Browsing by Author "Dhurjati, Prasad"
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Item Development of Physiologically Based Pharmacokinetic Model (PBPK) of BMP2 in Mice(OMICS Group, 2013) Utturkar, A.; Paul, B.; Akkiraju, H.; Bonor, J.; Dhurjati, Prasad; Nohe, Anja; Utturkar A, Paul B, Akkiraju H, Bonor J, Dhurjati P, Nohe A; Utturkar, A.; Paul, B.; Akkiraju, H.; Bonor, J.; Dhurjati, Prasad; Nohe, AnjaBone Morphogenetic protein 2 holds great promise for potential applications in the clinic. It is a potent growth factor for the use in the cervical spine surgery (FDA approved 2002) and has been marketed as “Infuse” for treating open tibial shaft fractures (FDA approved 2004). However, its use is limited by several significant side effects that maybe due to its potency and effect on different stem cell populations in the spine. BMP2 is expressed throughout the human body in several tissues and at a very high concentration in the blood. BMP receptors, especially BMP receptor type Ia, is ubiquitously expressed in most tissues. Currently, it is difficult to determine how BMP2 is physiologically distributed in mice or humans and no quantitative models are available. A Physiologically-Based Pharmaco-Kinetic (PBPK) model has been developed to determine steady-state distribution of BMP2 in mice. The multi-compartmental PBPK model represents relevant organ/tissues with physiological accuracy. The organs/tissue compartments chosen were brain, lung, heart, liver, pancreas, kidney, uterus, bone and fat. A blood compartment maintained connectivity among the various organs. Four processes characterized the change in the concentration of the protein in every compartment: blood flow in, blood flow out, protein turnover and receptor binding in the organ. The unique aspects of the model are the determination of elimination using receptor kinetics and generation using protein turnover. The model also predicts steady state concentrations of BMP2 in tissues in mice and may be used for possible scale-up of dosage regimens in humans.Item Novel Systems Modeling Methodology in Comparative Microbial Metabolomics: Identifying Key Enzymes and Metabolites Implicated in Autism Spectrum Disorders(MDPI AG, Basel, Switzerland, 2015-04-22) Heberling, Colin; Dhurjati, Prasad; Colin Heberling and Prasad Dhurjati; Heberling, Colin; Dhurjati, PrasadAutism spectrum disorders are a group of mental illnesses highly correlated with gastrointestinal dysfunction. Recent studies have shown that there may be one or more microbial “fingerprints” in terms of the composition characterizing individuals with autism, which could be used for diagnostic purposes. This paper proposes a computational approach whereby metagenomes characteristic of “healthy” and autistic individuals are artificially constructed via genomic information, analyzed for the enzymes coded within, and then these enzymes are compared in detail. This is a text mining application. A custom-designed online application was built and used for the comparative metabolomics study and made publically available. Several of the enzyme-catalyzing reactions involved with the amino acid glutamate were curiously missing from the “autism” microbiome and were coded within almost every organism included in the “control” microbiome. Interestingly, there exists a leading hypothesis regarding autism and glutamate involving a neurological excitation/inhibition imbalance; but the association with this study is unclear. The results included data on the transsulfuration and transmethylation pathways, involved with oxidative stress, also of importance to autism. The results from this study are in alignment with leading hypotheses in the field, which is impressive, considering the purely in silico nature of this study. The present study provides new insight into the complex metabolic interactions underlying autism, and this novel methodology has potential to be useful for developing new hypotheses. However, limitations include sparse genome data availability and conflicting literature experimental data. We believe our software tool and methodology has potential for having great utility as data become more available, comprehensive and reliable.Item Novel Systems Modeling Methodology in Comparative Microbial Metabolomics: Identifying Key Enzymes and Metabolites Implicated in Autism Spectrum Disorders(MDPI AG, 2015-04-22) Heberling, Colin; Dhurjati, Prasad; Colin Heberling, and Prasad Dhurjati.; Heberling, Colin; Dhurjati, PrasadAutism spectrum disorders are a group of mental illnesses highly correlated with gastrointestinal dysfunction. Recent studies have shown that there may be one or more microbial “fingerprints” in terms of the composition characterizing individuals with autism, which could be used for diagnostic purposes. This paper proposes a computational approach whereby metagenomes characteristic of “healthy” and autistic individuals are artificially constructed via genomic information, analyzed for the enzymes coded within, and then these enzymes are compared in detail. This is a text mining application. A custom-designed online application was built and used for the comparative metabolomics study and made publically available. Several of the enzyme-catalyzing reactions involved with the amino acid glutamate were curiously missing from the “autism” microbiome and were coded within almost every organism included in the “control” microbiome. Interestingly, there exists a leading hypothesis regarding autism and glutamate involving a neurological excitation/inhibition imbalance; but the association with this study is unclear. The results included data on the transsulfuration and transmethylation pathways, involved with oxidative stress, also of importance to autism. The results from this study are in alignment with leading hypotheses in the field, which is impressive, considering the purely in silico nature of this study. The present study provides new insight into the complex metabolic interactions underlying autism, and this novel methodology has potential to be useful for developing new hypotheses. However, limitations include sparse genome data availability and conflicting literature experimental data. We believe our software tool and methodology has potential for having great utility as data become more available, comprehensive and reliable.