Complex genetic dependencies among growth and neurological phenotypes in healthy children: Towards deciphering developmental mechanisms.

The genetic mechanisms of childhood development in its many facets remain largely undeciphered. In the population of healthy infants studied in the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) program, we have identified a range of dependencies among the observed phenotypes of fetal and early childhood growth, neurological development, and a number of genetic variants. We have quantified these dependencies using our information theory-based methods. The genetic variants show dependencies with single phenotypes as well as pleiotropic effects on more than one phenotype and thereby point to a large number of brain-specific and brain-expressed gene candidates. These dependencies provide a basis for connecting a range of variants with a spectrum of phenotypes (pleiotropy) as well as with each other. A broad survey of known regulatory expression characteristics, and other function-related information from the literature for these sets of candidate genes allowed us to assemble an integrated body of evidence, including a partial regulatory network, that points towards the biological basis of these general dependencies. Notable among the implicated loci are RAB11FIP4 (next to NF1), MTMR7 and PLD5, all highly expressed in the brain; DNMT1 (DNA methyl transferase), highly expressed in the placenta; and PPP1R12B and DMD (dystrophin), known to be important growth and development genes. While we cannot specify and decipher the mechanisms responsible for the phenotypes in this study, a number of connections for further investigation of fetal and early childhood growth and neurological development are indicated. These results and this approach open the door to new explorations of early human development.

Exposure-efficacy Relationships for Vedolizumab Induction Therapy in Patients with Ulcerative Colitis or Crohn's Disease.

BACKGROUND AND AIMS: A positive relationship between vedolizumab trough serum concentrations and clinical outcomes in patients with ulcerative colitis [UC] or Crohn's disease [CD] has been reported. Here we further explore exposure-efficacy relationships for vedolizumab induction therapy in post hoc analyses of GEMINI study data. METHODS: Vedolizumab trough concentrations at Week 6 or 10 were grouped in quartiles and clinical outcome rates calculated. Exposure-efficacy relationships at Week 6 and potential baseline covariate effects were explored using logistic regression and individual predicted cumulative average concentration through Week 6 [Caverage] as exposure measure. RESULTS: Higher vedolizumab concentrations were associated with higher clinical remission rates; the exposure-efficacy relationship was steeper for UC than CD. Unadjusted analyses overestimated the relationship, more so for CD. From covariate-adjusted models, average probability of remission at Week 6 increased by approximately 15% for UC and 10% for CD between Caverage values of 35 and 84 µg/ml [5th and 95th percentiles, respectively]. On average, patients with higher albumin, lower faecal calprotectin [UC only], lower C-reactive protein [CD only], and no previous tumour necrosis factor-α [TNFα] antagonist use had a higher remission probability. Previous TNFα antagonist use had the greatest impact; remission probability was approximately 10% higher in treatment-naïve patients. CONCLUSIONS: Higher vedolizumab serum concentrations were associated with higher remission rates after induction therapy in patients with moderately to severely active UC or CD. This relationship is affected by several factors, including previous TNFα antagonist use. Prospective studies are needed to assess vedolizumab dose individualisation and optimisation.

A linearization approach for the model-based analysis of combined aggregate and individual patient data.

The application of model-based meta-analysis in drug development has gained prominence recently, particularly for characterizing dose-response relationships and quantifying treatment effect sizes of competitor drugs. The models are typically nonlinear in nature and involve covariates to explain the heterogeneity in summary-level literature (or aggregate data (AD)). Inferring individual patient-level relationships from these nonlinear meta-analysis models leads to aggregation bias. Individual patient-level data (IPD) are indeed required to characterize patient-level relationships but too often this information is limited. Since combined analyses of AD and IPD allow advantage of the information they share to be taken, the models developed for AD must be derived from IPD models; in the case of linear models, the solution is a closed form, while for nonlinear models, closed form solutions do not exist. Here, we propose a linearization method based on a second order Taylor series approximation for fitting models to AD alone or combined AD and IPD. The application of this method is illustrated by an analysis of a continuous landmark endpoint, i.e., change from baseline in HbA1c at week 12, from 18 clinical trials evaluating the effects of DPP-4 inhibitors on hyperglycemia in diabetic patients. The performance of this method is demonstrated by a simulation study where the effects of varying the degree of nonlinearity and of heterogeneity in covariates (as assessed by the ratio of between-trial to within-trial variability) were studied. A dose-response relationship using an Emax model with linear and nonlinear effects of covariates on the emax parameter was used to simulate data. The simulation results showed that when an IPD model is simply used for modeling AD, the bias in the emax parameter estimate increased noticeably with an increasing degree of nonlinearity in the model, with respect to covariates. When using an appropriately derived AD model, the linearization method adequately corrected for bias. It was also noted that the bias in the model parameter estimates decreased as the ratio of between-trial to within-trial variability in covariate distribution increased. Taken together, the proposed linearization approach allows addressing the issue of aggregation bias in the particular case of nonlinear models of aggregate data.

