PAMAM dendrimers as mediators of dermal and transdermal drug delivery: a review.

OBJECTIVES: Poly(amidoamine) dendrimers have been widely investigated as potential nanomaterials that can enhance the skin permeation of topically applied drugs. This article reviews the studies that have used dendrimers as penetration enhancers and examines the mechanisms by which enhancement is claimed. KEY FINDINGS: A wide range of studies have demonstrated that, in certain circumstances and for certain drugs, the incorporation of dendrimers into a topically applied formulation can significantly increase the amount of drug passing into and through the skin. In some cases, dendrimers offered little or no enhancement of skin permeation, suggesting that the drug-dendrimer interaction and the selection of a specific dendrimer were central to ensuring optimal enhancement of skin permeation. Significant interactions between dendrimers and other formulation components were also reported in some cases. SUMMARY: Dendrimers offer substantial potential for enhancing drug delivery into and across the skin, putatively by mechanisms that include occlusion and changes to surface tension. However, most of these studies are conducted in vitro and limited progress has been made beyond such laboratory studies, some of which are conducted using membranes of limited relevance to humans, such as rodent skin. Thus, the outcomes and claims of such studies should be treated with caution.

Microneedle-Mediated Permeation Enhancement of Chlorhexidine Digluconate: Mechanistic Insights Through Imaging Mass Spectrometry.

PURPOSE: Chlorhexidine digluconate (CHG) is a first-line antiseptic agent typically applied to the skin as a topical solution prior to surgery due to its efficacy and safety profile. However, the physiochemical properties of CHG limits its cutaneous permeation, preventing it from reaching potentially pathogenic bacteria residing within deeper skin layers. Thus, the utility of a solid oscillating microneedle system, Dermapen®, and a CHG-hydroxyethylcellulose (HEC) gel were investigated to improve the intradermal delivery of CHG. METHODS: Permeation of CHG from the commercial product, Hibiscrub®, and HEC-CHG gels (containing 1% or 4% CHG w/w) was assessed in intact skin, or skin that had been pre-treated with microneedles of different array numbers, using an Franz diffusion cells and Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS). RESULTS: Gels containing 1% and 4% CHG resulted in significantly increased depth permeation of CHG compared to Hibiscrub® (4% w/v CHG) when applied to microneedle pre-treated skin, with the effect being more significant with the higher array number. ToF-SIMS analysis indicated that the depth of dermal penetration achieved was sufficient to reach the skin strata that typically harbours pathogenic bacteria, which is currently inaccessible by Hibiscrub®, and showed potential lateral diffusion within the viable epidermis. CONCLUSIONS: This study indicates that HEC-CHG gels applied to microneedle pre-treated skin may be a viable strategy to improve the permeation CHG into the skin. Such enhanced intradermal delivery may be of significant clinical utility for improved skin antisepsis in those at risk of a skin or soft tissue infection following surgical intervention.

Dendrimer-mediated permeation enhancement of chlorhexidine digluconate: Determination of in vitro skin permeability and visualisation of dermal distribution.

Chlorhexidine digluconate (CHG) is a cationic bisbiguanide used in the UK as the first-line skin antiseptic prior to surgery in the UK due to its favourable efficacy and safety profile, high affinity for skin binding and minimal reports of resistance. Despite this, bacteria remain within deeper skin layers, furrows and appendages that are considered inaccessible to CHG, due to its poor dermal penetration. In this study a third generation, polyamidoamine dendrimer (G3 PAMAM-NH2) was utilised to improve dermal penetration of CHG. A topical gel formulation was optimised to maximise CHG delivery (containing 0.5% gelling agent and 4% drug), followed by drug and dendrimer co-formulation into a commercially viable gel. The gel containing 4% CHG and 1 mM PAMAM dendrimer significantly increased the depth permeation of CHG compared to the commercial benchmark (Hibiscrub®, containing 4% w/v CHG) (p < 0.05). The optimised formulation was further characterised using Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS), which indicated that the depth of dermal penetration achieved was sufficient to reach the skin strata that typically harbours pathogenic bacteria, which is currently inaccessible by commercial CHG formulations. This study therefore indicates that a G3 PAMAM-NH2 dendrimer gel may be viable as a permeation enhancer of CHG, for improved skin antisepsis in those at risk of a skin or soft tissue infection as a result of surgical intervention.

Development of a Gaussian Process - feature selection model to characterise (poly)dimethylsiloxane (Silastic® ) membrane permeation.

