IU Indianapolis ScholarWorks :: Browsing by Author "Bies, Robert R."

Browsing by Author "Bies, Robert R."

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Adherence to escitalopram treatment in depression: a study of electronically compiled dosing histories in the ‘Depression: the search for phenotypes’ study
(Wolters Kluwer, 2012) Wessels, Alette M.; Jin, Yuyan; Pollock, Bruce G.; Frank, Ellen; Lange, Anne-Catherine; Vrijens, Bernard; Fagiolini, Andrea; Kupfer, David J.; Rucci, Paola; Kepple, Gail; Anderton, Joel; Buttenfield, Joan; Bies, Robert R.; Medicine, School of Medicine
Poor adherence to depression treatment is common. Understanding determinants of poor adherence to therapy is crucial to ensure optimal clinical outcomes. The aim of this study was to describe characteristics of dosing history in participants with depression receiving once daily escitalopram. Participants were randomly assigned to interpersonal psychotherapy (IPT) or pharmacotherapy. Participants assigned to IPT who did not evidence a response or remission had escitalopram added to their treatment. Adherence to pharmacotherapy was assessed using an electronically monitored pill cap (MEMS). Fifty-four participants on escitalopram alone and 32 on escitalopram+IPT were monitored. After 200 days, 71.7% of the participants in the escitalopram group and 54.8% of those in the escitalopram+IPT group were still engaged with the dosing regimen. Of those engaged in the dosing regimen, 17.9% (average over 210 days) of the participants did not take their medication (nonexecution). In 69% of the days participants took the correct dosage required. On average, participants had three drug holidays and the mean length of a holiday was 7 days per patient. No difference in adherence patterns was observed between patients receiving escitalopram alone vs. IPT+escitalopram. Early discontinuation of treatment and suboptimal daily execution of the prescribed regimen are the most common facets of poor adherence in this study population.
Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building
(Springer, 2012) Sherer, Eric A.; Sale, Mark E.; Pollock, Bruce G.; Belani, Chandra P.; Egorin, Merrill J.; Ivy, Percy S.; Lieberman, Jeffrey A.; Manuck, Stephen B.; Marder, Stephen R.; Muldoon, Matthew F.; Scher, Howard I.; Solit, David B.; Bies, Robert R.; Medicine, School of Medicine
A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.
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.
Biomarkers for Diagnosis and Prognosis of Sinusoidal Obstruction Syndrome after Hematopoietic Cell Transplantation.
(Elsevier, 2015-10) Akil, Ayman; Zhang, Qing; Mumaw, Christen L.; Raiker, Nisha; Yu, Jeffrey; de Mendizabal, Nieves Velez; Haneline, Laura S.; Robertson, Kent A.; Skiles, Jodi; Diaz-Ricart, Maribel; Carreras, Enric; Renbarger, Jamie; Hanash, Samir; Bies, Robert R.; Paczesny, Sophie; Department of Pediatrics, IU School of Medicine
Reliable, non-invasive methods for diagnosing and prognosing sinusoidal obstruction syndrome (SOS) early after hematopoietic cell transplantation (HCT) are needed. We used a quantitative mass spectrometry-based proteomics approach to identify candidate biomarkers of SOS by comparing plasma pooled from 20 patients with and 20 patients without SOS. Of 494 proteins quantified, we selected six proteins [L-Ficolin, vascular-cell-adhesion-molecule-1 (VCAM1), tissue-inhibitor of metalloproteinase-1, von Willebrand factor, intercellular-adhesion-molecule-1, and CD97] based on a differential heavy/light isotope ratio of at least 2 fold, information from the literature, and immunoassay availability. Next, we evaluated the diagnostic potential of these six proteins and five selected from the literature [suppression of tumorigenicity-2 (ST2), angiopoietin-2 (ANG2), hyaluronic acid (HA), thrombomodulin, and plasminogen activator inhibitor-1] in samples from 80 patients. The results demonstrate that together ST2, ANG2,
Clinical Trial Simulation to Evaluate Population Pharmacokinetics and Food Effect: Capturing Abiraterone and Nilotinib Exposures
(John Wiley & Sons, Inc., 2015-05) Li, Claire H.; Sherer, Eric A.; Lewis, Lionel D.; Bies, Robert R.; Department of Medicine, IU School of Medicine
