Clinical Care of Patients with Neurocognitive Disorders: A Qualitative Study of the Psychiatric Residency Training Experience.

OBJECTIVE: Few data describe how general psychiatry residencies prepare trainees to care for individuals with neurocognitive disorders (NCDs), despite increasing recognition of the need for psychiatrists to provide care for the growing numbers of patients with NCD. This study aims to identify training needs and approaches, as the resident experience is one important perspective that can be added to others, such as milestones developed by expert educators. METHODS: The authors conducted three focus groups of third- and fourth-year general adult psychiatry residency trainees from three different training programs in May and June of 2021. Focus groups consisted of three to eight unique participants per group. Qualitative data analysis techniques derived in grounded theory were utilized to identify themes. RESULTS: Four main themes emerged from the focus groups: unique challenges of NCD care, intrinsic rewards of working with families, perceived gaps in educational experiences, and limited comfort in future practice. CONCLUSIONS: Participants expressed that aspects of NCD care were fundamentally different than care for other mental health conditions encountered in psychiatry residency. They found the progressive nature of the disease to be particularly challenging, and they also expressed challenges with clinical interviews and establishing rapport with individuals with NCDs. However, working with families was especially rewarding. Regardless of training program, participants expressed a need for additional longitudinal and diversified training opportunities to prepare them for future practice in this area.

QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs.

S2R overexpression is associated with various forms of cancer as well as both neuropsychiatric disorders (e.g., schizophrenia) and neurodegenerative diseases (Alzheimer's disease: AD). In the present study, three ligand-based methods (QSAR modeling, pharmacophore mapping, and shape-based screening) were implemented to select putative S2R ligands from the DrugBank library comprising 2000+ entries. Four separate optimization algorithms (i.e., stepwise regression, Lasso, genetic algorithm (GA), and a customized extension of GA called GreedGene) were adapted to select descriptors for the QSAR models. The subsequent biological evaluation of selected compounds revealed that three FDA-approved drugs for unrelated therapeutic indications exhibited sub-1 uM binding affinity for S2R. In particular, the antidepressant drug nefazodone elicited a S2R binding affinity Ki = 140 nM. A total of 159 unique S2R ligands were retrieved from 16 publications for model building, validation, and testing. To our best knowledge, the present report represents the first case to develop comprehensive QSAR models sourced by pooling and curating a large assemblage of structurally diverse S2R ligands, which should prove useful for identifying new drug leads and predicting their S2R binding affinity prior to the resource-demanding tasks of chemical synthesis and biological evaluation.

Comprehensive 3D-QSAR Model Predicts Binding Affinity of Structurally Diverse Sigma 1 Receptor Ligands.

The Sigma 1 Receptor (S1R) has attracted intense interest as a pharmaceutical target for various therapeutic indications, including the treatment of neuropathic pain and the potentiation of opioid analgesia. Efforts by drug developers to rationally design S1R antagonists have been spurred recently by the 2016 publication of the high-resolution X-ray crystal structure of the ligand-bound human S1R. Until now, however, the absence in the published literature of a single, large-scale, and comprehensive quantitative structure-activity relationship (QSAR) model that encompasses a structurally diverse collection of S1R ligands has impaired rapid progress. To our best knowledge, the present study represents the first report of a statistically robust and highly predictive 3D-QSAR model (R2 = 0.92, Q2 = 0.62, Rpred2 = 0.81) based on the X-ray crystal structure of human S1R and constructed from a pooled compilation of 180 S1R antagonists that encompass five structurally diverse chemical families investigated using identical experimental protocols. Best practices, as recommended by the Organization for Economic Cooperation and Development (OECD: http://www.oecd.org/ ), were adopted for pooling data from disparate sources and for QSAR model development and both internal and external model validation. The practical utility of the final 3D-QSAR model was tested by virtual screening of the DrugBank database of FDA approved drugs supplemented by eight reported S1R antagonists. Among the top-ranked 40 DrugBank hits, four approved drugs which were previously unknown as S1R antagonists were tested using in vitro radiolabeled human S1R binding assays. Of these, two drugs (diphenhydramine and phenyltoloxamine) exhibited potent S1R binding affinity with Ki = 58 nM and 160 nM, respectively. As diphenhydramine is approved as an antiallergic, and phenyltoloxamine as an analgesic and sedative, each of these compounds represents a viable starting point for a drug discovery campaign aimed at the development of novel S1R antagonists for a wide range of therapeutic indications.

