Short Sleep Duration Disrupts Glucose Metabolism: Can Exercise Turn Back the Clock?

Short sleep duration is prevalent in modern society and may be contributing to type 2 diabetes prevalence. This review will explore the effects of sleep restriction on glycemic control, the mechanisms causing insulin resistance, and whether exercise can offset changes in glycemic control. Chronic sleep restriction may also contribute to a decrease in physical activity leading to further health complications.

Comparative Effectiveness of Bimekizumab in Psoriatic Arthritis: A Model-Based Meta-Analysis of American College of Rheumatology Response Criteria.

A model-based meta-analysis (MBMA) was conducted to compare the efficacy of bimekizumab with other psoriatic arthritis (PsA) treatment regimens using ≥ 20%/50%/70% improvements in American College of Rheumatology (ACR) criteria (ACR20/50/70) for patients with PsA. Forty-nine trials of 16 drugs were identified in the literature, comprising 21,340 patients. Trial-level covariates, including prior biologic use, concomitant methotrexate use, time since diagnosis, trial completion year, and active comparator were considered for exploratory models. The final model was selected using leave-one-out cross-validation (LOO CV) to assess predictive performance based on prespecified criteria. LOO CV was conducted for 15 trials; the final model demonstrated that 91.5% (952/1,040) of the observed treatment differences, and 96.1% of the observed ACR20/50/70 response rates were within the 95% prediction interval (PI). Median ACR50 response rates (95% PI) at week 16 in biologic-naïve patients were predicted to be 44% (40-49%) for bimekizumab 160 mg, among the highest of all treatments analyzed. Response rates for secukinumab 150 mg and risankizumab 150 mg were 28% (25-32%) and 27% (24-31%), respectively. The MBMA was also used to predict the probability of success (PoS) of potential head-to-head trials using ACR50 response as the end point with varying sample sizes: vs. secukinumab 150 mg, the PoS for bimekizumab 160 mg was 62% (N = 200) and 90% (N = 400). Versus risankizumab 150 mg, the PoS for bimekizumab 160 mg was 68% (N = 200) and 94% (N = 400). In summary, a predictive MBMA described ACR20/50/70 outcomes in PsA, allowing accurate and precise treatment comparisons and robust PoS calculations.

Sleep restriction by sleep timing late night or early wake: The impact on physical activity and dietary intake in adults.

PURPOSE: To date, few studies have assessed whether the timing of sleep restriction impacts physical activity and energy intake patterns. Thus, we aimed to quantify physical activity and energy intake during an early wake (EW) and late sleep (LS) period. METHODS: Fourteen participants who met the inclusion criteria (sleep 7-9 h/night and a BMI of <40 kg/m2) participated in 3 crossover free-living conditions: normal sleep (NS, 7-9 h), EW (2-h early wake-time), and LS (2-h late to sleep) for 4 nights. Sleep duration (via Actiwatch), energy intake (via food diaries), and physical activity (via hip accelerometry) were recorded for 4 days/4 nights throughout each condition. RESULTS: Sleep duration was reduced in both sleep restriction conditions compared to NS (p < 0.001) with no difference between sleep restriction conditions. Daily energy intake tended to increase in the LS condition (p = 0.056) but was unchanged during EW (p = 0.56). Fat (p = 0.031) and sodium (p = 0.039) intake were increased in the LS condition only compared to NS. During the EW condition, fat (p = 0.24) and sodium (p = 0.18) intake were not altered. No changes in carbohydrate or protein intake occurred between conditions. Daily steps tended to increase in the EW condition compared to NS (p = 0.058), while steps during the LS condition were unchanged (p = 0.28), with no differences between sleep restriction conditions. CONCLUSION: The timing of sleep curtailment differentially influences physical activity and EI the following day, such that EW results in increased physical activity, while LS leads to poorer dietary choices.

A Model-Based Meta-Analysis of 24 Antihyperglycemic Drugs for Type 2 Diabetes: Comparison of Treatment Effects at Therapeutic Doses.

