Quantitative structure-pharmacokinetic/pharmacodynamic relationships of corticosteroids in man

Dexamethasone Conjugates: Synthetic Approaches and Medical Prospects

Dexamethasone (DEX) is the most commonly prescribed glucocorticoid (GC) and has a wide spectrum of pharmacological activity. However, steroid drugs like DEX can have severe side effects on non-target organs. One strategy to reduce these side effects is to develop targeted systems with the controlled release by conjugation to polymeric carriers. This review describes the methods available for the synthesis of DEX conjugates (carbodiimide chemistry, solid-phase synthesis, reversible addition fragmentation-chain transfer [RAFT] polymerization, click reactions, and 2-iminothiolane chemistry) and perspectives for their medical application as GC drug or gene delivery systems for anti-tumor therapy. Additionally, the review focuses on the development of DEX conjugates with different physical-chemical properties as successful delivery systems in the target organs such as eye, joint, kidney, and others. Finally, polymer conjugates with improved transfection activity in which DEX is used as a vector for gene delivery in the cell nucleus have been described.

Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids

Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.

Application of a hemodialysis clearance prediction model using quantitative structure-pharmacokinetic relationship analysis

Hemodialysis (HD) is a method used to remove biogenic substances or blood components that cause disease and some drugs used by patients to treat their diseases. Therefore, dosing schedule must be planned according to HD clearance (CL_HD ) when medical treatment is provided to patients receiving HD. We aimed to clarify the physical properties (eg, octanol-water partition coefficient and molecular electronegativity) or pharmacokinetic parameters (eg, volume of distribution) of compounds affecting CL_HD and to construct a mathematical model to predict CL_HD . The analysis covered individual CL_HD data for nine compounds from the literature. The molecular descriptors which are physical properties or pharmacokinetic parameters were calculated using the structural formula of each compound, and searched for factors related to CL_HD among the calculated 148 molecular descriptors. Nonlinear mixed-effects model analysis with CL_HD as objective variable and molecular descriptors as explanatory variable was conducted to examine the factor affecting CL_HD and develop a model for predicting CL_HD . The logarithm of the brain/blood partition coefficient was detected as a factor affecting CL_HD . The predictive accuracy of CL_HD using the constructed mathematical model with the logarithm of the brain/blood partition coefficient as explanatory variable was adequate.

Calculated Log D Is Inversely Correlated With Select Camptothecin Clearance and Efficacy in Colon Cancer Xenografts

Quantitative structure-property relationships are often derived to identify molecular determinants of drug potency and facilitate drug design. However, compound activity is typically based on in vitro bioassays, and the influence of physicochemical properties on pharmacokinetic/pharmacodynamic (PK/PD) behavior is not considered. Here, we integrate PK/PD and quantitative structure-property relationship modeling to evaluate the role of lipophilicity in camptothecin antitumor responses in colon cancer xenografts. Drug exposure and tumor growth profiles for 5 camptothecins were extracted from the literature. A PK/PD model with time-dependent transduction was developed, which characterized PK and tumor growth inhibition. Correlations between drug lipophilicity (log D), in vitro potency (IC50), and in vivo efficacy and systemic clearance parameters were tested. Models were qualified using leave-one-out cross-validation. Efficacy and clearance of analogs decreased linearly with increasing log D values; efficacy exhibiting a steeper decline relative to clearance. Cross-validated R(2) for predicting in vivo efficacy was 0.55 and 0.18 using log D and in vitro IC50 as the descriptors. Lipophilicity may represent a better predictor of in vivo efficacy than in vitro IC50 measurements for camptothecins. The identified relationships between efficacy, clearance, and lipohilicity may help guide development of new camptothecin analogs and delivery systems with improved pharmacologic profiles.

