Human membrane transporter database: a Web-accessible relational database for drug transport studies and pharmacogenomics

Breast cancer, placenta and pregnancy

BACKGROUND

Breast cancer is one of the most frequently diagnosed malignancies during pregnancy. Tumours often present characteristics of high malignancy and are hormone receptor negative/HER2 positive or triple negative. In general, pregnancy, including the postpartum period, is associated with a transiently increased risk of developing breast cancer but followed by a long-lasting protective period. Placental metastases are very rare and, thus far, breast cancer metastases in the foetal compartment have not been described. To discuss these apparently contradictory observations, this narrative review resumes immunological and hormonal alterations during pregnancy potentially affecting breast cancer risk as well as tumour growth and behaviour.

OBSERVATIONS

Upregulation of breast cancer-associated genes involved in immunological and reproductive processes has been observed in parous women and is potentially responsible for a transiently increased risk in pregnancy. In contrast, maternal immunisation and immunoglobulin production against antigens expressed on trophoblast cells, such as specific glycosylation patterns of mucin-1 or RCAS1-associated truncated glycans, seem to prevent breast cancer development in later years. Animal and human studies indicate that T cells are involved in these processes. Several placenta-derived factors, especially kisspeptin, have direct anti-tumour effects. The pregnancy-related increase of estrogen, progesterone, and other hormones influence growth and characteristics of breast cancer while the role of further placenta-secreted factors is still controversially discussed.

CONCLUSION

Several factors and cells are involved in altered breast cancer risk during and after pregnancy and have potential for developing novel treatment strategies in future.

Pharmacokinetic aspects of retinal drug delivery

Drug delivery to the posterior eye segment is an important challenge in ophthalmology, because many diseases affect the retina and choroid leading to impaired vision or blindness. Currently, intravitreal injections are the method of choice to administer drugs to the retina, but this approach is applicable only in selected cases (e.g. anti-VEGF antibodies and soluble receptors). There are two basic approaches that can be adopted to improve retinal drug delivery: prolonged and/or retina targeted delivery of intravitreal drugs and use of other routes of drug administration, such as periocular, suprachoroidal, sub-retinal, systemic, or topical. Properties of the administration route, drug and delivery system determine the efficacy and safety of these approaches. Pharmacokinetic and pharmacodynamic factors determine the required dosing rates and doses that are needed for drug action. In addition, tolerability factors limit the use of many materials in ocular drug delivery. This review article provides a critical discussion of retinal drug delivery, particularly from the pharmacokinetic point of view. This article does not include an extensive review of drug delivery technologies, because they have already been reviewed several times recently. Instead, we aim to provide a systematic and quantitative view on the pharmacokinetic factors in drug delivery to the posterior eye segment. This review is based on the literature and unpublished data from the authors' laboratory.

Metrabase: a cheminformatics and bioinformatics database for small molecule transporter data analysis and (Q)SAR modeling

ABSTRACT

Both metabolism and transport are key elements defining the bioavailability and biological activity of molecules, i.e. their adverse and therapeutic effects. Structured and high quality experimental data stored in a suitable container, such as a relational database, facilitates easy computational processing and thus allows for high quality information/knowledge to be efficiently inferred by computational analyses. Our aim was to create a freely accessible database that would provide easy access to data describing interactions between proteins involved in transport and xenobiotic metabolism and their small molecule substrates and modulators. We present Metrabase, an integrated cheminformatics and bioinformatics resource containing curated data related to human transport and metabolism of chemical compounds. Its primary content includes over 11,500 interaction records involving nearly 3,500 small molecule substrates and modulators of transport proteins and, currently to a much smaller extent, cytochrome P450 enzymes. Data was manually extracted from the published literature and supplemented with data integrated from other available resources. Metrabase version 1.0 is freely available under a CC BY-SA 4.0 license at http://www-metrabase.ch.cam.ac.uk.

Human transporter database: comprehensive knowledge and discovery tools in the human transporter genes

Transporters are essential in homeostatic exchange of endogenous and exogenous substances at the systematic, organic, cellular, and subcellular levels. Gene mutations of transporters are often related to pharmacogenetics traits. Recent developments in high throughput technologies on genomics, transcriptomics and proteomics allow in depth studies of transporter genes in normal cellular processes and diverse disease conditions. The flood of high throughput data have resulted in urgent need for an updated knowledgebase with curated, organized, and annotated human transporters in an easily accessible way. Using a pipeline with the combination of automated keywords query, sequence similarity search and manual curation on transporters, we collected 1,555 human non-redundant transporter genes to develop the Human Transporter Database (HTD) (http://htd.cbi.pku.edu.cn). Based on the extensive annotations, global properties of the transporter genes were illustrated, such as expression patterns and polymorphisms in relationships with their ligands. We noted that the human transporters were enriched in many fundamental biological processes such as oxidative phosphorylation and cardiac muscle contraction, and significantly associated with Mendelian and complex diseases such as epilepsy and sudden infant death syndrome. Overall, HTD provides a well-organized interface to facilitate research communities to search detailed molecular and genetic information of transporters for development of personalized medicine.