Virtual Screening and Experimental Testing of B1 Metallo-ß-lactamase Inhibitors.

The global rise of metallo-ß-lactamases (MBLs) is problematic due to their ability to inactivate most ß-lactam antibiotics. MBL inhibitors that could be coadministered with and restore the efficacy of ß-lactams are highly sought after. In this study, we employ virtual screening of candidate MBL inhibitors without thiols or carboxylates to avoid off-target effects using the Avalanche software package, followed by experimental validation of the selected compounds. As target enzymes, we chose the clinically relevant B1 MBLs NDM-1, IMP-1, and VIM-2. Among 32 compounds selected from an approximately 1.5 million compound library, 6 exhibited IC50 values less than 40 µM against NDM-1 and/or IMP-1. The most potent inhibitors of NDM-1, IMP-1, and VIM-2 had IC50 values of 19 ± 2, 14 ± 1, and 50 ± 20 µM, respectively. While chemically diverse, the most potent inhibitors all contain combinations of hydroxyl, ketone, ester, amide, or sulfonyl groups. Docking studies suggest that these electron-dense moieties are involved in Zn(II) coordination and interaction with protein residues. These novel scaffolds could serve as the basis for further development of MBL inhibitors. A procedure for renaming NDM-1 residues to conform to the class B ß-lactamase (BBL) numbering scheme is also included.

Niclosamide is a Negative Allosteric Modulator of Group I Metabotropic Glutamate Receptors: Implications for Neuropathic Pain.

PURPOSE: Novel therapeutics are greatly needed that target specific pathological receptors and pathways involved in Neuropathic Pain (NP). Extending our previous work published in this Journal on Group I metabotropic glutamate receptor (mGluR) modulators, we now investigate the therapeutic potential of niclosamide in modulating aberrant glutamate transmission in NP. METHOD: Calcium mobilization assays and cross-receptor selectivity experiments are conducted to characterize the pharmacological activity of niclosamide. A focused series of niclosamide analogues is then prepared to elucidate key structural determinants that emerged from computational molecular modeling analysis on drug-receptor interactions. Finally, niclosamide and a carbamate derivative are studied to assess their efficacy in an NP-evoked mechanical hyperalgesia model in rats. RESULTS: Niclosamide is a low-nanomolar allosteric antagonist of Group I mGluRs with high selectivity for Group I over homologous Group III mGluRs. The phenolic hydroxyl group of niclosamide forms a crucial hydrogen bond with mGluR1/5. Its bioactive coplanar conformation is further stabilized by the nitro substituent on the B ring and an intramolecular bond. Mechanical hyperalgesia in NP rats is reversed by niclosamide through three different dosing routes. CONCLUSION: To our knowledge, this is the first report of the salicylanilide class of compounds as potential treatments for NP.

Elucidating the Role of Residue 67 in IMP-Type Metallo-ß-Lactamase Evolution.

