A modeling and simulation-based assessment of the impact of confounding factors on the readout of a sildenafil survival trial in pulmonary arterial hypertension.

Sildenafil (REVATIO®) was approved for the treatment of adult Pulmonary Arterial Hypertension (PAH) in the US and the EU. A pediatric study has been performed and sildenafil was approved in the EU for pediatric PAH. The long-term extension of this study revealed good survival but also an increased mortality with the high dose of sildenafil compared to lower doses. As a consequence, FDA required Pfizer to evaluate REVATIO®'s effect on the risk of death in adults with PAH. Following FDA's rationale a survival model was developed to characterize the exposure-mortality relationship and assess its potential impact on an ongoing survival trial in adults in the context of confounding factors. Clinical trial simulations were performed to assess the design of the survival trial in adults (AFFILIATE, NCT02060487), expected to last approximately 8 years according to both assumptions: absence or presence of an exposure-mortality relationship and to quantify the impact of confounding factors on its readout. Simulations showed that the trial would be robust in most conditions. But its interpretation will depend on the number of confounding factors such as additional treatments attempting to control disease progression.Clinical trial identifier NCT00159913 for STARTS-1, NCT00159874 for STARTS-2.

Comparison of tumor size assessments in tumor growth inhibition-overall survival models with second-line colorectal cancer data from the VELOUR study.

PURPOSE: To compare lesion-level and volumetric measures of tumor burden with sum of the longest dimensions (SLD) of target lesions on overall survival (OS) predictions using time-to-growth (TTG) as predictor. METHODS: Tumor burden and OS data from a phase 3 randomized study of second-line FOLFIRI ± aflibercept in metastatic colorectal cancer were available for 918 patients out of 1216 treated (75%). A TGI model that estimates TTG was fit to the longitudinal tumor size data (nonlinear mixed effect modeling) to estimate TTG with: SLD, sum of the measured lesion volumes (SV), individual lesion diameters (ILD), or individual lesion volumes (ILV). A parametric OS model was built with TTG estimates and assessed for prediction of the hazard ratio (HR) for survival. RESULTS: Individual lesions had consistent dynamics within individuals. Between-lesion variability in rate constants was lower (typically < 27% CV) than inter-patient variability (typically > 50% CV). Estimates of TTG were consistent (around 12 weeks) across tumor size assessments. TTG was highly significant in a log-logistic parametric model of OS (median over 12 months). When individual lesions were considered, TTG of the fastest progressing lesions best predicted OS. TTG obtained from the lesion-level analyses were slightly better predictors of OS than estimates from the sums, with ILV marginally better than ILD. All models predicted VELOUR HR equally well and all predicted study success. CONCLUSION: This analysis revealed consistent TGI profiles across all tumor size assessments considered. TTG predicted VELOUR HR when based on any of the tumor size measures.

A Model of Overall Survival Predicts Treatment Outcomes with Atezolizumab versus Chemotherapy in Non-Small Cell Lung Cancer Based on Early Tumor Kinetics.

Purpose: Standard endpoints often poorly predict overall survival (OS) with immunotherapies. We investigated the predictive performance of model-based tumor growth inhibition (TGI) metrics using data from atezolizumab clinical trials in patients with non-small cell lung cancer.Patients and Methods: OS benefit with atezolizumab versus docetaxel was observed in both POPLAR (phase II) and OAK (phase III), although progression-free survival was similar between arms. A multivariate model linking baseline patient characteristics and on-treatment tumor growth rate constant (KG), estimated using time profiles of sum of longest diameters (RECIST 1.1) to OS, was developed using POPLAR data. The model was evaluated to predict OAK outcome based on estimated KG at TGI data cutoffs ranging from 10 to 122 weeks.Results: In POPLAR, TGI profiles in both arms crossed at 25 weeks, with more shrinkage with docetaxel and slower KG with atezolizumab. A log-normal OS model, with albumin and number of metastatic sites as independent prognostic factors and estimated KG, predicted OS HR in subpopulations of patients with varying baseline PD-L1 expression in both POPLAR and OAK: model-predicted OAK HR (95% prediction interval), 0.73 (0.63-0.85), versus 0.73 observed. The POPLAR OS model predicted greater than 97% chance of success of OAK (significant OS HR, P < 0.05) from the 40-week data cutoff onward with 50% of the total number of tumor assessments when a successful study was predicted from 70 weeks onward based on observed OS.Conclusions: KG has potential as a model-based early endpoint to inform decisions in cancer immunotherapy studies. Clin Cancer Res; 24(14); 3292-8. ©2018 AACR.

