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1.
Mov Disord ; 2024 Aug 29.
Artículo en Inglés | MEDLINE | ID: mdl-39206961

RESUMEN

BACKGROUND: Progressive supranuclear palsy (PSP) is a neurodegenerative, late-onset disease that is challenging in terms of assessment. The Progressive Supranuclear Palsy Rating Scale (PSPRS), a 28-item clinician-reported scale, is the most established clinical outcome assessment method. Recently, the U.S. Food and Drug Administration (FDA) has proposed a subscale of 10 items as an alternative to full PSPRS. OBJECTIVES: To quantitatively evaluate and compare the properties of full PSPRS and the FDA subscale using item response theory. To develop a progression model of the disease and assess relative merits of study designs and analysis options. METHODS: Data of 979 patients from four interventional trials and two registries were available for analysis. Our investigation was divided into: (1) estimating informativeness of the 28 items; (2) estimating disease progression; and (3) comparing the scales, trial designs, and analysis options with respect to power to detect a clinically relevant treatment effect. RESULTS: PSPRS item scores had a low pairwise correlation (r = 0.17 ± 0.14) and the items irritability, sleep difficulty, and postural tremor were uncorrelated with the other items. The FDA-selected items displayed higher correlation (r = 0.35 ± 0.14) and were the basis for a longitudinal item response model including disease progression. Trial simulations indicated that identification of a disease-modifying treatment effect required less than half the study size if the analysis was based on longitudinal item information compared with total scores at end-of-treatment. CONCLUSION: A longitudinal item response model based on the FDA-selected PSPRS items is a promising tool in evaluating treatments for PSP. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

2.
J Pharmacokinet Pharmacodyn ; 51(1): 65-75, 2024 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-37943398

RESUMEN

Biological therapies may act as immunogenic triggers leading to the formation of anti-drug antibodies (ADAs). Population pharmacokinetic (PK) models can be used to characterize the relationship between ADA and drug disposition but often rely on the ADA bioassay results, which may not be sufficiently sensitive to inform on this characterization.In this work, a methodology that could help to further elucidate the underlying ADA production and impact on the drug disposition was explored. A mixed hidden-Markov model (MHMM) was developed to characterize the underlying (hidden) formation of ADA against the biologic, using certolizumab pegol (CZP), as a test drug. CZP is a PEGylated Fc free TNF-inhibitor used in the treatment of rheumatoid arthritis and other chronic inflammatory diseases.The bivariate MHMM used information from plasma drug concentrations and ADA measurements, from six clinical studies (n = 845), that were correlated through a bivariate Gaussian function to infer about two hidden states; production and no-production of ADA influencing PK. Estimation of inter-individual variability was not supported in this case. Parameters associated with the observed part of the model were reasonably well estimated while parameters associated with the hidden part were less precise. Individual state sequences obtained using a Viterbi algorithm suggested that the model was able to determine the start of ADA production for each individual, being a more assay-independent methodology than traditional population PK. The model serves as a basis for identification of covariates influencing the ADA formation, and thus has the potential to identify aspects that minimize its impact on PK and/or efficacy.


Asunto(s)
Antirreumáticos , Artritis Reumatoide , Humanos , Certolizumab Pegol/farmacocinética , Certolizumab Pegol/uso terapéutico , Anticuerpos , Artritis Reumatoide/tratamiento farmacológico , Algoritmos , Antirreumáticos/uso terapéutico
3.
J Pharmacokinet Pharmacodyn ; 51(1): 5-31, 2024 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-37573528

RESUMEN

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.