Inhaled corticosteroids: potency, dose equivalence and therapeutic index

Glucocorticosteroids are a group of structurally related molecules that includes natural hormones and synthetic drugs with a wide range of anti-inflammatory potencies. For synthetic corticosteroid analogues it is commonly assumed that the therapeutic index cannot be improved by increasing their glucocorticoid receptor binding affinity. The validity of this assumption, particularly for inhaled corticosteroids, has not been fully explored. Inhaled corticosteroids exert their anti-inflammatory activity locally in the airways, and hence this can be dissociated from their potential to cause systemic adverse effects. The molecular structural features that increase glucocorticoid receptor binding affinity and selectivity drive topical anti-inflammatory activity. However, in addition, these structural modifications also result in physicochemical and pharmacokinetic changes that can enhance targeting to the airways and reduce systemic exposure. As a consequence, potency and therapeutic index can be correlated. However, this consideration is not reflected in asthma treatment guidelines that classify inhaled corticosteroid formulations as low-, mid- and high dose, and imbed a simple dose equivalence approach where potency is not considered to affect the therapeutic index. This article describes the relationship between potency and therapeutic index, and concludes that higher potency can potentially improve the therapeutic index. Therefore, both efficacy and safety should be considered when classifying inhaled corticosteroid regimens in terms of dose equivalence. The historical approach to dose equivalence in asthma treatment guidelines is not appropriate for the wider range of molecules, potencies and device/formulations now available. A more robust method is needed that incorporates pharmacological principles.

Prediction of steady-state volume of distribution of acidic drugs by quantitative structure-pharmacokinetics relationships

The volume of distribution (VD) is one of the most important pharmacokinetic parameters of drugs. The present study employs quantitative structure-pharmacokinetics relationships (QSPkR) to derive models for VD prediction of acidic drugs. The steady-state volume of distribution (VD(ss)) values of 132 acidic drugs were collected, the chemical structures were described by 178 molecular descriptors, and QSPkR models were derived after variable selection by genetic algorithm and stepwise regression. Models were validated by cross-validation procedures and external test set. According to the molecular descriptors selected as the most predictive for VD(ss), the presence of seven- and nine-member cycles, atom type P(5+), SH groups, and large nonionized substituents increase the VD(ss), whereas atom types S(2+) and S(4+) and polar ionized substituents decrease it. Cross-validation and external validation studies on the QSPkR models derived in the present study showed good predictive ability with mean fold error values ranging from 1.58 (cross-validation) to 2.25 (external validation). The model performance is comparable to more complicated methods requiring in vitro or in vivo experiments and superior to the existing QSPkR models concerning acidic drugs. Apart from the prediction of VD in human, present models are also useful as a curator of available pharmacokinetic databases.

Population pharmacokinetics of landiolol hydrochloride in healthy subjects

Landiolol hydrochloride is a newly developed cardioselective, ultra short-acting beta(1)-adrenergic receptor blocking agent used for perioperative arrhythmia control. The objective of this study was to characterize the population pharmacokinetics of landiolol hydrochloride in healthy male subjects. A total of 420 blood concentration data points collected from 47 healthy male subjects were used for the population pharmacokinetic analysis. NONMEM was used for population pharmacokinetic analysis. In addition, the final pharmacokinetic model was evaluated using a bootstrap method and a leave-one-out cross validation method. The concentration time course of landiolol hydrochloride was best described by a two-compartment model with lag time. The final parameters were total body clearance (CL: 36.6 mL/min/kg), distribution volume of the central compartment (V1: 101 mL/kg), inter-compartmental clearance (16.1 mL/min/kg), distribution volume of the peripheral compartment (55.6 mL/kg), and lag time (0.82 min). The inter-individual variability in the CL and V1 were 21.8% and 46.3%, respectively. The residual variability was 22.1%. Model evaluation by the two different methods indicated that the final model was robust and parameter estimates were reasonable. The population pharmacokinetic model for landiolol hydrochloride in healthy subjects was developed and was shown to be appropriate by both bootstrap and leave-one-out cross validation methods.