Placental ABC transporters, cellular toxicity and stress in pregnancy

The human placenta, in addition to its roles as a nutrient transfer and endocrine organ, functions as a selective barrier to protect the fetus against the harmful effects of exogenous and endogenous toxins. Members of the ATP-binding cassette (ABC) family of transport proteins limit the entry of xenobiotics into the fetal circulation via vectorial efflux from the placenta to the maternal circulation. Several members of the ABC family, including proteins from the ABCA, ABCB, ABCC and ABCG subfamilies, have been shown to be functional in the placenta with clinically significant roles in xenobiotic efflux. However, recent findings suggest that these transporters also protect placental tissue by preventing the cellular accumulation of cytotoxic compounds such as lipids, sterols and their derivatives. Such protective functions are likely to be particularly important in pregnancies complicated by inflammatory or oxidative stress, where the generation of toxic metabolites is enhanced. For example, ABC transporters have been shown to protect against the harmful effects of hypoxia and oxidative stress through increased expression and efflux of oxysterols and glutathione conjugated xenobiotics. However, this protective capacity may be diminished in response to the same stressors. Several studies in primary human trophoblast cells and animal models have demonstrated decreased expression and activity of placental ABC transporters with inflammatory, oxidative or metabolic stress. Several clinical studies in pregnancies complicated by inflammatory conditions such as preeclampsia and gestational diabetes support these findings, although further studies are required to determine the clinical relevance of the relationships between placental ABC transporter expression and activity, and placental function in stressed pregnancies. Such studies are necessary to fully understand the consequences of pregnancy disorders on placental function and viability in order to optimise pregnancy care and maximise fetal growth and health.

Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions

A human alveolar macrophage genome-scale metabolic reconstruction was reconstructed from tailoring a global human metabolic network, Recon 1, by using computational algorithms and manual curation.

A genome-scale host–pathogen network of the human alveolar macrophage and Mycobacterium tuberculosis is presented. This involved integrating two genome-scale network reconstructions.

The reaction activity and gene essentiality predictions of the host–pathogen model represent a more accurate depiction of infection.

Integration of high-throughput data into a host-pathogen model followed by systems analysis was performed in order to elucidate major metabolic differences under different types of M. tuberculosis infection.

Mycobacterium tuberculosis (M. tb) is an insidious and highly persistent pathogen that affects one-third of the world's population (WHO, 2009). Metabolism is foundational to M. tb's infection ability and the ensuing host–pathogen interactions. In addition, M. tb has a heterogeneous clinical presentation and can infect virtually every tissue. Depending on the location of the infection, different metabolic pathways are active and inactive in both the host and pathogen cells. In this study, we sought to model the host–pathogen interactions of the human alveolar macrophage and M. tb as well as detail the metabolic differences in specific infection types using genome-scale metabolic reconstructions (Figure 4A).

Genome-scale metabolic reconstructions are knowledge bases of all known metabolic reactions of a given organism. Reconstructions have been shown to elucidate the mechanistic genotype-to-phenotype relationship through the integration of high-throughput and physiological data (Oberhardt et al, 2009). Genome-scale reconstructions are converted into mathematical models under the constraints-based reconstruction and analysis (COBRA) platform (Becker et al, 2007). COBRA models use network stoichiometry and steady-state mass balances to define a solution space of potential flux states that a network can take. Thus, the COBRA approach does not require kinetic parameters.

Recently, the global human metabolic network, Recon 1, has been reconstructed (Duarte et al, 2007). To understand the metabolic host–pathogen integrations of M. tb with its human host, we first tailored the global human metabolic network into a cell-specific metabolic reconstruction of the human alveolar macrophage. This was carried out using established computational algorithms (Becker and Palsson, 2008; Shlomi et al, 2008) and manual curation to confirm the included and excluded reactions. The human alveolar macrophage reconstruction, iAB-AMØ-1410, accounts for 1410 genes, 3012 intracellular reactions, and 2572 metabolites (Figure 4C). iAB-AMØ-1410 was able to accurately predict maximum ATP and NO production rates obtained from experimental data (Griscavage et al, 1993; Newsholme et al, 1999).