Asunto(s)
Farmacología , Humanos , Farmacocinética , Selección de Profesión
4.
J Pharmacokinet Pharmacodyn ; 50(4): 297-314, 2023 08.
Artículo en Inglés | MEDLINE | ID: mdl-36947282

RESUMEN

Model-based meta-analysis (MBMA) is an approach that integrates relevant summary level data from heterogeneously designed randomized controlled trials (RCTs). This study not only evaluated the predictability of a published MBMA for forced expiratory volume in one second (FEV1) and its link to annual exacerbation rate in patients with chronic obstructive pulmonary disease (COPD) but also included data from new RCTs. A comparative effectiveness analysis across all drugs was also performed. Aggregated level data were collected from RCTs published between July 2013 and November 2020 (n = 132 references comprising 156 studies) and combined with data used in the legacy MBMA (published RCTs up to July 2013 - n = 142). The augmented data (n = 298) were used to evaluate the predictive performance of the published MBMA using goodness-of-fit plots for assessment. Furthermore, the model was extended including drugs that were not available before July 2013, estimating a new set of parameters. The legacy MBMA model predicted the post-2013 FEV1 data well, and new estimated parameters were similar to those of drugs in the same class. However, the exacerbation model overpredicted the post-2013 mean annual exacerbation rate data. Inclusion of year when the study started on the pre-treatment placebo rate improved the model predictive performance perhaps explaining potential improvements in the disease management over time. The addition of new data to the legacy COPD MBMA enabled a more robust model with increased predictability performance for both endpoints FEV1 and mean annual exacerbation rate.


Asunto(s)
Enfermedad Pulmonar Obstructiva Crónica , Humanos , Enfermedad Pulmonar Obstructiva Crónica/tratamiento farmacológico , Volumen Espiratorio Forzado
5.
Pharm Res ; 39(8): 1779-1787, 2022 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-35233731

RESUMEN

PURPOSE: The current study aimed to illustrate how a non-linear mixed effect (NLME) model-based analysis may improve confidence in a Phase III trial through more precise estimates of the drug effect. METHODS: The FULFIL clinical trial was a Phase III study that compared 24 weeks of once daily inhaled triple therapy with twice daily inhaled dual therapy in patients with chronic obstructive pulmonary disease (COPD). Patient reported outcome data, obtained by using The Evaluating Respiratory Symptoms in COPD (E-RS:COPD) questionnaire, from the FULFIL study were analyzed using an NLME item-based response theory model (IRT). The change from baseline (CFB) in E-RS:COPD total score over 4-week intervals for each treatment arm was obtained using the IRT and compared with published results obtained with a mixed model repeated measures (MMRM) analysis. RESULTS: The IRT included a graded response model characterizing item parameters and a Weibull function combined with an offset function to describe the COPD symptoms-time course in patients receiving either triple therapy (n = 907) or dual therapy (n = 894). The IRT improved precision of the estimated drug effect compared to MMRM, resulting in a sample size of at least 3.64 times larger for the MMRM analysis to achieve the IRT precision in the CFB estimate. CONCLUSION: This study shows the advantage of IRT over MMRM with a direct comparison of the same primary endpoint for the two analyses using the same observed clinical trial data, resulting in an increased confidence in Phase III.


Asunto(s)
Enfermedad Pulmonar Obstructiva Crónica , Administración por Inhalación , Broncodilatadores/uso terapéutico , Humanos , Medición de Resultados Informados por el Paciente , Enfermedad Pulmonar Obstructiva Crónica/tratamiento farmacológico
6.
Pharm Res ; 36(9): 135, 2019 Jul 17.
Artículo en Inglés | MEDLINE | ID: mdl-31317279

RESUMEN

PURPOSE: The aim of this work was to allow combination of information from recent and historical trials in Parkinson's Disease (PD) by developing bridging methodology between two versions of the clinical endpoint. METHODS: A previously developed Item Response Model (IRM), that described longitudinal changes in Movement Disorder Society (MDS) sponsored revision of Unified Parkinson's Disease Rating Scale (UPDRS) [MDS-UPDRS] data from the De Novo PD cohort in Parkinson's Progression Markers Initiative, was first adapted to describe baseline UPDRS data from two clinical trials, one in subjects with early PD and another in subjects with advanced PD. Assuming similar IRM structure, items of the UPDRS version were mapped to those in the MDS-UPDRS version. Subsequently, the longitudinal changes in the placebo arm of the advanced PD study were characterized. RESULTS: The parameters reflecting differences in the shared items between endpoints were successfully estimated, and the model diagnostics indicated that mapping was better for early PD subjects (closer to De Novo cohort) than for advanced PD subjects. Disease progression for placebo in advanced PD patients was relatively shallow. CONCLUSION: An IRM able to handle two variants of clinical PD endpoints was developed; it can improve the utilization of data from diverse sources and diverse disease populations.


