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130 results on '"Bhhatarai, Barun"'

1. The activities of drug inactive ingredients on biological targets

Author

Pottel, Joshua, Armstrong, Duncan, Zou, Ling, Fekete, Alexander, Huang, Xi-Ping, Torosyan, Hayarpi, Bednarczyk, Dallas, Whitebread, Steven, Bhhatarai, Barun, Liang, Guiqing, Jin, Hong, Ghaemi, S Nassir, Slocum, Samuel, Lukacs, Katalin V, Irwin, John J, Berg, Ellen L, Giacomini, Kathleen M, Roth, Bryan L, Shoichet, Brian K, and Urban, Laszlo

Subjects
5.1 Pharmaceuticals, Development of treatments and therapeutic interventions, Animals, Drug Compounding, Drug Evaluation, Preclinical, Excipients, Humans, Molecular Targeted Therapy, General Science & Technology
Abstract

Excipients, considered "inactive ingredients," are a major component of formulated drugs and play key roles in their pharmacokinetics. Despite their pervasiveness, whether they are active on any targets has not been systematically explored. We computed the likelihood that approved excipients would bind to molecular targets. Testing in vitro revealed 25 excipient activities, ranging from low-nanomolar to high-micromolar concentration. Another 109 activities were identified by testing against clinical safety targets. In cellular models, five excipients had fingerprints predictive of system-level toxicity. Exposures of seven excipients were investigated, and in certain populations, two of these may reach levels of in vitro target potency, including brain and gut exposure of thimerosal and its major metabolite, which had dopamine D3 receptor dissociation constant K d values of 320 and 210 nM, respectively. Although most excipients deserve their status as inert, many approved excipients may directly modulate physiologically relevant targets.

Published
2020

2. Allosteric Inhibition of the IRE1α RNase Preserves Cell Viability and Function during Endoplasmic Reticulum Stress

Author

Ghosh, Rajarshi, Wang, Likun, Wang, Eric S, Perera, B Gayani K, Igbaria, Aeid, Morita, Shuhei, Prado, Kris, Thamsen, Maike, Caswell, Deborah, Macias, Hector, Weiberth, Kurt F, Gliedt, Micah J, Alavi, Marcel V, Hari, Sanjay B, Mitra, Arinjay K, Bhhatarai, Barun, Schürer, Stephan C, Snapp, Erik L, Gould, Douglas B, German, Michael S, Backes, Bradley J, Maly, Dustin J, Oakes, Scott A, and Papa, Feroz R

Subjects
Biochemistry and Cell Biology, Biological Sciences, Genetics, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Generic health relevance, Allosteric Regulation, Animals, Apoptosis, Cell Line, Endoplasmic Reticulum Stress, Endoribonucleases, Enzyme Activation, Humans, Islets of Langerhans, Male, Mice, Protein Kinase Inhibitors, Protein Serine-Threonine Kinases, Rats, Retina, Ribonucleases, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Depending on endoplasmic reticulum (ER) stress levels, the ER transmembrane multidomain protein IRE1α promotes either adaptation or apoptosis. Unfolded ER proteins cause IRE1α lumenal domain homo-oligomerization, inducing trans autophosphorylation that further drives homo-oligomerization of its cytosolic kinase/endoribonuclease (RNase) domains to activate mRNA splicing of adaptive XBP1 transcription factor. However, under high/chronic ER stress, IRE1α surpasses an oligomerization threshold that expands RNase substrate repertoire to many ER-localized mRNAs, leading to apoptosis. To modulate these effects, we developed ATP-competitive IRE1α Kinase-Inhibiting RNase Attenuators-KIRAs-that allosterically inhibit IRE1α's RNase by breaking oligomers. One optimized KIRA, KIRA6, inhibits IRE1α in vivo and promotes cell survival under ER stress. Intravitreally, KIRA6 preserves photoreceptor functional viability in rat models of ER stress-induced retinal degeneration. Systemically, KIRA6 preserves pancreatic β cells, increases insulin, and reduces hyperglycemia in Akita diabetic mice. Thus, IRE1α powerfully controls cell fate but can itself be controlled with small molecules to reduce cell degeneration.

