| 1. |
- Chua, S. J., et al.
(författare)
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Age-related natural fertility outcomes in women over 35 years: a systematic review and individual participant data meta-analysis
- 2020
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Ingår i: Human Reproduction. - : Oxford University Press (OUP). - 0268-1161 .- 1460-2350. ; 35:8, s. 1808-1820
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Tidskriftsartikel (refereegranskat)abstract
- STUDY QUESTION: What is the rate of natural conception leading to ongoing pregnancy or livebirth over 6-12 months for infertile women of age >= 35 years? SUMMARY ANSWER: Natural conception rates were still clinically relevant in women aged 35 years and above and were significantly higher in women with unexplained infertility compared to those with other diagnoses. WHAT IS KNOWN ALREADY: In recent years, increasing numbers of women have attempted to conceive at a later age, resulting in a commensurate increase in the need for ART. However, there is a lack of data on natural fertility outcomes (i.e. no interventions) in women with increasing age. STUDY DESIGN, SIZE, DURATION: A systematic review with individual participant data (IPD) meta-analysis was carried out. PubMed, MEDLINE, EMBASE, the Cochrane Library, clinicaltrials.gov were searched until 1 July 2018 including search terms 'fertility service', 'waiting list', 'treatment-independent' and 'spontaneous conception'. Language restrictions were not imposed. PARTICIPANTS/MATERIALS, SETTING, METHODS: Inclusion criteria were studies (at least partly) reporting on infertile couples with female partner of age >= 35 years who attended fertility services, underwent fertility workup (e.g. history, semen analysis, tubal status and ovulation status) and were exposed to natural conception (e.g. independent of treatment such as IVF, ovulation induction and tubal surgery). Studies that exclusively studied only one infertility diagnosis, without including other women presenting to infertility services for other causes of infertility, were excluded. For studies that met the inclusion criteria, study authors were contacted to provide IPD, after which fertility outcomes for women of age >= 35 years were retrieved. Time to pregnancy or livebirth and the effect of increasing age on fertility outcomes after adjustment for other prognostic factors were analysed. Quality of studies was graded with the Newcastle-Ottawa Scale (non-randomised controlled trials (RCTs)) or the Cochrane Risk of Bias tool (for RCTs). MAIN RESULTS AND THE ROLE OF CHANCE: We included nine studies (seven cohort studies and two RCTs) (n = 4379 women of at least age 35 years), with the observed composite primary outcome of ongoing pregnancy or livebirth occurring in 429 women (9.8%) over a median follow-up of 5 months (25th to 75th percentile: 2.5-8.5 months). Studies were of moderate to high quality. The probability of natural conception significantly decreased with any diagnosis of infertility, when compared with unexplained infertility. We found non-linear effects of female age and duration of infertility on ongoing pregnancy and tabulated the predicted probabilities for unexplained infertile women aged 35-42 years with either primary or secondary infertility and with a duration of infertility from 1 to 6 years. For a 35-year-old woman with 2 years of primary unexplained infertility, the predicted probability of natural conception leading to ongoing pregnancy or livebirth was 0.15 (95% CI 0.11-0.19) after 6 months and 0.24 (95% CI 0.17-0.30) after 12 months. For a 42-year-old woman, this decreased to 0.08 (95% CI 0.04-0.11) after 6 months and 0.13 (95% CI 0.07-0.18) after 12 months. LIMITATIONS, REASONS FOR CAUTION: In the studies selected, there were different study designs, recruitment strategies in different centres, protocols and countries and different methods of assessment of infertility. Data were limited for women above the age of 40 years. WIDER IMPLICATIONS OF THE FINDINGS: Women attending fertility services should be encouraged to pursue natural conception while waiting for treatment to commence and after treatment if it is unsuccessful. Our results may aid in counselling women, and, in particular, for those with unexplained infertility.
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| 2. |
- Taneja, A, et al.
(författare)
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Application of ED-optimality to screening experiments for analgesic compounds in an experimental model of neuropathic pain.
