ranging from 15-45% for a drug assumed to be dosed at its elimination half-life. One hundred replicate data sets of 100 subjects each were simulated for each case. Although estimates of clearance (CL) and variability in clearance were robust for most of the sampling time errors, there was an increase in bias and imprecision in overall parameter estimation as intersubject variability was increased. If there is interest in parameters other than CL, then the design of prospective population studies should include procedures for minimizing the error in the recording of sample times relative to dosing history.

Comparing non-hierarchical models: application to non-linear mixed effects modeling.

There is no method available to compare the fit of two non-hierarchical non-linear mixed effects models, although the common practice is to select the model with the lower objective function. Bootstrapping the log-likelihood differences (LLDs) of non-hierarchical models and constructing a bootstrap confidence interval on the LLDs is proposed for comparing the goodness-of-fit of such models. This is illustrated with different parameterizations of clearance models for an anti-infective agent in a longitudinal pharmacokinetic study which are compared. Additive and exponential models of creatinine clearance as a predictor of clearance are used as examples.

Analysis of animal pharmacokinetic data: performance of the one point per animal design.

A simulation study was carried out to determine the impact of various design factors on the accuracy and precision with which population pharmacokinetic parameters are estimated in preclinical pharmacokinetic studies. A drug given by intravenous bolus injection and having mono-exponential disposition characteristics was assumed. The factors investigated were (i) number of animals sampled at specified times with one observation taken per animal, (ii) error in observed concentration measurements, and (iii) doubling the number of observations per animal while varying the number of animals. Data were analyzed with the NONMEM program, and the least number of animals per time point (where each animal supplied one concentration-time point) required for accurate and precise parameter estimation was determined. The one observation per animal design yielded biased and imprecise estimates of variability, and residual variability could not be estimated. Increasing the error in the concentration measurement led to a significant deterioration in the accuracy and precision with which variability was estimated. Obtaining a second sample from each animal practically eliminated bias and facilitated the partitioning of interanimal variability and residual intraanimal variability, by introducing information about the latter. Doubling the total number of observations per animal required using half (i.e., 50) the total number of animals required for accurate and precise parameter estimation with the one sample per animal design.

Population pharmacokinetic modeling: the importance of informative graphics.

PURPOSE: The usefulness of several modelling methods were examined in the development of a population pharmacokinetics model for cefepime. METHODS: The analysis was done in six steps: (1) exploratory data analysis to examine distributions and correlations among covariates, (2) determination of a basic pharmacokinetic model using the NON-MEM program and obtaining Bayesian individual parameter estimates, (3) examination of the distribution of parameter estimates, (4) multiple linear regression (MLR) with case deletion diagnostics, generalized additive modelling (GAM), and tree-based modelling (TBM) for the selection of covariates and revealing structure in the data, (5) final NONMEM modelling to determine the population PK model, and (6) the evaluation of final parameter estimates. RESULTS: An examination of the distribution of individual clearance (CL) estimates suggested bimodality. Thus, the mixture model feature in NONMEM was used for the separation of subpopulations. MLR and GAM selected creatinine clearance (CRCL) and age, while TBM selected both of these covariates and weight as predictors of CL. The final NONMEM model for CL included only a linear relationship with CRCL. However, two subpopulations were identified that differed in slope and intercept. CONCLUSIONS: The findings suggest that using informative graphical and statistical techniques enhance the understanding of the data structure and lead to an efficient analysis of the data.

Experimental design and efficient parameter estimation in preclinical pharmacokinetic studies.

Monte Carlo simulation technique used to evaluate the effect of the arrangement of concentrations on the efficiency of estimation of population pharmacokinetic parameters in the preclinical setting is described. Although the simulations were restricted to the one compartment model with intravenous bolus input, they provide the basis of discussing some structural aspects involved in designing a destructive ("quantic") preclinical population pharmacokinetic study with a fixed sample size as is usually the case in such studies. The efficiency of parameter estimation obtained with sampling strategies based on the three and four time point designs were evaluated in terms of the percent prediction error, design number, individual and joint confidence intervals coverage for parameter estimates approaches, and correlation analysis. The data sets contained random terms for both inter- and residual intra-animal variability. The results showed that the typical population parameter estimates for clearance and volume were efficiently (accurately and precisely) estimated for both designs, while interanimal variability (the only random effect parameter that could be estimated) was inefficiently (inaccurately and imprecisely) estimated with most sampling schedules of the two designs. The exact location of the third and fourth time point for the three and four time point designs, respectively, was not critical to the efficiency of overall estimation of all population parameters of the model. However, some individual population pharmacokinetic parameters were sensitive to the location of these times.

