Browsing by Author "Stratford, Robert E., Jr."

Now showing 1 - 5 of 5
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    Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials
    (Wiley, 2021-09) Podichetty, Jagdeep T.; Silvola, Rebecca M.; Rodriguez-Romero, Violeta; Bergstrom, Richard F.; Vakilynejad, Majid; Bies, Robert R.; Stratford, Robert E., Jr.; Medicine, School of Medicine
    Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof-of-concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either "improvement," defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or "no improvement," defined as an inadequate treatment response (<20% reduction in total PANSS). A random forest algorithm performed best relative to other tree-based approaches in model ability to classify patients after 6 months of treatment. Although model ability to identify true positives, a measure of model sensitivity, was poor (<0.2), its specificity, true negative rate, was high (0.948). A second model, adapted from the first, was subsequently applied as a proof-of-concept for the ML approach to supplement trial enrollment by identifying patients not expected to improve based on their baseline diagnostic scores. In three virtual trials applying this screening approach, the percentage of patients predicted to improve ranged from 46% to 48%, consistently approximately double the CATIE response rate of 22%. These results show the promising application of ML to improve clinical trial efficiency and, as such, ML models merit further consideration and development.
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    Multiscale Model of Antiviral Timing, Potency, and Heterogeneity Effects on an Epithelial Tissue Patch Infected by SARS-CoV-2
    (MDPI, 2022-03-14) Gianlupi, Juliano Ferrari; Mapder, Tarunendu; Sego, T.J.; Sluka, James P.; Quinney, Sara K.; Craig, Morgan; Stratford, Robert E., Jr.; Glazier, James A.; Medicine, School of Medicine
    We extend our established agent-based multiscale computational model of infection of lung tissue by SARS-CoV-2 to include pharmacokinetic and pharmacodynamic models of remdesivir. We model remdesivir treatment for COVID-19; however, our methods are general to other viral infections and antiviral therapies. We investigate the effects of drug potency, drug dosing frequency, treatment initiation delay, antiviral half-life, and variability in cellular uptake and metabolism of remdesivir and its active metabolite on treatment outcomes in a simulated patch of infected epithelial tissue. Non-spatial deterministic population models which treat all cells of a given class as identical can clarify how treatment dosage and timing influence treatment efficacy. However, they do not reveal how cell-to-cell variability affects treatment outcomes. Our simulations suggest that for a given treatment regime, including cell-to-cell variation in drug uptake, permeability and metabolism increase the likelihood of uncontrolled infection as the cells with the lowest internal levels of antiviral act as super-spreaders within the tissue. The model predicts substantial variability in infection outcomes between similar tissue patches for different treatment options. In models with cellular metabolic variability, antiviral doses have to be increased significantly (>50% depending on simulation parameters) to achieve the same treatment results as with the homogeneous cellular metabolism.
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    Novel Quantification of Extracellular Vesicles with Unaltered Surface Membranes Using an Internalized Oligonucleotide Tracer and Applied Pharmacokinetic Multiple Compartment Modeling
    (Springer, 2021-10) De Luca, Thomas; Stratford, Robert E., Jr.; Edwards, Madison E.; Ferreira, Christina R.; Benson, Eric A.; Medicine, School of Medicine
    Purpose: We developed an accessible method for labeling small extracellular vesicles (sEVs) without disrupting endogenous ligands. Using labeled sEVs administered to conscious rats, we developed a multiple compartment pharmacokinetic model to identify potential differences in the disposition of sEVs from three different cell types. Methods: Crude sEVs were labeled with a non-homologous oligonucleotide and isolated from cell culture media using a commercial reagent. Jugular vein catheters were used to introduce EVs to conscious rats (n = 30) and to collect blood samples. Digital PCR was leveraged to allow for quantification over a wide dynamic range. Non-linear mixed effects analysis with first order conditional estimation - extended least squares (FOCE ELS) was used to estimate population-level parameters with associated intra-animal variability. Results: 86.5% ± 1.5% (mean ± S.E.) of EV particles were in the 45-195 nm size range and demonstrated protein and lipid markers of endosomal origin. Incorporated oligonucleotide was stable in blood and detectable over five half-lives. Data were best described by a three-compartment model with one elimination from the central compartment. We performed an observation-based simulated posterior predictive evaluation with prediction-corrected visual predictive check. Covariate and bootstrap analyses identified cell type having an influence on peripheral volumes (V2 and V3) and clearance (Cl3). Conclusions: Our method relies upon established laboratory techniques, can be tailored to a variety of biological questions regarding the pharmacokinetic disposition of extracellular vesicles, and will provide a complementary approach for the of study EV ligand-receptor interactions in the context of EV uptake and targeted therapeutics.