OBJECTIVES: The current study aims to determine the effect of physicochemical descriptor selection on models of polydimethylsiloxane permeation. METHODS: A total of 2942 descriptors were calculated for a data set of 77 chemicals. Data were processed to remove redundancy, single values, imbalanced and highly correlated data, yielding 1363 relevant descriptors. For four independent test sets, feature selection methods were applied and modelled via a variety of Machine Learning methods. KEY FINDINGS: Two sets of molecular descriptors which can provide improved predictions, compared to existing models, have been identified. Best permeation predictions were found with Gaussian Process methods. The molecular descriptors describe lipophilicity, partial charge and hydrogen bonding as key determinants of PDMS permeation. CONCLUSIONS: This study highlights important considerations in the development of relevant models and in the construction and use of the data sets used in such studies, particularly that highly correlated descriptors should be removed from data sets. Predictive models are improved by the methodology adopted in this study, notably the systematic evaluation of descriptors, rather than simply using any and all available descriptors, often based empirically on in vitro experiments. Such findings also have clear relevance to a number of other fields.

The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability.

OBJECTIVES: The aim of this study was to use Gaussian process regression (GPR) methods to quantify the effect of experimental temperature (Texp ) and choice of diffusion cell on model quality and performance. METHODS: Data were collated from the literature. Static and flow-through diffusion cell data were separated, and a series of GPR experiments was conducted. The effect of Texp was assessed by comparing a range of datasets where Texp either remained constant or was varied from 22 to 45 °C. KEY FINDINGS: Using data from flow-through diffusion cells results in poor model performance. Data from static diffusion cells resulted in significantly greater performance. Inclusion of data from flow-through cell experiments reduces overall model quality. Consideration of Texp improves model quality when the dataset used exhibits a wide range of experimental temperatures. CONCLUSIONS: This study highlights the problem of collating literature data into datasets from which models are constructed without consideration of the nature of those data. In order to optimise model quality data from only static, Franz-type, experiments should be used to construct the model and Texp should either be incorporated as a descriptor in the model if data are collated from a range of studies conducted at different temperatures.

Antimicrobial efficacy and mechanism of action of poly(amidoamine) (PAMAM) dendrimers against opportunistic pathogens.

The aim of this study was to investigate a range of poly(amidoamine) (PAMAM) dendrimer generations against Gram-positive and Gram-negative skin pathogens and to determine any differences in antimicrobial potency for different generations, characterising how differences in physicochemical properties influence antimicrobial efficacy. A range of tests were carried out, including viable count assays to determine half maximal inhibitory concentration (IC50) values for each dendrimer, membrane integrity studies and an inner membrane permeabilisation assay. This is supported by scanning electron microscopy imaging of the interactions observed between dendrimers and bacteria. The results of this study indicate that the antimicrobial efficacy of native PAMAM dendrimers is dependent on generation, concentration and terminal functionalities, for example, the concentration at 50% growth inhibition (MIC50) (µg/mL), against Staphylococcus aureus was between 26.77 for the G2-PAMAM-NH2 dendrimer and 2.881 for the G5-PAMAM-NH2 dendrimer. There was a strong correlation between membrane disruption and the determined biocidal activity, making it a key contributing mechanism of action. This study demonstrates that selection of the type of PAMAM dendrimer is important as their inherent antimicrobial efficacy varies according to their individual physicochemical properties. This understanding may pave the way for the development of enhanced dendrimer-based antimicrobial formulations and drug-delivery systems.

Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression.

OBJECTIVES: The aim of this study was to investigate how to improve predictions from Gaussian Process models by optimising the model hyperparameters. METHODS: Optimisation methods, including Grid Search, Conjugate Gradient, Random Search, Evolutionary Algorithm and Hyper-prior, were evaluated and applied to previously published data. Data sets were also altered in a structured manner to reduce their size, which retained the range, or 'chemical space' of the key descriptors to assess the effect of the data range on model quality. KEY FINDINGS: The Hyper-prior Smoothbox kernel results in the best models for the majority of data sets, and they exhibited significantly better performance than benchmark quantitative structure-permeability relationship (QSPR) models. When the data sets were systematically reduced in size, the different optimisation methods generally retained their statistical quality, whereas benchmark QSPR models performed poorly. CONCLUSIONS: The design of the data set, and possibly also the approach to validation of the model, is critical in the development of improved models. The size of the data set, if carefully controlled, was not generally a significant factor for these models and that models of excellent statistical quality could be produced from substantially smaller data sets.