A phase IIb dose-ranging study of the oral JAK inhibitor tofacitinib (CP-690,550) versus placebo in combination with background methotrexate in patients with active rheumatoid arthritis and an inadequate response to methotrexate alone.

OBJECTIVE: To compare the efficacy, safety, and tolerability of 6 dosages of oral tofacitinib (CP-690,550) with placebo for the treatment of active rheumatoid arthritis (RA) in patients receiving a stable background regimen of methotrexate (MTX) who have an inadequate response to MTX monotherapy. METHODS: In this 24-week, double-blind, phase IIb study, patients with active RA (n = 507) were randomized to receive placebo or tofacitinib (20 mg/day, 1 mg twice daily, 3 mg twice daily, 5 mg twice daily, 10 mg twice daily, or 15 mg twice daily). All patients continued to receive a stable dosage of MTX. The primary end point was the American College of Rheumatology 20% improvement criteria (ACR20) response rate at week 12. RESULTS: At week 12, ACR20 response rates for patients receiving all tofacitinib dosages ≥3 mg twice daily (52.9% for 3 mg twice daily, 50.7% for 5 mg twice daily, 58.1% for 10 mg twice daily, 56.0% for 15 mg twice daily, and 53.8% for 20 mg/day) were significantly (P ≤ 0.05) greater than those for placebo (33.3%). Improvements were sustained at week 24 for the ACR20, ACR50, and ACR70 responses, scores for the Health Assessment Questionnaire disability index, the 3-variable Disease Activity Score in 28 joints using the C-reactive protein level (DAS28-CRP), and a 3-variable DAS28-CRP of <2.6. The most common treatment-emergent adverse events occurring in >10% of patients in any tofacitinib group were diarrhea, upper respiratory tract infection, and headache; 21 patients (4.1%) experienced serious adverse events. Sporadic increases in transaminase levels, increases in cholesterol and serum creatinine levels, and decreases in neutrophil and hemoglobin levels were observed. CONCLUSION: In patients with active RA in whom the response to MTX has been inadequate, the addition of tofacitinib at a dosage ≥3 mg twice daily showed sustained efficacy and a manageable safety profile over 24 weeks.

Extending the latent variable model for extra correlated longitudinal dichotomous responses.

Since generalized nonlinear mixed-effects modeling methodology of ordered categorical data became available in the pharmacokinetic/pharmacodynamic (PK/PD) literature over a decade ago, pharmacometricians have been increasingly performing exposure-response analyses of such data to inform drug development. Also, as experiences with and scrutiny of these data have increased, pharmacometricians have noted fewer transitions (or greater correlations) between response values than predicted by the model. In this paper, we build on the latent variable (LV) approach, which is convenient for incorporating pharmacological concepts such as pharmacodynamic onset of drug effect, and present a PK/PD methodology which we term the multivariate latent variable (MLV) approach. This approach uses correlations between the latent residuals (LR) to address extra correlation or a fewer number of transitions, relative to if the LR were independent. Four approximation methods for handling dichotomous MLV data are formulated and then evaluated for accuracy and computation time using simulation studies. Some analytical results for models linear in the subject-specific random effects are also presented, and these provide insight into modeling such repeated measures data. Also, a case study previously analyzed using the LV approach is revisited using one of the MLV approximation methods and the results are discussed. Overall, consideration of the simulation and analytical results lead us to some conclusions we feel are applicable to many of the models and situations frequently encountered in analysis of such data: the MLV approach is a flexible method that can handle many different extra correlated data structures and therefore can more accurately predict the number of transitions between response values; incorrect modeling of the population covariances by implementing an LV model when extra correlation exists is not likely to (and in many cases does not) influence accuracy of the population (marginal) mean predictions; adequate prediction of the population mean probabilities achieves adequate predictions of the population variances, regardless of the correct specification of the population covariances--that is, if the LV model accurately predicts the means in the presence of extra correlation, it will accurately predict the variances; the between subject random effects component to the model describe the marginal covariances in responses--not the marginal variances as with continuous-type data. From these conclusions we make a general statement that it may not be necessary to model the extra correlation in every case using the MLV model, which requires technical implementation with currently available commercially or publically available software. The LV model may be sufficient for answering many of the typical questions arising during drug development. The MLV approach should be considered however if prediction or simulation of individual level data is an objective of the analysis.

Estimating transformations for repeated measures modeling of continuous bounded outcome data.

Continuous bounded outcome data are unlikely to meet the usual assumptions for mixed-effects models of normally distributed and independent subject-specific and residual random effects. Additionally, overly complicated model structures might be necessary to account adequately for non-drug (time-dependent) and drug treatment effects. A transformation strategy with a likelihood component for censoring is developed to promote the simplicity of model structures and to improve the plausibility of assumptions on the random effects. The approach is motivated by Health Assessment Questionnaire Disability Index (HAQ-DI) data from a study in subjects with rheumatoid arthritis and is evaluated using a simulation study.

Longitudinal aggregate data model-based meta-analysis with NONMEM: approaches to handling within treatment arm correlation.

