Arterial occlusion secondary to prior cardiac catheterization in a radial forearm flap-lessons learnt from an unusual case.

The radial forearm free flap (RFFF) is a workhorse flap for head and neck reconstruction. We present an unusual case of radial artery occlusion, likely from previous transradial cardiac catheterization, in a patient for whom an RFFF was raised for floor of mouth reconstruction following resection of squamous cell carcinoma. Pre-operative assessment with ultrasound Doppler and an Allen test was normal. The flap was raised uneventfully under tourniquet control. However, following flap elevation and tourniquet release, poor flap perfusion was noted, and cutback of the artery revealed a long segment of hard fibrous plaque within the lumen. Retrospective review of medical records showed a history of cardiac catheterization via the same radial artery. We discuss various measures that can prevent this occurrence, including careful pre-operative screening of previous procedures involving the radial artery, the reverse Allen test, Doppler ultrasound, and consideration of distal arterial exploration without a tourniquet.

Primary thrombolysis for free flap surgery in head and neck reconstruction: a case report and review.

The use of free flaps is an essential and reliable method of reconstruction in complex head and neck defects. Flap failure remains the most feared complication, the most common cause being pedicle thrombosis. Among other measures, thrombolysis is useful when manual thrombectomy has failed to restore flap perfusion, in the setting of late or established thrombosis, or in arterial thrombosis with distal clot propagation. We report a case of pedicle arterial thrombosis with distal clot propagation which occurred during reconstruction of a maxillectomy defect, and was successfully treated with thrombolysis using recombinant tissue plasminogen activator. We also review the literature regarding the use of thrombolysis in free flap surgery, and propose an algorithm for the salvage of free flaps in head and neck reconstruction.

Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents.

The detrimental health effects associated with tobacco use constitute a major public health concern. The addiction associated with nicotine found in tobacco products has led to difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the targets of nicotine and are responsible for addiction to tobacco products. However, it is unknown if the other >8000 tobacco constituents are addictive. Since it is time-consuming and costly to experimentally assess addictive potential of such larger number of chemicals, computationally predicting human nAChRs binding is important for in silico evaluation of addiction potential of tobacco constituents and needs structures of human nAChRs. Therefore, we constructed three-dimensional structures of the ligand binding domain of human nAChR α7 subtype and then developed a predictive model based on the constructed structures to predict human nAChR α7 binding activity of tobacco constituents. The predictive model correctly predicted 11 out of 12 test compounds to be binders of nAChR α7. The model is a useful tool for high-throughput screening of potential addictive tobacco constituents. These results could inform regulatory science research by providing a new validated predictive tool using cutting-edge computational methodology to high-throughput screen tobacco additives and constituents for their binding interaction with the human α7 nicotinic receptor. The tool represents a prediction model capable of screening thousands of chemicals found in tobacco products for addiction potential, which improves the understanding of the potential effects of additives.

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides.

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.

Experimental Data Extraction and in Silico Prediction of the Estrogenic Activity of Renewable Replacements for Bisphenol A.

Bisphenol A (BPA) is a ubiquitous compound used in polymer manufacturing for a wide array of applications; however, increasing evidence has shown that BPA causes significant endocrine disruption and this has raised public concerns over safety and exposure limits. The use of renewable materials as polymer feedstocks provides an opportunity to develop replacement compounds for BPA that are sustainable and exhibit unique properties due to their diverse structures. As new bio-based materials are developed and tested, it is important to consider the impacts of both monomers and polymers on human health. Molecular docking simulations using the Estrogenic Activity Database in conjunction with the decision forest were performed as part of a two-tier in silico model to predict the activity of 29 bio-based platform chemicals in the estrogen receptor-α (ERα). Fifteen of the candidates were predicted as ER binders and fifteen as non-binders. Gaining insight into the estrogenic activity of the bio-based BPA replacements aids in the sustainable development of new polymeric materials.

QSAR Models at the US FDA/NCTR.

