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Browsing by Author "Reisfeld, Brad, advisor"

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    A new approach to addressing two problems in pharmacokinetics and pharmacodynamics using machine learning
    (Colorado State University. Libraries, 2020) Habib, Sohaib, author; Reisfeld, Brad, advisor; Munsky, Brian, committee member; Shipman, Patrick, committee member
    In this work, machine learning was applied to develop solutions for two problems related to drug pharmacokinetics (PK) and pharmacodynamics (PD). The first problem was finding a way to easily predict important pharmacological measures accurately representative of those from simulation results computed via a sophisticated model for drug absorption via oral dosing. This model (OpenCAT: Open source Compartmental And Transit model) comprises a system of differential equations describing the absorption of drugs into the gastrointestinal tract, including such factors as drug dissolution and spatially-distributed absorption, metabolism, and transport. For this problem, a machine learning framework was built to develop a self-contained random forest representation of the model predictions that could be queried for critical PK parameters such as maximum plasma concentration (Cmax), time at which the maximum concentration occurs (tmax), and the area under the concentration-time curve (AUC). The random-forest representation was able to generate predictions for the targeted PK parameters close to the solution of the original OpenCAT model over a wide range of drug characteristics. The second problem involved predicting the pharmacodynamics (cholinesterase reactivation) of antidotes for nerve agents. In this case, a machine learning framework was built to use experimental data and corresponding theoretically-derived chemical descriptors to predict the pharmacodynamics of new candidate antidotes against both tested and untested nerve agents. Overall, this project has demonstrated the utility of machine learning approaches in the fields of drug pharmacokinetics and pharmacodynamics.
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    Computational modeling of the pharmacokinetics and pharmacodynamics of selected xenobiotics
    (Colorado State University. Libraries, 2016) Zurlinden, Todd J., author; Reisfeld, Brad, advisor; Hays, Sean, committee member; Shipman, Patrick, committee member; Munsky, Brian, committee member
    The determination of important endpoints in toxicology and pharmacology continues to involve the acquisition of large amounts of data through resource-intensive experimental studies involving a large number of resources. Because of this, only a small fraction of chemicals in the environment and marketplace can reasonably be evaluated for safety, and many promising drug candidates must be eliminated from consideration based on inadequate evaluation. Promisingly, advances in biologically-based computational models are beginning to allow researchers to estimate these endpoints and make useful extrapolations using a limited set of experimental data. The work described in this dissertation examined how computational models can provide meaningful insight and quantitation of important pharmacological and toxicological endpoints related to toxicity and pharmacological efficacy. To this end, physiologically-based pharmacokinetic and pharmacodynamic models were developed and applied for several pharmaceutical agents and environmental toxicants to predict significant, and diverse, biological endpoints. First, physiologically-based modeling allowed for the evaluation of various dosing regimens of rifapentine, a drug that is showing great promise for the treatment of tuberculosis, by comparing lung-specific concentration predictions to experimentally-derived thresholds for antibacterial activity. Second, physiologically-based pharmacokinetic modeling, coupled with Bayesian inference, was used as part of a methodology to characterize genetic differences in acetaminophen pharmacokinetics and also to help clinicians predict an ingested dose of this drug under overdose conditions. Third, a methodology for using physiologically-based pharmacokinetic modeling to predict health-based cognitive endpoints was demonstrated for chronic exposure to chlorpyrifos, an organophosphorus insecticide. The environmental public health indicators derived from this work allowed for biomarkers of exposure to be used to predict neurobehavioral changes following long-term exposure to this chemical. Finally, computational modeling was used to develop a mechanistically-plausible pharmacodynamic model for hepatoprotective and pro-inflammatory events to relate trichloroethylene dosing conditions to observed pathologies associated with auto-immune hepatitis.
