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| 2. |
- Albitar, Orwa, et al.
(author)
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Pharmacometric modeling of drug adverse effects : an application of mixture models in schizophrenia spectrum disorder patients treated with clozapine
- 2023
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In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Nature. - 1567-567X .- 1573-8744. ; 50:1, s. 21-31
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Journal article (peer-reviewed)abstract
- Clozapine has superior efficacy to other antipsychotics yet is underutilized due to its adverse effects, such as neutropenia, weight gain, and tachycardia. The current investigation aimed to introduce a pharmacometric approach to simultaneously model drug adverse effects, with examples from schizophrenia spectrum patients receiving clozapine. The adverse drug effects were represented as a function of time by incorporating a mixture model to describe individual susceptibility to the adverse effects. Applications of the proposed method were presented by analyzing retrospective data from patients’ medical records in Psychiatric Clinic, Penang General Hospital. Tachycardia, weight gain, and absolute neutrophils count (ANC) decrease were best described by an offset, a piecewise linear, and a transient surge function, respectively. 42.9% of the patients had all the adverse effects, including weight gain (0.01 kg/m2 increase every week over a baseline of 24.7 kg/m2 until stabilizing at 279 weeks), ANC decrease (20% decrease from 4540 cells/µL week 12-20.8), and tachycardia (14% constant increase over a baseline of 87.9 bpm for a clozapine maintenance dose of 450 mg daily). 32.5% of the patients had only tachycardia, while the remaining 24.6% had none of the adverse effects. A new pharmacometric approach was proposed to describe adverse drug effects with examples of clozapine-induced weight gain, ANC drop, and tachycardia. The current approach described the longitudinal time changes of continuous data while assessing patient susceptibility. Furthermore, the model revealed the possible co-existence of ANC drop and weight gain; thus, neutrophil monitoring might predict future changes in body weight.
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- Alskär, Oskar, et al.
(author)
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Model-Based Interspecies Scaling of Glucose Homeostasis
- 2017
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In: CPT. - : John Wiley & Sons. - 2163-8306. ; 6:11, s. 778-786
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Journal article (peer-reviewed)abstract
- Being able to scale preclinical pharmacodynamic response to clinical would be beneficial in drug development. In this work, the integrated glucose insulin (IGI) model, developed on clinical intravenous glucose tolerance test (IVGTT) data, describing dynamic glucose and insulin concentrations during glucose tolerance tests, was scaled to describe data from similar tests performed in healthy rats, mice, dogs, pigs, and humans. Several approaches to scaling the dynamic glucose and insulin were investigated. The theoretical allometric exponents of 0.75 and 1, for clearances and volumes, respectively, could describe the data well with some species-specific adaptations: dogs and pigs showed slower first phase insulin secretion than expected from the scaling, pigs also showed more rapid insulin dependent glucose elimination, and rodents showed differences in glucose effectiveness. The resulting scaled IGI model was shown to accurately predict external preclinical IVGTT data and may be useful in facilitating translations of preclinical research into the clinic.
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| 4. |
- Claussen, Anetta, et al.
(author)
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Impact of demographics and disease progression on the relationship between glucose and HbA1c
- 2017
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In: European Journal of Pharmaceutical Sciences. - : ELSEVIER SCIENCE BV. - 0928-0987 .- 1879-0720. ; 104, s. 417-423
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Journal article (peer-reviewed)abstract
- Context: Several studies have shown that the relationship between mean plasma glucose (MPG) and glycated haemoglobin (HbA1c) may vary across populations. Especially race has previously been referred to shift the regression line that links MPG to HbA1c at steady-state (Herman & Cohen, 2012).Objective: To assess the influence of demographic and disease progression-related covariates on the intercept of the estimated linear MPG-HbA1c relationship in a longitudinal model.Data: Longitudinal patient-level data from 16 late-phase trials in type 2 diabetes with a total of 8927 subjects was used to study covariates for the relationship between MPG and HbA1c. The analysed covariates included age group, HMI, gender, race, diabetes duration, and pre-trial treatment. Differences between trials were taken into account by estimating a trial-to-trial variability component.Participants: Participants included 47% females and 20% above 65 years. 77% were Caucasian, 9% were Asian, 5% were Black and the remaining 9% were analysed together as other races.Analysis: Estimates of the change in the intercept of the MPG-HbA1c relationship due to the mentioned covariates were determined using a longitudinal model.Results: The analysis showed that pre-trial treatment with insulin had the most pronounced impact associated with a 0.34% higher HbA1c at a given MPG. However, race, diabetes duration and age group also had an impact on the MPG-HbA1c relationship.Conclusion: Our analysis shows that the relationship between MPG and HbA1c is relatively insensitive to covariates, but shows small variations across populations, which may be relevant to take into account when predicting HbA1c response based on MPG measurements in clinical trials.
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| 5. |
- Eriksson, Jan, et al.
