Peter Veng-Pedersen

Mean Time Parameters in Pharmacokinetics

SummaryA mean time parameter in pharmacokinetics defines the average time taken for 1 or more kinetic events to occur. Due to the complexity of the subject, a great number of different mean time parameters may be defined. Three of these parameters which appear to be of greatest interest are discussed: mean residence time (MRT), mean transit time (MTT) and mean arrival time (MAT). Formal definitions for these parameters are presented and various methods of evaluating them are described. The concepts of kinetic spaces, of importance in dealing with mean time parameters, are broadly defined. The discussion of the theory behind mean time parameters begins generally with fundamental core relationships, valid for both stochastic and non-stochastic systems, and successively introduces increasing degrees of kinetic specificity, ending with a discussion of mean time parameters of specific pharmacokinetic models. The limitations and assumptions involved in the use of mean time parameters in the various models are highlighted, with examples to clarify the concepts discussed. Area under the moment curve/area under the concentration-time curve (AUMC/AUC), commonly used as a definition for the MRT of drug molecules in the body, should not serve as a definition but should instead be considered as a method of evaluating this parameter.The literature on mean time parameters as they relate to absorption, distribution, elimination, metabolites, dosing times and drug accumulation is discussed. The clinical implications of mean time parameters are also considered, particularly in relation to the prediction, evaluation and interpretation of pharmacokinetic data.

A polyexponential deconvolution method. Evaluation of the «gastrointestinal bioavailability» and mean In vivo dissolution time of some ibuprofen dosage forms

A new deconvolution algorithm (DCON) suitable for pharmacokinetic applications is presented. It requires that both the impulse and input responses, typically systemic drug levels, be well described by polyexponential equations. DCON has a wider range of applications than an earlier method (DECONV) from which it is derived. A FORTRAN program is provided, making implementation of the technique a simple matter. DCON is demonstrated to evaluate the “GI bioavailability,” defined as the rate and the extent of gastrointestinal drug release, of various ibuprofen dosage forms. The GI drug release kinetics exemplifies a pharmacokinetic system which cannot be evaluated using the previous deconvolution algorithm (DECONV) because of an initial zero drug level response. This limitation is not found in DCON. It is also demonstrated how the mean in vivo dissolution time MDT can be evaluated by deconvolution.

Noncompartmentally-based pharmacokinetic modeling

OBJECTIVE To present an overview of noncompartmentally-based modeling which is a modeling that makes use of systems analysis, predominantly linear systems analysis (LSA). FINDINGS Fundamental elements of LSA presented from a linear operational viewpoint have a sound foundation in molecular stochastic independence (MSI). Powerful LSA procedures based on MSI presented such as convolution, deconvolution and disposition decomposition analysis (DDA) enable PK predictions and evaluations of drug input and delivery using models with simple general structures and few verifiable assumptions. DDA nonparametrically differentiates the unit impulse response (UIR) into generalized elimination and distribution functions. DDA applied in a linear and nonlinear context is central to many LSA procedures such as analytically exact direct deconvolution, nonparametric evaluation of drug elimination and distribution, steady state predictions, evaluation of mean time parameters for drug delivery and disposition (mean residence time, mean transit time, mean arrival time), relative tissue affinity (residence time coefficients), nonparametric exact clearance correction of UIR, and time variant convolution and deconvolution. The general response mapping operation procedure of LSA presented provides a powerful rational alternative to problematic structured modeling of multivariate PK systems. CONCLUSION The wide arsenal of underutilized LSA-based kinetic analysis tools provide a rational, powerful alternative to traditional kinetic modeling.

Linear and nonlinear system approaches in pharmacokinetics: How much do they have to offer? I. General considerations

System approaches in pharmacokinetics are defined as generalizing and simplifying modeling approaches that mathematically model a general property of the pharmacokinetic system without modeling specifically the individual kinetic processes responsible for the general property considered. The rationale for the use of system approaches is discussed and the kinetic basis of some of the approaches is presented. An overview of the approaches is presented together with a comparison to classical approaches involving specific pharmacokinetic models. Examples are given from different application areas involving problems in linear and nonlinear pharmacokinetics and in pharmacodynamics. The advantages, disadvantages, and limitations of the system approaches are discussed. In several application areas the system approach offers some rational methods and procedures with distinct advantages over more traditional approaches.