Structure-activity relationships and quantitative structure-activity relationships for breast cancer resistance protein (ABCG2)

Breast cancer resistance protein (ABCG2), the newest ABC transporter, was discovered independently by three groups in the late 1990s. ABCG2 is widely distributed in the body with expression in the brain, intestine, and liver, among others. ABCG2 plays an important role by effluxing drugs at the blood-brain, blood-testis, and maternal-fetal barriers and in the efflux of xenobiotics at the small intestine and kidney proximal tubule brush border and liver canalicular membranes. ABCG2 transports a wide variety of substrates including HMG-CoA reductase inhibitors, antibiotics, and many anticancer agents and is one contributor to multidrug resistance in cancer cells. Quantitative structure-activity relationship (QSAR) models and structure-activity relationships (SARs) are often employed to predict ABCG2 substrates and inhibitors prior to in vitro and in vivo studies. QSAR models correlate in vivo biological activity to physicochemical properties of compounds while SARs attempt to explain chemical moieties or structural features that contribute to or are detrimental to the biological activity. Most ABCG2 datasets available for in silico modeling are comprised of congeneric series of compounds; the results from one series usually cannot be applied to another series of compounds. This review will focus on in silico models in the literature used for the prediction of ABCG2 substrates and inhibitors.

Prediction of biliary excretion in rats and humans using molecular weight and quantitative structure-pharmacokinetic relationships

The aims were (1) to evaluate the molecular weight (MW) dependence of biliary excretion and (2) to develop quantitative structure-pharmacokinetic relationships (QSPKR) to predict biliary clearance (CL(b)) and percentage of administered dose excreted in bile as parent drug (PD(b)) in rats and humans. CL(b) and PD(b) data were collected from the literature for rats and humans. Receiver operating characteristic curve analysis was utilized to determine whether a MW threshold exists for PD(b). Stepwise multiple linear regression (MLR) was used to derive QSPKR models. The predictive performance of the models was evaluated by internal validation using the leave-one-out method and external test groups. A MW threshold of 400 Da was determined for PD(b) for anions in rats, while 475 Da was the cutoff for anions in humans. MW thresholds were not present for cations or cations/neutral compounds in either rats or humans. The QSPKR model for human CL(b) showed a significant correlation (R (2) = 0.819) with good prediction performance (Q (2) = 0.722). The model was further assessed using a test group, yielding a geometric mean fold-error of 2.68. QSPKR models with significant correlation and good predictability were also developed for CL(b) in rats and PD(b) data for anions or cation/neutral compounds in rats and humans. Both CL(b) and PD(b) data were further evaluated for subsets of MRP2 or P-glycoprotein substrates, and significant relationships were derived. QSPKR models were successfully developed for biliary excretion of non-congeneric compounds in rats and humans, providing a quantitative prediction of biliary clearance of compounds.

Glucocorticoid receptor binding: a biphasic dependence on molecular size as revealed by the bilinear LinBiExp model

For corticosteroids, receptor-binding affinity (RBA) at the glucocorticoid receptor (GR) is a major determinant of therapeutic potential. Here, the results of a comprehensive quantitative analysis of relative RBA (rRBA) data obtained from more than a hundred active structures are reported. Because of a clear biphasic size-dependence, the recently introduced linearized biexponential (LinBiExp) model provided very good fit: for steroids that satisfy the main binding criteria at the GR, it accounts for close to 80% of the variability in the free energy of binding DeltaG0 (or logrRBA) data by using only two descriptors: calculated molecular volume and an indicator variable for the presence of 6alpha/9alpha-halogen or cyclic 16,17-acetal moieties. Accordingly, binding is strongest for corticosteroids close to an ideal size that is large enough to provide as large nonspecific (van der Waals-type) interactions as possible, but is not too large to have difficulty fitting due to size-limitations at the binding site. Binding affinity is dramatically increased by 6alpha- or 9alpha-halogenation or introduction of a cyclic 16,17-acetal moiety (in average, about 7-fold), but there is no significant increase after the first substitution. Known highly active glucocorticoids, such as betamethasone 17-monopropionate, fluticasone propionate, or mometasone furoate, indeed satisfy both of these criteria. For small-enough structures, the obtained size-dependency (slope) of the free energy of binding suggest that, as long as only nonspecific interactions are involved, addition of a methylene-sized non-hydrogen atom to the ligand structure increases DeltaG0 on average by about 1.5 kJ/mol, corresponding to an almost doubling of the binding affinity.