Model-based meta-analysis was used to compare glycemic control, weight changes, and hypoglycemia risk across 24 antihyperglycemic drugs used to treat type 2 diabetes. Electronic searches identified 229 randomized controlled studies comprising 121,914 patients. To ensure fair and unbiased treatment comparisons, the analyses adjusted for important differences between studies, including duration of treatment, baseline glycated hemoglobin, and drug dosages. At the approved doses, glycemic control was typically greatest with glucagon-like peptide 1 receptor agonists (GLP-1RAs), and least with dipeptidyl peptidase-4 (DPP-4) inhibitors. Weight loss was highly variable across GLP-1RAs but was similar across sodium-glucose cotransporter 2 (SGLT2) inhibitors. Large weight increases were observed with sulfonylureas and thiazolidinediones. Hypoglycemia risk was highest with sulfonylureas, although gliclazide was notably lower. Hypoglycemia risk for DPP-4 inhibitors, SGLT2 inhibitors, and thiazolidinediones was generally very low but increased slightly for both GLP-1RAs and metformin. In summary, important differences between and within drug classes were identified.

A New Paradigm. "Learn - Learn More"; Dose-Exposure-Response at the Center of Drug Development and Regulatory Approval.

In his seminal paper, Lewis Sheiner introduced the "Learning versus Confirming" paradigm. From that foundation, this work proposes why the precise estimation of the dose-exposure-response (D-E-R) for both efficacy and safety endpoints should be the ultimate goal for most drug development programs. The subsequent identification and approval of an optimal dose regimen range will provide a pragmatic framework for delivering personalized medicine based on dose titration for each and every patient.

D optimal designs for three Poisson dose-response models.

The objective of this paper was to find and investigate the performance of the D optimal designs for three Poisson dose-response models. Phase II dose ranging studies are pivotal in the drug development program, being used to select dose(s) for phase III. Count data is encountered in a number of clinical areas. The Poisson distribution provides an intuitive platform for modelling such data, especially when combined with random effects which allow subjects to differ in their response rates. This work investigated three Poisson dose-response models of increasing complexity. A simple E(max) model was used to describe the drug effect, and D optimal designs under a range of different parameter values (scenarios) were found. The relative performances between scenarios were assessed using: the precision of all parameters, the precision of the drug effect parameters, and the percent coefficient of variation (%CV) of the ED(50) parameter. The results showed that the D optimal designs were similar across models and scenarios, with the D optimal designs consisting of placebo, the maximum dose, and a dose just below the ED(50). However the relative performance of the optimal designs was very different. For example, with 1,000 subjects, the %CV of the ED(50) parameter ranged from 1.4 to 91 %. Performance typically improved with higher baseline counts, smaller random effects, and larger E(max). This work introduces a framework for determining and evaluating the performance of D optimal designs for phase II dose ranging studies with count data as the primary endpoint.

Performance comparison of various maximum likelihood nonlinear mixed-effects estimation methods for dose-response models.

Estimation methods for nonlinear mixed-effects modelling have considerably improved over the last decades. Nowadays, several algorithms implemented in different software are used. The present study aimed at comparing their performance for dose-response models. Eight scenarios were considered using a sigmoid E(max) model, with varying sigmoidicity and residual error models. One hundred simulated datasets for each scenario were generated. One hundred individuals with observations at four doses constituted the rich design and at two doses, the sparse design. Nine parametric approaches for maximum likelihood estimation were studied: first-order conditional estimation (FOCE) in NONMEM and R, LAPLACE in NONMEM and SAS, adaptive Gaussian quadrature (AGQ) in SAS, and stochastic approximation expectation maximization (SAEM) in NONMEM and MONOLIX (both SAEM approaches with default and modified settings). All approaches started first from initial estimates set to the true values and second, using altered values. Results were examined through relative root mean squared error (RRMSE) of the estimates. With true initial conditions, full completion rate was obtained with all approaches except FOCE in R. Runtimes were shortest with FOCE and LAPLACE and longest with AGQ. Under the rich design, all approaches performed well except FOCE in R. When starting from altered initial conditions, AGQ, and then FOCE in NONMEM, LAPLACE in SAS, and SAEM in NONMEM and MONOLIX with tuned settings, consistently displayed lower RRMSE than the other approaches. For standard dose-response models analyzed through mixed-effects models, differences were identified in the performance of estimation methods available in current software, giving material to modellers to identify suitable approaches based on an accuracy-versus-runtime trade-off.

Dopamine D2 occupancy as a biomarker for antipsychotics: quantifying the relationship with efficacy and extrapyramidal symptoms.