Published
2014

4. Divergent allosteric control of the IRE1α endoribonuclease using kinase inhibitors.

Author

Wang, Likun, Perera, B, Hari, Sanjay, Bhhatarai, Barun, Backes, Bradley, Seeliger, Markus, Schürer, Stephan, Maly, Dustin, Papa, Feroz, and Oakes, Scott

Subjects
Adaptor Proteins, Signal Transducing, Catalysis, Cells, Cultured, Cross-Linking Reagents, DNA-Binding Proteins, Down-Regulation, Endoplasmic Reticulum Stress, Endoribonucleases, Humans, Intracellular Signaling Peptides and Proteins, Isoenzymes, Molecular Conformation, Mutation, Phosphorylation, Protein Kinase Inhibitors, Protein Serine-Threonine Kinases, RNA Splicing, Regulatory Factor X Transcription Factors, Ribonucleases, Transcription Factors, Unfolded Protein Response, Up-Regulation, X-Box Binding Protein 1
Abstract

Under endoplasmic reticulum stress, unfolded protein accumulation leads to activation of the endoplasmic reticulum transmembrane kinase/endoRNase (RNase) IRE1α. IRE1α oligomerizes, autophosphorylates and initiates splicing of XBP1 mRNA, thus triggering the unfolded protein response (UPR). Here we show that IRE1αs kinase-controlled RNase can be regulated in two distinct modes with kinase inhibitors: one class of ligands occupies IRE1αs kinase ATP-binding site to activate RNase-mediated XBP1 mRNA splicing even without upstream endoplasmic reticulum stress, whereas a second class can inhibit the RNase through the same ATP-binding site, even under endoplasmic reticulum stress. Thus, alternative kinase conformations stabilized by distinct classes of ATP-competitive inhibitors can cause allosteric switching of IRE1αs RNase--either on or off. As dysregulation of the UPR has been implicated in a variety of cell degenerative and neoplastic disorders, small-molecule control over IRE1α should advance efforts to understand the UPRs role in pathophysiology and to develop drugs for endoplasmic reticulum stress-related diseases.

Published
2012

5. Alternative approaches for identifying acute systemic toxicity: Moving from research to regulatory testing

Author

Hamm, Jon, Sullivan, Kristie, Clippinger, Amy J., Strickland, Judy, Bell, Shannon, Bhhatarai, Barun, Blaauboer, Bas, Casey, Warren, Dorman, David, Forsby, Anna, Garcia-Reyero, Natàlia, Gehen, Sean, Graepel, Rabea, Hotchkiss, Jon, Lowit, Anna, Matheson, Joanna, Reaves, Elissa, Scarano, Louis, Sprankle, Catherine, Tunkel, Jay, Wilson, Dan, Xia, Menghang, Zhu, Hao, and Allen, David

Published
2017
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6. Abstract 1142: Discovery of FMC-376 a novel orally bioavailable inhibitor of activated KRASG12C

Author

Patel, Snahel, primary, Bhhatarai, Barun, additional, Calses, Philamer, additional, Erlanson, Daniel, additional, Everley, Robert, additional, Fong, Susan, additional, Gerken, Phil, additional, Hermann, Johannes C., additional, Le, Tiep, additional, Liu, Li-kai, additional, McMahon, Evan, additional, Neve, Richard M., additional, Phan, Tony, additional, Roberts, Allison, additional, Shanafelt, Mikayla, additional, Siemsgluess, Sophie, additional, Staunton, Jocelyn, additional, Wang, Yan, additional, Wang, Weiru, additional, Williams, Monika, additional, and Webster, Kevin R., additional

Published
2023
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12. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