Quantitative structure-activity relationship (QSAR) has been used in the scientific research community for many decades and applied to drug discovery and development in the industry. QSAR technologies are advancing fast and attracting possible applications in regulatory science. To facilitate the development of reliable QSAR models, the FDA had invested a lot of efforts in constructing chemical databases with a variety of efficacy and safety endpoint data, as well as in the development of computational algorithms. In this chapter, we briefly describe some of the often used databases developed at the FDA such as EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), LTKB (Liver Toxicity Knowledge Base), and CERES (Chemical Evaluation and Risk Estimation System) and the technologies adopted by the agency such as Mold(2) program for calculation of a large and diverse set of molecular descriptors and decision forest algorithm for QSAR model development. We also summarize some QSAR models that have been developed for safety evaluation of the FDA-regulated products.

Structures of androgen receptor bound with ligands: advancing understanding of biological functions and drug discovery.

INTRODUCTION: Androgen receptor (AR) is a ligand-dependent transcription factor and a member of the nuclear receptor superfamily. It plays a vital role in male sexual development and regulates gene expression in various tissues, including prostate. Androgens are compounds that exert their biological effects via interaction with AR. Binding of androgens to AR initiates conformational changes in AR that affect binding of co-regulator proteins and DNA. AR agonists and antagonists are widely used in a variety of clinical applications (i.e. hypogonadism and prostate cancer therapy). AREAS COVERED: This review provides a close look at structures of AR-ligand complexes and mutations in the receptor that have been revealed, discusses current challenges in the field, and sheds light on future directions. EXPERT OPINION: AR is one of the primary targets for the treatment of prostate cancer, as AR antagonists inhibit prostate cancer growth. However, these drugs are not effective for long-term treatment and lead to castration-resistant prostate cancer. The structures of AR-ligand complexes are an invaluable scientific asset that enhances our understanding of biological functions and mechanisms of androgenic and anti-androgenic chemicals as well as promotes the discovery of superior drug candidates.

A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals.

Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.

Pathway Analysis Revealed Potential Diverse Health Impacts of Flavonoids that Bind Estrogen Receptors.

Flavonoids are frequently used as dietary supplements in the absence of research evidence regarding health benefits or toxicity. Furthermore, ingested doses could far exceed those received from diet in the course of normal living. Some flavonoids exhibit binding to estrogen receptors (ERs) with consequential vigilance by regulatory authorities at the U.S. EPA and FDA. Regulatory authorities must consider both beneficial claims and potential adverse effects, warranting the increases in research that has spanned almost two decades. Here, we report pathway enrichment of 14 targets from the Comparative Toxicogenomics Database (CTD) and the Herbal Ingredients' Targets (HIT) database for 22 flavonoids that bind ERs. The selected flavonoids are confirmed ER binders from our earlier studies, and were here found in mainly involved in three types of biological processes, ER regulation, estrogen metabolism and synthesis, and apoptosis. Besides cancers, we conjecture that the flavonoids may affect several diseases via apoptosis pathways. Diseases such as amyotrophic lateral sclerosis, viral myocarditis and non-alcoholic fatty liver disease could be implicated. More generally, apoptosis processes may be importantly evolved biological functions of flavonoids that bind ERs and high dose ingestion of those flavonoids could adversely disrupt the cellular apoptosis process.

Applying network analysis and Nebula (neighbor-edges based and unbiased leverage algorithm) to ToxCast data.

BACKGROUND: ToxCast data have been used to develop models for predicting in vivo toxicity. To predict the in vivo toxicity of a new chemical using a ToxCast data based model, its ToxCast bioactivity data are needed but not normally available. The capability of predicting ToxCast bioactivity data is necessary to fully utilize ToxCast data in the risk assessment of chemicals. OBJECTIVES: We aimed to understand and elucidate the relationships between the chemicals and bioactivity data of the assays in ToxCast and to develop a network analysis based method for predicting ToxCast bioactivity data. METHODS: We conducted modularity analysis on a quantitative network constructed from ToxCast data to explore the relationships between the assays and chemicals. We further developed Nebula (neighbor-edges based and unbiased leverage algorithm) for predicting ToxCast bioactivity data. RESULTS: Modularity analysis on the network constructed from ToxCast data yielded seven modules. Assays and chemicals in the seven modules were distinct. Leave-one-out cross-validation yielded a Q(2) of 0.5416, indicating ToxCast bioactivity data can be predicted by Nebula. Prediction domain analysis showed some types of ToxCast assay data could be more reliably predicted by Nebula than others. CONCLUSIONS: Network analysis is a promising approach to understand ToxCast data. Nebula is an effective algorithm for predicting ToxCast bioactivity data, helping fully utilize ToxCast data in the risk assessment of chemicals.

