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1.
Alzheimers Dement ; 20(3): 1538-1549, 2024 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-38032015

RESUMEN

INTRODUCTION: Alzheimer's disease (AD), the leading cause of dementia worldwide, represents a human and financial impact for which few effective drugs exist to treat the disease. Advances in molecular imaging have enabled assessment of cerebral glycolytic metabolism, and network modeling of brain region have linked to alterations in metabolic activity to AD stage. METHODS: We performed 18 F-FDG positron emission tomography (PET) imaging in 4-, 6-, and 12-month-old 5XFAD and littermate controls (WT) of both sexes and analyzed region data via brain metabolic covariance analysis. RESULTS: The 5XFAD model mice showed age-related changes in glucose uptake relative to WT mice. Analysis of community structure of covariance networks was different across age and sex, with a disruption of metabolic coupling in the 5XFAD model. DISCUSSION: The current study replicates clinical AD findings and indicates that metabolic network covariance modeling provides a translational tool to assess disease progression in AD models.


Asunto(s)
Enfermedad de Alzheimer , Animales , Femenino , Masculino , Ratones , Envejecimiento/metabolismo , Enfermedad de Alzheimer/diagnóstico por imagen , Enfermedad de Alzheimer/metabolismo , Encéfalo/diagnóstico por imagen , Encéfalo/metabolismo , Fluorodesoxiglucosa F18/metabolismo , Redes y Vías Metabólicas , Tomografía de Emisión de Positrones/métodos
2.
Br J Clin Pharmacol ; 88(4): 1441-1451, 2022 02.
Artículo en Inglés | MEDLINE | ID: mdl-34337764

RESUMEN

AIMS: Physiologically based pharmacokinetic (PBPK) models have been previously developed for betamethasone and buprenorphine for pregnant women. The goal of this work was to replicate and reassess these models using data from recently completed studies. METHODS: Betamethasone and buprenorphine PBPK models were developed in Simcyp V19 based on prior publications using V17 and V15. Ability to replicate models was verified by comparing predictions in V19 to those previously published. Once replication was verified, models were reassessed by comparing predictions to observed data from additional studies in pregnant women. Model performance was based upon visual inspection of concentration vs. time profiles, and comparison of pharmacokinetic parameters. Models were deemed reproducible if parameter estimates were within 10% of previously reported values. External validations were considered acceptable if the predicted area under the concentration-time curve (AUC) and peak plasma concentration fell within 2-fold of the observed. RESULTS: The betamethasone model was successfully replicated using Simcyp V19, with ratios of reported (V17) to reproduced (V19) peak plasma concentration of 0.98-1.04 and AUC of 0.95-1.07. The model-predicted AUC ratios ranged from 0.98-1.79 compared to external data. The previously published buprenorphine PBPK model was not reproducible, as we predicted intravenous clearance of 70% that reported previously (both in Simcyp V15). CONCLUSION: While high interstudy variability was observed in the newly available clinical data, the PBPK model sufficiently predicted changes in betamethasone exposure across gestation. Model reproducibility and reassessment with external data are important for the advancement of the discipline. PBPK modelling publications should contain sufficient detail and clarity to enable reproducibility.


Asunto(s)
Buprenorfina , Modelos Biológicos , Área Bajo la Curva , Betametasona , Buprenorfina/farmacocinética , Simulación por Computador , Femenino , Humanos , Embarazo , Reproducibilidad de los Resultados
3.
BMC Pregnancy Childbirth ; 22(1): 926, 2022 Dec 09.
Artículo en Inglés | MEDLINE | ID: mdl-36482347