Surface Characterization of Activated Charcoal by X-Ray Photoelectron Spectroscopy (XPS): Correlation with Phenobarbital Adsorption Data

X-ray photoelectron spectroscopy (XPS) was used to identify the functional states of carbon existing on the surfaces of various activated charcoals. The relative percentages of carbon, oxygen, and detectable trace elements comprising the activated charcoal surfaces were determined. Analysis of the carbon core-electron binding energy region revealed the existence of one hydrocarbon state (C–H, C–C are indistinguishable) and three oxygen-containing functional states. These states were hydroxyls or ethers (C–O), carbon-yls (C = O), and carboxylic acids or esters (O–C = O). The C–O functional state contributed approximately 60–70% to the total percentage of oxygen-containing states. A very good correlation existed between the apparent areas occupied on the adsorbent surface per phenobarbital molecule and the relative percentages of the C–O functional state. Previously reported heat of displacement results for phenobarbital adsorption are now explained since the C–O state appears to be the primary site involved in the binding of phenobarbital by the activated charcoals.

Theorems and implications of a model independent elimination/distribution function decomposition of linear and some nonlinear drug dispositions. I. Derivations and theoretical analysis

The approach presented enables a model independent representation of the pharmacokinetics of drugs with a linear disposition and some drugs with a nonlinear disposition. The approach is based on a decomposition of the drug disposition into an elimination function q(c) and a distribution function h(t). The qfunction represents the net effect of all disposition processes which work toward a reduction in the systemic drug level. The hfunction represents the net effect of all disposition processes which slow down the rate of decline of the systemic drug level by returning drug from the peripheral environment to the systemic circulation. Several theorems relating qand hto the drug disposition are presented which uniquely define these functions mathematically. The disposition decomposition is of particular significance in three main areas of pharmacokinetics: (1) evaluation of drug absorption, (2) drug level predictions including steady state predictions, and (3)elucidation of drug disposition kinetics. The practical significance of the decomposition method in these three areas is discussed, and various procedures for the application of the method are proposed. The decomposition method represents a model independent alternative to pharmacokinetic models such as linear compartmental models, the recirculation model, and some physiologic models. This also includes nonlinear forms of such models, as long as the nonlinearity is due to a central nonlinear elimination. The greatest promise and significance of the disposition decomposition approach appears to be its application to nonlinear pharmacokinetics. In contrast to linear pharmacokinetics the kinetic analysis in such cases has been limited to model dependent methods employing specific pharmacokinetic models, due to the lack of model independent alternatives. The novel development presented offers such alternatives. For some applications these alternatives appear more rational in the sense that the analysis becomes more general and objective and may be based on fewer assumptions.

Erythropoietic Response to Endogenous Erythropoietin in Premature Very Low Birth Weight Infants

Despite the common occurrence of anemia in very low birth weight (VLBW) infants, the erythropoiesis and Hb production rates and their relationship to plasma erythropoietin (EPO) concentrations remain unknown in these subjects. To determine these quantities, all blood removed by phlebotomy and administered by red blood cell (RBC) transfusion over the first 30 days of life was recorded in 14 ventilated VLBW infants born at 24 to 28 weeks of gestation. Discarded blood from frequent clinically ordered laboratory blood samples was used to construct plasma EPO, Hb, and RBC concentration-time profiles for each infant. A pharmacodynamic Hb mass balance model that accounted for the dynamic hematological conditions experienced by these infants was simultaneously fitted to the plasma EPO, Hb, and RBC concentrations from each individual subject, while accounting for subject growth. Based on the model estimates, an average of 4.69 g of Hb was produced over the first 30 days of life, compared with 5.97 g removed by phlebotomies and 12.3 g administered by transfusions. These high transfusion amounts were consistent with a relatively short RBC life span and rapidly expanding blood volume with infant growth. The estimated mean body weight-scaled Hb production rate dropped nearly 3-fold after birth to 0.144 g/day·(kg)3/4. Although only estimated in a subset of the subjects, the mean plasma EPO EC50 of 28.5 mU/ml and maximal Hb production rate (Emax) indicated that a severalfold increase in Hb production rate could be achieved with only a modest increase in plasma EPO concentrations.