Prevalence of KRAS, BRAF, PI3K and EGFR mutations among Asian patients with metastatic colorectal cancer.

Mutations in oncogenes along the epidermal growth factor receptor (EGFR) signaling pathway have been implicated in the resistance to cetuximab in patients with metastatic colorectal cancer (mCRC). However, the relative significance of these mutations based on their frequencies of occurrence in the Singaporean population remains unclear. In the present study, the prevalence of Kirsten rat sarcoma viral oncogene homolog (KRAS), v-Raf murine sarcoma viral oncogene homolog B (BRAF), phosphoinositide 3-kinase (PI3K) and EGFR somatic mutations were determined among Singaporean patients with mCRC. DNA extracted from 45 pairs of surgically resected tumor and normal mucosa samples was subjected to direct sequencing or restriction fragment length polymorphism. Associations of the genetic mutations with various clinicopathological parameters were further explored. Mutations in either codon 12 or 13 of KRAS were confirmed as prominent phenomena among the included Singaporean mCRC patients, at a prevalence comparable with that of Caucasian and patients of other Asian ethnicities [33.3% (90% confidence interval, 21.8-44.9%)]. KRAS mutation was not associated with clinicopathological features, including age, gender and ethnicity of patients, or the tumor site, differentiation and mucinous status. Conversely, the prevalence of BRAF (0%), PI3K (2.2%) and EGFR (0%) mutations were low. The results of the present study indicate that KRAS mutations are prevalent among the studied population, and confirm the low prevalence of BRAF, PI3K and EGFR mutations. KRAS should be prioritized as an investigational gene for future studies of predictive biomarkers of cetuximab response among Singaporean patients with mCRC.

Comparing genetic variants detected in the 1000 genomes project with SNPs determined by the International HapMap Consortium.

Single-nucleotide polymorphisms (SNPs) determined based on SNP arrays from the international HapMap consortium (HapMap) and the genetic variants detected in the 1000 genomes project (1KGP) can serve as two references for genomewide association studies (GWAS). We conducted comparative analyses to provide a means for assessing concerns regarding SNP array-based GWAS findings as well as for realistically bounding expectations for next generation sequencing (NGS)-based GWAS. We calculated and compared base composition, transitions to transversions ratio, minor allele frequency and heterozygous rate for SNPs from HapMap and 1KGP for the 622 common individuals. We analysed the genotype discordance between HapMap and 1KGP to assess consistency in the SNPs from the two references. In 1KGP, 90.58% of 36,817,799 SNPs detected were not measured in HapMap. More SNPs with minor allele frequencies less than 0.01 were found in 1KGP than HapMap. The two references have low disc ordance (generally smaller than 0.02) in genotypes of common SNPs, with most discordance from heterozygous SNPs. Our study demonstrated that SNP array-based GWAS findings were reliable and useful, although only a small portion of genetic variances were explained. NGS can detect not only common but also rare variants, supporting the expectation that NGS-based GWAS will be able to incorporate a much larger portion of genetic variance than SNP arrays-based GWAS.

Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets.