Author

Mansouri, Kamel, primary, Kleinstreuer, Nicole, additional, Abdelaziz, Ahmed M., additional, Alberga, Domenico, additional, Alves, Vinicius M., additional, Andersson, Patrik L., additional, Andrade, Carolina H., additional, Bai, Fang, additional, Balabin, Ilya, additional, Ballabio, Davide, additional, Benfenati, Emilio, additional, Bhhatarai, Barun, additional, Boyer, Scott, additional, Chen, Jingwen, additional, Consonni, Viviana, additional, Farag, Sherif, additional, Fourches, Denis, additional, García-Sosa, Alfonso T., additional, Gramatica, Paola, additional, Grisoni, Francesca, additional, Grulke, Chris M., additional, Hong, Huixiao, additional, Horvath, Dragos, additional, Hu, Xin, additional, Huang, Ruili, additional, Jeliazkova, Nina, additional, Li, Jiazhong, additional, Li, Xuehua, additional, Liu, Huanxiang, additional, Manganelli, Serena, additional, Mangiatordi, Giuseppe F., additional, Maran, Uko, additional, Marcou, Gilles, additional, Martin, Todd, additional, Muratov, Eugene, additional, Nguyen, Dac-Trung, additional, Nicolotti, Orazio, additional, Nikolov, Nikolai G., additional, Norinder, Ulf, additional, Papa, Ester, additional, Petitjean, Michel, additional, Piir, Geven, additional, Pogodin, Pavel, additional, Poroikov, Vladimir, additional, Qiao, Xianliang, additional, Richard, Ann M., additional, Roncaglioni, Alessandra, additional, Ruiz, Patricia, additional, Rupakheti, Chetan, additional, Sakkiah, Sugunadevi, additional, Sangion, Alessandro, additional, Schramm, Karl-Werner, additional, Selvaraj, Chandrabose, additional, Shah, Imran, additional, Sild, Sulev, additional, Sun, Lixia, additional, Taboureau, Olivier, additional, Tang, Yun, additional, Tetko, Igor V., additional, Todeschini, Roberto, additional, Tong, Weida, additional, Trisciuzzi, Daniela, additional, Tropsha, Alexander, additional, Van Den Driessche, George, additional, Varnek, Alexandre, additional, Wang, Zhongyu, additional, Wedebye, Eva B., additional, Williams, Antony J., additional, Xie, Hongbin, additional, Zakharov, Alexey V., additional, Zheng, Ziye, additional, and Judson, Richard S., additional

Published
2020
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14. Evaluation of OASIS QSAR models using ToxCast™ in vitro estrogen and androgen receptor binding data and application in an integrated endocrine screening approach

Author

Bhhatarai, Barun, Wilson, Daniel M., Price, Paul S., Marty, Sue, Parks, Amanda K., and Carney, Edward

Subjects
Function tests (Medicine) -- Evaluation, Hormone receptors -- Properties, Protein binding -- Health aspects, Environmental issues, Health
Abstract

BACKGROUND: Integrative testing strategies (ITSs) for potential endocrine activity can use tiered in silico and in vitro models. Each component of an ITS should be thoroughly assessed. OBJECTIVES: We used the data from three in vitro ToxCast™ binding assays to assess OASIS, a quantitative structure-activity relationship (QSAR) platform covering both estrogen receptor (ER) and androgen receptor (AR) binding. For stronger binders (described here as [AC.sub.50] < 1 µM), we also examined the relationship of QSAR predictions of ER or AR binding to the results from 18 ER and 10 AR transactivation assays, 72 ER-binding reference compounds, and the in vivo uterotrophic assay. METHODS: NovaScreen binding assay data for ER (human, bovine, and mouse) and AR (human, chimpanzee, and rat) were used to assess the sensitivity, specificity, concordance, and applicability domain of two OASIS QSAR models. The binding strength relative to the QSAR-predicted binding strength was examined for the ER data. The relationship of QSAR predictions of binding to transactivation-and pathway-based assays, as well as to in vivo uterotrophic responses, was examined. RESULTS: The QSAR models had both high sensitivity (> 75%) and specificity (> 86%) for ER as well as both high sensitivity (92-100%) and specificity (70-81%) for AR. For compounds within the domains of the ER and AR QSAR models that bound with [AC.sub.50] < 1 µM, the QSAR models accurately predicted the binding for the parent compounds. The parent compounds were active in all transactivation assays where metabolism was incorporated and, except for those compounds known to require metabolism to manifest activity, all assay platforms where metabolism was not incorporated. Compounds in-domain and predicted to bind by the ER QSAR model that were positive in ToxCast™ ER binding at [AC.sub.50] < 1 µM were active in the uterotrophic assay. CONCLUSIONS: We used the extensive ToxCast™ HTS binding data set to show that OASIS ER and AR QSAR models had high sensitivity and specificity when compounds were in-domain of the models. Based on this research, we recommend a tiered screening approach wherein a) QSAR is used to identify compounds in-domain of the ER or AR binding models and predicted to bind; b) those compounds are screened in vitro to assess binding potency; and c) the stronger binders ([AC.sub.50] < 1 µM) are screened in vivo. This scheme prioritizes compounds for integrative testing and risk assessment. Importantly, compounds that are not in-domain, that are predicted either not to bind or to bind weakly, that are not active in in vitro, that require metabolism to manifest activity, or for which in vivo AR testing is in order, need to be assessed differently. http://dx.doi.org/10.1289/EHP184, Introduction The ability to quickly profile and prioritize large numbers of compounds for potential hazards, including endocrine receptor binding activity, improved with the advent of predictive in vitro high-throughput screening [...]