- 2012
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:6, s. 673-681
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Tidskriftsartikel (refereegranskat)abstract
- In spite of the evidence regarding high variability in the response to evoked pain, little attention has been paid to its impact on the screening of drugs for inflammatory and neuropathic pain. In this study, we explore the feasibility of introducing optimality concepts to experimental protocols, enabling estimation of parameter and model uncertainty. Pharmacokinetic (PK) and pharmacodynamic data from different experiments in rats were pooled and modelled using nonlinear mixed effects modelling. Pain data on gabapentin and placebo-treated animals were generated in the complete Freund's adjuvant model of neuropathic pain. A logistic regression model was applied to optimise sampling times and dose levels to be used in an experimental protocol. Drug potency (EC(50)) and interindividual variability (IIV) were considered the parameters of interest. Different experimental designs were tested and validated by SSE (stochastic simulation and estimation) taking into account relevant exposure ranges. The pharmacokinetics of gabapentin was described by a two-compartment PK model with first order absorption (CL = 0.159 l h(-1), V(2) = 0.118 l, V(3) = 0.253 l, Ka = 0.26 h(-1), Q = 1.22 l h(-1)). Drug potency (EC(50)) for the anti-allodynic effects was estimated to be 1400 ng ml(-1). Protocol optimisation improved bias and precision of the EC50 by 6 and 11.9. %, respectively, whilst IIV estimates showed improvement of 31.89 and 14.91 %, respectively. Our results show that variability in behavioural models of evoked pain response leads to uncertainty in drug potency estimates, with potential impact on the ranking of compounds during screening. As illustrated for gabapentin, ED-optimality concepts enable analysis of discrete data taking into account experimental constraints.
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| 3. |
- De Cock, Roosmarijn F W, et al.
(författare)
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A Neonatal Amikacin Covariate Model Can Be Used to Predict Ontogeny of Other Drugs Eliminated Through Glomerular Filtration in Neonates
- 2014
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Ingår i: Pharmaceutical research. - : Springer Science and Business Media LLC. - 0724-8741 .- 1573-904X. ; 31:3, s. 754-767
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSERecently, a covariate model characterizing developmental changes in clearance of amikacin in neonates has been developed using birth bodyweight and postnatal age. The aim of this study was to evaluate whether this covariate model can be used to predict maturation in clearance of other renally excreted drugs.METHODSFive different neonatal datasets were available on netilmicin, vancomycin, tobramycin and gentamicin. The extensively validated covariate model for amikacin clearance was used to predict clearance of these drugs. In addition, independent reference models were developed based on a systematic covariate analysis.RESULTSThe descriptive and predictive properties of the models developed using the amikacin covariate model were good, and fairly similar to the independent reference models (goodness-of-fit plots, NPDE). Moreover, similar clearance values were obtained for both approaches. Finally, the same covariates as in the covariate model of amikacin, i.e. birth bodyweight and postnatal age, were identified on clearance in the independent reference models.CONCLUSIONSThis study shows that pediatric covariate models may contain physiological information since information derived from one drug can be used to describe other drugs. This semi-physiological approach may be used to optimize sparse data analysis and to derive individualized dosing algorithms for drugs in children.
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| 4. |
- Duong, Janna K., et al.
(författare)
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The variability in beta-cell function in placebo-treated subjects with type 2 diabetes : application of the weight-HbA1c-insulin-glucose (WHIG) model
- 2017
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Ingår i: British Journal of Clinical Pharmacology. - : WILEY-BLACKWELL. - 0306-5251 .- 1365-2125. ; 83:3, s. 487-497
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Tidskriftsartikel (refereegranskat)abstract
- AIM The weight-glycosylated haemoglobin (HbA1C)-insulin-glucose (WHIG) model describes the effects of changes in weight on insulin sensitivity (IS) in newly diagnosed, obese subjects receiving placebo treatment. Thismodel was applied to a wider population of placebo-treated subjects, to investigate factors influencing the variability in IS and beta-cell function. METHODS The WHIG model was applied to the WHIG dataset (Study 1) and two other placebo datasets (Studies 2 and 3). Studies 2 and 3 consisted of nonobese subjects and subjects with advanced type 2 diabetes mellitus (T2DM). Body weight, fasting serum insulin (FSI), fasting plasma glucose (FPG) and HbA1c were used for nonlinear mixed-effects modelling (using NONMEM v7.2 software). Sources of interstudy variability (ISV) and potential covariates (age, gender, diabetes duration, ethnicity, compliance) were investigated. RESULTS An ISV for baseline parameters (body weight and beta-cell function) was required. The baseline beta-cell function was significantly lower in subjects with advanced T2DM (median difference: Study 2: 15.6%, P < 0.001; Study 3: 22.7%, P < 0.001) than in subjects with newly diagnosed T2DM (Study 1). A reduction in the estimated insulin secretory response in subjects with advanced T2DM was observed but diabetes duration was not a significant covariate. CONCLUSION The WHIG model can be used to describe the changes in weight, IS and beta-cell function in the diabetic population. IS remained relatively stable between subjects but a large ISV in beta-cell function was observed. There was a trend towards decreasing beta-cell responsiveness with diabetes duration, and further studies, incorporating subjects with a longer history of diabetes, are required.