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    Population model analysis of chiral inversion and degradation of bupropion enantiomers, and application to enantiomer specific fraction unbound determination in rat plasma and brain
    (Elsevier, 2021) Bhattacharya, Chandrali; Masters, Andrea R.; Bach, Christine; Stratford, Robert E., Jr.; Medicine, School of Medicine
    Pharmacologic effects elicited by drugs most directly relate to their unbound concentrations. Measurement of binding in blood, plasma and target tissues are used to estimate these concentrations by determining the fraction of total concentration in a biological matrix that is not bound. In the case of attempting to estimate R- and S-bupropion concentrations in plasma and brain following racemic bupropion administration, reversible chiral inversion and irreversible degradation of the enantiomers were hypothesized to confound attempts at unbound fraction estimation. To address this possibility, a kinetic modeling approach was used to quantify inversion and degradation specific processes for each enantiomer from separate incubations of each enantiomer in the two matrices, and in pH 7.4 buffer, which is also used in binding experiments based on equilibrium dialysis. Modeling analyses indicated that chiral inversion kinetics were two to four-fold faster in plasma and brain than degradation, with only inversion observed in buffer. Inversion rate was faster for S-bupropion in the three media; whereas, degradation rates were similar for the two enantiomers in plasma and brain, with overall degradation in plasma approximately 2-fold higher than in brain homogenate. Incorporation of degradation and chiral inversion kinetic terms into a model to predict enantiomer-specific binding in plasma and brain revealed that, despite existence of these two processes, empirically derived estimates of fraction unbound were similar to model-derived values, leading to a firm conclusion that observed extent of plasma and brain binding are accurate largely because binding kinetics are faster than parallel degradation and chiral inversion processes.
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    Simultaneous Pharmacokinetic Analysis of Nitrate and its Reduced Metabolite, Nitrite, Following Ingestion of Inorganic Nitrate in a Mixed Patient Population
    (SpringerLink, 2020-11) Coggan, Andrew R.; Racette, Susan B.; Thies, Dakkota; Peterson, Linda R.; Stratford, Robert E., Jr.; Kinesiology, School of Health and Human Sciences
    Purpose: The pharmacokinetic properties of plasma NO3- and its reduced metabolite, NO2-, have been separately described, but there has been no reported attempt to simultaneously model their pharmacokinetics following NO3- ingestion. This report describes development of such a model from retrospective analyses of concentrations largely obtained from primary endpoint efficacy trials. Methods: Linear and non-linear mixed effects analyses were used to statistically define concentration dependency on time, dose, as well as patient and study variables, and to integrate NO3- and NO2- concentrations from studies conducted at different times, locations, patient groups, and several studies in which sample range was limited to a few hours. Published pharmacokinetic studies for both substances were used to supplement model development. Results: A population pharmacokinetic model relating NO3- and NO2- concentrations was developed. The model incorporated endogenous levels of the two entities, and determined these were not influenced by exogenous NO3- delivery. Covariate analysis revealed intersubject variability in NO3- exposure was partially described by body weight differences influencing volume of distribution. The model was applied to visualize exposure versus response (muscle contraction performance) in individual patients. Conclusions: Extension of the present first-generation model, to ultimately optimize NO3- dose versus pharmacological effects, is warranted.