Dendrimer pre-treatment enhances the skin permeation of chlorhexidine digluconate: Characterisation by in vitro percutaneous absorption studies and Time-of-Flight Secondary Ion Mass Spectrometry.

Skin penetration and localisation of chlorhexidine digluconate (CHG) within the skin have been investigated in order to better understand and optimise the delivery using a nano polymeric delivery system of this topically-applied antimicrobial drug. Franz-type diffusion cell studies using in vitro porcine skin and tape stripping procedures were coupled with Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) to visualise the skin during various treatments with CHG and polyamidoamine dendrimers (PAMAM). Pre-treatment of the skin with PAMAM dendrimers significantly increased the amount and depth of permeation of CHG into the skin in vitro. The effect observed was not concentration dependant in the range 0.5-10mM PAMAM. This could be important in terms of the efficiency of treatment of bacterial infection in the skin. It appears that the mechanism of enhancement is due to the PAMAM dendrimer disrupting skin barrier lipid conformation or by occluding the skin surface. Franz-type diffusion cell experiments are complimented by the detailed visualisation offered by the semi-quantitative ToF-SIMS method which provides excellent benefits in terms of sensitivity and fragment ion specificity. This allows a more accurate depth profile of chlorhexidine permeation within the skin to be obtained and potentially affords the opportunity to map the co-localisation of permeants with skin structures, thus providing a greater ability to characterise skin absorption and to understand the mechanism of permeation, providing opportunities for new and more effective therapies.

Support vector regression to estimate the permeability enhancement of potential transdermal enhancers.

OBJECTIVES: Searching for chemicals that will safely enhance transdermal drug delivery is a significant challenge. This study applies support vector regression (SVR) for the first time to estimating the optimal formulation design of transdermal hydrocortisone formulations. METHODS: The aim of this study was to apply SVR methods with two different kernels in order to estimate the enhancement ratio of chemical enhancers of permeability. KEY FINDINGS: A statistically significant regression SVR model was developed. It was found that SVR with a nonlinear kernel provided the best estimate of the enhancement ratio for a chemical enhancer. CONCLUSIONS: Support vector regression is a viable method to develop predictive models of biological processes, demonstrating improvements over other methods. In addition, the results of this study suggest that a global approach to modelling a biological process may not necessarily be the best method and that a 'mixed-methods' approach may be best in optimising predictive models.

Simulated drug discovery process to conduct a synoptic assessment of pharmacy students.

OBJECTIVE. To implement and assess a task-based learning exercise that prompts pharmacy students to integrate their understanding of different disciplines. DESIGN. Master of pharmacy (MPharm degree) students were provided with simulated information from several preclinical science and from clinical trials and asked to synthesize this into a marketing authorization application for a new drug. Students made a link to pharmacy practice by creating an advice leaflet for pharmacists. ASSESSMENT. Students' ability to integrate information from different disciplines was evaluated by oral examination. In 2 successive academic years, 96% and 82% of students demonstrated an integrated understanding of their proposed new drug. Students indicated in a survey that their understanding of the links between different subjects improved. CONCLUSION. Simulated drug discovery provides a learning environment that emphasizes the connectivity of the preclinical sciences with each other and the practice of pharmacy.

Distribution and visualisation of chlorhexidine within the skin using ToF-SIMS: a potential platform for the design of more efficacious skin antiseptic formulations.

PURPOSE: In order to increase the efficacy of a topically applied antimicrobial compound the permeation profile, localisation and mechanism of action within the skin must first be investigated. METHODS: Time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to visualise the distribution of a conventional antimicrobial compound, chlorhexidine digluconate, within porcine skin without the need for laborious preparation, radio-labels or fluorescent tags. RESULTS: High mass resolution and high spatial resolution mass spectra and chemical images were achieved when analysing chlorhexidine digluconate treated cryo-sectioned porcine skin sections by ToF-SIMS. The distribution of chlorhexidine digluconate was mapped throughout the skin sections and our studies indicate that the compound appears to be localised within the stratum corneum. In parallel, tape strips taken from chlorhexidine digluconate treated porcine skin were analysed by ToF-SIMS to support the distribution profile obtained from the skin sections. CONCLUSIONS: ToF-SIMS can act as a powerful complementary technique to map the distribution of topically applied compounds within the skin.

An evaluation of the potential of linear and nonlinear skin permeation models for the prediction of experimentally measured percutaneous drug absorption.