Published
2016
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15. CoMPARA : Collaborative Modeling Project for Androgen Receptor Activity

Author

Mansouri, Kamel, Kleinstreuer, Nicole, Abdelaziz, Ahmed M., Alberga, Domenico, Alves, Vinicius M., Andersson, Patrik L., Andrade, Carolina H., Bai, Fang, Balabin, Ilya, Ballabio, Davide, Benfenati, Emilio, Bhhatarai, Barun, Boyer, Scott, Chen, Jingwen, Consonni, Viviana, Farag, Sherif, Fourches, Denis, García-Sosa, Alfonso T., Gramatica, Paola, Grisoni, Francesca, Grulke, Chris M., Hong, Huixiao, Horvath, Dragos, Hu, Xin, Huang, Ruili, Jeliazkova, Nina, Li, Jiazhong, Li, Xuehua, Liu, Huanxiang, Manganelli, Serena, Mangiatordi, Giuseppe F., Maran, Uko, Marcou, Gilles, Martin, Todd, Muratov, Eugene, Nguyen, Dac-Trung, Nicolotti, Orazio, Nikolov, Nikolai G., Norinder, Ulf, Papa, Ester, Petitjean, Michel, Pür, Geven, Pogodin, Pavel, Poroikov, Vladimir, Qiao, Xianliang, Richard, Ann M., Roncaglioni, Alessandra, Ruiz, Patricia, Rupakheti, Chetan, Sakkiah, Sugunadevi, Sangion, Alessandro, Schramm, Karl-Werner, Selvaraj, Chandrabose, Shah, Imran, Sild, Sulev, Sun, Lixia, Taboureau, Olivier, Tang, Yun, Tetko, Igor V., Todeschini, Roberto, Tong, Weida, Trisciuzzi, Daniela, Tropsha, Alexander, Van Den Driessche, George, Varnek, Alexandre, Wang, Zhongyu, Wedebye, Eva B., Williams, Antony J., Xie, Hongbin, Zakharov, Alexey V., Zheng, Ziye, Judson, Richard S., Mansouri, Kamel, Kleinstreuer, Nicole, Abdelaziz, Ahmed M., Alberga, Domenico, Alves, Vinicius M., Andersson, Patrik L., Andrade, Carolina H., Bai, Fang, Balabin, Ilya, Ballabio, Davide, Benfenati, Emilio, Bhhatarai, Barun, Boyer, Scott, Chen, Jingwen, Consonni, Viviana, Farag, Sherif, Fourches, Denis, García-Sosa, Alfonso T., Gramatica, Paola, Grisoni, Francesca, Grulke, Chris M., Hong, Huixiao, Horvath, Dragos, Hu, Xin, Huang, Ruili, Jeliazkova, Nina, Li, Jiazhong, Li, Xuehua, Liu, Huanxiang, Manganelli, Serena, Mangiatordi, Giuseppe F., Maran, Uko, Marcou, Gilles, Martin, Todd, Muratov, Eugene, Nguyen, Dac-Trung, Nicolotti, Orazio, Nikolov, Nikolai G., Norinder, Ulf, Papa, Ester, Petitjean, Michel, Pür, Geven, Pogodin, Pavel, Poroikov, Vladimir, Qiao, Xianliang, Richard, Ann M., Roncaglioni, Alessandra, Ruiz, Patricia, Rupakheti, Chetan, Sakkiah, Sugunadevi, Sangion, Alessandro, Schramm, Karl-Werner, Selvaraj, Chandrabose, Shah, Imran, Sild, Sulev, Sun, Lixia, Taboureau, Olivier, Tang, Yun, Tetko, Igor V., Todeschini, Roberto, Tong, Weida, Trisciuzzi, Daniela, Tropsha, Alexander, Van Den Driessche, George, Varnek, Alexandre, Wang, Zhongyu, Wedebye, Eva B., Williams, Antony J., Xie, Hongbin, Zakharov, Alexey V., Zheng, Ziye, and Judson, Richard S.