The objectives of this study were to determine (1) the accuracy with which individual patient level exposure can be determined and (2) whether a known food effect can be identified in a trial simulation of a typical population pharmacokinetic trial. Clinical trial simulations were undertaken using NONMEM VII to assess a typical oncology pharmacokinetic trial design. Nine virtual trials for each compound were performed for combinations of different level of between-occasion variability, number of patients in the trial and magnitude of a food covariate on oral clearance. Less than 5% and 20% bias and precision were obtained in individual clearance estimated for both abiraterone and nilotinib using this design. This design resulted biased and imprecise population clearance estimates for abiraterone. The between-occasion variability in most trials was captured with less than 30% of percent bias and precision. The food effect was detectable as a statistically significant covariate on oral clearance for abiraterone and nilotinib with percent bias and precision of the food covariate less than 20%. These results demonstrate that clinical trial simulation can be used to explore the ability of specific trial designs to evaluate the power to identify individual and population level exposures,covariate and variability effects.
Identification and mechanistic investigation of clinically important myopathic drug-drug interactions
(2014) Han, Xu; Flockhart, David A.; Bies, Robert R.; Desta, Zeruesenay; Li, Lang; Queener, Sherry F.; Quinney, Sara K.; Zhang, Jian-Ting
Drug-drug interactions (DDIs) refer to situations where one drug affects the pharmacokinetics or pharmacodynamics of another. DDIs represent a major cause of morbidity and mortality. A common adverse drug reaction (ADR) that can result from, or be exacerbated by DDIs is drug-induced myopathy. Identifying DDIs and understanding their underlying mechanisms is key to the prevention of undesirable effects of DDIs and to efforts to optimize therapeutic outcomes. This dissertation is dedicated to identification of clinically important myopathic DDIs and to elucidation of their underlying mechanisms. Using data mined from the published cytochrome P450 (CYP) drug interaction literature, 13,197 drug pairs were predicted to potentially interact by pairing a substrate and an inhibitor of a major CYP isoform in humans. Prescribing data for these drug pairs and their associations with myopathy were then examined in a large electronic medical record database. The analyses identified fifteen drug pairs as DDIs significantly associated with an increased risk of myopathy. These significant myopathic DDIs involved clinically important drugs including alprazolam, chloroquine, duloxetine, hydroxychloroquine, loratadine, omeprazole, promethazine, quetiapine, risperidone, ropinirole, trazodone and simvastatin. Data from in vitro experiments indicated that the interaction between quetiapine and chloroquine (risk ratio, RR, 2.17, p-value 5.29E-05) may result from the inhibitory effects of quetiapine on chloroquine metabolism by cytochrome P450s (CYPs). The in vitro data also suggested that the interaction between simvastatin and loratadine (RR 1.6, p-value 4.75E-07) may result from synergistic toxicity of simvastatin and desloratadine, the major metabolite of loratadine, to muscle cells, and from the inhibitory effect of simvastatin acid, the active metabolite of simvastatin, on the hepatic uptake of desloratadine via OATP1B1/1B3. Our data not only identified unknown myopathic DDIs of clinical consequence, but also shed light on their underlying pharmacokinetic and pharmacodynamic mechanisms. More importantly, our approach exemplified a new strategy for identification and investigation of DDIs, one that combined literature mining using bioinformatic algorithms, ADR detection using a pharmacoepidemiologic design, and mechanistic studies employing in vitro experimental models.
Modeling and simulation applications with potential impact in drug development and patient care
(2014) Li, Claire; Bies, Robert R.; Foroud, Tatiana; Li, Lang; Renbarger, Jamie L.