Antibiotic resistance in bacteria is ever changing and adapting, as once-novel ß-lactam antibiotics are losing their efficacy, primarily due to the production of ß-lactamases. Metallo-ß-lactamases (MBLs) efficiently inactivate a broad range of ß-lactam antibiotics, including carbapenems, and are often coexpressed with other antibacterial resistance factors. The rapid dissemination of MBLs and lack of novel antibacterials pose an imminent threat to global health. In an effort to better counter these resistance-conferring ß-lactamases, an investigation of their natural evolution and resulting substrate specificity was employed. In this study, we elucidated the effects of different amino acid substitutions at position 67 in IMP-type MBLs on the ability to hydrolyze and confer resistance to a range of ß-lactam antibiotics. Wild-type ß-lactamases IMP-1 and IMP-10 and mutants IMP-1-V67A and IMP-1-V67I were characterized biophysically and biochemically, and MICs for Escherichia coli cells expressing these enzymes were determined. We found that all variants exhibited catalytic efficiencies (kcat/Km) equal to or higher than that of IMP-1 against all tested ß-lactams except penicillins, against which IMP-1 and IMP-1-V67I showed the highest kcat/Km values. The substrate-specific effects of the different amino acid substitutions at position 67 are discussed in light of their side chain structures and possible interactions with the substrates. Docking calculations were employed to investigate interactions between different side chains and an inhibitor used as a ß-lactam surrogate. The differences in binding affinities determined experimentally and computationally seem to be governed by hydrophobic interactions between residue 67 and the inhibitor and, by inference, the ß-lactam substrates.

Identification of Nitazoxanide as a Group I Metabotropic Glutamate Receptor Negative Modulator for the Treatment of Neuropathic Pain: An In Silico Drug Repositioning Study.

PURPOSE: Drug repositioning strategies were employed to explore new therapeutic indications for existing drugs that may exhibit dual negative mGluR1/5 modulating activities as potential treatments for neuropathic pain. METHOD: A customized in silico-in vitro-in vivo drug repositioning scheme was assembled and implemented to search available drug libraries for compounds with dual mGluR1/5 antagonistic activities, that were then evaluated using in vitro functional assays and, for validated hits, in an established animal model for neuropathic pain. RESULTS: Tizoxanide, the primary active metabolite of the FDA approved drug nitazoxanide, fit in silico pharmacophore models constructed for both mGluR1 and mGluR5. Subsequent calcium (Ca++) mobilization functional assays confirmed that tizoxanide exhibited appreciable antagonist activity for both mGluR1 and mGluR5 (IC50 = 1.8 µM and 1.2 µM, respectively). The in vivo efficacy of nitazoxanide administered by intraperitoneal injection was demonstrated in a rat model for neuropathic pain. CONCLUSION: The major aim of the present study was to demonstrate the utility of an in silico-in vitro-in vivo drug repositioning protocol to facilitate the repurposing of approved drugs for new therapeutic indications. As an example, this particular investigation successfully identified nitazoxanide and its metabolite tizoxanide as dual mGluR1/5 negative modulators. A key finding is the vital importance for drug screening libraries to include the structures of drug active metabolites, such as those emanating from prodrugs which are estimated to represent 5-7% of marketed drugs.

Avalanche for shape and feature-based virtual screening with 3D alignment.

This report introduces a new ligand-based virtual screening tool called Avalanche that incorporates both shape- and feature-based comparison with three-dimensional (3D) alignment between the query molecule and test compounds residing in a chemical database. Avalanche proceeds in two steps. The first step is an extremely rapid shape/feature based comparison which is used to narrow the focus from potentially millions or billions of candidate molecules and conformations to a more manageable number that are then passed to the second step. The second step is a detailed yet still rapid 3D alignment of the remaining candidate conformations to the query conformation. Using the 3D alignment, these remaining candidate conformations are scored, re-ranked and presented to the user as the top hits for further visualization and evaluation. To provide further insight into the method, the results from two prospective virtual screens are presented which show the ability of Avalanche to identify hits from chemical databases that would likely be missed by common substructure-based or fingerprint-based search methods. The Avalanche method is extended to enable patent landscaping, i.e., structural refinements to improve the patentability of hits for deployment in drug discovery campaigns.

Machine learning models to predict ligand binding affinity for the orexin 1 receptor.