Pharmacokinetics of methylprednisolone after intravenous and intramuscular administration in rats

Methylprednisolone (MPL) pharmacokinetics was examined in adrenalectomized (ADX) and normal rats to assess the feasibility of intramuscular (i.m.) dosing for use in pharmacodynamic studies. Several study phases were pursued. Parallel group studies were performed in normal and ADX rats given 50 mg/kg MPL (i.v. or i.m.) and blood samples were collected up to 6 h. Data from studies where normal rats were dosed with 50 mg/kg MPL i.m. and killed over either 6 or 96 h were combined to determine muscle site and plasma MPL concentrations. Lastly, ADX rats were dosed with 50 mg/kg MPL i.m. and killed over 18 h to assess hepatic tyrosine aminotransferase (TAT) dynamics. MPL exhibited bi-exponential kinetics after i.v. dosing with a terminal slope of 2.1 h(-1). The i.m. drug was absorbed slowly with two first-order absorption rate constants, 1.26 and 0.219 h(-1) indicating flip-flop kinetics with overall 50% bioavailability. The kinetics of MPL at the injection site exhibited slow, dual absorption rates. Although i.m. MPL showed lower bioavailability compared with other corticosteroids in rats, TAT dynamics revealed similar i.m. and i.v. response profiles. The more convenient intramuscular dosing can replace the i.v. route without causing marked differences in pharmacodynamics.

The role of mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modelling in translational research of biologics

Lack of predictability of clinical efficacy and safety is an important problem facing pharmaceutical research today. Translational PK-PD has the ability to integrate data generated from diverse test platforms during discovery and development in a mechanistic framework. Therefore, successful implementation of translational PK-PD modelling and simulation early in the development cycle could have a substantial impact on overall efficiency and success of pharmaceutical research. Three case studies are presented, which outline successful implementation of the translational PK-PD methodology in the rational development of biotherapeutics across various stages of discovery and development. Emerging developments within the field are also discussed.

Applying pattern recognition methods plus quantum and physico-chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids

The great cost associated with the development of new anabolic-androgenic steroid (AASs) makes necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, quantum, and physicochemical molecular descriptors, plus linear discriminant analysis (LDA) were used to analyze the anabolic/androgenic activity of structurally diverse steroids and to discover novel AASs, as well as also to give a structural interpretation of their anabolic-androgenic ratio (AAR). The obtained models are able to correctly classify 91.67% (86.27%) of the AASs in the training (test) sets, respectively. The results of predictions on the 10% full-out cross-validation test also evidence the robustness of the obtained model. Moreover, these classification functions are applied to an "in house" library of chemicals, to find novel AASs. Two new AASs are synthesized and tested for in vivo activity. Although both AASs are less active than some commercially AASs, this result leaves a door open to a virtual variational study of the structure of the two compounds, to improve their biological activity. The LDA-assisted QSAR models presented here, could significantly reduce the number of synthesized and tested AASs, as well as could increase the chance of finding new chemical entities with higher AAR.