Asunto(s)
Enfermedad de Parkinson/clasificación , Índice de Severidad de la Enfermedad , Estudios de Cohortes , Evaluación de la Discapacidad , Humanos , Modelos Teóricos , Enfermedad de Parkinson/fisiopatología , Placebos
8.
J Pharmacokinet Pharmacodyn ; 43(3): 305-14, 2016 06.
Artículo en Inglés | MEDLINE | ID: mdl-27165151

RESUMEN

Parameter variation in pharmacometric analysis studies can be characterized as within subject parameter variability (WSV) in pharmacometric models. WSV has previously been successfully modeled using inter-occasion variability (IOV), but also stochastic differential equations (SDEs). In this study, two approaches, dynamic inter-occasion variability (dIOV) and adapted stochastic differential equations, were proposed to investigate WSV in pharmacometric count data analysis. These approaches were applied to published count models for seizure counts and Likert pain scores. Both approaches improved the model fits significantly. In addition, stochastic simulation and estimation were used to explore further the capability of the two approaches to diagnose and improve models where existing WSV is not recognized. The results of simulations confirmed the gain in introducing WSV as dIOV and SDEs when parameters vary randomly over time. Further, the approaches were also informative as diagnostics of model misspecification, when parameters changed systematically over time but this was not recognized in the structural model. The proposed approaches in this study offer strategies to characterize WSV and are not restricted to count data.


Asunto(s)
Ensayos Clínicos como Asunto/estadística & datos numéricos , Modelos Estadísticos , Farmacocinética , Farmacología/estadística & datos numéricos , Procesos Estocásticos , Simulación por Computador , Humanos , Cadenas de Markov
9.
Pharm Res ; 31(8): 2152-65, 2014 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-24595495

RESUMEN

PURPOSE: This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer's disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT). METHODS: A baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model. RESULTS: IRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis. CONCLUSION: A combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data.


Asunto(s)
Enfermedad de Alzheimer/tratamiento farmacológico , Enfermedad de Alzheimer/epidemiología , Ensayos Clínicos Fase III como Asunto/normas , Bases de Datos Factuales/normas , Modelos Biológicos , Estadística como Asunto/normas , Enfermedad de Alzheimer/psicología , Atorvastatina , Trastornos del Conocimiento/tratamiento farmacológico , Trastornos del Conocimiento/epidemiología , Ácidos Heptanoicos/uso terapéutico , Humanos , Estudios Longitudinales , Pirroles/uso terapéutico
10.
J Pharmacokinet Pharmacodyn ; 41(3): 223-38, 2014 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-24801864

RESUMEN

NONMEM is the most widely used software for population pharmacokinetic (PK)-pharmacodynamic (PD) analyses. The latest version, NONMEM 7 (NM7), includes several sampling-based estimation methods in addition to the classical methods. In this study, performance of the estimation methods available in NM7 was investigated with respect to bias, precision, robustness and runtime for a diverse set of PD models. Simulations of 500 data sets from each PD model were reanalyzed with the available estimation methods to investigate bias and precision. Simulations of 100 data sets were used to investigate robustness by comparing final estimates obtained after estimations starting from the true parameter values and initial estimates randomly generated using the CHAIN feature in NM7. Average estimation time for each algorithm and each model was calculated from the runtimes reported by NM7. The method giving the lowest bias and highest precision across models was importance sampling, closely followed by FOCE/LAPLACE and stochastic approximation expectation-maximization. The methods relative robustness differed between models and no method showed clear superior performance. FOCE/LAPLACE was the method with the shortest runtime for all models, followed by iterative two-stage. The Bayesian Markov Chain Monte Carlo method, used in this study for point estimation, performed worst in all tested metrics.