Literature data are often reported as multiple (longitudinal) mean outcomes observed in several groups of patients within a study. Observations within a study are correlated because the patients come from a common population, and the mean observations over time within a treatment arm are correlated because they are based on the same set of patients. As a result, model-based meta-analysis may require more than two levels of random effects to correctly characterize this correlation structure. Using simulation, we explored and evaluated ways to implement multi-level random effects in NONMEM. Simulation models that were linear and non-linear in the random effects were investigated. We compared estimation models that included study and/or treatment arm-level random effects, with and without residual correlation. With all estimation strategies, the fixed random effects parameters were accurately estimated. With regard to correctly characterizing the variability, models that accounted for correlation within a study and treatment arm over time were the best in some situations, while models that accounted for study-level correlation only were better in others. Models that included only treatment arm-level random effects were not superior in any scenario.

Central nervous system pharmacokinetics of the Mdr1 P-glycoprotein substrate CP-615,003: intersite differences and implications for human receptor occupancy projections from cerebrospinal fluid exposures.

The central nervous system (CNS) distribution and transport mechanisms of the investigational drug candidate CP-615,003 (N-[3-fluoro-4-[2-(propylamino)ethoxy]phenyl]-4,5,6,7-tetrahydro-4-oxo-1H-indole-3-carboxamide) and its active metabolite CP-900,725 have been characterized. Brain distribution of CP-615,003 and CP-900,725 was low in rats and mice (brain-to-serum ratio < 0.2). Cerebrospinal fluid (CSF)-to-serum ratios of CP-615,003 were 6- to 8-fold lower than the plasma unbound fraction in rats and dogs. In vitro, CP-615,003 displayed quinidine-like efflux in MDR1-expressing Madin-Darby canine kidney II cells. The brain-to-serum ratio of CP-615,003 in mdr1a/1b (-/-) mice was approximately 7 times that in their wild-type counterparts, confirming that impaired CNS distribution was explained by P-gp efflux transport. In contrast, P-gp efflux did not explain the impaired CNS penetration of CP-900,725. Intracerebral microdialysis was used to characterize rat brain extracellular fluid (ECF) distribution. Interestingly, the ECF-to-serum ratio of the P-gp substrate CP-615,003 was 7-fold below the CSF-to-serum ratio, whereas this disequilibrium was not observed for CP-900,725. In a clinical study, steady-state CSF exposures were measured after administration of 100 mg of CP-615,003 b.i.d. The human CSF-to-plasma ratios of CP-615,003 and CP-900,725 were both approximately 10-fold below their ex vivo plasma unbound fractions, confirming impaired human CNS penetration. Preliminary estimates of CNS receptor occupancy from human CSF concentrations were sensitive to assumptions regarding the magnitude of the CSF-ECF gradient for CP-615,003 in humans. In summary, this case provides an example of intersite differences in CNS pharmacokinetics of a P-gp substrate and potential implications for projection of human CNS receptor occupancy of transporter substrates from CSF pharmacokinetic data when direct imaging-based approaches are not feasible.

Generalized additive models for cancer mapping with incomplete covariates.

Maps depicting cancer incidence rates have become useful tools in public health research, giving valuable information about the spatial variation in rates of disease. Typically, these maps are generated using count data aggregated over areas such as counties or census blocks. However, with the proliferation of geographic information systems and related databases, it is becoming easier to obtain exact spatial locations for the cancer cases and suitable control subjects. The use of such point data allows us to adjust for individual-level covariates, such as age and smoking status, when estimating the spatial variation in disease risk. Unfortunately, such covariate information is often subject to missingness. We propose a method for mapping cancer risk when covariates are not completely observed. We model these data using a logistic generalized additive model. Estimates of the linear and non-linear effects are obtained using a mixed effects model representation. We develop an EM algorithm to account for missing data and the random effects. Since the expectation step involves an intractable integral, we estimate the E-step with a Laplace approximation. This framework provides a general method for handling missing covariate values when fitting generalized additive models. We illustrate our method through an analysis of cancer incidence data from Cape Cod, Massachusetts. These analyses demonstrate that standard complete-case methods can yield biased estimates of the spatial variation of cancer risk.

Bayesian methods for a three-state model for rodent carcinogenicity studies.

The objective of a chronic rodent bioassay is to assess the impact of a chemical compound on the development of tumors. However, most tumor types are not observable prior to necropsy, making direct estimation of the tumor incidence rate problematic. In such cases, estimation can proceed only if the study incorporates multiple interim sacrifices or we make use of simplified parametric or nonparametric models. In addition, it is widely accepted that other factors, such as weight, can be related to both dose level and tumor onset, confounding the association of interest. However, there is not typically enough information in the current study to assess such effects. The addition of historical data can help alleviate this problem. In this article, we propose a novel Bayesian semiparametric model for the analysis of data from rodent carcinogenicity studies. We develop informative prior distributions for covariate effects through the use of historical control data and outline a Gibbs sampling scheme. We implement the model by analyzing data from a National Toxicology Program chronic rodent bioassay.