The orexin 1 receptor (OX1R) is a G-protein coupled receptor that regulates a variety of physiological processes through interactions with the neuropeptides orexin A and B. Selective OX1R antagonists exhibit therapeutic effects in preclinical models of several behavioral disorders, including drug seeking and overeating. However, currently there are no selective OX1R antagonists approved for clinical use, fueling demand for novel compounds that act at this target. In this study, we meticulously curated a dataset comprising over 1300 OX1R ligands using a stringent filter and criteria cascade. Subsequently, we developed highly predictive quantitative structure-activity relationship (QSAR) models employing the optimized hyper-parameters for the random forest machine learning algorithm and twelve 2D molecular descriptors selected by recursive feature elimination with a 5-fold cross-validation process. The predictive capacity of the QSAR model was further assessed using an external test set and enrichment study, confirming its high predictivity. The practical applicability of our final QSAR model was demonstrated through virtual screening of the DrugBank database. This revealed two FDA-approved drugs (isavuconazole and cabozantinib) as potential OX1R ligands, confirmed by radiolabeled OX1R binding assays. To our best knowledge, this study represents the first report of highly predictive QSAR models on a large comprehensive dataset of diverse OX1R ligands, which should prove useful for the discovery and design of new compounds targeting this receptor.

Pregnane X receptor mediates dyslipidemia induced by the HIV protease inhibitor amprenavir in mice.

Human immunodeficiency virus (HIV) protease inhibitors (PIs) have been used successfully in extending the life span of people infected with HIV. The use of PIs has also been associated with dyslipidemia and an increased risk of cardiovascular disease, but the underlying mechanisms remain elusive. Several PIs have been implicated in activating the nuclear receptor pregnane X receptor (PXR), which acts as a xenobiotic sensor to regulate xenobiotic metabolism in the liver and intestine. Recent studies indicate that PXR may also play an important role in the regulation of lipid homeostasis. In the present study, we identified amprenavir, a widely used HIV PI, as a potent PXR-selective agonist. Computational docking studies combined with site-direct mutagenesis identified several key residues within the ligand-binding pocket of PXR that constitute points of interaction with amprenavir. Amprenavir efficiently activated PXR and induced PXR target gene expression in vitro and in vivo. Short-term exposure to amprenavir significantly increased plasma total cholesterol and atherogenic low-density lipoprotein cholesterol levels in wild-type mice, but not in PXR-deficient mice. Amprenavir-mediated PXR activation stimulated the expression of several key intestinal genes involved in lipid homeostasis. These findings provide critical mechanistic insight for understanding the impact of PIs on cardiovascular disease and demonstrate a potential role of PXR in mediating the adverse effects of HIV PIs in humans.

Fragment-based Shape Signatures: a new tool for virtual screening and drug discovery.

Since its introduction in 2003, the Shape Signatures method has been successfully applied in a number of drug design projects. Because it uses a ray-tracing approach to directly measure molecular shape and properties (as opposed to relying on chemical structure), it excels at scaffold hopping, and is extraordinarily easy to use. Despite its advantages, a significant drawback of the method has hampered its application to certain classes of problems; namely, when the chemical structures considered are large and contain heterogeneous ring-systems, the method produces descriptors that tend to merely measure the overall size of the molecule, and begin to lose selective power. To remedy this, the approach has been reformulated to automatically decompose compounds into fragments using ring systems as anchors, and to likewise partition the ray-trace in accordance with the fragment assignments. Subsequently, descriptors are generated that are fragment-based, and query and target molecules are compared by mapping query fragments onto target fragments in all ways consistent with the underlying chemical connectivity. This has proven to greatly extend the selective power of the method, while maintaining the ease of use and scaffold-hopping capabilities that characterized the original implementation. In this work, we provide a full conceptual description of the next generation Shape Signatures, and we underline the advantages of the method by discussing its practical applications to ligand-based virtual screening. The new approach can also be applied in receptor-based mode, where protein-binding sites (partitioned into subsites) can be matched against the new fragment-based Shape Signatures descriptors of library compounds.

Recent advances in drug discovery efforts targeting the sigma 1 receptor system: Implications for novel medications designed to reduce excessive drug and food seeking.