Asunto(s)
Sesgo , Farmacocinética , Programas Informáticos/normas , Algoritmos , Humanos , Imidazoles/farmacología , Modelos Estadísticos , Norepinefrina/metabolismo , Reproducibilidad de los Resultados , Simpaticolíticos/farmacología
11.
Clin Transl Sci ; 16(11): 2310-2322, 2023 11.
Artículo en Inglés | MEDLINE | ID: mdl-37718498

RESUMEN

The Mayo Clinical Score is used in clinical trials to describe the clinical status of patients with ulcerative colitis (UC). It comprises four subscores: rectal bleeding (RB), stool frequency (SF), physician's global assessment, and endoscopy (ENDO). According to recent US Food and Drug Administration guidelines (Ulcerative colitis: developing drugs for treatment, Guidance Document, https://www.fda.gov/regulatory-information/s. 2022), clinical response and remission should be based on modified Mayo Score (mMS) relying on RB, SF, and ENDO. Typically, ENDO is performed at the beginning and end of each phase, whereas RB and SF are more frequently available. Item response theory (IRT) models allow the shared information to be used for prediction of all subscores at each observation time; therefore, it leverages information from RB and SF to predict ENDO. A UC disease IRT model was developed based on four etrolizumab phase III studies to describe the longitudinal mMS subscores, placebo response, and remission at the end of induction and maintenance. For each subscore, a bounded integer model was developed. The placebo response was characterized by a mono-exponential function acting on all mMS subscores similarly. The final model reliably predicted longitudinal mMS data. In addition, remission was well-predicted by the model, with only 5% overprediction at the end of induction and 3% underprediction at the end of maintenance. External evaluation of the final model using placebo arms from five different studies indicated adequate performance for both longitudinal mMS subscores and remission status. These results suggest utility of the current disease model for informed decision making in UC clinical development, such as assisting future clinical trial designs and evaluations.


Asunto(s)
Colitis Ulcerosa , Humanos , Colitis Ulcerosa/diagnóstico , Colitis Ulcerosa/tratamiento farmacológico , Recto , Inducción de Remisión , Heces , Efecto Placebo , Hemorragia Gastrointestinal , Resultado del Tratamiento , Método Doble Ciego
12.
J Pharmacol Exp Ther ; 339(3): 878-85, 2011 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-21890509

RESUMEN

Transient lower esophageal sphincter relaxation (TLESR) is the major mechanism for gastroesophageal reflux. Characterizations of candidate compounds for reduction of TLESRs are traditionally done through summary exposure and response measures and would benefit from model-based analyses of exposure-TLESR events relationships. Pharmacokinetic (PK)-pharmacodynamic (PD) modeling approaches treating TLESRs either as count data or repeated time-to-event (RTTE) data were developed and compared in terms of their ability to characterize system and drug characteristics. Vehicle data comprising 294 TLESR events were collected from nine dogs. Compound [(R)-(+)-[2,3-dihydro-5-methyl-3-(4-morpholinylmethyl)pyrrolo[1,2,3-de]-1,4-benzoxazin-6-yl]-1-naphthalenylmethanone mesylate (WIN55212-2)] data containing 66 TLESR events, as well as plasma concentrations, were obtained from four dogs. Each experiment lasted for 45 min and was initiated with a meal. Counts in equispaced 5- and 1-min intervals were modeled based on a Poisson probability distribution model. TLESR events were analyzed with the RTTE model. The PK was connected to the PD with a one-compartment model. Vehicle data were described by a baseline and a surge function; the surge peak was determined to be approximately 9.69 min by all approaches, and its width in time at half-maximal intensity was 5 min (1-min count and RTTE) or 10 min (5-min count). TLESR inhibition by WIN55212-2 was described by an I(max) model, with an IC(50) of on average 2.39 nmol · l(-1). Modeling approaches using count or RTTE data linked to a dynamic PK-PD representation of exposure are superior to using summary PK and PD measures and are associated with a higher power for detecting a statistically significant drug effect.


