Browsing by Author "Sherer, Eric A."

Now showing 1 - 6 of 6
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    The accuracy and completeness for receipt of colorectal cancer care using Veterans Health Administration administrative data.
    (BMC, 2016) Sherer, Eric A.; Fisher, Deborah A.; Barnd, Jeffrey; Jackson, George L.; Provenzale, Dawn; Haggstrom, David A.; Department of Medicine, IU School of Medicine
    The National Comprehensive Cancer Network and the American Society of Clinical Oncology have established guidelines for the treatment and surveillance of colorectal cancer (CRC), respectively. Considering these guidelines, an accurate and efficient method is needed to measure receipt of care.
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    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.
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    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.
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    A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection
    (Wiley, 2015-01) Sale, Mark; Sherer, Eric A.; Department of Medicine, IU School of Medicine
    The current algorithm for selecting a population pharmacokinetic/pharmacodynamic model is based on the well-established forward addition/backward elimination method. A central strength of this approach is the opportunity for a modeller to continuously examine the data and postulate new hypotheses to explain observed biases. This algorithm has served the modelling community well, but the model selection process has essentially remained unchanged for the last 30 years. During this time, more robust approaches to model selection have been made feasible by new technology and dramatic increases in computation speed. We review these methods, with emphasis on genetic algorithm approaches and discuss the role these methods may play in population pharmacokinetic/pharmacodynamic model selection.
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    Population pharmacokinetic analysis of 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG) in adult patients with solid tumors
    (Springer, 2012) Aregbe, Abdulateef O.; Sherer, Eric A.; Egorin, Merrill J.; Scher, Howard I.; Solit, David B.; Ramanathan, Ramesh K.; Ramalingam, Suresh; Belani, Chandra P.; Ivy, Percy S.; Bies, Robert R.; Medicine, School of Medicine
    Purpose: To identify sources of exposure variability for the tumor growth inhibitor 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG) using a population pharmacokinetic analysis. Methods: A total 67 solid tumor patients at 2 centers were given 1 h infusions of 17-DMAG either as a single dose, daily for 3 days, or daily for 5 days. Blood samples were extensively collected and 17-DMAG plasma concentrations were measured by liquid chromatography/mass spectrometry. Population pharmacokinetic analysis of the 17-DMAG plasma concentration with time was performed using nonlinear mixed effect modeling to evaluate the effects of covariates, inter-individual variability, and between-occasion variability on model parameters using a stepwise forward addition then backward elimination modeling approach. The inter-individual exposure variability and the effects of between-occasion variability on exposure were assessed by simulating the 95 % prediction interval of the AUC per dose, AUC(0-24 h), using the final model and a model with no between-occasion variability, respectively, subject to the five day 17-DMAG infusion protocol with administrations of the median observed dose. Results: A 3-compartment model with first order elimination (ADVAN11, TRANS4) and a proportional residual error, exponentiated inter-individual variability and between occasion variability on Q2 and V1 best described the 17-DMAG concentration data. No covariates were statistically significant. The simulated 95% prediction interval of the AUC(0-24 h) for the median dose of 36 mg/m(2) was 1,059-9,007 mg/L h and the simulated 95 % prediction interval of the AUC(0-24 h) considering the impact of between-occasion variability alone was 2,910-4,077 mg/L h. Conclusions: Population pharmacokinetic analysis of 17-DMAG found no significant covariate effects and considerable inter-individual variability; this implies a wide range of exposures in the population and which may affect treatment outcome. Patients treated with 17-DMAG may require therapeutic drug monitoring which could help achieve more uniform exposure leading to safer and more effective therapy.
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    Tailoring Surveillance Colonoscopy in Patients with Advanced Adenomas
    (Elsevier, 2021) Kahi, Charles J.; Myers, Laura J.; Stump, Timothy E.; Imler, Timothy D.; Sherer, Eric A.; Larson, Jason; Imperiale, Thomas F.; Medicine, School of Medicine
    Background and Aims Patients with advanced colorectal adenomas (AA) are directed to undergo intensive surveillance. However, the benefit derived from surveillance may be outweighed by the risk of death from non-colorectal cancer (CRC) causes, leading to uncertainty on how best to individualize follow-up. The aim of this study was to derive a risk prediction model and risk index that estimates and stratifies the risk for non-colorectal cancer mortality (NCM) subsequent to diagnosis and removal of AA. Methods We conducted a retrospective cohort study of Veterans > 40 years who had colonoscopy for diagnostic or screening indications at 13 VAMCs between 2002 and 2009, and had one or more AAs. The primary outcome was non-CRC mortality (NCM) using a fixed follow-up time period of 5 years. Logistic regression using the lasso technique was used to identify factors independently associated with non-CRC mortality (NCM), and an index based on points from regression coefficients was constructed to estimate risk of 5-year NCM. Results We identified 2,943 Veterans with AA (mean age (SD) 63 (8.6) years, 98% male, 74% white), with an overall 5-year mortality of 16.7%, which was nearly all due to NCM (16.6%). Age, comorbidity burden, specific comorbid conditions, and hospitalization within the preceding year were independently associated with NCM. The risk prediction model had a goodness of fit (calibration) p-value of 0.41, and c-statistic (discrimination) of 0.74 (95% CI, 0.71-0.76). Based on comparable 5-year risks of NCM, the scores comprised 3 risk categories: low (score of 0-1), intermediate (score of 2-4) and high (score of ≥ 5), in which NCM occurred in 6.5%, 14.1%, and 33.2%, respectively. Conclusions We derived a risk prediction model that identifies Veterans at high risk of NCM within 5 years, and who are thus unlikely to benefit from further surveillance.