Psychiatric disorders characterized by uncontrolled reward seeking, such as substance use disorders (SUDs), alcohol use disorder (AUD) and some eating disorders, impose a significant burden on individuals and society. Despite their high prevalence and substantial morbidity and mortality rates, treatment options for these disorders remain limited. Over the past two decades, there has been a gradual accumulation of evidence pointing to the sigma-1 receptor (S1R) system as a promising target for therapeutic interventions designed to treat these disorders. S1R is a chaperone protein that resides in the endoplasmic reticulum, but under certain conditions translocates to the plasma membrane. In the brain, S1Rs are expressed in several regions important for reward, and following translocation, they physically associate with several reward-related GPCRs, including dopamine receptors 1 and 2 (D1R and D2R). Psychostimulants, alcohol, as well as palatable foods, all alter expression of S1R in regions important for motivated behavior, and S1R antagonists generally decrease behavioral responses to these rewards. Recent advances in structural modeling have permitted the development of highly-selective S1R antagonists with favorable pharmacokinetic profiles, thus providing a therapeutic avenue for S1R-based medications. Here, we provide an up-to-date overview of work linking S1R with motivated behavior for drugs of abuse and food, as well as evidence supporting the clinical utility of S1R antagonists to reduce their excessive consumption. We also highlight potential challenges associated with targeting the S1R system, including the need for a more comprehensive understanding of the underlying neurobiology and careful consideration of the pharmacological properties of S1R-based drugs.

Potent inhibitors of human organic anion transporters 1 and 3 from clinical drug libraries: discovery and molecular characterization.

Transporter-mediated drug-drug interactions in the kidney dramatically influence the pharmacokinetics and other clinical effects of drugs. Human organic anion transporters 1 (hOAT1) and 3 (hOAT3) are the major transporters in the basolateral membrane of kidney proximal tubules, mediating the rate-limiting step in the elimination of a broad spectrum of drugs. In the present study, we screened two clinical drug libraries against hOAT1 and hOAT3. Of the 727 compounds screened, 92 compounds inhibited hOAT1 and 262 compounds inhibited hOAT3. When prioritized based on the peak unbound plasma concentrations of these compounds, three inhibitors for hOAT1 and seven inhibitors for hOAT3 were subsequently identified with high inhibitory potency (>95%). Computational analyses revealed that inhibitors and noninhibitors can be differentiated from each other on the basis of several physicochemical features, including number of hydrogen-bond donors, number of rotatable bonds, and topological polar surface area (TPSA) for hOAT1; and molecular weight, number of hydrogen-bond donors and acceptors, TPSA, partition coefficient (log P(7.4)), and polarizability for hOAT3. Pharmacophore modeling identified two common structural features associated with inhibitors for hOAT1 and hOAT3, viz., an anionic hydrogen-bond acceptor atom, and an aromatic center separated by ∼5.7 Å. Such model provides mechanistic insights for predicting new OAT inhibitors.

Human ADA3 regulates RARalpha transcriptional activity through direct contact between LxxLL motifs and the receptor coactivator pocket.

The alternation/deficiency in activation-3 (ADA3) is an essential component of the human p300/CBP-associated factor (PCAF) and yeast Spt-Ada-Gcn5-acetyltransferase (SAGA) histone acetyltransferase complexes. These complexes facilitate transactivation of target genes by association with transcription factors and modification of local chromatin structure. It is known that the yeast ADA3 is required for nuclear receptor (NR)-mediated transactivation in yeast cells; however, the role of mammalian ADA3 in NR signaling remains elusive. In this study, we have investigated how the human (h) ADA3 regulates retinoic acid receptor (RAR) α-mediated transactivation. We show that hADA3 interacts directly with RARα in a hormone-dependent manner and this interaction contributes to RARα transactivation. Intriguingly, this interaction involves classical LxxLL motifs in hADA3, as demonstrated by both 'loss' and 'gain' of function mutations, as well as a functional coactivator pocket of the receptor. Additionally, we show that hADA3 associates with RARα target gene promoter in a hormone-dependent manner and ADA3 knockdown impairs RARß2 expression. Furthermore, a structural model was established to illustrate an interaction network within the ADA3/RARα complex. These results suggest that hADA3 is a bona fide transcriptional coactivator for RARα, acting through a conserved mechanism involving direct contacts between NR boxes and the receptor's co-activator pocket.