UNLABELLED: OBJECTIVES The developments in combinatorial chemistry have led to a rapid increase in drug design and discovery and, ultimately, the production of many potential molecules that require evaluation. Hence, there has been much interest in the use of mathematical models to predict dermal absorption. Therefore, the aim of this study was to test the performance of both linear and nonlinear models to predict the skin permeation of a series of 11 compounds. METHODS: The modelling in this study was carried out by the application of both quantitative structure permeability relationships and Gaussian process-based machine learning methods to predict the flux and permeability coefficient of the 11 compounds. The actual permeation of these compounds across human skin was measured using Franz cells and a standard protocol with high performance liquid chromatography analysis. Statistical comparison between the predicted and experimentally-derived values was performed using mean squared error and the Pearson sample correlation coefficient. KEY FINDINGS: The findings of this study would suggest that the models failed to accurately predict permeation and in some cases were not within two- or three-orders of magnitude of the experimentally-derived values. However, with this set of compounds the models were able to effectively rank the permeants. CONCLUSIONS: Although not suitable for accurately predicting permeation the models may be suitable for determining a rank order of permeation, which may help to select candidate molecules for in-vitro screening. However, it is important to note that such predictions need to take into account actual relative drug candidate potencies.

The application and limitations of mathematical modelling in the prediction of permeability across mammalian skin and polydimethylsiloxane membranes.

OBJECTIVES: Predicting the rate of percutaneous absorption of a drug is an important issue with the increasing use of the skin as a means of moderating and controlling drug delivery. One key feature of this problem domain is that human skin permeability (as K(p)) has been shown to be inherently non-linear when mathematically related to the physicochemical parameters of penetrants. As such, the aims of this study were to apply and evaluate Gaussian process (GP) regression methods to datasets for membranes other than human skin, and to explore how the nature of the dataset may influence its analysis. METHODS: Permeability data for absorption across rodent and pig skin, and artificial membranes (polydimethylsiloxane, PDMS, i.e. Silastic) membranes was collected from the literature. Two quantitative structure-permeability relationship (QSPR) models were used to compare with the GP models. Further performance metrics were computed in terms of all predictions, and a range of covariance functions were examined: the squared exponential (SE), neural network (NNone) and rational quadratic (QR) covariance functions, along with two simple cases of Matern covariance function (Matern3 and Matern5) where the polynomial order is set to 1 and 2, respectively. As measures of performance, the correlation coefficient (CORR), negative log estimated predictive density (NLL, or negative log loss) and mean squared error (MSE) were employed. KEY FINDINGS: The results demonstrated that GP models with different covariance functions outperform QSPR models for human, pig and rodent datasets. For the artificial membranes, GPs perform better in one instance, and give similar results in other experiments (where different covariance parameters produce similar results). In some cases, the GP predictions for some of the artificial membrane dataset are poorly correlated, suggesting that the physicochemical parameters employed in this study might not be appropriate for developing models that represent this membrane. CONCLUSIONS: While the results of this study indicate that permeation across rodent (mouse and rat) and pig skin is, in a statistical sense, similar, and that the artificial membranes are poor replacements of human or animal skin, the overriding issue raised in this study is the nature of the dataset and how it can influence the results, and subsequent interpretation, of any model produced for particular membranes. The size of the datasets, in both absolute and comparative senses, appears to influence model quality. Ideally, to generate viable cross-comparisons the datasets for different mammalian membranes should, wherever possible, exhibit as much commonality as possible.

The application of feature selection to the development of Gaussian process models for percutaneous absorption.

OBJECTIVES: The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. METHODS: Gaussian processes, including automatic relevance detection (GPRARD) methods, were employed to develop models of percutaneous absorption that identified key physicochemical descriptors of percutaneous absorption. Using MatLab software, the statistical performance of these models was compared with single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs). Feature selection methods were used to examine in more detail the physicochemical parameters used in this study. A range of statistical measures to determine model quality were used. KEY FINDINGS: The inherently nonlinear nature of the skin data set was confirmed. The Gaussian process regression (GPR) methods yielded predictive models that offered statistically significant improvements over SLN and QSPR models with regard to predictivity (where the rank order was: GPR > SLN > QSPR). Feature selection analysis determined that the best GPR models were those that contained log P, melting point and the number of hydrogen bond donor groups as significant descriptors. Further statistical analysis also found that great synergy existed between certain parameters. It suggested that a number of the descriptors employed were effectively interchangeable, thus questioning the use of models where discrete variables are output, usually in the form of an equation. CONCLUSIONS: The use of a nonlinear GPR method produced models with significantly improved predictivity, compared with SLN or QSPR models. Feature selection methods were able to provide important mechanistic information. However, it was also shown that significant synergy existed between certain parameters, and as such it was possible to interchange certain descriptors (i.e. molecular weight and melting point) without incurring a loss of model quality. Such synergy suggested that a model constructed from discrete terms in an equation may not be the most appropriate way of representing mechanistic understandings of skin absorption.

