Prediction of apparent volume of distribution in obesity

A standard height-weight table permits the separation of the obese body into two compartments: a lean and a fat. Depending upon the physicochemical properties of a drug, such as pKa, apparent partition coefficient and extent of protein binding, the distribution of a drug could either be in the lean compartment or in the whole body. Based on this thesis, several equations, either published in the literature or developed for this report, all based on the physico-chemical properties of a drug and the physiological changes in the obese subject, were used to compare the apparent volume of distribution, Vz as predicted by these equations with the Vz values of several drugs reported in the literature for obese subjects. Duncan's multiple range test was used for testing literature values vs. predicted data. Examples from the literature are reported and discussed. The validity of these equations needs to be verified as more information on pharmacokinetic parameters in obesity becomes available.     

Prediction of blood-brain distribution: effect of ionization

Two simple multiple linear regression models were proposed to calculate the logarithm of the blood to brain concentration ratio (log BB) of drugs or drug-like compounds. The drugs were classified into two groups according to their ionization state in blood, and the significant parameters were selected using the train sets for each group. For un-ionizable compounds, the logarithm of distribution coefficient in octanol-water in pH 7.4 (log D(7.4)) and molecular weight are the significant parameters, whereas for ionizable compounds, log D(7.4) and number of hydrogen bond acceptor are significant parameters. The developed models were validated and their prediction capabilities checked using an external dataset of 25 compounds. In addition to the acceptable prediction errors, comparison of the external data analysis results with previously proposed models confirmed superior prediction capability of newly developed models.     

Prediction of drug distribution into human milk from physicochemical characteristics

Decisions about the safety of breast feeding during maternal ingestion of drugs require knowledge of the amount of drug which might be present in the milk. For many drugs this has not been studied, and mothers are usually advised against breast feeding. In many cases this is undoubtedly unnecessary, as the total dose to which the baby is exposed is often negligible. It would be very helpful, therefore, to be able to predict the approximate amount of drug which might be present in milk. Existing theory of pH partitioning enables estimation of the distribution of unbound drug, i.e. milk: plasma unbound ratios. However, these ratios are poor estimates of the concentration ratios for whole milk, because whole milk contains proteins and lipid in which drugs will distribute in amounts which depend on their particular physicochemical properties. To predict the milk: plasma concentration ratios for whole milk the amount of drug present in the protein and lipid phases must be considered along with the unbound drug distribution. A 'phase distribution model' has therefore been developed which permits estimation of whole milk: plasma concentration ratios. The model requires a knowledge of the unbound drug concentration ratio, the plasma and milk unbound fractions and the milk lipid: ultrafiltrate partition coefficient. Evaluation of the model by comparison of predicted whole milk ratio values with literature milk: plasma area under the curve (AUC) ratios indicated a trend to overprediction for acidic and neutral drugs and underprediction for basic drugs. Transformation of the phase distribution equation by taking logarithms results in a relationship which can be analysed by multiple linear regression to derive predictive equations for acidic and basic drugs which take into account the relative contributions of each component of the model. Regression of the logarithms of the literature milk: plasma AUC values against the independent variables resulted in good correlations for acidic and basic drugs. The independent variables explained 93.1% and 82.9% of the variance in the values for acidic and basic drugs, respectively, with random scatter of residuals. The equations, together with those to predict unbound fractions of drug in milk and milk lipid: ultrafiltrate partition coefficients, enable the ratio of the milk: plasma AUCs to be estimated for any acidic or basic drug for which the distribution into human milk is not known, using the pKa, octanol: water partition coefficient and plasma protein binding values of the drug. The data set for neutral drugs (n = 3) was too small to develop a correlation equation.(ABSTRACT TRUNCATED AT 400 WORDS)     

Prediction of thiopental induction dose based on caffeine volume of distribution

Similarities in the physicochemical properties of caffeine and thiopental would suggest that the apparent volume of distribution of caffeine (aVd) may be comparable to the initial volume of distribution of thiopental. It is the initial volume of distribution of thiopental that is critical in the early minutes of anesthetic induction. A comparison of the aVd of caffeine and thiopental induction dose was made in 21 male New Zealand white rabbits. The aVd of caffeine was determined from serial saliva determinations following intravenous injection of caffeine (7.5 mg/kg). The loss of the pupillary light reflex was used as the end point for induction with thiopental. A statistically significant correlation (r = .722, P less than .0001) was found between the aVd of caffeine and thiopental induction dose. Also, both thiopental induction dose and caffeine aVd decreased significantly with age in these animals. These findings provide a basis for development of an uninvasive test for predicting thiopental dose in humans.     