Abstract

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast (TM) metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast (TM)/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accura

Published
2020
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16. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

Author

Mansouri, K, Kleinstreuer, N, Abdelaziz, A, Alberga, D, Alves, V, Andersson, P, Andrade, C, Bai, F, Balabin, I, Ballabio, D, Benfenati, E, Bhhatarai, B, Boyer, S, Chen, J, Consonni, V, Farag, S, Fourches, D, García-Sosa, A, Gramatica, P, Grisoni, F, Grulke, C, Hong, H, Horvath, D, Hu, X, Huang, R, Jeliazkova, N, Li, J, Li, X, Liu, H, Manganelli, S, Mangiatordi, G, Maran, U, Marcou, G, Martin, T, Muratov, E, Nguyen, D, Nicolotti, O, Nikolov, N, Norinder, U, Papa, E, Petitjean, M, Piir, G, Pogodin, P, Poroikov, V, Qiao, X, Richard, A, Roncaglioni, A, Ruiz, P, Rupakheti, C, Sakkiah, S, Sangion, A, Schramm, K, Selvaraj, C, Shah, I, Sild, S, Sun, L, Taboureau, O, Tang, Y, Tetko, I, Todeschini, R, Tong, W, Trisciuzzi, D, Tropsha, A, Van Den Driessche, G, Varnek, A, Wang, Z, Wedebye, E, Williams, A, Xie, H, Zakharov, A, Zheng, Z, Judson, R, Mansouri, Kamel, Kleinstreuer, Nicole, Abdelaziz, Ahmed M., Alberga, Domenico, Alves, Vinicius M., Andersson, Patrik L., Andrade, Carolina H., Bai, Fang, Balabin, Ilya, Ballabio, Davide, Benfenati, Emilio, Bhhatarai, Barun, Boyer, Scott, Chen, Jingwen, Consonni, Viviana, Farag, Sherif, Fourches, Denis, García-Sosa, Alfonso T., Gramatica, Paola, Grisoni, Francesca, Grulke, Chris M., Hong, Huixiao, Horvath, Dragos, Hu, Xin, Huang, Ruili, Jeliazkova, Nina, Li, Jiazhong, Li, Xuehua, Liu, Huanxiang, Manganelli, Serena, Mangiatordi, Giuseppe F., Maran, Uko, Marcou, Gilles, Martin, Todd, Muratov, Eugene, Nguyen, Dac-Trung, Nicolotti, Orazio, Nikolov, Nikolai G., Norinder, Ulf, Papa, Ester, Petitjean, Michel, Piir, Geven, Pogodin, Pavel, Poroikov, Vladimir, Qiao, Xianliang, Richard, Ann M., Roncaglioni, Alessandra, Ruiz, Patricia, Rupakheti, Chetan, Sakkiah, Sugunadevi, Sangion, Alessandro, Schramm, Karl-Werner, Selvaraj, Chandrabose, Shah, Imran, Sild, Sulev, Sun, Lixia, Taboureau, Olivier, Tang, Yun, Tetko, Igor V., Todeschini, Roberto, Tong, Weida, Trisciuzzi, Daniela, Tropsha, Alexander, Van Den Driessche, George, Varnek, Alexandre, Wang, Zhongyu, Wedebye, Eva B., Williams, Antony J., Xie, Hongbin, Zakharov, Alexey V., Zheng, Ziye, Judson, Richard S., Mansouri, K, Kleinstreuer, N, Abdelaziz, A, Alberga, D, Alves, V, Andersson, P, Andrade, C, Bai, F, Balabin, I, Ballabio, D, Benfenati, E, Bhhatarai, B, Boyer, S, Chen, J, Consonni, V, Farag, S, Fourches, D, García-Sosa, A, Gramatica, P, Grisoni, F, Grulke, C, Hong, H, Horvath, D, Hu, X, Huang, R, Jeliazkova, N, Li, J, Li, X, Liu, H, Manganelli, S, Mangiatordi, G, Maran, U, Marcou, G, Martin, T, Muratov, E, Nguyen, D, Nicolotti, O, Nikolov, N, Norinder, U, Papa, E, Petitjean, M, Piir, G, Pogodin, P, Poroikov, V, Qiao, X, Richard, A, Roncaglioni, A, Ruiz, P, Rupakheti, C, Sakkiah, S, Sangion, A, Schramm, K, Selvaraj, C, Shah, I, Sild, S, Sun, L, Taboureau, O, Tang, Y, Tetko, I, Todeschini, R, Tong, W, Trisciuzzi, D, Tropsha, A, Van Den Driessche, G, Varnek, A, Wang, Z, Wedebye, E, Williams, A, Xie, H, Zakharov, A, Zheng, Z, Judson, R, Mansouri, Kamel, Kleinstreuer, Nicole, Abdelaziz, Ahmed M., Alberga, Domenico, Alves, Vinicius M., Andersson, Patrik L., Andrade, Carolina H., Bai, Fang, Balabin, Ilya, Ballabio, Davide, Benfenati, Emilio, Bhhatarai, Barun, Boyer, Scott, Chen, Jingwen, Consonni, Viviana, Farag, Sherif, Fourches, Denis, García-Sosa, Alfonso T., Gramatica, Paola, Grisoni, Francesca, Grulke, Chris M., Hong, Huixiao, Horvath, Dragos, Hu, Xin, Huang, Ruili, Jeliazkova, Nina, Li, Jiazhong, Li, Xuehua, Liu, Huanxiang, Manganelli, Serena, Mangiatordi, Giuseppe F., Maran, Uko, Marcou, Gilles, Martin, Todd, Muratov, Eugene, Nguyen, Dac-Trung, Nicolotti, Orazio, Nikolov, Nikolai G., Norinder, Ulf, Papa, Ester, Petitjean, Michel, Piir, Geven, Pogodin, Pavel, Poroikov, Vladimir, Qiao, Xianliang, Richard, Ann M., Roncaglioni, Alessandra, Ruiz, Patricia, Rupakheti, Chetan, Sakkiah, Sugunadevi, Sangion, Alessandro, Schramm, Karl-Werner, Selvaraj, Chandrabose, Shah, Imran, Sild, Sulev, Sun, Lixia, Taboureau, Olivier, Tang, Yun, Tetko, Igor V., Todeschini, Roberto, Tong, Weida, Trisciuzzi, Daniela, Tropsha, Alexander, Van Den Driessche, George, Varnek, Alexandre, Wang, Zhongyu, Wedebye, Eva B., Williams, Antony J., Xie, Hongbin, Zakharov, Alexey V., Zheng, Ziye, and Judson, Richard S.