Model-based drug development has become an essential element to potentially make drug development more productive by assessing the data using mathematical and statistical approaches to construct and utilize models to increase the understanding of the drug and disease. The modeling and simulation approach not only quantifies the exposure-response relationship, and the level of variability, but also identifies the potential contributors to the variability. I hypothesized that the modeling and simulation approach can: 1) leverage our understanding of pharmacokinetic-pharmacodynamic (PK-PD) relationship from pre-clinical system to human; 2) quantitatively capture the drug impact on patients; 3) evaluate clinical trial designs; and 4) identify potential contributors to drug toxicity and efficacy. The major findings for these studies included: 1) a translational PK modeling approach that predicted clozapine and norclozapine central nervous system exposures in humans relating these exposures to receptor binding kinetics at multiple receptors; 2) a population pharmacokinetic analysis of a study of sertraline in depressed elderly patients with Alzheimer’s disease that identified site specific differences in drug exposure contributing to the overall variability in sertraline exposure; 3) the utility of a longitudinal tumor dynamic model developed by the Food and Drug Administration for predicting survival in non-small cell lung cancer patients, including an exploration of the limitations of this approach; 4) a Monte Carlo clinical trial simulation approach that was used to evaluate a pre-defined oncology trial with a sparse drug concentration sampling schedule with the aim to quantify how well individual drug exposures, random variability, and the food effects of abiraterone and nilotinib were determined under these conditions; 5) a time to event analysis that facilitated the identification of candidate genes including polymorphisms associated with vincristine-induced neuropathy from several association analyses in childhood acute lymphoblastic leukemia (ALL) patients; and 6) a LASSO penalized regression model that predicted vincristine-induced neuropathy and relapse in ALL patients and provided the basis for a risk assessment of the population. Overall, results from this dissertation provide an improved understanding of treatment effect in patients with an assessment of PK/PD combined and with a risk evaluation of drug toxicity and efficacy.
Modeling Sitagliptin Effect on Dipeptidyl Peptidase 4 (DPP4) Activity in Adults with Hematological Malignancies After Umbilical Cord Blood (UCB) Hematopoietic Cell Transplant (HCT)
(Springer International Publishing, 2014-03) Vélez de Mendizábal, Nieves; Strother, Robert M.; Farag, Sherif S.; Broxmeyer, Hal E.; Messina-Graham, Steven; Chitnis, Shripad D.; Bies, Robert R.; Department of Medicine, IU School of Medicine
Background and Objectives— Dipeptidyl peptidase-4 (DPP4) inhibition is a potential strategy to increase the engraftment rate of hematopoietic stem/progenitor cells. A recent clinical trial using sitagliptin, a DPP4 inhibitor approved for type 2 diabetes mellitus, has shown to be a promising approach in adults with hematological malignancies after umbilical cord blood (UCB) hematopoietic cell transplant (HCT). Based on data from this clinical trial, a semi-mechanistic model was developed to simultaneously describe DPP4 activity after multiple doses of sitagliptin in subjects with hematological malignancies after a single-unit UCB HCT. Methods— The clinical study included 24 patients that received myeloablative conditioning followed by 4 oral sitagliptin 600mg with single-unit UCB HCT. Using a nonlinear mixed effects approach, a semi-mechanistic pharmacokinetic/pharmacodynamic model was developed to describe DPP4 activity from this trial data using NONMEM 7.2. The model was used to drive Monte-Carlo simulations to probe various dosage schedules and the attendant DPP4 response. Results— The disposition of sitagliptin in plasma was best described by a 2-compartment model. The relationship between sitagliptin concentration and DPP4 activity was best described by an indirect response model with a negative feedback loop. Simulations showed that twice a day or three times a day dosage schedules were superior to once daily schedule for maximal DPP4 inhibition at the lowest sitagliptin exposure. Conclusion— This study provides the first pharmacokinetic/pharmacodynamic model of sitagliptin in the context of HCT, and provides a valuable tool for exploration of optimal dosing regimens, critical for improving time to engraftment in patients after UCB HCT.
