1
|
Wu IHC, Strong LL, Nguyen NT, Cho D, John J, McNeill LH. Psychosocial Stressors, Depression, and Physical Activity among African Americans. Am J Health Behav 2019; 43:717-728. [PMID: 31239015 PMCID: PMC10486259 DOI: 10.5993/ajhb.43.4.6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Objectives: In this study, we examined how racial discrimination and neighborhood perceptions relate to physical activity and sedentary behavior mediated through depression symptoms. Methods: Data were from the first year of a longitudinal cohort study, Project Creating a Higher Understanding of cancer Research and Community Health (CHURCH), based on a convenience community sample of church-attending African Americans collected between April 2012 and March 2013 (N = 370) in Houston, Texas. Measures included racial discrimination, perceived neighborhood problems and vigilance, depression (CES-D), physical activity (IPAQ-short), and sedentary behavior. Results: Main effects from the structural equation model showed that racial discrimination (b = .20, p < .01) was related to greater depression symptoms. The same pattern emerged for neighborhood problems, but the effect was not significant (b = .20, p = .07). Further, depression symptoms were related to less physical activity (b = -.62, p = .03) and greater sedentary behavior (b = .64, p < .01). Indirect effects showed that depression mediated the relationship between racial discrimination and neighborhood problems on physical activity and sedentary behavior. Conclusions: Depression symptoms are an important mechanism by which racial discrimination and perceived neighborhood problems impact physical activity and sedentary behavior.
Collapse
Affiliation(s)
- Ivan H C Wu
- Postdoctoral Research Fellow, University of Texas MD Anderson Cancer Center, Department of Health Disparities Research, Houston, TX;,
| | - Larkin L Strong
- Assistant Professor, University of Texas MD Anderson Cancer Center, Department of Health Disparities Research, Houston, TX
| | - Nga T Nguyen
- Senior Statistical Analyst, University of Texas MD Anderson Cancer Center, Department of Biostatistics, Houston, TX
| | - Dalnim Cho
- Instructor, University of Texas MD Anderson Cancer Center, Department of Health Disparities Research, Houston, TX
| | - Jemima John
- Postdoctoral Research Fellow, University of Texas MD Anderson Cancer Center, Department of Health Disparities Research, Houston, TX
| | - Lorna H McNeill
- Associate Professor and Chair, University of Texas MD Anderson Cancer Center, Department of Health Disparities Research, Houston, TX
| |
Collapse
|
2
|
Neoadjuvant therapy for locally advanced gastric cancer patients. A population pharmacodynamic modeling. PLoS One 2019; 14:e0215970. [PMID: 31071108 PMCID: PMC6508715 DOI: 10.1371/journal.pone.0215970] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 04/05/2019] [Indexed: 01/27/2023] Open
Abstract
Background Perioperative chemotherapy (CT) or neoadjuvant chemoradiotherapy (CRT) in patients with locally advanced gastric (GC) or gastroesophageal junction cancer (GEJC) has been shown to improve survival compared to an exclusive surgical approach. However, most patients retain a poor prognosis due to important relapse rates. Population pharmacokinetic-pharmacodynamic (PK/PD) modeling may allow identifying at risk-patients. We aimed to develop a mechanistic PK/PD model to characterize the relationship between the type of neoadjuvant therapy, histopathologic response and survival times in locally advanced GC and GEJC patients. Methods Patients with locally advanced GC and GEJC treated with neoadjuvant CT with or without preoperative CRT were analyzed. Clinical response was assessed by CT-scan and EUS. Pathologic response was defined as a reduction on pTNM stage compared to baseline cTNM. Metastasis development risk and overall survival (OS) were described using the population approach with NONMEM 7.3. Model evaluation was performed through predictive checks. Results A low correlation was observed between clinical and pathologic TNM stage for both T (R = 0.32) and N (R = 0.19) categories. A low correlation between clinical and pathologic response was noticed (R = -0.29). The OS model adequately described the observed survival rates. Disease recurrence, cTNM stage ≥3 and linitis plastica absence, were correlated to a higher risk of death. Conclusion Our model adequately described clinical response profiles, though pathologic response could not be predicted. Although the risk of disease recurrence and survival were linked, the identification of alternative approaches aimed to tailor therapeutic strategies to the individual patient risk warrants further research.