Class modeling analysis of heparin 1H NMR spectral data using the soft independent modeling of class analogy and unequal class modeling techniques.

To differentiate heparin samples with varying amounts of dermatan sulfate (DS) impurities and oversulfated chondroitin sulfate (OSCS) contaminants, proton NMR spectral data for heparin sodium active pharmaceutical ingredient samples from different manufacturers were analyzed using multivariate chemometric techniques. A total of 168 samples were divided into three groups: (a) Heparin, [DS] ≤ 1.0% and [OSCS] = 0%; (b) DS, [DS] > 1.0% and [OSCS] = 0%; (c) OSCS, [OSCS] > 0% with any content of DS. The chemometric models were constructed and validated using two well-established methods: soft independent modeling of class analogy (SIMCA) and unequal class modeling (UNEQ). While SIMCA modeling was conducted using the entire set of variables extracted from the NMR spectral data, UNEQ modeling was combined with variable reduction using stepwise linear discriminant analysis to comply with the requirement that the number of samples per class exceed the number of variables in the model by at least 3-fold. Comparison of the results from these two modeling approaches revealed that UNEQ had greater sensitivity (fewer false positives) while SIMCA had greater specificity (fewer false negatives). For Heparin, DS, and OSCS, respectively, the sensitivity was 78% (56/72), 74% (37/50), and 85% (39/46) from SIMCA modeling and 88% (63/72), 90% (45/50), and 91% (42/46) from UNEQ modeling. Importantly, the specificity of both the SIMCA and UNEQ models was 100% (46/46) for Heparin with respect to OSCS; no OSCS-containing sample was misclassified as Heparin. The specificity of the SIMCA model (45/50, or 90%) was superior to that of the UNEQ model (27/50, or 54%) for Heparin with respect to DS samples. However, the overall prediction ability of the UNEQ model (85%) was notably better than that of the SIMCA model (76%) for the Heparin vs DS vs OSCS classes. The models were challenged with blends of heparin spiked with nonsulfated, partially sulfated, or fully oversulfated chondroitin sulfate A, dermatan sulfate, or heparan sulfate at the 1.0, 5.0, and 10.0 wt % levels. The results from the present study indicate that the combination of (1)H NMR spectral data and class modeling techniques (viz., SIMCA and UNEQ) represents a promising strategy for assessing the quality of commercial heparin samples with respect to impurities and contaminants. The methodologies show utility for applications beyond heparin to other complex products.

Rapid discovery of inhibitors of Toxoplasma gondii using hybrid structure-based computational approach.

Toxoplasma (T.) gondii, the causative agent of toxoplasmosis, is a ubiquitous opportunistic pathogen that infects individuals worldwide, and is a leading cause of severe congenital neurologic and ocular disease in humans. No vaccine to protect humans is available, and hypersensitivity and toxicity limit the use of the few available medicines. Therefore, safer and more effective medicines to treat toxoplasmosis are urgently needed. Using the Hybrid Structure Based (HSB) method, we have previously identified small molecule inhibitors of P. falciparum that seem to target a novel protein-protein interaction between the Myosin tail interacting protein and myosin light chain. This pathway has been hypothesized to be involved in invasion of host erythrocytes by the parasite and is broadly conserved among the apicomplexans. Guided by similar computational drug design approaches, we investigated this series of small molecules as potential inhibitors of T. gondii. Compound C3-21, identified as the most active inhibitor in this series, exhibited an IC(50) value ~500 nM against T. gondii. Among the 16 structural analogs of C3-21 tested thus far, nine additional compounds were identified with IC(50) values <10.0 µM. In vitro assays have revealed that C3-21 markedly limits intracellular growth of T. gondii tachyzoites, but has no effect on host cell human foreskin fibroblasts (HFF) at concentrations more than a log greater than the concentration that inhibits the parasites.

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information.

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

Determination of galactosamine impurities in heparin samples by multivariate regression analysis of their (1)H NMR spectra.