RESUMEN

BACKGROUND: The objective of this study was to determine if the lack of exposure to individual antidepressants at certain times in pregnancy improved maternal and infant outcomes. METHODS: This was a retrospective cohort study of 2741 pregnant women prescribed antidepressant(s) before or during pregnancy. Data were obtained from electronic medical records. Analysis was limited to women prescribed one of five antidepressants (bupropion, citalopram, escitalopram, fluoxetine, sertraline). Period of exposure was determined using prescription order dates. Primary outcomes were neonatal intensive care unit (NICU) admission and adaptation syndrome in the newborn. Logistic regression, adjusted for maternal age, race, and insurance, compared consistent exposure throughout pregnancy versus (A) no exposure in the third trimester, (B) no exposure early in pregnancy, and (C) exposure in the midtrimester alone. RESULTS: Compared to women prescribed an antidepressant continually throughout pregnancy, NICU admission was less likely for women lacking exposure in the third trimester if they had been taking bupropion (aOR 0.43, 95% CI 0.21-0.90) or escitalopram (aOR 0.49, 95% CI 0.28-0.85). Women previously taking escitalopram but lacking third trimester exposure also had lower odds of adaptation syndrome (aOR 0.19, 95% CI 0.07-0.48). No differences were found in other outcomes for women taking other antidepressants or for any outcomes for women who lacked early pregnancy drug exposure compared to exposure throughout pregnancy. CONCLUSION: For the five antidepressants included in this study, lack of exposure early or late in pregnancy compared to consistent exposure throughout pregnancy generally did not change newborn outcomes. The exceptions were bupropion and escitalopram, where lack of exposure in the third trimester associated with lower rates of adaptation syndrome or NICU admission. These data may help pregnant women with depression in need of drug therapy to have informed discussions with providers about the potential risks and benefits to continuing or stopping drugs at different times during pregnancy.


Asunto(s)
Familia , Embarazo , Recién Nacido , Femenino , Humanos , Estudios Retrospectivos
4.
Cancer Rep (Hoboken) ; 6(1): e1686, 2023 01.
Artículo en Inglés | MEDLINE | ID: mdl-35906899

RESUMEN

BACKGROUND: Chemotherapeutic drug concentrations vary across different regions of tumors and this is thought to be involved in development of chemotherapy resistance. Insufficient drug delivery to some regions of the tumor may be due to spatial differences in expression of genes involved in the disposition, transport, and detoxification of drugs (pharmacogenes). Therefore, in this study, we analyzed the spatial expression of 286 pharmacogenes in six breast cancer tissues using the recently developed Visium spatial transcriptomics platform to (1) determine if these pharmacogenes are expressed heterogeneously across tumor tissue and (2) to determine which pharmacogenes have the most spatial expression heterogeneity. METHODS AND RESULTS: The spatial transcriptomics technology sequences the transcriptome of 55 um diameter barcoded sections (spots) across a tissue sample. We analyzed spatial gene expression profiles of four biobank-sourced breast tumor samples in addition to two breast tumor sample datasets from 10× Genomics. We define heterogeneity as the interquartile range of read counts. Collectively, we identified 8887 spots in tumor regions, 3814 in stroma, 44 in lymphocytes, and 116 in normal regions based on pathologist annotation of the tissues. We showed statistically significant differences in expression of pharmacogenes in tumor regions compared to surrounding non-tumor regions. We also observed that the most heterogeneously expressed genes within tumor regions were involved in reactive oxygen species (ROS) handling and detoxification mechanisms. GPX4, GSTP1, MGST3, SOD1, CYP4Z1, CYB5R3, GSTK1, and NAT1 showed the most heterogeneous expression within tumor regions. CONCLUSIONS: The heterogeneous expression of these pharmacogenes may have important implications for cancer therapy due to their ability to impact drug distribution and efficacy throughout the tumor. Our results suggest that chemoresistance caused by expression of GPX4, GSTP1, MGST3, and SOD1 may be intrinsic, not acquired, since the heterogeneity is not specific to chemotherapy-treated samples or cell type. Additionally, we identified candidate chemoresistance pharmacogenes that can be further tested through focused follow-up studies.