Asunto(s)
Benzoxazinas/farmacología , Benzoxazinas/farmacocinética , Agonistas de Receptores de Cannabinoides , Esfínter Esofágico Inferior/fisiopatología , Reflujo Gastroesofágico/fisiopatología , Morfolinas/farmacología , Morfolinas/farmacocinética , Naftalenos/farmacología , Naftalenos/farmacocinética , Animales , Bloqueadores de los Canales de Calcio/farmacología , Modelos Animales de Enfermedad , Perros , Esfínter Esofágico Inferior/fisiología , Femenino , Masculino , Modelos Biológicos , Relajación Muscular/efectos de los fármacos , Relajación Muscular/fisiología , Presión , Receptores de Cannabinoides/fisiología , Factores de Tiempo
13.
CPT Pharmacometrics Syst Pharmacol ; 10(4): 309-317, 2021 04.
Artículo en Inglés | MEDLINE | ID: mdl-33951753

RESUMEN

This study tested the hypothesis that analyzing longitudinal item scores of the Unified Parkinson's Disease Rating Scale could allow a smaller trial size and describe a drug's effect on symptom progression. Two historical studies of the dopaminergic drug ropinirole were analyzed: a cross-over formulation comparison trial in 161 patients with early-stage Parkinson's disease, and a 24-week, parallel-group, placebo-controlled efficacy trial in 393 patients with advanced-stage Parkinson's disease. We applied item response theory to estimate the patients' symptom severity and developed a longitudinal model using the symptom severity to describe the time course of the placebo response and the drug effect on the time course. Similarly, we developed a longitudinal model using the total score. We then compared sample size needs for drug effect detection using these two different models. Total score modeling estimated median changes from baseline at 24 weeks (90% confidence interval) of -3.7 (-5.4 to -2.0) and -9.3 (-11 to -7.3) points by placebo and ropinirole. Comparable changes were estimated (with slightly higher precision) by item-score modeling as -2.0 (-4.0 to -1.0) and -9.0 (-11 to -8.0) points. The treatment duration was insufficient to estimate the symptom progression rate; hence the drug effect on the progression could not be assessed. The trial sizes to detect a drug effect with 80% power on total score and on symptom severity were estimated (at the type I error level of 0.05) as 88 and 58, respectively. Longitudinal item response analysis could markedly reduce sample size; it also has the potential for assessing drug effects on disease progression in longer trials.


Asunto(s)
Agonistas de Dopamina/farmacología , Desarrollo de Medicamentos/métodos , Indoles/farmacología , Pruebas de Estado Mental y Demencia/estadística & datos numéricos , Enfermedad de Parkinson/tratamiento farmacológico , Adulto , Anciano , Anciano de 80 o más Años , Estudios de Casos y Controles , Estudios Cruzados , Agonistas de Dopamina/administración & dosificación , Agonistas de Dopamina/uso terapéutico , Femenino , Humanos , Indoles/administración & dosificación , Indoles/uso terapéutico , Estudios Longitudinales , Masculino , Persona de Mediana Edad , Placebos/administración & dosificación , Índice de Severidad de la Enfermedad
14.
AAPS J ; 23(4): 79, 2021 06 02.
Artículo en Inglés | MEDLINE | ID: mdl-34080077

RESUMEN

This study aimed to illustrate how a new methodology to assess clinical trial outcome measures using a longitudinal item response theory-based model (IRM) could serve as an alternative to mixed model repeated measures (MMRM). Data from the EXACT (Exacerbation of chronic pulmonary disease tool) which is used to capture frequency, severity, and duration of exacerbations in COPD were analyzed using an IRM. The IRM included a graded response model characterizing item parameters and functions describing symptom-time course. Total scores were simulated (month 12) using uncertainty in parameter estimates. The 50th (2.5th, 97.5th) percentiles of the resulting simulated differences in average total score (drug minus placebo) represented the estimated drug effect (95%CI), which was compared with published MMRM results. Furthermore, differences in sample size, sensitivity, specificity, and type I and II errors between approaches were explored. Patients received either oral danirixin 75 mg twice daily (n = 45) or placebo (n = 48) on top of standard of care over 52 weeks. A step function best described the COPD symptoms-time course in both trial arms. The IRM improved precision of the estimated drug effect compared to MMRM, resulting in a sample size of 2.5 times larger for the MMRM analysis to achieve the IRM precision. The IRM showed a higher probability of a positive predictive value (34%) than MMRM (22%). An item model-based analysis data gave more precise estimates of drug effect than MMRM analysis for the same endpoint in this one case study.