Some chemicals in the environment possess the potential to interact with the endocrine system in the human body. Multiple receptors are involved in the endocrine system; estrogen receptor α (ERα) plays very important roles in endocrine activity and is the most studied receptor. Understanding and predicting estrogenic activity of chemicals facilitates the evaluation of their endocrine activity. Hence, we have developed a decision forest classification model to predict chemical binding to ERα using a large training data set of 3308 chemicals obtained from the U.S. Food and Drug Administration's Estrogenic Activity Database. We tested the model using cross validations and external data sets of 1641 chemicals obtained from the U.S. Environmental Protection Agency's ToxCast project. The model showed good performance in both internal (92% accuracy) and external validations (∼ 70-89% relative balanced accuracies), where the latter involved the validations of the model across different ER pathway-related assays in ToxCast. The important features that contribute to the prediction ability of the model were identified through informative descriptor analysis and were related to current knowledge of ER binding. Prediction confidence analysis revealed that the model had both high prediction confidence and accuracy for most predicted chemicals. The results demonstrated that the model constructed based on the large training data set is more accurate and robust for predicting ER binding of chemicals than the published models that have been developed using much smaller data sets. The model could be useful for the evaluation of ERα-mediated endocrine activity potential of environmental chemicals.

Machine Learning Methods for Predicting HLA-Peptide Binding Activity.

As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA-peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA-peptide binding prediction. We also summarize the descriptors based on which the HLA-peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA-peptide binding prediction method based on network analysis.

Estrogenic activity data extraction and in silico prediction show the endocrine disruption potential of bisphenol A replacement compounds.

Bisphenol A (BPA) replacement compounds are released to the environment and cause widespread human exposure. However, a lack of thorough safety evaluations on the BPA replacement compounds has raised public concerns. We assessed the endocrine disruption potential of BPA replacement compounds in the market to assist their safety evaluations. A literature search was conducted to ascertain the BPA replacement compounds in use. Available experimental estrogenic activity data of these compounds were extracted from the Estrogenic Activity Database (EADB) to assess their estrogenic potential. An in silico model was developed to predict the estrogenic activity of compounds lacking experimental data. Molecular dynamics (MD) simulations were performed to understand the mechanisms by which the estrogenic compounds bind to and activate the estrogen receptor (ER). Forty-five BPA replacement compounds were identified in the literature. Seven were more estrogenic and five less estrogenic than BPA, while six were nonestrogenic in EADB. A two-tier in silico model was developed based on molecular docking to predict the estrogenic activity of the 27 compounds lacking data. Eleven were predicted as ER binders and 16 as nonbinders. MD simulations revealed hydrophobic contacts and hydrogen bonds as the main interactions between ER and the estrogenic compounds.

Human sex hormone-binding globulin binding affinities of 125 structurally diverse chemicals and comparison with their binding to androgen receptor, estrogen receptor, and α-fetoprotein.

One endocrine disruption mechanism is through binding to nuclear receptors such as the androgen receptor (AR) and estrogen receptor (ER) in target cells. The concentration of a chemical in serum is important for its entry into the target cells to bind the receptors, which is regulated by the serum proteins. Human sex hormone-binding globulin (SHBG) is the major transport protein in serum that can bind androgens and estrogens and thus change a chemical's availability to enter the target cells. Sequestration of an androgen or estrogen in the serum can alter the chemical elicited AR- and ER-mediated responses. To better understand the chemical-induced endocrine activity, we developed a competitive binding assay using human pregnancy plasma and measured the binding to the human SHBG for 125 structurally diverse chemicals, most of which were known to bind AR and ER. Eighty seven chemicals were able to bind the human SHBG in the assay, whereas 38 chemicals were nonbinders. Binding data for human SHBG are compared with that for rat α-fetoprotein, ER and AR. Knowing the binding profiles between serum and nuclear receptors will improve assessment of a chemical's potential for endocrine disruption. The SHBG binding data reported here represent the largest data set of structurally diverse chemicals tested for human SHBG binding. Utilization of the SHBG binding data with AR and ER binding data could enable better evaluation of endocrine disrupting potential of chemicals through AR- and ER-mediated responses since sequestration in serum could be considered.

Competitive molecular docking approach for predicting estrogen receptor subtype α agonists and antagonists.