Nicotine and cotinine exposure from electronic cigarettes: a population approach
(Springer-Verlag, 2015-06) Vélez de Mendizábal, Nieves; Jones, David R.; Jahn, Andy; Bies, Robert R.; Brown, Joshua W.; Department of Medicine, IU School of Medicine
BACKGROUND AND OBJECTIVES: Electronic cigarettes (e-cigarettes) are a recent technology that has gained rapid acceptance. Still, little is known about them in terms of safety and effectiveness. A basic question is how effectively they deliver nicotine; however, the literature is surprisingly unclear on this point. Here, a population pharmacokinetic model was developed for nicotine and its major metabolite cotinine with the aim to provide a reliable framework for the simulation of nicotine and cotinine concentrations over time, based solely on inhalation airflow recordings and individual covariates [i.e., weight and breath carbon monoxide (CO) levels]. METHODS: This study included ten adults self-identified as heavy smokers (at least one pack of cigarettes per day). Plasma nicotine and cotinine concentrations were measured at regular 10-min intervals for 90 min while human subjects inhaled nicotine vapor from a modified e-cigarette. Airflow measurements were recorded every 200 ms throughout the session. A population pharmacokinetic model for nicotine and cotinine was developed based on previously published pharmacokinetic parameters and the airflow recordings. All of the analyses were performed with the non-linear mixed-effect modeling software NONMEM(®) version 7.2. RESULTS: The results show that e-cigarettes deliver nicotine effectively, although the pharmacokinetic profiles are lower than those achieved with regular cigarettes. Our pharmacokinetic model effectively predicts plasma nicotine and cotinine concentrations from the inhalation volume, and initial breath CO. CONCLUSION: E-cigarettes are effective at delivering nicotine. This new pharmacokinetic model of e-cigarette usage might be used for pharmacodynamic analysis where the pharmacokinetic profiles are not available.
Pharmacokinetic modeling of tranexamic acid for patients undergoing cardiac surgery with normal renal function and model simulations for patients with renal impairment
(Wiley, 2015-07) Yang, Qi; Jerath, Angela; Bies, Robert R.; Wąsowicz, Marcin; Pang, K. Sandy; Department of Medicine, IU School of Medicine
Tranexamic acid (TXA), an effective anti-fibrinolytic agent that is cleared by glomerular filtration, is used widely for cardiopulmonary bypass (CPB) surgery. However, an effective dosing regimen has not been fully developed in patients with renal impairment. The aims of this study were to characterize the inter-patient variability associated with pharmacokinetic parameters and to recommend a new dosing adjustment based on the BART dosing regimen for CPB patients with chronic renal dysfunction (CRD). Recently published data on CPB patients with normal renal function (n = 15) were re-examined with a two-compartment model using the ADAPT5® and NONMEMVII® to identify covariates that explain inter-patient variability and to ascertain whether sampling strategies might affect parameter estimation. A series of simulations was performed to adjust the BART dosing regimen for CPB patients with renal impairment. Based on the two-compartmental model, the number of samples obtained after discontinuation of TXA infusion was found not to be critical in parameter estimation (p > 0.05). Both body weight and creatinine clearance were identified as significant covariates (p < 0.005). Simulations showed significantly higher than normal TXA concentrations in CRD patients who received the standard dosing regimen in the BART trial. Adjustment of the maintenance infusion rate based on the percent reduction in renal clearance resulted in predicted plasma TXA concentrations that were safe and therapeutic (~100 mg·L(-1) ). Our proposed dosing regimen, with consideration of renal function, is predicted to maintain effective target plasma concentrations below those associated with toxicity for patients with renal failure for CPB.

Browsing by Author "Bies, Robert R."

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