Neural networks in pharmacodynamic modeling. Is current modeling practice of complex kinetic systems at a dead end

Neural networks (NN) are computational systems implemented in software or hardware that attempt to simulate the neurological processing abilities of biological systems, in particular the brain. Computational NN are classified as parallel distributed processing systems that for many tasks are recognized to have superior processing capability to the classical sequential Von Neuman computer model. NN are recognized mainly in terms of their adaptive learning and selforganization features and their nonlinear processing capability and are considered most suitable to deal with complex multivariate systems that are poorly understood and difficult to model by classical inductive,logically structured modeling techniques. A NN is applied to demonstrate one of the potentially many applications of NN for modeling complex kinetic systems. The NN was used to predict the effect of alfentanil on the heart rate resulting from a complex infusion scheme applied to six rabbits. Drug input-drug effect data resulting from a repeated, triple infusion rate scheme lasting from 30 to 180 min was used to train the NN to recognize and emulate the input-effect behavior of the system. With the NN memory fixed from the 30- to 180-min learning phase the NN was then tested for its ability to predict the effect resulting from a multiple infusion rate scheme applied in the subsequent 180 to 300 min of the experiment. The NNs ability to emulate the system (30–180 min) was excellent and its predictive extrapolation capability (180–300 min) was very good (mean relative prediction accuracy of 78%). The NN was best in predicting the higher intensity effect and was able to identify and predict an overshoot phenomenon likely caused by a withdrawal effect from acute tolerance. Current modeling philosophy and practice is discussed on the basis of the alternative offered by NN in the modeling of complex kinetic systems. In modeling such systems it is questioned whether traditional modeling practice that insists on structure relevance and conceptually pleasing structures has any practical advantages over the empirical NN approach that largely ignores structure relevance but concentrates on the emulation of the behaviorof the kinetic system. The traditional searching for appropriate models of complex kinetic systems is a painstakingly slow process. In contrast, the search for empirical models using NN will continue to improve, limited only by technological advances supporting the very promising NN developments.

2,2',3,5',6-Pentachlorobiphenyl (PCB 95) and its hydroxylated metabolites are enantiomerically enriched in female mice.

Epidemiological and laboratory studies link polychlorinated biphenyls and their metabolites to adverse neurodevelopmental outcomes. Several neurotoxic PCB congeners are chiral and undergo enantiomeric enrichment in mammalian species, which may modulate PCB developmental neurotoxicity. This study measures levels and enantiomeric enrichment of PCB 95 and its hydroxylated metabolites (OH-PCBs) in adult female C57Bl/6 mice following subchronic exposure to racemic PCB 95. Tissue levels of PCB 95 and OH-PCBs increased with increasing dose. Dose-dependent enantiomeric enrichment of PCB 95 was observed in brain and other tissues. OH-PCBs also displayed enantiomeric enrichment in blood and liver, but were not detected in adipose and brain. In light of data suggesting enantioselective effects of chiral PCBs on molecular targets linked to PCB developmental neurotoxicity, our observations highlight the importance of accounting for PCB and OH-PCB enantiomeric enrichment in the assessment of PCB developmental neurotoxicity.

Carbamazepine level-A in vivo-in vitro correlation (IVIVC) : A scaled convolution based predictive approach

A method is presented for prediction of the systemic drug concentration profile from in vitro release/dissolution data for a drug formulation. The method is demonstrated using four different tablet formulations containing 200 mg carbamazepine (CZM), each administered in a four way cross‐over manner to 20 human subjects, with 15 blood samples drawn to determine the resulting concentration profile. Amount versus time dissolution data were obtained by a 75 rpm paddle method for each formulation. Differentiation, with respect to time, of a monotonic quadratic spline fitted to the dissolution data provided the dissolution rate curve. The dissolution curve was through time and magnitude scaling mapped into a drug concentration curve via a convolution by a single exponential, and the estimated unit impulse response function. The method was tested by cross‐validation, where the in vivo concentration profiles for each formulation were predicted based on correlation parameters determined from in vivo–in vitro data from the remaining three formulations. The mean prediction error (MPE), defined as the mean value of 100% x(observed−predicted)/observed was calculated for all 240 cross‐validation predictions. The mean values of MPE were in the range of 10–36% (average 22%) with standard deviations (S.D.s) in the range of 9–33% (average 13%), indicating a good prediction performance of the proposed in vivo–in vitro correlation (IVIVC) method. Copyright