(author)
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Altered glucose-dependent secretion of glucagon and ACTH is associated with insulin resistance, assessed by population analysis
- 2023
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In: Endocrine Connections. - : Bioscientifica. - 2049-3614. ; 12:4
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Journal article (peer-reviewed)abstract
- This study aimed to characterize how the dysregulation of counter-regulatory hormones can contribute to insulin resistance and potentially to diabetes. Therefore, we investigated the association between insulin sensitivity and the glucose- and insulin-dependent secretion of glucagon, adrenocorticotropic hormone (ACTH), and cortisol in non-diabetic individuals using a population model analysis. Data, from hyperinsulinemic-hypoglycemic clamps, were pooled for analysis, including 52 individuals with a wide range of insulin resistance (reflected by glucose infusion rate 20-60 min; GIR(20-60min)). Glucagon secretion was suppressed by glucose and, to a lesser extent, insulin. The GIR(20-60min) and BMI were identified as predictors of the insulin effect on glucagon. At normoglycemia (5 mmol/L), a 90% suppression of glucagon was achieved at insulin concentrations of 16.3 and 43.4 mu U/mL in individuals belonging to the highest and lowest quantiles of insulin sensitivity, respectively. Insulin resistance of glucagon secretion explained the elevated fasting glucagon for individuals with a low GIR(20-60min). ACTH secretion was suppressed by glucose and not affected by insulin. The GIR(20-60min) was superior to other measures as a predictor of glucose-dependent ACTH secretion, with 90% suppression of ACTH secretion by glucose at 3.1 and 3.5 mmol/L for insulin-sensitive and insulin-resistant individuals, respectively. This difference may appear small but shifts the suppression range into normoglycemia for individuals with insulin resistance, thus, leading to earlier and greater ACTH/cortisol response when the glucose falls. Based on modeling of pooled glucose-clamp data, insulin resistance was associated with generally elevated glucagon and a potentiated cortisol-axis response to hypoglycemia, and over time both hormonal pathways may therefore contribute to dysglycemia and possibly type 2 diabetes.
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| 6. |
- Germovsek, Eva, et al.
(author)
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A Time-to-Event Model Relating Integrated Craving to Risk of Smoking Relapse Across Different Nicotine Replacement Therapy Formulations
- 2021
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In: Clinical Pharmacology and Therapeutics. - : John Wiley & Sons. - 0009-9236 .- 1532-6535. ; 109:2, s. 416-423
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Journal article (peer-reviewed)abstract
- Smoking increases the risk of cancer and other diseases, causing an estimated 7 million deaths per year. Nicotine replacement therapy (NRT) reduces craving for smoking, therefore, increasing an individual's probability to remain abstinent. In this work, we for the first time quantitatively described the relationship between craving and smoking abstinence, using retrospectively collected data from 19 studies, including 3 NRT formulations (inhaler, mouth spray, and patch) and a combination of inhaler and patch. Smokers motivated to quit were included in the NRT or placebo arms. Integrated craving (i.e., craving over a period of time) was assessed with 4-category, 5-category, or 100-mm visual analogue scale. The bounded integer model was used to assess latent craving from all scales. A time-to-event model linked predicted integrated craving to the hazard of smoking relapse. Available data included 9,323 adult subjects, observed for 3 weeks up to 2 years. At the study end, 9% (11% for NRT and 5% for placebo), on average, remained abstinent according to the protocol definition. A Gompertz-Makeham hazard best described the data, with a hazard of smoking relapse decreasing over time. Latent integrated craving was positively related to the hazard of smoking relapse, through a sigmoidal maximum effect function. For the same craving, being on NRT was found to reduce the hazard of relapse by an additional 30% compared with placebo. This work confirmed that low craving is associated with a high probability of remaining smoking abstinent and that NRT, in addition to reducing craving, increases the probability of remaining smoking abstinent.
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| 8. |
- Germovsek, Eva, et al.
(author)
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Relating Nicotine Plasma Concentration to Momentary Craving Across Four Nicotine Replacement Therapy Formulations
- 2020
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In: Clinical Pharmacology and Therapeutics. - : WILEY. - 0009-9236 .- 1532-6535. ; 107:1, s. 238-245
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Journal article (peer-reviewed)abstract
- Tobacco use is a major health concern. To assist smoking cessation, nicotine replacement therapy (NRT) is used to reduce nicotine craving. We quantitatively described the relationship between nicotine pharmacokinetics (PKs) from NRTs and momentary craving, linking two different pharmacodynamic (PD) scales for measuring craving. The dataset comprised retrospective data from 17 clinical studies and included 1,077 adult smokers with 39,802 craving observations from four formulations: lozenge, gum, mouth spray, and patch. A PK/PD model was developed that linked individual predicted nicotine concentrations with the categorical and visual analogue PD scales through a joint bounded integer model. A maximum effect model, accounting for acute tolerance development, successfully related nicotine concentrations to momentary craving. Results showed that all formulations were similarly effective in reducing craving, albeit with a fourfold lower potency for the patch. Women were found to have a higher maximal effect of nicotine to reduce craving, compared with men.
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| 9. |
- Ghadzi, Siti Maisharah Sheikh, et al.
(author)
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Implications for Drug Characterization in Glucose Tolerance Tests Without Insulin : Simulation Study of Power and Predictions Using Model-Based Analysis
- 2017
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In: CPT. - : WILEY. - 2163-8306. ; 6:10, s. 686-694
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Journal article (peer-reviewed)abstract
- In antihyperglycemic drug development, drug effects are usually characterized using glucose provocations. Analyzing provocation data using pharmacometrics has shown powerful, enabling small studies. In preclinical drug development, high power is attractive due to the experiment sizes; however, insulin is not always available, which potentially impacts power and predictive performance. This simulation study was performed to investigate the implications of performing model-based drug characterization without insulin. The integrated glucose-insulin model was used to simulate and re-estimated oral glucose tolerance tests using a crossover design of placebo and study compound. Drug effects were implemented on seven different mechanisms of action (MOA); one by one or in two-drug combinations. This study showed that exclusion of insulin may severely reduce the power to distinguish the correct from competing drug effect, and to detect a primary or secondary drug effect, however, it did not affect the predictive performance of the model.
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