Asunto(s)
Modelos Biológicos , Piperidinas/farmacocinética , Enfermedad Pulmonar Obstructiva Crónica/tratamiento farmacológico , Proyectos de Investigación , Sulfonas/farmacocinética , Administración Oral , Anciano , Interpretación Estadística de Datos , Femenino , Humanos , Masculino , Persona de Mediana Edad , Piperidinas/administración & dosificación , Placebos/administración & dosificación , Enfermedad Pulmonar Obstructiva Crónica/diagnóstico , Tamaño de la Muestra , Índice de Severidad de la Enfermedad , Sulfonas/administración & dosificación , Brote de los Síntomas , Resultado del Tratamiento
15.
J Pharmacokinet Pharmacodyn ; 36(4): 353-66, 2009 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-19653080

RESUMEN

There has been little evaluation of maximum likelihood approximation methods for non-linear mixed effects modelling of count data. The aim of this study was to explore the estimation accuracy of population parameters from six count models, using two different methods and programs. Simulations of 100 data sets were performed in NONMEM for each probability distribution with parameter values derived from a real case study on 551 epileptic patients. Models investigated were: Poisson (PS), Poisson with Markov elements (PMAK), Poisson with a mixture distribution for individual observations (PMIX), Zero Inflated Poisson (ZIP), Generalized Poisson (GP) and Negative Binomial (NB). Estimations of simulated datasets were completed with Laplacian approximation (LAPLACE) in NONMEM and LAPLACE/Gaussian Quadrature (GQ) in SAS. With LAPLACE, the average absolute value of the bias (AVB) in all models was 1.02% for fixed effects, and ranged 0.32-8.24% for the estimation of the random effect of the mean count (lambda). The random effect of the overdispersion parameter present in ZIP, GP and NB was underestimated (-25.87, -15.73 and -21.93% of relative bias, respectively). Analysis with GQ 9 points resulted in an improvement in these parameters (3.80% average AVB). Methods implemented in SAS had a lower fraction of successful minimizations, and GQ 9 points was considerably slower than 1 point. Simulations showed that parameter estimates, even when biased, resulted in data that were only marginally different from data simulated from the true model. Thus all methods investigated appear to provide useful results for the investigated count data models.


Asunto(s)
Biometría , Funciones de Verosimilitud , Análisis Numérico Asistido por Computador , Distribuciones Estadísticas , Teorema de Bayes , Distribución Binomial , Simulación por Computador , Humanos , Cadenas de Markov , Método de Montecarlo , Distribución Normal , Distribución de Poisson , Programas Informáticos
16.
J Pharmacokinet Pharmacodyn ; 36(5): 461-77, 2009 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-19798550

RESUMEN

The number of counts (events) per unit of time is a discrete response variable that is generally analyzed with the Poisson distribution (PS) model. The PS model makes two assumptions: the mean number of counts (lambda) is assumed equal to the variance, and counts occurring in non-overlapping intervals are assumed independent. However, many counting outcomes show greater variability than predicted by the PS model, a phenomenon called overdispersion. The purpose of this study was to implement and explore, in the population context, different distribution models accounting for overdispersion and Markov patterns in the analysis of count data. Daily seizures count data obtained from 551 subjects during the 12-week screening phase of a double-blind, placebo-controlled, parallel-group multicenter study performed in epileptic patients with medically refractory partial seizures, were used in the current investigation. The following distribution models were fitted to the data: PS, Zero-Inflated PS (ZIP), Negative Binomial (NB), and Zero-Inflated Negative Binomial (ZINB) models. Markovian features were introduced estimating different lambdas and overdispersion parameters depending on whether the previous day was a seizure or a non-seizure day. All analyses were performed with NONMEM VI. All models were successfully implemented and all overdispersed models improved the fit with respect to the PS model. The NB model resulted in the best description of the data. The inclusion of Markovian features in lambda and in the overdispersion parameter improved the fit significantly (P < 0.001). The plot of the variance versus mean daily seizure count profiles, and the number of transitions, are suggested as model performance tools reflecting the capability to handle overdispersion and Markovian features, respectively.