Quantitative structure-pharmacokinetic/pharmacodynamic relationships

Quantitative structure-activity relationships have long been considered a vital component of drug discovery and development, providing insight into the role of molecular properties in the biological activity of similar and unrelated compounds. Recognition that in vitro bioassay and/or pre-clinical activity are insufficient for anticipating which compounds are suitable leads for further development has shifted the focus toward integrated pharmacokinetic (PK) and pharmacodynamic (PD) processes. Over the last decade, considerable progress has been made in constructing empirical and mechanistic quantitative structure-PK relationships (QSPKR), as well as diverse mechanism-based pharmacodynamic models of drug effects. In this review, traditional and contemporary approaches to developing QSPKR models are discussed, along with selected examples of attempts to couple QSPKR and pharmacodynamic models to anticipate the intensity and time-course of the pharmacological effects of new or related compounds, or quantitative structure-pharmacodynamic relationships modeling. Such models are in accordance with the goals of systems biology and the ideal of designing drugs and delivery systems from first principles.

General Linearized Biexponential Model for QSAR Data Showing Bilinear-Type Distribution

A major impediment of many QSAR-type analyses is that the data show a maximum or minimum and can no longer be adequately described by linear functions that provide unrivaled simplicity and usually give good description over more restricted ranges. Here, a general linearized biexponential (LinBiExp) model is proposed that can adequately describe data showing bilinear-type distribution as a function of not just often-employed lipophilicity descriptors (e.g., log P) but as a function of any descriptor (e.g., molecular volume). Contrary to Hansch-type parabolic models, LinBiExp allows the natural extension of linear models and fitting of asymmetrical data. It is also more general and intuitive than Kubinyi's model as it has a more natural functional form. It was obtained by a differential equation-based approach starting from very general assumptions that cover both static equilibria and first-order kinetic processes and that involve abstract processes through which the concentration of the compound of interest in an assumed "effect" compartment is connected to its "external" concentration. Physicochemical aspects placing LinBiExp within the framework of linear free energy relationship (LFER) approaches are presented together with illustrative applications in various fields such as toxicity, antimicrobial activity, anticholinergic activity, and glucocorticoid receptor binding.

truPK – human pharmacokinetic models for quantitative ADME prediction

The use of in silico prediction of absorption, distribution, metabolism and excretion (ADME) properties is gaining acceptance as a useful assessment tool for early identification of likely drug candidate failures. However, it has been difficult to locate reliable models for the prediction of human pharmacokinetics (PK) in silico Currently available methods for estimating ADME and toxicity properties, such as in vitro and animal models, are not very predictive of what is observed in the clinic. Existing in silico ADME prediction tools concentrate on physicochemical properties, such as solubility, log P, rule-of-five compliance, Caco-2 permeability, blood-brain barrier and so on, or only classify drug-like candidates as 'poor', 'medium' or 'good' for a PK parameter, without ascribing values. Although physiology-based pharmacokinetic -models can predict ADME properties, they rely on using various measured properties as input for better accuracy. Strand Genomics has developed a tool, truPK, that predicts the properties of a molecule (bioavailability, protein binding, volume of distribution, elimination half-life and absorption rate) that affect its dose and dose frequency in humans. truPK's five models built using sophisticated machine methods have predicted with > 75% accuracies in external validation sets.

Quantitative structure-activity relationship and quantitative structure-pharmacokinetics relationship of 1,4-dihydropyridines and pyridines as multidrug resistance modulators

PURPOSE

The aim of this study was to develop quantitative structure-activity/pharmacokinetic relationships (QSAR/QSPKR) for a series of synthesized 1,4-dihydropyridines (DHPs) and pyridines as P-glycoprotein (P-gp) inhibitors.

METHODS

Molecular descriptors of test compounds were generated by 3D molecular modeling using SYBYL and KowWin programs. Forward inclusion coupled with multiple linear regression (MLR) was used to derive a QSAR equation for Ca2+ channel binding. A multivariate statistical technique, partial least square (PLS) regression, was applied to derive a QSAR model for P-gp inhibition and QSPKR models. Cross-validation using the "leave-one-out" method was performed to evaluate the predictive performance of models.