Prediction of aminoglycoside distribution space in neonates by multiple frequency bioelectrical impedance analysis

Objective: Aminoglycoside antibiotics have a narrow margin of safety between therapeutic and toxic levels. The current study used multiple frequency bioelectrical impedance analysis to develop prediction equations for gentamicin distribution space in neonates. Methods: Gentamicin pharmacokinetic parameters and bioimpedance were measured in 14 infants in the neonatal intensive care unit. Stepwise regression analysis was used to develop predictive models, using impedance quotients (F²/R), weight and gestational age as variables, whose predictive performance was then tested in a second group of ten infants. Results: The prediction model with the smallest bias and highest concordance correlation was that which included F²/R₀ and weight. This bias of 50 ml or 6.7% was less than half of that found using a model including weight alone. Conclusion: A bioelectrical impedance-based prediction equation for prediction of gentamicin distribution space in neonates was produced. Although this prediction equation represents only a small improvement over that using weight alone, this is of clinical significance due to the narrow margin between therapeutic and toxic levels for gentamicin. A clinical trial to confirm the value of this methodology is now warranted. Received: 12 April 1999 / Accepted in revised form: 27 July 1999     

Prediction of drug distribution in vivo on the basis of in vitro binding data

For 11 drugs it was investigated whether tissue distribution in vivo can be predicted by use of binding data obtained in vitro. The selection of drugs represented a broad spectrum of physicochemical and pharmacokinetic properties thought to be important for distribution of drugs in vivo. The extent of binding to plasma and to tissue-homogenates of rabbits was determined in vitro. The drug concentrations in plasma, liver, lungs, kidneys, and skeletal muscle of rabbits were determined in vivo after i.v. administration of the drug. The tissue-plasma partition ratios measured in vivo were compared with the theoretical tissue-plasma partition ratios calculated from the in vitro binding data. For all drugs investigated the muscle-plasma partition ratio could be reasonably well predicted by the in vitro binding data. In liver, lungs, and kidneys good agreement was found between measured and predicted tissue-plasma ratio for anionic drugs; marked differences, however, were observed between measured and predicted tissue-plasma ratios of lipophilic cationic drugs. A significant correlation was found between binding of drugs to muscle tissue in vitro and the volume of distribution of the unbound drug (Vf), opening the possibility to approximate Vf from in vitro binding studies with rabbit muscle tissue.     

Pharmacokinetics of verapamil in lactating rabbits. Prediction of verapamil distribution into rabbit milk

In this work, we have studied the pharmacokinetics and milk penetration of verapamil following intravenous administration in lactating rabbits. Milk-to-serum drug concentration ratios (M/B(obs)) have been determined using area under the milk and serum concentration-time profiles, and the resulting values have then been compared with those obtained by theoretical classical diffusion milk transfer models that were described by Fleishaker et al. [J. Pharm. Sci. 76 (1987) 189.], Atkinson and Begg [Br. J. Clin. Pharmacol. 25 (1990) 495.], and Stebler and Guentert [Pharm. Res. 9 (1992) 1299.]. The pharmacokinetic profile of verapamil in lactating rabbits following endovenous administration is described in the form of a two-compartment model. Moreover, we detected an important milk transfer after endovenous administration of verapamil in lactating rabbits. M/B(obs) was near 15. The classical diffusional models mentioned were not able to predict this extensive transfer of verapamil into rabbit milk. However, when the classical Fleishaker equation was modified and a stepwise regression was carried out, we found that the M/B(obs) value could be predicted using the plasma and milk protein binding.     

Prediction of volume of distribution at steady state in humans: comparison of different approaches

INTRODUCTION: Reasonable prediction of volume of distribution at steady state (Vd(ss)) in humans is required for screening drug candidates, evaluating drug safety, and estimating first-in-human doses. AREAS COVERED: This review summarizes methods for the prediction of human Vd(ss) and tissue plasma partition coefficients (K(p)). The methods reviewed includes allometric scaling, physiologically based models, correlation with animal Vd(ss) and in silico models. The assumptions, equations, input data required, advantages, and limitations of each approach are discussed. Due to the variations among test datasets, some studies have reached inconsistent conclusions. Hence, a comprehensive comparison of various approaches using a large and exhaustive dataset is warranted to address the controversies in human Vd(ss) prediction. EXPERT OPINION: Compared with allometric scaling, the Oie-Tozer method and correlations between human and animal Vd(ss) are more accurate. All the three methods can be used for accurate predictions of human Vd(ss) just prior to first-in-human studies. Although in vivo animal data are not required, tissue composition-based approaches and inter-tissue correlation of K(p) provide reasonable predictive accuracies and are promising for physiologically based pharmacokinetic modeling. The in silico models are most suitable for high-throughput screening of compounds, which are at an early stage of development.     