Interpretation of simulation studies for efficient estimation of population pharmacokinetic parameters.

OBJECTIVE: To develop new approaches for evaluating results obtained from simulation studies used to determine sampling strategies for efficient estimation of population pharmacokinetic parameters. METHODS: One-compartment kinetics with intravenous bolus injection was assumed and the simulated data (one observation made on each experimental unit [human subject or animal]), were analyzed using NONMEM. Several approaches were used to judge the efficiency of parameter estimation. These included: (1) individual and joint confidence intervals (CIs) coverage for parameter estimates that were computed in a manner that would reveal the influence of bias and standard error (SE) on interval estimates; (2) percent prediction error (%PE) approach; (3) the incidence of high pair-wise correlations; and (4) a design number approach. The design number (phi) is a new statistic that provides a composite measure of accuracy and precision (using SE). RESULTS: The %PE approach is useful only in examining the efficiency of estimation of a parameter considered independently. The joint CI coverage approach permitted assessment of the accuracy and reliability of all model parameter estimates. The phi approach is an efficient method of achieving an accurate estimate of parameter(s) with good precision. Both the phi for individual parameter estimation and the overall phi for the estimation of model parameters led to optimal experimental design. CONCLUSIONS: Application of these approaches to the analyses of the results of the study was found useful in determining the best sampling design (from a series of two sampling times designs within a study) for efficient estimation of population pharmacokinetic parameters.

Ivermectin: a long-acting microfilaricidal agent.

Ivermectin is a macrocyclic lactone (fermentation) product and actinomycete (Streptomyces avermitilis) that possesses an unusually broad spectrum of potent activity against several species of nematodes, arachnids, and insects that parasitize domestic animals. From clinical trials in humans it has been found to be microfilaricidal, killing microfilariae of Onchocerca volvulus (the parasite causing onchocerciasis), and interrupting its transmission by the black fly vector. Dermal microfilariae density in patients are reduced to near zero levels for 6-12 months after a single oral dose of ivermectin 0.15-0.2 mg/kg. Its precise mechanism of action is unknown. It has a time to maximum concentration of 2.7-4.3 h, and an elimination half-life of 28 +/- 10 h. When compared with an oral solution the tablet dosage form has a relative bioavailability of approximately 60 percent. Not much is known about its metabolism in humans, and the unchanged drug is not detected in the urine. Controlled clinical trials have shown ivermectin to be associated with milder side effects than diethylcarbamazine, the current drug of choice for onchocerciasis therapy. It does not cause the severe Mazzoti-type (anaphylactoid) reactions that are associated with diethylcarbamazine use. Ivermectin is effective, safer, and more tolerable than diethylcarbamazine. It should, therefore, replace diethylcarbamazine as the drug of choice for onchocerciasis therapy.

Determination of saliva: total plasma chloroquine levels relationship by high performance liquid chromatography.

The use of saliva chloroquine concentrations measurement as a noninvasive technique in the evaluation of the pharmacokinetics of the drug was investigated. Chloroquine concentrations in saliva and plasma were measured in eight healthy volunteers after a single oral dose of two tablets of chloroquine sulfate. The saliva: total plasma concentrations (S/P) ratio was found to be approximately constant in the absorption (0.4 +/- 0.07), distribution (0.47 +/- 0.08), and elimination (0.46 +/- 0.05) phases. Thus, saliva sampling for chloroquine concentrations was found to be a useful noninvasive technique for the estimation of all the pharmacokinetic parameters of the drug and hence, for chloroquine pharmacokinetic studies.