Asunto(s)
Neoplasias de la Mama , Femenino , Humanos , Mama/cirugía , Mama/patología , Neoplasias de la Mama/tratamiento farmacológico , Neoplasias de la Mama/genética , Neoplasias de la Mama/patología , Perfilación de la Expresión Génica , Transcriptoma
5.
Pharmacotherapy ; 41(11): 907-914, 2021 11.
Artículo en Inglés | MEDLINE | ID: mdl-34587291

RESUMEN

OBJECTIVE: To compare associations between individual antidepressants and newborn outcomes. DESIGN: Retrospective cohort study. SETTING: Deliveries in a large, US medical system. POPULATION: Women who received at least one antidepressant prescription 3 months prior to conception through delivery. METHODS: Eligible women had maternal characteristics and newborn outcomes extracted from medical record data. Exposure was defined by the timing of the prescription during pregnancy. MAIN OUTCOME MEASURES: Newborn outcomes (any adaptation syndrome, neonatal intensive care unit (NICU) admission) were analyzed for each antidepressant and compared using standard statistics and multivariable regression compared to exposure to bupropion. Odds of outcomes based on timing of exposure were also explored. RESULTS: A total of 3,694 women were analyzed. Rates of any adaptation syndrome (p < 0.001), NICU admission (p < 0.001), and transient tachypnea of newborn (TTN) (p = 0.006) were significantly different between drugs. Infants exposed to duloxetine had the highest rates of NICU admissions (39.6%) and adaptation syndromes (15.1%). Venlafaxine-exposed infants had the highest rates of TTN (18.2%). Controlling for maternal age, race, insurance, and gestational age at delivery, early pregnancy antidepressant exposure was associated with adaptation syndrome and NICU admission for both duloxetine (adjusted odds ratio (aOR) 2.31 [95% Confidence Interval (CI) 1.11-4.80] and aOR 2.47 [95% CI 1.40-4.34], respectively) and escitalopram (aOR 1.72 [95% CI 1.09-2.70] and aOR 1.64 [95% CI 1.21-2.22], respectively). Exposure in the third trimester was associated with any adaptation syndrome for citalopram, duloxetine, escitalopram, fluoxetine, sertraline, and venlafaxine and NICU admission for bupropion, citalopram, duloxetine, escitalopram, and fluoxetine. CONCLUSION: Duloxetine and escitalopram appear to have the strongest associations with any adaptation syndrome and NICU admission whereas bupropion and sertraline tended to have among the lowest risks of these outcomes. These results can help providers and patients discuss choice of individual antidepressant drugs during pregnancy.


Asunto(s)
Antidepresivos , Efectos Tardíos de la Exposición Prenatal , Antidepresivos/efectos adversos , Bupropión/efectos adversos , Citalopram/efectos adversos , Clorhidrato de Duloxetina/efectos adversos , Escitalopram/efectos adversos , Femenino , Fluoxetina/efectos adversos , Humanos , Recién Nacido , Embarazo , Efectos Tardíos de la Exposición Prenatal/epidemiología , Estudios Retrospectivos , Sertralina/efectos adversos , Resultado del Tratamiento , Clorhidrato de Venlafaxina/efectos adversos
6.
Clin Transl Sci ; 14(5): 1864-1874, 2021 09.
Artículo en Inglés | MEDLINE | ID: mdl-33939284

RESUMEN

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.


Asunto(s)
Antipsicóticos/uso terapéutico , Aprendizaje Automático , Selección de Paciente , Esquizofrenia/tratamiento farmacológico , Adolescente , Adulto , Anciano , Ensayos Clínicos Fase II como Asunto/estadística & datos numéricos , Conjuntos de Datos como Asunto , Femenino , Humanos , Masculino , Persona de Mediana Edad , Prueba de Estudio Conceptual , Esquizofrenia/diagnóstico , Resultado del Tratamiento , Adulto Joven
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