Abstract

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP’s list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCastTM metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCastTM/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy ass

Published
2020

18. Alternative approaches for identifying acute systemic toxicity: Moving from research to regulatory testing

Author

LS IRAS Tox CMT/ATX, dIRAS RA-1, Hamm, Jon, Sullivan, Kristie, Clippinger, Amy J, Strickland, Judy, Bell, Shannon, Bhhatarai, Barun, Blaauboer, B, Casey, Warren, Dorman, David, Forsby, Anna, Garcia-Reyero, Natàlia, Gehen, Sean, Graepel, Rabea, Hotchkiss, Jon, Lowit, Anna, Matheson, Joanna, Reaves, Elissa, Scarano, Louis, Sprankle, Catherine, Tunkel, Jay, Wilson, Dan, Xia, Menghang, Zhu, Hao, Allen, David, LS IRAS Tox CMT/ATX, dIRAS RA-1, Hamm, Jon, Sullivan, Kristie, Clippinger, Amy J, Strickland, Judy, Bell, Shannon, Bhhatarai, Barun, Blaauboer, B, Casey, Warren, Dorman, David, Forsby, Anna, Garcia-Reyero, Natàlia, Gehen, Sean, Graepel, Rabea, Hotchkiss, Jon, Lowit, Anna, Matheson, Joanna, Reaves, Elissa, Scarano, Louis, Sprankle, Catherine, Tunkel, Jay, Wilson, Dan, Xia, Menghang, Zhu, Hao, and Allen, David