Collapse
|
3
|
Ibrahim MMA, Largajolli A, Kjellsson MC, Karlsson MO. Translation Between Two Models; Application with Integrated Glucose Homeostasis Models. Pharm Res 2019; 36:86. [PMID: 31001701 DOI: 10.1007/s11095-019-2592-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 02/18/2019] [Indexed: 11/25/2022]
Abstract
PURPOSE For some biological systems, there exist several models with somewhat different features and perspectives. We propose an evaluation method for NLME models by analyzing real and simulated data from the model of main interest using a structurally different, but similar, NLME model. We showcase this method using the Integrated Glucose Insulin (IGI) model and the Integrated Minimal Model (IMM). Additionally, we try to map parameters carrying similar information between the two models. METHODS A bootstrap of real data and simulated datasets from both the IMM and IGI models were analyzed with the two models. Important parameters of the IMM were mapped to IGI parameters using a large IMM simulated dataset analyzed under the IGI model. RESULTS Comparison of the parameters estimated from real data and data simulated with the IMM and analyzed with the IGI model demonstrated differences between real and IMM-simulated data. Comparison of the parameters estimated from real data and data simulated with the IGI model and analyzed with the IMM also demonstrated differences but to a lower extent. The strongest parameter correlations were found for: insulin-dependent glucose clearance (IGI) ~ insulin sensitivity (IMM); insulin-independent glucose clearance (IGI) ~ glucose effectiveness (IMM); and insulin effect parameter (IGI) ~ insulin action (IMM). CONCLUSIONS We demonstrated a new approach to investigate models' ability to simulate real-life-like data, and the information captured in each model in comparison to real data, and the IMM clinically used parameters were successfully mapped to their corresponding IGI parameters.
Collapse
Affiliation(s)
- Moustafa M A Ibrahim
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 23, Uppsala, Sweden.,Department of Pharmacy Practice, Helwan University, Cairo, Egypt
| | - Anna Largajolli
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 23, Uppsala, Sweden
| | - Maria C Kjellsson
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 23, Uppsala, Sweden
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 23, Uppsala, Sweden.
| |
Collapse
|
4
|
Bizzotto R, Zamuner S. Analysis of variability in length of sleep state bouts reveals memory-free sleep subcomponents consistent among primary insomnia patients. J Neurophysiol 2018; 119:1836-1851. [PMID: 29384456 DOI: 10.1152/jn.00649.2017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The statistical distributions of bout lengths for the different (macro) sleep states (wake, N1, N2, N3, and REM sleep) are essential to understanding whether any memory-free subcomponent ("micro state") is involved in the organization of sleep. Micro state detection can be prevented by the fusion of data including various sources of variability, in particular by the differences in sleep architecture between individuals, along sleep time (or nighttime), or between different nights. In this analysis, a mathematical model of sleep was adopted to disentangle these features and advance the understanding of the dynamics and mechanisms of sleep and its states. The analysis involved 116 primary insomnia patients taking placebo before going to bed and undergoing polysomnography for one night. The individual sequences of macro sleep states had been previously modeled with a mixed-effect nonhomogeneous modified Markov chain model, from which individual conditional probability distributions for the bout durations were derived in this analysis as functions of sleep time. The probability distributions, affected by neither subject, night-time, nor multiple-night pooling, substantially changed at ¼ and ¾ sleep time, had modified exponential shape, and were best described as the sum of one to four exponentials, depending on the sleep state. The time constants and proportions of bouts contributing to each exponential were similar in the different subjects, changing over sleep time. Variability in bout durations thus indicated the presence of multiple memory-free sleep subcomponents whose mean residence times and access probabilities could be identified and shown to be consistent among the studied subjects. NEW & NOTEWORTHY We present a new methodology for deriving, from polysomnography data, the individual conditional probability for the duration of the bouts of wake, N1, N2, N3, and REM sleep. We evaluated the variability of this probability within and between primary insomnia patients and along sleep time. The multiexponential shapes of the probability distributions within the individuals revealed memory-free mechanisms and sleep subcomponents with consistent features in the studied population.