RESULTS

For Ca2+ channel binding, the MLR equation indicated a good correlation between observed and predicted values (R2 = 0.90), and cross-validation confirmed the predictive ability of the model (Q2 = 0.67). For P-gp reversal, the model obtained by PLS could account for most of the variation in P-gp inhibition (R2 = 0.76) with fair predictive performance (Q2 = 0.62). Nine structurally related 1,4-DHP drugs were used for QSPKR analysis. The models could explain the majority of the variation in clearance (R2 = 0.90), and cross-validation confirmed the prediction ability (Q2 = 0.69).

CONCLUSION

QSAR/QSPKR models were developed, and the QSAR models were capable of identifying synthesized 1,4-DHPs and pyridines with potent P-gp inhibition and reduced Ca2+ channel binding. The QSPKR models provide insight into the contribution of electronic, steric, and lipophilic factors to the clearance of DHPs.

Betamethasone pharmacokinetics after two prodrug formulations in sheep: implications for antenatal corticosteroid use

Maternal administration of betamethasone to enhance fetal lung maturation for women who threaten preterm labor is common clinical practice. However, recommendations regarding the choice of betamethasone formulations for perinatal use are vague. The disposition of betamethasone from two commonly used antenatal formulations is poorly understood. We therefore designed a study to capture the true pharmacokinetic profiles of betamethasone from these fast acting and dual-release formulations. Betamethasone in sheep plasma was measured by a newly designed, highly sensitive liquid chromatography/tandem mass spectrometry assay after intramuscular injection (n = 4) of 0.25 mg/kg betamethasone phosphate and 0.5 mg/kg betamethasone phosphate/acetate formulations. Compartmental modeling was performed using the ADAPT II program. Betamethasone pharmacokinetics could be captured for 24 h for the phosphate and for 5 days for the phosphate/acetate formulations. The phosphate formulation profile had the appearance of a traditional Bateman function with a terminal half-life of 4 h, whereas the phosphate/acetate formulation produced a biexponential decline with a terminal half-life of 14 h. The latter is much longer than is commonly reported and has been missed in the literature due to assay limitations. Extrapolations to humans indicate that although both formulations might have similar therapeutic indices, the dual formulation might be associated with a lower safety profile. In light of this newly identified long terminal half-life for the betamethasone dual formulation, dosing practices for betamethasone in pregnancy need to be reassessed.

Pharmacokinetics and pharmacodynamics of systemically administered glucocorticoids

Glucocorticoids have pleiotropic effects that are used to treat diverse diseases such as asthma, rheumatoid arthritis, systemic lupus erythematosus and acute kidney transplant rejection. The most commonly used systemic glucocorticoids are hydrocortisone, prednisolone, methylprednisolone and dexamethasone. These glucocorticoids have good oral bioavailability and are eliminated mainly by hepatic metabolism and renal excretion of the metabolites. Plasma concentrations follow a biexponential pattern. Two-compartment models are used after intravenous administration, but one-compartment models are sufficient after oral administration.The effects of glucocorticoids are mediated by genomic and possibly nongenomic mechanisms. Genomic mechanisms include activation of the cytosolic glucocorticoid receptor that leads to activation or repression of protein synthesis, including cytokines, chemokines, inflammatory enzymes and adhesion molecules. Thus, inflammation and immune response mechanisms may be modified. Nongenomic mechanisms might play an additional role in glucocorticoid pulse therapy. Clinical efficacy depends on glucocorticoid pharmacokinetics and pharmacodynamics. Pharmacokinetic parameters such as the elimination half-life, and pharmacodynamic parameters such as the concentration producing the half-maximal effect, determine the duration and intensity of glucocorticoid effects. The special contribution of either of these can be distinguished with pharmacokinetic/pharmacodynamic analysis. We performed simulations with a pharmacokinetic/pharmacodynamic model using T helper cell counts and endogenous cortisol as biomarkers for the effects of methylprednisolone. These simulations suggest that the clinical efficacy of low-dose glucocorticoid regimens might be increased with twice-daily glucocorticoid administration.