Heparin, a widely used anticoagulant primarily extracted from animal sources, contains varying amounts of galactosamine impurities. Currently, the United States Pharmacopeia (USP) monograph for heparin purity specifies that the weight percent of galactosamine (%Gal) may not exceed 1%. In the present study, multivariate regression (MVR) analysis of (1)H NMR spectral data obtained from heparin samples was employed to build quantitative models for the prediction of %Gal. MVR analysis was conducted using four separate methods: multiple linear regression, ridge regression, partial least squares regression, and support vector regression (SVR). Genetic algorithms and stepwise selection methods were applied for variable selection. In each case, two separate prediction models were constructed: a global model based on dataset A which contained the full range (0-10%) of galactosamine in the samples and a local model based on the subset dataset B for which the galactosamine level (0-2%) spanned the 1% USP limit. All four regression methods performed equally well for dataset A with low prediction errors under optimal conditions, whereas SVR was clearly superior among the four methods for dataset B. The results from this study show that (1)H NMR spectroscopy, already a USP requirement for the screening of contaminants in heparin, may offer utility as a rapid method for quantitative determination of %Gal in heparin samples when used in conjunction with MVR approaches.

Identification of heparin samples that contain impurities or contaminants by chemometric pattern recognition analysis of proton NMR spectral data.

Chemometric analysis of a set of one-dimensional (1D) (1)H nuclear magnetic resonance (NMR) spectral data for heparin sodium active pharmaceutical ingredient (API) samples was employed to distinguish USP-grade heparin samples from those containing oversulfated chondroitin sulfate (OSCS) contaminant and/or unacceptable levels of dermatan sulfate (DS) impurity. Three chemometric pattern recognition approaches were implemented: classification and regression tree (CART), artificial neural network (ANN), and support vector machine (SVM). Heparin sodium samples from various manufacturers were analyzed in 2008 and 2009 by 1D (1)H NMR, strong anion-exchange high-performance liquid chromatography, and percent galactosamine in total hexosamine tests. Based on these data, the samples were divided into three groups: Heparin, DS ≤ 1.0% and OSCS = 0%; DS, DS > 1.0% and OSCS = 0%; and OSCS, OSCS > 0% with any content of DS. Three data sets corresponding to different chemical shift regions (1.95-2.20, 3.10-5.70, and 1.95-5.70 ppm) were evaluated. While all three chemometric approaches were able to effectively model the data in the 1.95-2.20 ppm region, SVM was found to substantially outperform CART and ANN for data in the 3.10-5.70 ppm region in terms of classification success rate. A 100% prediction rate was frequently achieved for discrimination between heparin and OSCS samples. The majority of classification errors between heparin and DS involved cases where the DS content was close to the 1.0% DS borderline between the two classes. When these borderline samples were removed, nearly perfect classification results were attained. Satisfactory results were achieved when the resulting models were challenged by test samples containing blends of heparin APIs spiked with non-, partially, or fully oversulfated chondroitin sulfate A, heparan sulfate, or DS at the 1.0%, 5.0%, and 10.0% (w/w) levels. This study demonstrated that the combination of 1D (1)H NMR spectroscopy with multivariate chemometric methods is a nonsubjective, statistics-based approach for heparin quality control and purity assessment that, once standardized, minimizes the need for expert analysts.

NetCSSP: web application for predicting chameleon sequences and amyloid fibril formation.

The calculation of contact-dependent secondary structure propensity (CSSP) is a unique and sensitive method that detects non-native secondary structure propensities in protein sequences. This method has applications in predicting local conformational change, which typically is observed in core sequences of protein aggregation and amyloid fibril formation. NetCSSP implements the latest version of the CSSP algorithm and provides a Flash chart-based graphic interface that enables an interactive calculation of CSSP values for any user-selected regions in a given protein sequence. This feature also can quantitatively estimate the mutational effect on changes in native or non-native secondary structural propensities in local sequences. In addition, this web tool provides precalculated non-native secondary structure propensities for over 1,400,000 fragments that are seven-residues long, collected from PDB structures. They are searchable for chameleon subsequences that can serve as the core of amyloid fibril formation. The NetCSSP web tool is available at http://cssp2.sookmyung.ac.kr/.