Collapse
Affiliation(s)
- Roberto Bizzotto
- Neuroscience Institute, National Research Council , Padua , Italy
| | - Stefano Zamuner
- Clinical Pharmacology Modeling and Simulation, GlaxoSmithKline, Stevenage , United Kingdom
| |
Collapse
|
5
|
Tsamandouras N, Wendling T, Rostami-Hodjegan A, Galetin A, Aarons L. Incorporation of stochastic variability in mechanistic population pharmacokinetic models: handling the physiological constraints using normal transformations. J Pharmacokinet Pharmacodyn 2015; 42:349-73. [PMID: 26006250 DOI: 10.1007/s10928-015-9418-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Accepted: 05/16/2015] [Indexed: 10/23/2022]
Abstract
The utilisation of physiologically-based pharmacokinetic models for the analysis of population data is an approach with progressively increasing impact. However, as we move from empirical to complex mechanistic model structures, incorporation of stochastic variability in model parameters can be challenging due to the physiological constraints that may arise. Here, we investigated the most common types of constraints faced in mechanistic pharmacokinetic modelling and explored techniques for handling them during a population data analysis. An efficient way to impose stochastic variability on the parameters of interest without neglecting the underlying physiological constraints is through the assumption that they follow a distribution with support and properties matching the underlying physiology. It was found that two distributions that arise through transformations of the normal, the logit-normal generalisation and the logistic-normal, are excellent for such an application as not only they can satisfy the physiological constraints but also offer high flexibility during characterisation of the parameters' distribution. The statistical properties and practical advantages/disadvantages of these distributions for such an application were clearly displayed in the context of different modelling examples. Finally, a simulation study clearly illustrated the practical gains of the utilisation of the described techniques, as omission of population variability in physiological systems parameters leads to a biased/misplaced stochastic model with mechanistically incorrect variance structure. The current methodological work aims to facilitate the use of mechanistic/physiologically-based models for the analysis of population pharmacokinetic clinical data.
Collapse
Affiliation(s)
- Nikolaos Tsamandouras
- Centre for Applied Pharmacokinetic Research, Manchester Pharmacy School, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, UK,
| | | | | | | | | |
Collapse
|
6
|
Sy SKB, Wang X, Derendorf H. Introduction to Pharmacometrics and Quantitative Pharmacology with an Emphasis on Physiologically Based Pharmacokinetics. ACTA ACUST UNITED AC 2014. [DOI: 10.1007/978-1-4939-1304-6_1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
|
7
|
Steven Ernest C, Nyberg J, Karlsson MO, Hooker AC. Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model. J Pharmacokinet Pharmacodyn 2014; 41:639-54. [PMID: 25308776 DOI: 10.1007/s10928-014-9391-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Accepted: 10/03/2014] [Indexed: 11/25/2022]
Abstract
D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIM(total)). The reference designs were placebo, 0.1, 1-, 6-, 10- and 20-mg dosing for a 2- to 6-way crossover study in six dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE). Contrary to expectations, the predicted parameter uncertainty obtained via FIM(total) was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIM(total). Through the use of an approximate analytic solution and weighting schemes, the FIM(total) for a non-homogeneous, dichotomous Markov-chain phase advanced sleep model was computed and provided more efficient trial designs and increased nonlinear mixed-effects modeling parameter precision.