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| 6. |
- Stevens, Jasper, et al.
(författare)
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Mechanism-based PK-PD model for the prolactin biological system response following an acute dopamine inhibition challenge : quantitative extrapolation to humans
- 2012
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:5, s. 463-477
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this investigation was to develop a mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) model for the biological system prolactin response following a dopamine inhibition challenge using remoxipride as a paradigm compound. After assessment of baseline variation in prolactin concentrations, the prolactin response of remoxipride was measured following (1) single intravenous doses of 4, 8 and 16 mg/kg and (2) following double dosing of 3.8 mg/kg with different time intervals. The mechanistic PK-PD model consisted of: (i) a PK model for remoxipride concentrations in brain extracellular fluid; (ii) a pool model incorporating prolactin synthesis, storage in lactotrophs, release into- and elimination from plasma; (iii) a positive feedback component interconnecting prolactin plasma concentrations and prolactin synthesis; and (iv) a dopamine antagonism component interconnecting remoxipride brain extracellular fluid concentrations and stimulation of prolactin release. The most important findings were that the free brain concentration drives the prolactin release into plasma and that the positive feedback on prolactin synthesis in the lactotrophs, in contrast to the negative feedback in the previous models on the PK-PD correlation of remoxipride. An external validation was performed using a dataset obtained in rats following intranasal administration of 4, 8, or 16 mg/kg remoxipride. Following simulation of human remoxipride brain extracellular fluid concentrations, pharmacodynamic extrapolation from rat to humans was performed, using allometric scaling in combination with independent information on the values of biological system specific parameters as prior knowledge. The PK-PD model successfully predicted the system prolactin response in humans, indicating that positive feedback on prolactin synthesis and allometric scaling thereof could be a new feature in describing complex homeostatic mechanisms.
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| 7. |
- Taneja, A, et al.
(författare)
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Optimised protocol design for the screening of analgesic compounds in neuropathic pain.
- 2012
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:6, s. 661-671
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Tidskriftsartikel (refereegranskat)abstract
- We have previously shown how screening experiments for neuropathic pain can be optimised taking into account parameter and model uncertainty. Here we demonstrate how optimised protocols can be used to screen and rank candidate molecules. The concept is illustrated by pregabalin as a new chemical entity and gabapentin as a reference compound. ED-optimality was applied to a logistic regression model describing the relationship between drug exposure and response to evoked pain in the complete Freund's adjuvant (CFA) model in rats. Design variables for optimisation of the experimental protocol included dose levels and sampling times. Prior information from the reference compound was used in conjunction with relative in vitro potency as priors. Results from simulated scenarios were then combined with fitting of experimental data to estimate precision and bias of model parameters for the empirical and optimised designs. The pharmacokinetics of pregabalin was described by a two-compartment model. The expected value of EC(50) of pregabalin was 637.5 ng ml(-1). Model-based analysis of the data yielded median (range) of EC(50) values of 1,125 (898-2412) ng ml(-1) for the empirical protocol and 755 (189-756) ng ml(-1) for the optimised design. In contrast to current practice, optimal design entails different sampling schedule across dose levels. ED-optimised designs should become standard practice in the screening of candidate molecules. It ensures lower bias when estimating the drug potency, facilitating accurate ranking and selection of compounds for further development.
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