The second step to studying host–pathogen interactions was integration of the human alveolar macrophage reconstruction with an existing genome-scale metabolic model of M. tb, iNJ661 (Jamshidi and Palsson, 2007). Interfacial constraints were set to create a phagosomal environment that was hypoxic, nitrosative, rich in fatty acids, and poor in carbohydrates. From the onset, it was apparent that some oxygen (<15% of in vitro uptake) was required for proper simulations. In addition, algorithmic tailoring of the M. tb biomass objective function was performed to better represent an infectious state. The integrated host–pathogen metabolic reconstruction was dubbed iAB-AMØ-1410-Mt-661.

Analysis of the integrated host–pathogen metabolic reconstruction resulted in three main findings. First, by setting interfacial constraints and tailoring the biomass objective function, the solution space better represents an infectious state. Without adding artificial constraints to the host portion of the integrated model, the iAB-AMØ-1410 solution space is greatly reduced (Figure 4B). Macrophage glycolysis and nitric oxide production are up-regulated and macrophage ATP production, nucleotide synthesis, and amino-acid metabolism are suppressed. In addition, M. tb glycolysis is suppressed and isocitrate lyase is up-regulated for generation of acetyl-CoA. Fatty acid oxidation pathways and production of mycolic acids are increased, while production of nucleotides, peptidoglycans, and phenolic glycolipids are reduced. The modified solution space of the alveolar macrophage and M. tb better represents the infectious state.

Second, the host-pathogen model more accurately predicts M. tb gene deletion tests than the current in vitro model, iNJ661. The host-pathogen model predicted 11 essential genes and 37 unessential genes differently than iNJ661. A total of 22 of the differentially predicted genes have been experimentally characterized (Sassetti and Rubin, 2003; Sohaskey, 2008). The host-pathogen model correctly predicted 18 of the 22 genes. Thus, iAB-AMØ-1410-Mt-661 is a more accurate platform for studying infectious states of M. tb.

Finally, we sought to determine metabolic differences in both the macrophage and M. tb between three different types of infection: latent, pulmonary, and meningeal. Transcription profiling data of the macrophage for the three infections (Thuong et al, 2008) were integrated in the context of the host–pathogen network to elucidate the reaction activity of the three infections. There was wide heterogeneity in the three infection states; some of these differences are highlighted. Macrophage hyaluronan synthase and export were only active in the pulmonary infection. This is potentially interesting from a pharmaceutical viewpoint as hyaluronan has been implicated as a potential carbon source for extracellular M. tb (Hirayama et al, 2009). In addition, we detected metabolic activity differences in M. tb pathways that have been previously discussed as potential drug targets (Eoh et al, 2007; Boshoff et al, 2008). Polyprenyl metabolic reactions were only active in the latent state infection, while de novo synthesis of nicotinamide cofactors was only active in latent and meningeal M. tb infections.

Host-pathogen modeling represents a novel approach for studying metabolic interactions during infection. iAB-AMØ-1410-Mt-661 is a more accurate platform for understanding the biology and pathophysiology of M. tb infection. Most importantly, genome-scale metabolic reconstructions can act as scaffolds for integrating high-throughput data. Particularly, in this study we were able to discern reaction activity differences between different infection types.

Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome-scale metabolic models have shown utility in integrating -omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host–pathogen interactions using in silico mass-balanced, genome-scale models has not been performed. We, therefore, constructed a cell-specific alveolar macrophage model, iAB-AMØ-1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host–pathogen genome-scale reconstruction, iAB-AMØ-1410-Mt-661. The integrated host–pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High-throughput data from infected macrophages were mapped onto the host–pathogen network and were able to describe three distinct pathological states. Integrated host–pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed.

Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism

The first computational approach for the rapid generation of genome-scale tissue-specific models from a generic species model.

A genome scale model of human liver metabolism, which is comprehensively tested and validated using cross-validation and the ability to carry out complex hepatic metabolic functions.

The model's flux predictions are shown to correlate with flux measurements across a variety of hormonal and dietary conditions, and are successfully used to predict biomarker changes in genetic metabolic disorders, both with higher accuracy than the generic human model.

The study of normal human metabolism and its alterations is central to the understanding and treatment of a variety of human diseases, including diabetes, metabolic syndrome, neurodegenerative disorders, and cancer. A promising systems biology approach for studying human metabolism is through the development and analysis of large-scale stoichiometric network models of human metabolism. The reconstruction of these network models has followed two main paths: the former being the reconstruction of generic (non-tissue specific) models, characterizing the complete metabolic potential of human cells, based mostly on genomic data to trace enzyme-coding genes (Duarte et al, 2007; Ma et al, 2007), and the latter is the reconstruction of cell type- and tissue-specific models (Wiback and Palsson, 2002; Chatziioannou et al, 2003; Vo et al, 2004), based on a similar methodology to that described above, with the extra complexity of manual curation of literature evidence for the cell/system specificity of metabolic enzymes and pathways.