Asunto(s)
Cadenas de Markov , Farmacocinética , Adolescente , Adulto , Anciano , Algoritmos , Análisis de Varianza , Niño , Interpretación Estadística de Datos , Método Doble Ciego , Femenino , Predicción , Humanos , Masculino , Persona de Mediana Edad , Modelos Estadísticos , Distribución de Poisson , Población , Convulsiones/epidemiología , Programas Informáticos , Adulto Joven
17.
AAPS J ; 21(4): 60, 2019 04 26.
Artículo en Inglés | MEDLINE | ID: mdl-31028495

RESUMEN

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease with approximately 174 million cases worldwide. Electronic questionnaires are increasingly used for collecting patient-reported-outcome (PRO) data about disease symptoms. Our aim was to leverage PRO data, collected to record COPD disease symptoms, in a general modelling framework to enable interpretation of PRO observations in relation to disease progression and potential to predict exacerbations. The data were collected daily over a year, in a prospective, observational study. The e-questionnaire, the EXAcerbations of COPD Tool (EXACT®) included 14 items (i.e. questions) with 4 or 5 ordered categorical response options. An item response theory (IRT) model was used to relate the responses from each item to the underlying latent variable (which we refer to as disease severity), and on each item level, Markov models (MM) with 4 or 5 categories were applied to describe the dependence between consecutive observations. Minimal continuous time MMs were used and parameterised using ordinary differential equations. One hundred twenty-seven COPD patients were included (median age 67 years, 54% male, 39% current smokers), providing approximately 40,000 observations per EXACT® item. The final model suggested that, with time, patients more often reported the same scores as the previous day, i.e. the scores were more stable. The modelled COPD disease severity change over time varied markedly between subjects, but was small in the typical individual. This is the first IRT model with Markovian properties; our analysis proved them necessary for predicting symptom-defined exacerbations.


Asunto(s)
Progresión de la Enfermedad , Modelos Teóricos , Medición de Resultados Informados por el Paciente , Enfermedad Pulmonar Obstructiva Crónica , Anciano , Simulación por Computador , Registros Electrónicos de Salud , Femenino , Humanos , Masculino , Estudios Prospectivos , Enfermedad Pulmonar Obstructiva Crónica/diagnóstico , Enfermedad Pulmonar Obstructiva Crónica/terapia , Índice de Severidad de la Enfermedad , Encuestas y Cuestionarios
18.
AAPS J ; 21(5): 85, 2019 07 08.
Artículo en Inglés | MEDLINE | ID: mdl-31286293

RESUMEN

In this work, a previously developed pegfilgrastim (PG) population pharmacokinetic-pharmacodynamic (PKPD) model was used to evaluate potential factors of importance in the assessment of PG PK and PD similarity. Absolute neutrophil count (ANC) was the modelled PD variable. A two-way cross-over study was simulated where a reference PG and a potentially biosimilar test product were administered to healthy volunteers. Differences in delivered dose amounts or potency between the products were simulated. A different baseline absolute neutrophil count (ANC) was also considered. Additionally, the power to conclude PK or PD similarity based on areas under the PG concentration-time curve (AUC) and ANC-time curve (AUEC) were calculated. Delivered dose differences between the products led to a greater than dose proportional differences in AUC but not in AUEC, respectively. A 10% dose difference from a 6 mg dose resulted in 51% and 7% differences in AUC and AUEC, respectively. These differences were more pronounced with low baseline ANC. Potency differences up to 50% were not associated with large differences in either AUCs or AUECs. The power to conclude PK similarity was affected by the simulated dose difference; with a 4% dose difference from 6 mg the power was approximately 29% with 250 subjects. The power to conclude PD similarity was high for all delivered dose differences and sample sizes.