Prediction of the volumes of distribution of basic drugs in humans based on data from animals

The apparent volume of distribution-after distribution equilibrium and the ratio of distributive tissue volume to the unbound fraction in the tissue (VT/fuT) of 10 weak basic drugs, i.e., chlorpromazine, imipramine, propranolol, disopyramide, lidocaine, quinidine, meperidine, pentazocine, chlorpheniramine, and methacyclin were compared in animal species and humans. In these two parameters, a statistically significant correlation between animals and humans was obtained, when the parameters were plotted on a log-log scale. The correlation coefficient between VT/fuT was significantly higher than that between the apparent volumes of distribution (p less than 0.05). In general, there was little difference between VT/fuT of various basic drugs in animals and that in humans. Prediction of the apparent volume of distribution in humans using animal data of VT/fuT, plasma unbound fraction, blood volume, and blood-to-plasma concentration ratio in humans was successful for most of drugs studied.     

Prediction of the volumes of distribution of basic drugs in humans based on data from animals

The apparent volume of distribution-after distribution equilibrium and the ratio of distributive tissue volume to the unbound fraction in the tissue (V_T/fu_T)of 10 weak basic drugs, i.e., chlorpromazine, imipramine, propranolol, disopyramide, lidocaine, quinidine, meperidine, pentazocine, chlorpheniramine, and methacyclin were compared in animal species and humans. In these two parameters, a statistically significant correlation between animals and humans was obtained, when the parameters were plotted on a log-log scale. The correlation coefficient between V_T/fu_T was significantly higher than that between the apparent volumes of distribution (p<0.05). In general, there was little difference between V_T/fu_T of various basic drugs in animals and that in humans. Prediction of the apparent volume of distribution in humans using animal data of V_T/fu_T,plasma unbound fraction, blood volume, and blood-to-plasma concentration ratio in humans was successful for most of drugs studied.     

Prediction of volume of distribution in preclinical species and humans: application of simplified physiologically based algorithms

1. The present study was aimed at developing simplified physiologically based semi-mechanistic algorithms to predict V_ss and interspecies scaling factors to predict tissue-K_ps which require minimum input parameters, diminish the computing complexity and have better predictability.

2. V_ss of 86 structurally diverse compounds in preclinical species and 27 compounds in humans were predicted using only lung- and muscle-K_p as inputs. Interspecies scaling factor (s) were developed based on fold-differences in individual tissue lipid contents, relative organ blood flow: relative organ weight ratio between two species. Tissue-K_ps were predicted for 34 compounds using the newly developed interspecies scaling factors.

3. The predicted-to-experimental V_ss values for all the 113 compounds was 1.3?±?0.9 with 83% values being within a factor of two. The tissue-K_ps in rat, dog and human were predicted using experimental tissue-K_p data in rodents and interspecies scaling factors and here also, 83% of tissue-K_ps were within two-fold of the experimental values.

4. In conclusion, simplified physiologically based algorithms have been developed to predict both volume of distribution and tissue-K_ps, in which required input parameters as well as computing complexity have been noticeably reduced.

     

Prediction of distribution coefficients from structure. Comparison of calculated and experimental data for various drugs.

The efficiency of the program PrologD to predict distribution coefficients (D) at any pH and pairing ion concentration has been tested using experimental logD values for various drugs measured under standard conditions of buffers and ionic strength. Clonidine derivatives, fluoroquinolones and beta-blockers were included as particular pharmacological classes within the testing data set. Calculations were performed using the three logP estimation options implemented in the program. PrologD proved to be very efficient and can be of great advantage in drug research. Prediction patterns and correlations between experimental and calculated data indicate acceptable results for more than 80% of the data. In addition, comparable studies using the different options permitted suggestions for the more suitable logP estimation method in respect of the particular classes of compounds.