Published
2017

34. The QSPR-THESAURUS : The Online Platform of the CADASTER Project

Author

Brandmaier, Stefan, Peijnenburg, Willie, Durjava, Mojca K., Kolar, Boris, Gramatica, Paola, Papa, Ester, Bhhatarai, Barun, Kovarich, Simona, Cassani, Stefano, Roy, Partha Pratim, Rahmberg, Magnus, Öberg, Tomas, Jeliazkova, Nina, Golsteijn, Laura, Comber, Mike, Charochkina, Larisa, Novotarskyi, Sergii, Sushko, Iurii, Abdelaziz, Ahmed, D’Onofrio, Elisa, Kunwar, Prakash, Ruggiu, Fiorella, Tetko, Igor V., Brandmaier, Stefan, Peijnenburg, Willie, Durjava, Mojca K., Kolar, Boris, Gramatica, Paola, Papa, Ester, Bhhatarai, Barun, Kovarich, Simona, Cassani, Stefano, Roy, Partha Pratim, Rahmberg, Magnus, Öberg, Tomas, Jeliazkova, Nina, Golsteijn, Laura, Comber, Mike, Charochkina, Larisa, Novotarskyi, Sergii, Sushko, Iurii, Abdelaziz, Ahmed, D’Onofrio, Elisa, Kunwar, Prakash, Ruggiu, Fiorella, and Tetko, Igor V.

Abstract

The aim of the CADASTER project (CAse Studies on the Development and Application of in Silico Techniques for Environmental Hazard and Risk Assessment) was to exemplify REACH-related hazard assessments for four classes of chemical compound, namely, polybrominated diphenylethers, per and polyfluorinated compounds, (benzo)triazoles, and musks and fragrances. The QSPR-THESAURUS website (http: / /qspr-thesaurus.eu) was established as the project's online platform to upload, store, apply, and also create, models within the project. We overview the main features of the website, such as model upload, experimental design and hazard assessment to support risk assessment, and integration with other web tools, all of which are essential parts of the QSPR-THESAURUS.

Published
2014
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36. The QSPR-THESAURUS: The Online Platform of the CADASTER Project

Author

Brandmaier, Stefan, primary, Peijnenburg, Willie, additional, Durjava, Mojca K., additional, Kolar, Boris, additional, Gramatica, Paola, additional, Papa, Ester, additional, Bhhatarai, Barun, additional, Kovarich, Simona, additional, Cassani, Stefano, additional, Roy, Partha Pratim, additional, Rahmberg, Magnus, additional, Öberg, Tomas, additional, Jeliazkova, Nina, additional, Golsteijn, Laura, additional, Comber, Mike, additional, Charochkina, Larisa, additional, Novotarskyi, Sergii, additional, Sushko, Iurii, additional, Abdelaziz, Ahmed, additional, D'Onofrio, Elisa, additional, Kunwar, Prakash, additional, Ruggiu, Fiorella, additional, and Tetko, Igor V., additional

Published
2014
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37. CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals

Author

Bhhatarai, Barun, Teetz, Wolfram, Liu, Tao, Öberg, Tomas, Jeliazkova, Nina, Kochev, Nikolay, Pukalov, Ognyan, Tetko, Igor, Kovarich, Simona, Papa, Ester, Gramatica, Paola, Bhhatarai, Barun, Teetz, Wolfram, Liu, Tao, Öberg, Tomas, Jeliazkova, Nina, Kochev, Nikolay, Pukalov, Ognyan, Tetko, Igor, Kovarich, Simona, Papa, Ester, and Gramatica, Paola

Abstract

Quantitative structure property relationship (QSPR) studies on per- and polyfluorinated chemicals (PFCs) on melting point (MP) and boiling point (BP) are presented. The training and prediction chemicals used for developing and validating the models were selected from Syracuse PhysProp database and literatures. The available experimental data sets were split in two different ways: a) random selection on response value, and b) structural similarity verified by self-organizing-map (SOM), in order to propose reliable predictive models, developed only on the training sets and externally verified on the prediction sets. Individual linear and non-linear approaches based models developed by different CADASTER partners on 0D-2D Dragon descriptors, E-state descriptors and fragment based descriptors as well as consensus model and their predictions are presented. In addition, the predictive performance of the developed models was verified on a blind external validation set (EV-set) prepared using PERFORCE database on 15 MP and 25 BP data respectively. This database contains only long chain perfluoro-alkylated chemicals, particularly monitored by regulatory agencies like US-EPA and EU-REACH. QSPR models with internal and external validation on two different external prediction/validation sets and study of applicability-domain highlighting the robustness and high accuracy of the models are discussed. Finally, MPs for additional 303 PFCs and BPs for 271 PFCs were predicted for which experimental measurements are unknown.