BACKGROUND: Endocrine disrupting chemicals (EDCs) are exogenous compounds that interfere with the endocrine system of vertebrates, often through direct or indirect interactions with nuclear receptor proteins. Estrogen receptors (ERs) are particularly important protein targets and many EDCs are ER binders, capable of altering normal homeostatic transcription and signaling pathways. An estrogenic xenobiotic can bind ER as either an agonist or antagonist to increase or inhibit transcription, respectively. The receptor conformations in the complexes of ER bound with agonists and antagonists are different and dependent on interactions with co-regulator proteins that vary across tissue type. Assessment of chemical endocrine disruption potential depends not only on binding affinity to ERs, but also on changes that may alter the receptor conformation and its ability to subsequently bind DNA response elements and initiate transcription. Using both agonist and antagonist conformations of the ERα, we developed an in silico approach that can be used to differentiate agonist versus antagonist status of potential binders. METHODS: The approach combined separate molecular docking models for ER agonist and antagonist conformations. The ability of this approach to differentiate agonists and antagonists was first evaluated using true agonists and antagonists extracted from the crystal structures available in the protein data bank (PDB), and then further validated using a larger set of ligands from the literature. The usefulness of the approach was demonstrated with enrichment analysis in data sets with a large number of decoy ligands. RESULTS: The performance of individual agonist and antagonist docking models was found comparable to similar models in the literature. When combined in a competitive docking approach, they provided the ability to discriminate agonists from antagonists with good accuracy, as well as the ability to efficiently select true agonists and antagonists from decoys during enrichment analysis. CONCLUSION: This approach enables evaluation of potential ER biological function changes caused by chemicals bound to the receptor which, in turn, allows the assessment of a chemical's endocrine disrupting potential. The approach can be used not only by regulatory authorities to perform risk assessments on potential EDCs but also by the industry in drug discovery projects to screen for potential agonists and antagonists.

Whole genome sequencing of 35 individuals provides insights into the genetic architecture of Korean population.

BACKGROUND: Due to a significant decline in the costs associated with next-generation sequencing, it has become possible to decipher the genetic architecture of a population by sequencing a large number of individuals to a deep coverage. The Korean Personal Genomes Project (KPGP) recently sequenced 35 Korean genomes at high coverage using the Illumina Hiseq platform and made the deep sequencing data publicly available, providing the scientific community opportunities to decipher the genetic architecture of the Korean population. METHODS: In this study, we used two single nucleotide variant (SNV) calling pipelines: mapping the raw reads obtained from whole genome sequencing of 35 Korean individuals in KPGP using BWA and SOAP2 followed by SNV calling using SAMtools and SOAPsnp, respectively. The consensus SNVs obtained from the two SNV pipelines were used to represent the SNVs of the Korean population. We compared these SNVs to those from 17 other populations provided by the HapMap consortium and the 1000 Genomes Project (1KGP) and identified SNVs that were only present in the Korean population. We studied the mutation spectrum and analyzed the genes of non-synonymous SNVs only detected in the Korean population. RESULTS: We detected a total of 8,555,726 SNVs in the 35 Korean individuals and identified 1,213,613 SNVs detected in at least one Korean individual (SNV-1) and 12,640 in all of 35 Korean individuals (SNV-35) but not in 17 other populations. In contrast with the SNVs common to other populations in HapMap and 1KGP, the Korean only SNVs had high percentages of non-silent variants, emphasizing the unique roles of these Korean only SNVs in the Korean population. Specifically, we identified 8,361 non-synonymous Korean only SNVs, of which 58 SNVs existed in all 35 Korean individuals. The 5,754 genes of non-synonymous Korean only SNVs were highly enriched in some metabolic pathways. We found adhesion is the top disease term associated with SNV-1 and Nelson syndrome is the only disease term associated with SNV-35. We found that a significant number of Korean only SNVs are in genes that are associated with the drug term of adenosine. CONCLUSION: We identified the SNVs that were found in the Korean population but not seen in other populations, and explored the corresponding genes and pathways as well as the associated disease terms and drug terms. The results expand our knowledge of the genetic architecture of the Korean population, which will benefit the implementation of personalized medicine for the Korean population.