Collapse
Affiliation(s)
- C Steven Ernest
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden,
| | | | | | | |
Collapse
|
8
|
Sy SKB, Heuberger J, Shilbayeh S, Conrado DJ, Derendorf H. A Markov chain model to evaluate the effect of CYP3A5 and ABCB1 polymorphisms on adverse events associated with tacrolimus in pediatric renal transplantation. AAPS JOURNAL 2013; 15:1189-99. [PMID: 23990505 DOI: 10.1208/s12248-013-9528-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2013] [Accepted: 08/12/2013] [Indexed: 12/19/2022]
Abstract
The SNP A6986G of the CYP3A5 gene (*3) results in a non-functional protein due to a splicing defect whereas the C3435T was associated with variable expression of the ABCB1 gene, due to protein instability. Part of the large interindividual variability in tacrolimus efficacy and toxicity can be accounted for by these genetic factors. Seventy-two individuals were examined for A6986G and C3435T polymorphism using a PCR-RFLP-based technique to estimate genotype and allele frequencies in the Jordanian population. The association of age, hematocrit, platelet count, CYP3A5, and ABCB1 polymorphisms with tacrolimus dose- and body-weight-normalized levels in the subset of 38 pediatric renal transplant patients was evaluated. A Markov model was used to evaluate the time-dependent probability of an adverse event occurrence by CYP3A5 phenotypes and ABCB1 genotypes. The time-dependent probability of adverse event was about double in CYP3A5 non-expressors compared to the expressors for the first 12 months of therapy. The CYP3A5 non-expressors had higher corresponding normalized tacrolimus levels compared to the expressors in the first 3 months. The correlation trend between probability of adverse events and normalized tacrolimus concentrations for the two CYP3A5 phenotypes persisted for the first 9 months of therapy. The differences among ABCB1 genotypes in terms of adverse events and normalized tacrolimus levels were only observed in the first 3 months of therapy. The information on CYP3A5 genotypes and tacrolimus dose requirement is important in designing effective programs toward management of tacrolimus side effects particularly for the initial dose when tacrolimus blood levels are not available for therapeutic drug monitoring.
Collapse
Affiliation(s)
- Sherwin K B Sy
- Department of Pharmaceutics, College of Pharmacy, University of Florida, P.O. Box 100494, 1600 Archer Road, Gainesville, Florida, 32610, USA
| | | | | | | | | |
Collapse
|
9
|
Diack C, Ackaert O, Ploeger BA, van der Graaf PH, Gurrell R, Ivarsson M, Fairman D. A hidden Markov model to assess drug-induced sleep fragmentation in the telemetered rat. J Pharmacokinet Pharmacodyn 2011; 38:697-711. [PMID: 21909798 PMCID: PMC3215869 DOI: 10.1007/s10928-011-9215-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2010] [Accepted: 08/12/2011] [Indexed: 11/03/2022]
Abstract
Drug-induced sleep fragmentation can cause sleep disturbances either via their intended pharmacological action or as a side effect. Examples of disturbances include excessive daytime sleepiness, insomnia and nightmares. Developing drugs without these side effects requires insight into the mechanisms leading to sleep disturbance. The characterization of the circadian sleep pattern by EEG following drug exposure has improved our understanding of these mechanisms and their translatability across species. The EEG shows frequent transitions between specific sleep states leading to multiple correlated sojourns in these states. We have developed a Markov model to consider the high correlation in the data and quantitatively compared sleep disturbance in telemetered rats induced by methylphenidate, which is known to disturb sleep, and of a new chemical entity (NCE). It was assumed that these drugs could either accelerate or decelerate the transitions between the sleep states. The difference in sleep disturbance of methylphenidate and the NCE were quantitated and different mechanisms of action on rebound sleep were identified. The estimated effect showed that both compounds induce sleep fragmentation with methylphenidate being fivefold more potent compared to the NCE.
Collapse
Affiliation(s)
- C Diack
- LAP&P Consultants, Leiden, The Netherlands.
| | | | | | | | | | | | | |
Collapse
|