Formulation and characterization of a captopril ethyl ester drug-in-adhesive-type patch for percutaneous absorption.

BACKGROUND: The ethyl ester of captopril has been shown to exhibit enhanced permeation across human skin compared to the parent drug. A drug-in-adhesive patch formulation of a captopril ethyl ester was therefore developed for optimum drug release. METHOD: A wide range of transdermal patches were prepared using two commercially available bioadhesive polymers. Investigational screening was conducted on the patches using microscopy, texture profile analysis, and infrared spectroscopy. Drug release profiles of suitable patches were obtained using both polydimethylsiloxane (Silastic) and porcine skin in vitro. RESULTS: Diffusion results across Silastic showed a gradual plateau in flux with increased drug loading that may be attributable to intramolecular interactions while flux across porcine skin was seen to increase with increasing patch thickness and attained a therapeutic level. CONCLUSIONS: This study demonstrated that adhesion and drug loading are significant factors in optimizing a topical patch formulation for the delivery of a captopril prodrug.

The fertility of ram sperm held for 24h at 5 degrees C prior to cryopreservation.

Diluted ram sperm can be held for 24h at 5 degrees C prior to cryopreservation without impacting cryosurvival rates, however, the effects this storage has on subsequent fertility are unknown. These studies were conducted to evaluate the fertility of semen held for 24h (to mimic shipping semen to a cryopreservation center), prior to freezing. Semen from Suffolk rams (n=3 in experiment 1 and n=6 in experiment 2) with initial motility of greater than 70%, were diluted to 200 x 10(6)sperm/mL, in one step, with a Tris-egg yolk-glycerol diluent. In experiment 1, diluted samples were cooled to 5 degrees C over 2h, and then divided. Sperm in one fraction were loaded into 0.5mL straws, frozen (T0) and stored in liquid nitrogen until thawing. Sperm in the second fraction were held at 5 degrees C for 24h (T24) before being frozen. In experiment 2 ejaculates were collected and divided into two fractions. Sperm in one fraction were treated with cholesterol-loaded cyclodextrin (CLC) and sperm in the other served as control. Both fractions were diluted, cooled, and cryopreserved as described in experiment 1. Stage of the estrous cycle was synchronized in ewes (n=196) using controlled internal drug releasing devices (CIDR) for 12d and at CIDR removal each ewe was administered PMSG (500IU in experiment 1 and 350IU in experiment 2) immediately before insemination. Ewes were stratified by age and randomly assigned to one of the semen treatments; experiment 1: Fresh (F), T0, or T24; experiment 2: F, T24, or CLC, and inseminated laparoscopically 56h after CIDR removal. Differences in fertility were detected between experiments, but not for treatments within experiments. Differences in fertility were also observed due to ewe age, with the 3-year-old ewes having the greatest fertility (50.7%) and 6-year-old ewes having the least fertility (9.6%; P<0.05). Differences in the prolificacy rates due to semen treatment were also observed but differences due to ewe age were not detected. Therefore, sperm can be held at 5 degrees C for 24h prior to cryopreservation without altering sperm fertility.

The application of Gaussian processes in the prediction of percutaneous absorption.

OBJECTIVES: The aim was to assess mathematically the nature of a skin permeability dataset and to determine the utility of Gaussian processes in developing a predictive model for skin permeability, comparing it with existing methods for deriving predictive models. METHODS: Principal component analysis was carried out in order to determine the nature of the dataset. MatLab software was used to assess the performance of Gaussian process, single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs) using a range of statistical measures. KEY FINDINGS: Principal component analysis showed that the dataset is inherently non-linear. The Gaussian process model yielded a predictive model that provides a significantly more accurate estimate of skin absorption than previous models, particularly QSPRs (which were consistently worse than Gaussian process or SLN models), and does so across a wider range of molecular properties. Gaussian process models appear particularly capable of providing excellent predictions where previous studies have shown QSPRs to fail, such as where penetrants have high log P and high molecular weight. CONCLUSIONS: A non-linear approach was more appropriate than QSPRs or SLNs for the analysis of the dataset employed herein, as the prediction and confidence values in the prediction given by the Gaussian process are better than with other methods examined. Gaussian process provides a novel way of analysing skin absorption data that is substantially more accurate, statistically robust and reflective of our empirical understanding of skin absorption than the QSPR methods so far applied to skin absorption.