Published
2011
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48. QSAR and Molecular Modeling Studies of HIV Protease Inhibitors.

Author

Gupta, Satya Prakash, Garg, Rajni, and Bhhatarai, Barun

Abstract

Acquired immunodeficiency syndrome (AIDS) and its related disorders, caused by retrovirus human immunodeficiency virus (HIV) are a major health concern worldwide. HIV protease is one of the major viral targets for the development of new chemotherapeutics. Currently, many HIV protease inhibitors are used in combination with HIV reverse transcriptase inhibitors. However, the use of current drugs regimens has several shortcomings, such as adherence, tolerability, long-term toxicity and drug- and cross-resistance. HIV is also known to have several mutants. Therefore, the development of new inhibitors that are less toxic, more tolerable, convenient and active against drug-resistant viruses is highly desirable. Several in-silico techniques are utilized in the process of drug design and development. One such technique is quantitative structure-activity relationship (QSAR). QSAR models reveal significant correlations between the biological activity and physico-chemical parameters and molecular descriptors. QSAR model can stand alone, support other approaches or be examined in tandem with equations of a similar mechanism to truly reveal its power. This work is devoted to a discussion of QSAR and molecular modeling studies especially those pertaining to 3D-QSAR on HIV protease inhibitors. It provides an overview of new ideas and their applications as they appear in the recent literature. Examples are given of peptidic and non-peptidic protease inhibitors. The role of hydrophobic, steric and electronic interactions in the design of HIV protease inhibitors is discussed. Linking a non-peptidic moiety to a peptidic backbone may provide a way of highlighting regions of interest. Some studies on such hybrid inhibitors are also discussed. Finally, studies on mutant protease data are included as they appear to be of utmost importance. [ABSTRACT FROM AUTHOR]

Published
2006
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49. Prediction of Aqueous Solubility, Vapor Pressure and Critical Micelle Concentration for Aquatic Partitioning of Perfluorinated Chemicals.

Author

Bhhatarai, Barun and Gramatica, Paola

Subjects
*PERFLUOROCARBONS, *POLLUTION, *SOLUBILITY, *VAPOR pressure, *CRITICAL micelle concentration, *AQUATIC ecology, *BIOCONCENTRATION
Abstract

The majority of perfluorinated chemicals (PFCs) are of increasing risk to biota and environment due to their physicochemical stability, wide transport in the environment and difficulty in biodegradation. It is necessary to identify and prioritize these harmful PFCs and to characterize their physicochemical properties that govern the solubility, distribution and fate of these chemicals in an aquatic ecosystem. Therefore, available experimental data (10-35 compounds) of three important properties: aqueous solubility (AqS), vapor pressure (VP) and critical micelle concentration (CMC) on per- and polyfluorinated compounds were collected for quantitative structure-property relationship (QSPR) modeling. Simple and robust models based on theoretical molecular descriptors were developed and externally validated for predictivity. Model predictions on selected PFCs were compared with available experimental data and other published in silico predictions. The structural applicability domains (AD) of the models were verified on a bigger data set of 221 compounds. The predicted properties of the chemicals that are within the AD, are reliable, and they help to reduce the wide data gap that exists. Moreover, the predictions of AqS, VP, and CMC of most common PFCs were evaluated to understand the aquatic partitioning and to derive a relation with the available experimental data of bioconcentration factor (BCF). [ABSTRACT FROM AUTHOR]

Published
2011
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50. A QSAR Study of HIV Protease Inhibitors Using Theoretical Descriptors

Author

C. Basak, Subhash, Mills, Denise, Garg, Rajni, and Bhhatarai, Barun

Abstract

This paper reports the development of quantitative structure-activity relationship (QSAR) models for a set of 170 chemicals using mathematical descriptors which can be calculated directly from molecular structure without the input of any other experimental data. The calculated descriptors include topostructural (TS), topochemical (TC), and quantum chemical (QC). Because the situation is rank deficient i.e. the number of independent variables (descriptors) is larger than the number of compounds, three robust linear statistical modeling methods capable of handling such situations, viz., principal components regression (PCR), partial least square (PLS), and ridge regression (RR) were used for QSAR formulation. Results show that PLS and RR gave better q2 values as compared to the PCR method. Of the three classes of descriptors, the TC indices were the best predictors of anti-HIV activity and the QC indices were the least effective.

Published
2010

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