For currently available antipsychotic drugs, blockade of dopamine D(2) receptors is a critical component for achieving antipsychotic efficacy, but it is also a driving factor in the development of extrapyramidal symptoms (EPS). To inform the clinical development of asenapine, generic mathematical models have been developed for predicting antipsychotic efficacy and EPS tolerability based on D(2) receptor occupancy. Clinical data on pharmacokinetics, D(2) receptor occupancy, efficacy, and EPS for several antipsychotics were collected from the public domain. Asenapine data were obtained from in-house trials. D(2) receptor occupancy data were restricted to published positron emission tomography studies that included blood sampling for pharmacokinetics. Clinical efficacy data were restricted to group mean endpoint data from short-term placebo-controlled trials, whereas EPS evaluation also included some non-placebo-controlled trials. A generally applicable model connecting antipsychotic dose, pharmacokinetics, D(2) receptor occupancy, Positive and Negative Syndrome Scale (PANSS) response, and effect on Simpson-Angus Scale (SAS) was then developed. The empirical models describing the D(2)-PANSS and D(2)-SAS relationships were used successfully to aid dose selection for asenapine phase II and III trials. A broader use can be envisaged as a dose selection tool for new antipsychotics with D(2) antagonist properties in the treatment of schizophrenia.

An example of optimal phase II design for exposure response modelling.

This paper presents an example of how optimal design methodology was used to help design a phase II clinical study. The planned analysis would relate the clinical endpoint to exposure (measured via the area under the curve (AUC)), rather than dose. Optimal design methodology was used to compare a number of candidate phase II designs, and an algorithm for finding optimal designs was employed. The sigmoidal E(max) with baseline (E0) model was used to relate the clinical endpoint to individual subject AUCs, and the primary metrics were D optimality and the standard error (SE) of the AUC required to yield a clinically relevant change in the clinical endpoint. The performance of the candidate designs were compared across four different 'true' exposure response relationships (determined from the analysis of an earlier proof of concept (PoC) study). The results suggested the total sample size should be increased from the planned 540 individuals, and that the optimal design with 700 individuals would be equivalent to 812 individuals with the reference design (a 16% gain). The performance with this design was considered acceptable, although all designs performed poorly if the true exposure response relationship was very flat. This work allowed a prospective assessment of the likely performance and precision from the exposure response modelling prior to the start of the phase II study, and hence allowed the design to be revised to ensure the subsequent analysis would be of most value.

Versatile dual reporter gene systems for investigating stop codon readthrough in plants.

BACKGROUND: Translation is most often terminated when a ribosome encounters the first in-frame stop codon (UAA, UAG or UGA) in an mRNA. However, many viruses (and some cellular mRNAs) contain "stop" codons that cause a proportion of ribosomes to terminate and others to incorporate an amino acid and continue to synthesize a "readthrough", or C-terminally extended, protein. This dynamic redefinition of codon meaning is dependent on specific sequence context. METHODOLOGY: We describe two versatile dual reporter systems which facilitate investigation of stop codon readthrough in vivo in intact plants, and identification of the amino acid incorporated at the decoded stop codon. The first is based on the reporter enzymes NAN and GUS for which sensitive fluorogenic and histochemical substrates are available; the second on GST and GFP. CONCLUSIONS: We show that the NAN-GUS system can be used for direct in planta measurements of readthrough efficiency following transient expression of reporter constructs in leaves, and moreover, that the system is sufficiently sensitive to permit measurement of readthrough in stably transformed plants. We further show that the GST-GFP system can be used to affinity purify readthrough products for mass spectrometric analysis and provide the first definitive evidence that tyrosine alone is specified in vivo by a 'leaky' UAG codon, and tyrosine and tryptophan, respectively, at decoded UAA, and UGA codons in the Tobacco mosaic virus (TMV) readthrough context.

Performance in population models for count data, part I: maximum likelihood approximations.

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.

High-level expression of human immunodeficiency virus antigens from the tobacco and tomato plastid genomes.