On this background, we present in this study, to the best of our knowledge, the first computational approach for a rapid generation of genome-scale tissue-specific models. The method relies on integrating the previously reconstructed generic human models with a variety of high-throughput molecular ‘omics' data, including transcriptomic, proteomic, metabolomic, and phenotypic data, as well as literature-based knowledge, characterizing the tissue in hand (Figure 1). Hence, it can be readily used to quite rapidly build and use a large array of human tissue-specific models. The resulting model satisfies stoichiometric, mass-balance, and thermodynamic constraints. It serves as a functional metabolic network that can then be used to explore the metabolic state of a tissue under various genetic and physiological conditions, simulating enzymatic inhibition or drug applications through standard constraint-based modeling methods, without requiring additional context-specific molecular data.

We applied this approach to build a genome scale model of liver metabolism, which is then comprehensively tested and validated. The model is shown to be able to simulate complex hepatic metabolic functions, as well as depicting the pathological alterations caused by urea cycle deficiencies. The liver model was applied to predict measured intra-cellular metabolic fluxes given measured metabolite uptake and secretion rates at different hepatic metabolic conditions. The predictions were tested using a comprehensive set of flux measurements performed by (Chan et al, 2003), showing that the liver model obtained more accurate predictions compared to those obtained by the original, generic human model (an overall prediction accuracy of 0.67 versus 0.46). Furthermore, it was applied to identify metabolic biomarkers for liver in-born errors of metabolism—once again, displaying superiority vs. the predictions generated by the generic human model (accuracy of 0.67 versus 0.59).

From a biotechnological standpoint, the liver model generated here can serve as a basis for future studies aiming to optimize the functioning of bio artificial liver devices. The application of the method to rapidly construct metabolic models of other human tissues can obviously lead to many other important clinical insights, e.g., concerning means for metabolic salvage of ischemic heart and brain tissues. Last but not least, the application of the new method is not limited to the realm of human modeling; it can be used to generate tissue models for any multi-tissue organism for which a generic model exists, such as the Mus musculus (Quek and Nielsen, 2008; Sheikh et al, 2005) and the model plant Arabidopsis thaliana (Poolman et al, 2009).

The computational study of human metabolism has been advanced with the advent of the first generic (non-tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm generates a tissue-specific model from the generic human model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome-scale stoichiometric model of hepatic metabolism. The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.

GATMD: γ-aminobutyric acid transporter mutagenesis database

Since the cloning of the first γ-aminobutyric acid (GABA) transporter (GAT1; SLC6A1) from rat brain in 1990, more than 50 published studies have provided structure-function information on investigator-designed rat and mouse GAT1 mutants. To date, more than 200 of 599 GAT1 residues have been subjected to mutagenesis experiments by substitution with different amino acids, and the resulting transporter functional properties have significantly advanced our understanding of the mechanism of Na+- and Cl⁻-coupled GABA transport by this important member of the neurotransmitter:sodium symporter family. Moreover, many studies have addressed the functional consequences of amino acid deletion or insertion at various positions along the primary sequence. The enormity of this growing body of structure-function information has prompted us to develop GABA Transporter Mutagenesis Database (GATMD), a web-accessible, relational database of manually annotated biochemical, functional and pharmacological data reported on GAT1-the most intensely studied GABA transporter isoform. As of the last update of GATMD, 52 GAT1 mutagenesis papers have yielded 3360 experimental records, which collectively contain a total of ∼100 000 annotated parameters. Database URL: http://physiology.sci.csupomona.edu/GATMD/

Timing and Duration of Drug Exposure Affects Outcomes of a Drug-Nutrient Interaction During Ontogeny