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    Physiologically based pharmacokinetic modeling for prediction of pharmacokinetic parameters of capreomycin
    (Colorado State University. Libraries, 2010) Metzler, Catherine, author; Reisfeld, Brad, advisor; DeGroote, Mary Ann, committee member; Prasad, Ashok, committee member
    Tuberculosis (TB) is a global public health epidemic that is increasingly dangerous and difficult to treat due in large part to drug-resistant strains. New pharmaceutical options must be considered, including capreomycin, an antibiotic discovered in the 1950s but rarely used. Due to more effective, less renal-toxic drugs, capreomycin has not been used as a primary antibiotic in tuberculosis. However, capreomycin has reemerged due to the increase in multi drug resistant TB (MDRTB). Because of its importance in treating drug-resistant strains of TB, improving the understanding of the effective dosages and resulting side effects of capreomycin is necessary. By using a validated model, drugs of interest like capreomycin could be rapidly evaluated for initial recommendations thus reducing drug development time. Using physiologically-based pharmacokinetic (PBPK) models as predictors would be economically and time efficient. In this study, a PBPK model in combination with experimental concentration profiles in mice was used to predict capreomycin pharmacokinetic parameters. Through scale-up of the model to human physiology, and implementation of the hypothesized pharmacokinetic parameters, human organ concentration profiles were predicted and compared to literature data to assess the model capabilities. The model and parameters are anticipated to be useful in predicting the disposition of capreomycin in the mouse via various dosing regimens. Although the model is useful in making pharmaeokinetic predictions in the mouse, the parameter values will need to be adjusted appropriately to be useful for estimating ADME in humans.
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    Physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling of 3,3',4,4',5-pentachlorobiphenyl (PCB126)
    (Colorado State University. Libraries, 2008) Lohitnavy, Manupat, author; Yang, Raymond H. S., advisor; Reisfeld, Brad, advisor
    3,3',4',4',5-Pentachlorobiphenyl (PCB126) is a persistent environment carcinogen. Despite its high lipophilicity, PCB126 was primarily recovered from liver. In addition, PCB126 could achieve its steady state in the liver in a relatively short period of time. Using a three-dimensional quantitative structure-activity relationship (3D-QSAR) model, PCB126 was predicted to be a Mrp2 substrate with a relatively high binding affinity (Km) value. With this newly emerging knowledge, we incorporated a Mrp2-mediated excretion process into our physiologically-based pharmacokinetic (PBPK) model of PCB126. Our model could describe numerous tissue concentration-time courses in different dosing conditions. Our PBPK model revealed an important role of Mrp2 in PCB126 disposition. In addition, to establish a correlation between PCB126 pharmacokinetics and its pharmacodynamic (PD) endpoint (i.e. hepatocarcinogenic effect), we used a chosen internal dose surrogate [i.e. area under the curve of PCB126 in liver (AUCLiver)] to predict the PD effect of PCB126. With this PBPK/PD model, correlation between the AUCLiver and our liver glutathione- S-transferase placental form positive (GSTP+) foci development data was demonstrated. We also conducted a pharmacokinetic interaction study between PCB126 and methotrexate (MTX), a known Mrp2 substrate, by exposing rats with multiple oral doses of PCB126 followed by an oral single dose of MTX. Liver samples were collected and analyzed for hepatic MTX and PCB126 concentration levels. Using a PBPK modeling technique incorporating with competitive inhibition processes between the two chemicals at the level of hepatic Mrp2, liver concentration-time courses of both chemicals were successfully simulated. To further investigate PD effects of PCB126 within liver GSTP + foci, we conducted an experiment by exposing rats with PCB126 using our modified liver foci bioassay up to 6 months. Liver foci positive or negative for GSTP+, transforming growth factor-α+ (TGFα+) and transforming growth factor-β Type 2 receptor (TGFβ2Rc-) were investigated. In rats treated with PCB126, time-dependent changes in all of three biomarkers were observed. Interestingly, when the GSTP+ foci were categorized into four phenotypic groups according to their TGFα and TGFβ2Rc expression, GSTP+ foci with TGFα expression and absence of TGFβ2Rc expression had significantly higher hepatocyte division rates than those of GSTP+ foci without TGFα expression and with TGFβ2Rc expression.