Asunto(s)
Biosimilares Farmacéuticos/administración & dosificación , Filgrastim/administración & dosificación , Modelos Biológicos , Polietilenglicoles/administración & dosificación , Área Bajo la Curva , Biosimilares Farmacéuticos/farmacocinética , Biosimilares Farmacéuticos/farmacología , Estudios Cruzados , Relación Dosis-Respuesta a Droga , Filgrastim/farmacocinética , Filgrastim/farmacología , Humanos , Recuento de Leucocitos , Neutrófilos/metabolismo , Polietilenglicoles/farmacocinética , Polietilenglicoles/farmacología
19.
AAPS J ; 20(5): 91, 2018 08 15.
Artículo en Inglés | MEDLINE | ID: mdl-30112626

RESUMEN

Neutropenia and febrile neutropenia (FN) are serious side effects of cytotoxic chemotherapy which may be alleviated with the administration of recombinant granulocyte colony-stimulating factor (GCSF) derivatives, such as pegfilgrastim (PG) which increases absolute neutrophil count (ANC). In this work, a population pharmacokinetic-pharmacodynamic (PKPD) model was developed based on data obtained from healthy volunteers receiving multiple administrations of PG. The developed model was a bidirectional PKPD model, where PG stimulated the proliferation, maturation, and margination of neutrophils and where circulating neutrophils in turn increased the elimination of PG. Simulations from the developed model show disproportionate changes in response with changes in dose. A dose increase of 10% from the 6 mg therapeutic dose taken as a reference leads to area under the curve (AUC) increases of ~50 and ~5% for PK and PD, respectively. A full random effects covariate model showed that little of the parameter variability could be explained by sex, age, body size, and race. As a consequence, little of the secondary parameter variability (Cmax and AUC of PG and ANC) could be explained by these covariates.


Asunto(s)
Proliferación Celular/efectos de los fármacos , Filgrastim/administración & dosificación , Filgrastim/farmacocinética , Modelos Biológicos , Neutropenia/tratamiento farmacológico , Neutrófilos/efectos de los fármacos , Polietilenglicoles/administración & dosificación , Polietilenglicoles/farmacocinética , Factores de Edad , Tamaño Corporal , Ensayos Clínicos como Asunto , Simulación por Computador , Relación Dosis-Respuesta a Droga , Femenino , Humanos , Inactivación Metabólica , Recuento de Leucocitos , Masculino , Neutropenia/sangre , Neutropenia/etnología , Neutrófilos/metabolismo , Grupos Raciales , Factores Sexuales
20.
AAPS J ; 19(3): 837-845, 2017 05.
Artículo en Inglés | MEDLINE | ID: mdl-28247193

RESUMEN

In the current work, we present the methodology for development of an Item Response Theory model within a non-linear mixed effects framework to characterize the longitudinal changes of the Movement Disorder Society (sponsored revision) of Unified Parkinson's Disease Rating Scale (MDS-UPDRS) endpoint in Parkinson's disease (PD). The data were obtained from Parkinson's Progression Markers Initiative database and included 163,070 observations up to 48 months from 430 subjects belonging to De Novo PD cohort. The probability of obtaining a score, reported for each of the items in the questionnaire, was modeled as a function of the subject's disability. Initially, a single latent variable model was explored to characterize the disease progression over time. However, based on the understanding of the questionnaire set-up and the results of a residuals-based diagnostic tool, a three latent variable model with a mixture implementation was able to adequately describe longitudinal changes not only at the total score level but also at each individual item level. The linear progression rates obtained for the patient-reported items and the non-sided items were similar, each of which roughly take about 50 months for a typical subject to progress linearly from the baseline by one standard deviation. However for the sided items, it was found that the better side deteriorates quicker than the disabled side. This study presents a framework for analyzing MDS-UPDRS data, which can be adapted to more traditional UPDRS data collected in PD clinical trials and result in more efficient designs and analyses of such studies.


Asunto(s)
Modelos Estadísticos , Enfermedad de Parkinson/diagnóstico , Índice de Severidad de la Enfermedad , Adulto , Anciano , Anciano de 80 o más Años , Femenino , Humanos , Masculino , Persona de Mediana Edad
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