Significant drug-nutrient interactions are possible when drugs and nutrients share the same absorption and disposition mechanisms. During postnatal development, the outcomes of drug-nutrient interactions may change with postnatal age since these processes undergo ontogenesis through the postnatal period. Our study investigated the dependence of a significant drug-nutrient interaction (cefepime-carnitine) on the timing and duration of drug exposure relative to postnatal age. Rat pups were administered cefepime (5 mg/kg) twice daily subcutaneously according to different dosing schedules (postnatal day 1-4, 1-8, 8-11, 8-20, or 1-20). Cefepime significantly reduced serum and heart L-carnitine levels in postnatal day 1-4, 1-8 and 8-11 groups and caused severe degenerative changes in ventricular myocardium in these groups. Cefepime also altered the ontogeny of several key L-carnitine homeostasis pathways. The qualitative and quantitative changes in levels of hepatic γ-butyrobetaine hydroxylase mRNA and activity, hepatic trimethyllysine hydroxlase mRNA, intestinal organic cation/carnitine transporter (Octn) mRNA, and renal Octn2 mRNA depended on when during postnatal development the cefepime exposure occurred and duration of exposure. Despite lower levels of heart L-carnitine in earlier postnatal groups, levels of carnitine palmitoyltransferase mRNA and activity, heart Octn2 mRNA and ATP levels in all treatment groups remained unchanged with cefepime exposure. However, changes in other high energy phosphate substrates were noted and reductions in the phosphocreatine/ATP ratio were found in rat pups with normal serum L-carnitine levels. In summary, our data suggest a significant drug-nutrient transport interaction in developing neonates, the nature of which depends on the timing and duration of exposure relative to postnatal age.

Prediction of functional class of proteins and peptides irrespective of sequence homology by support vector machines

Various computational methods have been used for the prediction of protein and peptide function based on their sequences. A particular challenge is to derive functional properties from sequences that show low or no homology to proteins of known function. Recently, a machine learning method, support vector machines (SVM), have been explored for predicting functional class of proteins and peptides from amino acid sequence derived properties independent of sequence similarity, which have shown promising potential for a wide spectrum of protein and peptide classes including some of the low- and non-homologous proteins. This method can thus be explored as a potential tool to complement alignment-based, clustering-based, and structure-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using SVM for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented.

Network-based prediction of human tissue-specific metabolism

Direct in vivo investigation of mammalian metabolism is complicated by the distinct metabolic functions of different tissues. We present a computational method that successfully describes the tissue specificity of human metabolism on a large scale. By integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network, we predict tissue-specific metabolic activity in ten human tissues. This reveals a central role for post-transcriptional regulation in shaping tissue-specific metabolic activity profiles. The predicted tissue specificity of genes responsible for metabolic diseases and tissue-specific differences in metabolite exchange with biofluids extend markedly beyond tissue-specific differences manifest in enzyme-expression data, and are validated by large-scale mining of tissue-specificity data. Our results establish a computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.

Future directions for drug transporter modelling

Since the late 1980s computational methods such as quantitative structure-activity relationship (QSAR) and pharmacophore approaches have become more widely applied to assess interactions between drug-like molecules and transporters, starting with P-glycoprotein (P-gp). Identifying molecules that interact with P-gp and other transporters is important for drug discovery, but it is normally ascertained using laborious in vitro and in vivo studies. Computational QSAR and pharmacophore models can be used to screen commercial databases of molecules rapidly and suggest those likely to bind as substrates or inhibitors for transporters. These predictions can then be readily verified in vitro, thus representing a more efficient route to screening. Recently, the application of this approach has seen the identification of new substrates and inhibitors for several transporters. The successful application of computational models and pharmacophore models in particular to predict transporter binding accurately represents a way to anticipate drug-drug interactions of novel molecules from molecular structure. These models may also see incorporation in future pharmacokinetic-pharmacodynamic models to improve predictions of in vivo drug effects in patients. The implications of early assessment of transporter activity, current advances in QSAR, and other computational methods for future development of these and systems-based approaches will be discussed.

Comparative gene expression profiles of intestinal transporters in mice, rats and humans

We have studied gene expression profiles of intestinal transporters in model animals and humans. Total RNA was isolated from duodenum and the mRNA expression was measured using Affymetrix GeneChip oligonucleotide arrays. Detected genes from the intestine of mice, rats, and humans were about 60% of 22,690 sequences, 40% of 8739, and 47% of 12,559, respectively. A total of 86 genes involving transporters expressed in mice, 50 genes in rats, and 61 genes in humans were detected. Mice exhibited abundant mRNA expressions for peptide transporter HPT1, amino acid transporters CSNU3, CT1 and ASC1, nucleoside transporter CNT2, organic cation transporter SFXN1, organic anion transporter NBC3, glucose transporter SGLT1, and fatty acid transporters FABP1 and FABP2. Rats showed high expression profiles of peptide transporter PEPT1, amino acid transporters CSNU1 and 4F2HC, nucleoside transporter CNT2, organic cation transporter OCT5, organic anion transporter SDCT1, glucose transporter GLUT2 and GLUT5, and folate carrier FOLT. In humans, the highly expressed genes were peptide transporter HPT1, amino acid transporters LAT3, 4F2HC and PROT, nucleoside transporter CNT2, organic cation transporter OCTN2, organic anion transporters NADC1, NBC1 and SBC2, glucose transporters SGLT1 and GLUT5, multidrug resistance-associated protein RHO12, fatty acid transporters FABP1 and FABP2, and phosphate carrier PHC. Overall these data reveal diverse transcriptomic profiles for intestinal transporters among these species. Therefore, this transcriptional data may lead to more effective use of the laboratory animals as a model for oral drug development.