Inhaled corticosteroids in childhood asthma: long-term effects on growth and adrenocortical function

Inhaled corticosteroids (ICS) are the most potent of all the available inhaled treatments, and are effective medications for long-term control of asthma. However, their use in children is limited by the risk of systemic adverse effects. Although results reported in the literature on the adverse effects of ICS are conflicting and often restricted to a small number of cases with a limited follow-up, most of them show an early decrease in growth velocity without significant influence on final adult height. Partial adrenal suppression has also been demonstrated in children treated with ICS for more than 2 months. Only children with mild persistent, moderate, or severe asthma not controlled by non-corticosteroid drugs should be treated with ICS for long periods. The dose of ICS must be individually adjusted to minimize the possible adverse effects on growth, and all children with asthma receiving long-term treatment with ICS must be regularly evaluated for growth impairment, which may necessitate dose reduction or drug replacement.

Relative immunosuppressive potency of therapeutic corticosteroids measured by whole blood lymphocyte proliferation

The in vitro immunosuppressive potency of several therapeutic corticosteroids was investigated using a whole-blood lymphocyte proliferation assay. The selected steroids included beclomethasone dipropionate (BDP), betamethasone (BET), budesonide (BUD), dexamethasone (DEX), flunisolide (FLU), fluticasone propionate (FTP), hydrocortisone (HC), methylprednisolone (MPL), prednisolone (PNL), and triamcinolone acetonide (TAA). The ability of phytohemagglutinin to stimulate lymphocytes in whole blood samples obtained from six drug-free male volunteers was tested against drug concentrations ranging from approximately 0.2 to 2000 nM. Data analysis was conducted employing an inhibitory Hill function and nonlinear regression analysis in ADAPT II. The order of drug concentrations producing 50% inhibition (IC(50)) from highest to lowest was HC > PNL > MPL > BDP > BET > DEX > TAA > FLU > BUD > FTP. These IC(50) values are an inverse measure of drug potency, with FTP representing the most potent corticosteroid examined. A linear correlation was found between the log-transformed 1/IC(50) values and the log of the relative receptor affinity (RRA) values obtained from the literature (r(2) = 0.95). These results extend previous studies to include relatively new corticosteroids and further support the use of RRA values as an indirect measure of relative in vitro immunosuppressive potency.

Integrated QSPR--pharmacodynamic model of genomic effects of several corticosteroids

The results from a quantitative structure-property relationship (QSPR) model was integrated into a fifth-generation pharmacokinetic/pharmacodynamic (PK/PD) model of corticosteroid receptor/gene-mediated effects. The proposed model was developed using previously reported tyrosine aminotransferase (TAT) activity data following a 50 mg/kg intravenous dose of methylprednisolone in male adrenalectomized (ADX) rats. Induced TAT activity is a classical measure of corticosteroid genomic effects and the typical time course shows an initial lag-time, a slow rise to peak response, and a gradual return toward baseline values. The TAT activity profiles were subsequently predicted for two additional steroids (dexamethasone and hydrocortisone), which were confirmed experimentally. Two groups of male ADX Wistar rats (n = 18 each) were given either 0.1 mg/kg dexamethasone or 50 mg/kg hydrocortisone by penile vein injections. Plasma drug concentrations and liver TAT activity were measured at various time points. Baseline TAT activity was significantly lower in this study as compared to previous reports. Model simulations well captured the pharmacodynamic data once initial conditions were corrected for observed baseline values. Additional TAT profiles reported in the literature for prednisolone were also reasonably predicted using the final model. This study serves as a demonstration of how in vitro pharmacologic data and QSPR modeling results may be incorporated into existing mechanistic PK/PD models to anticipate the effects of other chemically related compounds.