Transgene expression from the plant's plastid genome represents a promising strategy in molecular farming because of the plastid's potential to accumulate foreign proteins to high levels and the increased biosafety provided by the maternal mode of organelle inheritance. In this article, we explore the potential of transplastomic plants to produce human immunodeficiency virus (HIV) antigens as potential components of an acquired immunodeficiency syndrome (AIDS) vaccine. It is shown that the HIV antigens p24 (the major target of T-cell-mediated immune responses in HIV-positive individuals) and Nef can be expressed to high levels in plastids of tobacco, a non-food crop, and tomato, a food crop with an edible fruit. Optimized p24-Nef fusion gene cassettes trigger antigen protein accumulation to up to approximately 40% of the plant's total protein, demonstrating the great potential of transgenic plastids to produce AIDS vaccine components at low cost and high yield.

Optimal adaptive design in clinical drug development: a simulation example.

The objective of this article is to demonstrate optimal adaptive design as a methodology for improving the performance of phase II dose-response studies. Optimal adaptive design uses both information prior to the study and data accrued during the study to continuously update and refine the study design. Dose-response models include linear, log-linear, 4-parameter sigmoidal E(max), and exponential models. Where the response has both a placebo effect and plateau at higher doses, only the 4-parameter sigmoidal E(max) model behaves acceptably and hence is used to illustrate the methodology. Across 13 hypothetical dose-response scenarios considered, it was shown that the capability of the adaptive designs to "learn" the true dose response resulted in performances up to 180% more efficient than the best fixed optimal designs. This work exposes the common misconception that adaptive designs are somehow "risky." As shown in this simple simulation example, the converse is true. Adaptive designs perform extremely well both when prior information is accurate and inaccurate. This leads to improved dose-response models and dose selection in phase III. This benefits sponsors, regulators, and subjects alike by reducing sample size, increasing information, and providing better dose guidance.

Mitochondrial malate dehydrogenase from the thermophilic, filamentous fungus Talaromyces emersonii.

Mitochondrial malate dehydrogenase (m-MDH; EC 1.1.1.37), from mycelial extracts of the thermophilic, aerobic fungus Talaromyces emersonii, was purified to homogeneity by sequential hydrophobic interaction and biospecific affinity chromatography steps. Native m-MDH was a dimer with an apparent monomer mass of 35 kDa and was most active at pH 7.5 and 52 degrees C in the oxaloacetate reductase direction. Substrate specificity and kinetic studies demonstrated the strict specificity of this enzyme, and its closer similarity to vertebrate m-MDHs than homologs from invertebrate or mesophilic fungal sources. The full-length m-MDH gene and its corresponding cDNA were cloned using degenerate primers derived from the N-terminal amino acid sequence of the native protein and multiple sequence alignments from conserved regions of other m-MDH genes. The m-MDH gene is the first oxidoreductase gene cloned from T. emersonii and is the first full-length m-MDH gene isolated from a filamentous fungal species and a thermophilic eukaryote. Recombinant m-MDH was expressed in Escherichia coli, as a His-tagged protein and was purified to apparent homogeneity by metal chelate chromatography on an Ni2+-nitrilotriacetic acid matrix, at a yield of 250 mg pure protein per liter of culture. The recombinant enzyme behaved as a dimer under nondenaturing conditions. Expression of the recombinant protein was confirmed by Western blot analysis using an antibody against the His-tag. Thermal stability studies were performed with the recombinant protein to investigate if results were consistent with those obtained for the native enzyme.

A randomized study of the effect of oral lamotrigine and hydromorphone on pain and hyperalgesia following heat/capsaicin sensitization.

In this randomized double-blind placebo-controlled study, the analgesic effect of oral lamotrigine (400 mg) on cutaneous sensitization induced with the heat/capsaicin sensitization model was compared with the effect of oral hydromorphone (8 mg) in healthy volunteers. In a separate session, intravenous remifentanil (0.10 microg.kg(-1).min(-1)) and placebo were administered. This session was used as an additional reference comparator. Outcome measures were the areas of secondary hyperalgesia to brush and von Frey hair stimulation and the painfulness of noxious thermal stimulation in nonsensitized skin. Compared with placebo, both intravenous remifentanil and oral hydromorphone significantly suppressed secondary hyperalgesia and acute thermal nociception. Oral lamotrigine did not reduce secondary hyperalgesia or acute thermal nociception but produced side effects of severity comparable with that of oral hydromorphone. Although lamotrigine is efficacious in the management of some types of chronic neuropathic pain, the lack of effect of this agent on human experimental pain suggests that its analgesic effects depend on nerve injury-associated abnormalities, which cannot be simulated in healthy human volunteers.