Tumor cell targeting of transferrin-PEG-TNF-alpha conjugate via a receptor-mediated delivery system: design, synthesis, and biological evaluation

PEGylation is a procedure of growing interest for enhancing the therapeutic and biotechnological potential of peptides and proteins. Transferrin (Tf) has been proposed to be useful for targeting cancer cells. The aim of this study was to modify PEGylated recombinant human tumor necrosis factor alpha (PEG-TNF-alpha) with Tf to form Tf-PEG-TNF-alpha conjugates, which would maintain the advantages of PEGylation and also achieve the function of active targeting to tumor cells. In PEGylation reactions with 5-, 20-, 40-, and 60-fold molar excess of 3.4 kDa N-hydroxysuccinimide-PEG-maleimide (PT1, PT2, PT3, and PT4, respectively), PEG-TNF-alpha conjugates with different PEG chains were synthesized. A perfusion chromatography technique using a cation-exchange column was introduced to purify PEG-TNF-alpha conjugates. PT4 with about five PEG chains was selected as a lead candidate due to highest extent of PEGylation and maximum reaction yield. Thiolated Tf was conjugated to the maleimide group at the distal end of the PEG chains on the PEG-TNF-alpha conjugates, with the resulting Tf-PEG-TNF-alpha conjugates after purification containing approximately one Tf ligand on one TNF-alpha molecule. The conjugate of Tf and PT4 (TPT4) was selected to assess the specificity and affinity to transferrin receptor (TfR) on two kinds of tumor cells, K562 and KB. Both the receptor binding assays and the competition experiments were performed using radioligand binding analysis. The results demonstrated that TPT4 as well as Tf bound specifically to the TfR on the tumor cell surface and the affinity of the conjugate to TfR was similar to that of native Tf. In contrast, PEG-TNF-alpha demonstrated no specificity. The biodistribution and antitumor effects were investigated in S-180 tumor-bearing mice. It was found that TPT4 could markedly alter in vivo behavioral characteristics of TNF-alpha. Compared with TNF-alpha and PT4, extravasated TPT4 in tumor tissues exhibited a significantly delayed blood clearance and the highest intratumoral TNF-alpha levels. Furthermore, the inhibitory rate of tumor of TPT4 enhanced 5.3- and 1.8-fold over that of TNF-alpha and PT4, indicating that TPT4 exhibited the highest antitumor activity. These results suggested that Tf-PEG-TNF-alpha was a useful long circulating conjugate with the capabilities of specific receptor binding resulting in enhanced antitumor activity of TNF-alpha.

Application of data mining approaches to drug delivery

Computational approaches play a key role in all areas of the pharmaceutical industry from data mining, experimental and clinical data capture to pharmacoeconomics and adverse events monitoring. They will likely continue to be indispensable assets along with a growing library of software applications. This is primarily due to the increasingly massive amount of biology, chemistry and clinical data, which is now entering the public domain mainly as a result of NIH and commercially funded projects. We are therefore in need of new methods for mining this mountain of data in order to enable new hypothesis generation. The computational approaches include, but are not limited to, database compilation, quantitative structure activity relationships (QSAR), pharmacophores, network visualization models, decision trees, machine learning algorithms and multidimensional data visualization software that could be used to improve drug delivery after mining public and/or proprietary data. We will discuss some areas of unmet needs in the area of data mining for drug delivery that can be addressed with new software tools or databases of relevance to future pharmaceutical projects.

Internet resources related to drug action and human response: a review

It has been demonstrated that numerous proteins interact with drugs or their metabolites. Knowledge of these proteins is necessary to understand the mechanisms of drug action and human response. Progress in modern genetics, molecular biology, biochemistry and pharmacology is generating a comprehensive mechanistic understanding of drug-target interaction on the molecular level. This is valuable for researchers and pharmaceutical companies in their efforts to improve the efficacy of existing drugs and to discover new ones. Most recently, the integration of a systems biology approach into drug discovery processes calls for more holistic knowledge and easily accessible resources of the proteins that are important in drug action and human response. We have reviewed many publicly accessible internet resources of these proteins, according to their roles in drug action and human response, such as therapeutic effect, adverse reaction, absorption, distribution, metabolism and excretion.

Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity

Protein sequence contains clues to its function. Functional prediction from sequence presents a challenge particularly for proteins that have low or no sequence similarity to proteins of known function. Recently, machine learning methods have been explored for predicting functional class of proteins from sequence-derived properties independent of sequence similarity, which showed promising potential for low- and non-homologous proteins. These methods can thus be explored as potential tools to complement alignment- and clustering-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using machine learning methods for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented, which need to be interpreted with caution as they are dependent on such factors as datasets used and choice of parameters.

Expression of the steroid and xenobiotic receptor and its possible target gene, organic anion transporting polypeptide-A, in human breast carcinoma

Steroid and xenobiotic receptor (SXR) or human pregnane X receptor (hPXR) has been shown to play an important role in the regulation of genes related to xenobiotic detoxification, such as cytochrome P450 3A4 and multidrug resistance gene 1. Cytochrome P450 enzymes, conjugation enzymes, and transporters are all considered to be involved in the resistance of breast carcinoma to chemotherapeutic or endocrine agents. However, the expression of SXR/hPXR proteins and that of its target genes and their biological or clinical significance have not been examined in human breast carcinomas. Therefore, we first examined SXR/hPXR expression in 60 breast carcinomas using immunohistochemistry and quantitative reverse transcription-PCR. We then searched for possible SXR/hPXR target genes using microarray analysis of carcinoma cells captured by laser microscissors. SXR/hPXR was detected in carcinoma tissues but not in nonneoplastic and stromal cells of breast tumors. A significant positive correlation was detected between the SXR/hPXR labeling index and both the histologic grade and the lymph node status of the carcinoma cases. Furthermore, in estrogen receptor-positive cases, SXR/hPXR expression was also positively correlated with expression of the cell proliferation marker, Ki-67. Microarray analysis showed that organic anion transporting polypeptide-A (OATP-A) was most closely correlated with SXR/hPXR gene expression, and both OATP-A mRNA and protein were significantly associated with SXR/hPXR in both breast carcinoma tissues and its cell lines. These results suggest that SXR/hPXR and its target gene, such as OATP-A, may play important roles in the biology of human breast cancers.

Fish drug analysis--Phish-Pharm: a searchable database of pharmacokinetics data in fish

Information about drug residues and pharmacokinetic parameters in aquatic species is relatively sparse. In addition, it is difficult to rapidly compare data between studies due to differences in experimental conditions, such as water temperatures and salinity. To facilitate the study of aquatic species drug metabolism, we constructed a Fish Drug/Chemical Analysis Phish-Pharm (FDA-PP) database. This database consists of more than 400 articles that include data from 90 species (64 genera) of fish. Data fields include genus, species, water temperatures, the average animal weight, sample types analyzed, drug (or chemical) name, dosage, route of administration, metabolites identified, method of analysis, protein binding, clearance, volume of distribution in a central compartment (Vc) or volume of distribution at steady-state (Vd), and drug half-lives (t((1/2))). Additional fields list the citation, authors, title, and Internet links. The document will be periodically updated, and users are invited to submit additional data. Updates will be announced in future issues of The AAPS Journal. This database will be a valuable resource to investigators of drug metabolism in aquatic species as well as government and private organizations involved in the drug approval process for aquatic species.

Computational prediction of human drug metabolism

There is an urgent requirement within the pharmaceutical and biotechnology industries, regulatory authorities and academia to improve the success of molecules that are selected for clinical trials. Although absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties are some of the many components that contribute to successful drug discovery and development, they represent factors for which we currently have in vitro and in vivo data that can be modelled computationally. Understanding the possible toxicity and the metabolic fate of xenobiotics in the human body is particularly important in early drug discovery. There is, therefore, a need for computational methodologies for uncovering the relationships between the structure and the biological activity of novel molecules. The convergence of numerous technologies, including high-throughput techniques, databases, ADME/Tox modelling and systems biology modelling, is leading to the foundation of systems-ADME/Tox. Results from experiments can be integrated with predictions to globally simulate and understand the likely complete effects of a molecule in humans. The development and early application of major components of MetaDrug (GeneGo, Inc.) software will be described, which includes rule-based metabolite prediction, quantitative structure-activity relationship models for major drug metabolising enzymes, and an extensive database of human protein-xenobiotic interactions. This represents a combined approach to predicting drug metabolism. MetaDrug can be readily used for visualising Phase I and II metabolic pathways, as well as interpreting high-throughput data derived from microarrays as networks of interacting objects. This will ultimately aid in hypothesis generation and the early triaging of molecules likely to have undesirable predicted properties or measured effects on key proteins and cellular functions.