Maternal obesity accelerates fetal pancreatic beta-cell but not alpha-cell development in sheep: prenatal consequences.

Maternal obesity affects offspring weight, body composition, and organ function, increasing diabetes and metabolic syndrome risk. We determined effects of maternal obesity and a high-energy diet on fetal pancreatic development. Sixty days prior to breeding, ewes were assigned to control [100% of National Research Council (NRC) recommendations] or obesogenic (OB; 150% NRC) diets. At 75 days gestation, OB ewes exhibited elevated insulin-to-glucose ratios at rest and during a glucose tolerance test, demonstrating insulin resistance compared with control ewes. In fetal studies, ewes ate their respective diets from 60 days before to 75 days after conception when animals were euthanized under general anesthesia. OB and control ewes increased in body weight by approximately 43% and approximately 6%, respectively, from diet initiation until necropsy. Although all organs were heavier in fetuses from OB ewes, only pancreatic weight increased as a percentage of fetal weight. Blood glucose, insulin, and cortisol were elevated in OB ewes and fetuses on day 75. Insulin-positive cells per unit pancreatic area were 50% greater in fetuses from OB ewes as a result of increased beta-cell mitoses rather than decreased programmed cell death. Lambs of OB ewes were born earlier but weighed the same as control lambs; however, their crown-to-rump length was reduced, and their fat mass was increased. We conclude that increased systemic insulin in fetuses from OB ewes results from increased glucose exposure and/or cortisol-induced accelerated fetal beta-cell maturation and may contribute to premature beta-cell function loss and predisposition to obesity and metabolic disease in offspring.

Effect of maternal fatness on fetal steroids and semi-quantitative real-time PCR expression of receptor genes in sheep.

Sexual differentiation of the brain occurs between d 30 and 70 in the fetal lamb. The objective of this experiment was to determine if maternal fatness affects fetal steroid production and expression of their receptors which may ultimately alter endocrine systems postnatally. Fetuses were collected from ewes fed at either 100% (Control; n=5) or 150% (Fat; n=6) of NRC recommendations from 60 d prior to breeding until collection at 75 d of gestation. Hypothalamic and amygdala neural tissues were collected from twin male/female fetuses. Serum concentrations of testosterone were greater (P<0.001) in male fetuses compared to female fetuses. Further, male fetuses from Fat ewes had greater (P<0.05) serum concentrations of testosterone than male fetuses from Control ewes, but differences in testicular steroidogenic enzyme mRNA were not detected (P=0.18). Quantity of hypothalamic mRNA for estrogen receptor (ER) beta tended (P=0.1) to be influenced by a sex by treatment interaction. Messenger RNA for ER-beta was greater in female fetuses than male fetuses from Control ewes (P=0.05). Although amount of ER-beta mRNA did not differ among male fetuses (P=0.7), amounts tended to be less (P=0.07) in female fetuses from Fat ewes compared to those from Control ewes, and did not differ (P> or =0.8) from male fetuses. Hypothalamic ER-alpha mRNA tended (P=0.1) to be less in fetuses from Fat ewes compared to Control fetuses but was not influenced (P=0.3) by fetal sex or their interaction. Amount of mRNA for hypothalamic progesterone receptor tended (P=0.06) to be greater in male fetuses than female fetuses and tended to be less (P=0.06) in fetuses from Fat ewes than in Control fetuses, but did not differ by any sex by treatment interaction (P=0.6). Hypothalamic RNA for the androgen receptor did not differ by sex, dam nutritional treatment, or the interaction. Likewise, amygdala RNA for the estrogen or androgen receptor did not differ (P> or =0.3) by sex, treatment, or their interaction. Dam fatness appears to decrease the expression of progesterone receptor, ER-alpha, and decrease amount of ER-beta in the female fetuses while increasing circulating concentrations of testosterone in male fetuses. Altered expression of hypothalamic receptor genes by the uterine environment may affect adult responses to stress, sexual behavior and/or the pattern of gonadotropin release in response to gonadal steroids.