Systems-ADME/Tox: resources and network approaches

The increasing cost of drug development is partially due to our failure to identify undesirable compounds at an early enough stage of development. The application of higher throughput screening methods have resulted in the generation of very large datasets from cells in vitro or from in vivo experiments following the treatment with drugs or known toxins. In recent years the development of systems biology, databases and pathway software has enabled the analysis of the high-throughput data in the context of the whole cell. One of the latest technology paradigms to be applied alongside the existing in vitro and computational models for absorption, distribution, metabolism, excretion and toxicology (ADME/Tox) involves the integration of complex multidimensional datasets, termed toxicogenomics. The goal is to provide a more complete understanding of the effects a molecule might have on the entire biological system. However, due to the sheer complexity of this data it may be necessary to apply one or more different types of computational approaches that have as yet not been fully utilized in this field. The present review describes the data generated currently and introduces computational approaches as a component of ADME/Tox. These methods include network algorithms and manually curated databases of interactions that have been separately classified under systems biology methods. The integration of these disparate tools will result in systems-ADME/Tox and it is important to understand exactly what data resources and technologies are available and applicable. Examples of networks derived with important drug transporters and drug metabolizing enzymes are provided to demonstrate the network technologies.

Bioinformatics in drug development and assessment

Bioinformatics is playing an increasingly important role in nearly all aspects of drug discovery, drug assessment, and drug development. This growing importance lies not only in the role that bioinformatics plays in handling large volumes of data, but also in the utility of bioinformatics tools to predict, analyze, or help interpret clinical and preclinical findings. This review focuses on describing and evaluating some of the newer or more important bioinformatics resources (i.e., databases and software) that are of growing importance to understanding or predicting drug metabolism, especially with respect to the absorption, distribution, metabolism, excretion, (ADME), and toxicity (T) of both existing drugs and potential drug leads. Detailed descriptions and critical assessments of a number of potentially useful bioinformatics/cheminformatics databases and predictive ADMET software tools are provided. Additionally, several pharmaceutically important applications of both the databases and software are highlighted. Given the rapid growth in this area and the rapid changes that are taking place, a special emphasis is placed on freely available or Web-accessible resources.

What do we mean by Internet access? A framework for health researchers

Much is written about Internet access, Web access, Web site accessibility, and access to online health information. The term access has, however, a variety of meanings to authors in different contexts when applied to the Internet, the Web, and interactive health communication. We have summarized those varied uses and definitions and consolidated them into a framework that defines Internet and Web access issues for health researchers. We group issues into two categories: connectivity and human interface. Our focus is to conceptualize access as a multicomponent issue that can either reduce or enhance the public health utility of electronic communications.

A mechanistic approach to understanding the factors affecting drug absorption: a review of fundamentals

This article provides an overview of the patient-specific and drug-specific variables that can affect drug absorption following oral product administration. The oral absorption of any chemical entity reflects a complex spectrum of events. Factors influencing product bioavailability include drug solubility, permeability, and the rate of in vivo dissolution. In this regard, the Biopharmaceutics Classification System has proven to be an important tool for predicting compounds likely to be associated with bioavailability problems. It also helps in identifying those factors that may alter the rate and extent of drug absorption. Product bioavailability can also be markedly influenced by patient attributes such as the integrity of the gastrointestinal tract, physiological status, site of drug absorption, membrane transporters, presystemic drug metabolism (intrinsic variables), and extrinsic variables such as the effect of food or concomitant medication. Through an awareness of a drug's physicochemical properties and the physiological processes affecting drug absorption, the skilled pharmaceutical scientist can develop formulations that will maximize product availability. By appreciating the potential impact of patient physiological status, phenotype, age, gender, and lifestyle, dosing regimens can be tailored to better meet the needs of the individual patient.

Pharmacogenomics of drug transporters: the next drug delivery challenge

Scientifically, the third millennium begins with a major triumph--the publishing of the human genomic map, which is destined to have a momentous impact on the quality of life in our time. Disease prevention, individualized medicine, and genotyped-based medicine will soon become a reality. Pharmacogenetics, the forerunner of pharmacogenomics, began in the 1950s with a series of observations relating drug response to various genetic factors. It took almost two more decades for scientists to discover that cytochrome p450 2D6 was responsible for the metabolism of many drugs. This landmark discovery helped focus attention on how gene expression could impact the response to drugs. The stage was set for a revolution in therapeutics some 30 years later as the Human Genome Project crossed the finishing line triumphantly. A parallel development in drug delivery that may also benefit from the fruits of the Human Genome Project is the growing acceptance/awareness of drug transporters as a gateway to epithelial drug transport. This presentation addresses an area in need of attention: the possible impact of genetic polymorphism of drug transporters in pharmacokinetics and the challenge it poses in drug delivery.