Modeling and Simulation of Pivotal Clinical Trials Using Linked Models for Multiple Endpoints in Chronic Obstructive Pulmonary Disease With Roflumilast.

Roflumilast is a selective phosphodiesterase 4 inhibitor (PDE4i) for the treatment of severe chronic obstructive pulmonary disease (COPD). In 2 large phase 3 trials in a broader population of COPD patients (BY217/M2-111, ClinicalTrials.gov: NCT00076089 and BY217/M2-112, ClinicalTrials.gov: NCT00430729), treatment with roflumilast reduced the rate of exacerbations; however, the reduction did not reach statistical significance. Two linked dose-response models for the primary (annualized COPD exacerbation counts) and secondary (change from baseline in forced expiratory volume in 1 second [FEV1 ]) end points were therefore developed to characterize and quantify effect sizes and the patient characteristics influencing them. The models showed that disease severity and bronchitis, particularly the severity of bronchitis expressed in cough-and-sputum scores, were good predictors of exacerbation rates and differential benefit of roflumilast in exacerbation reduction. The models were used to support the rational design of 2 phase 3 randomized, placebo-controlled clinical trials (BY217/M2-124, ClinicalTrials.gov: NCT00297102 and BY217/M2-125, ClinicalTrials.gov: NCT00297115) by identifying the most appropriate patient population using clinical trial simulations. Model predictions for both end points were found to be highly accurate - as confirmed by the results from these trials, which led to the approval of roflumilast as the first oral PDE4i for the treatment of COPD in patients associated with chronic bronchitis and a history of exacerbations.

Modeling and simulation of maintenance treatment in first-line non-small cell lung cancer with external validation.

BACKGROUND: Maintenance treatment (MTx) in responders following first-line treatment has been investigated and practiced for many cancers. Modeling and simulation may support interpretation of interim data and development decisions. We aimed to develop a modeling framework to simulate overall survival (OS) for MTx in NSCLC using tumor growth inhibition (TGI) data. METHODS: TGI metrics were estimated using longitudinal tumor size data from two Phase III first-line NSCLC studies evaluating bevacizumab and erlotinib as MTx in 1632 patients. Baseline prognostic factors and TGI metric estimates were assessed in multivariate parametric models to predict OS. The OS model was externally validated by simulating a third independent NSCLC study (n = 253) based on interim TGI data (up to progression-free survival database lock). The third study evaluated pemetrexed + bevacizumab vs. bevacizumab alone as MTx. RESULTS: Time-to-tumor-growth (TTG) was the best TGI metric to predict OS. TTG, baseline tumor size, ECOG score, Asian ethnicity, age, and gender were significant covariates in the final OS model. The OS model was qualified by simulating OS distributions and hazard ratios (HR) in the two studies used for model-building. Simulations of the third independent study based on interim TGI data showed that pemetrexed + bevacizumab MTx was unlikely to significantly prolong OS vs. bevacizumab alone given the current sample size (predicted HR: 0.81; 95 % prediction interval: 0.59-1.09). Predicted median OS was 17.3 months and 14.7 months in both arms, respectively. These simulations are consistent with the results of the final OS analysis published 2 years later (observed HR: 0.87; 95 % confidence interval: 0.63-1.21). Final observed median OS was 17.1 months and 13.2 months in both arms, respectively, consistent with our predictions. CONCLUSIONS: A robust TGI-OS model was developed for MTx in NSCLC. TTG captures treatment effect. The model successfully predicted the OS outcomes of an independent study based on interim TGI data and thus may facilitate trial simulation and interpretation of interim data. The model was built based on erlotinib data and externally validated using pemetrexed data, suggesting that TGI-OS models may be treatment-independent. The results supported the use of longitudinal tumor size and TTG as endpoints in early clinical oncology studies.

Model-based prediction of progression-free survival in patients with first-line renal cell carcinoma using week 8 tumor size change from baseline.

PURPOSE: To assess the link between early tumor shrinkage (ETS) and progression-free survival (PFS) based on historical first-line metastatic renal cell carcinoma (mRCC) data. METHODS: Tumor size data from 921 patients with first-line mRCC who received interferon-alpha, sunitinib, sorafenib or axitinib in two Phase III studies were modeled. The relationship between model-based estimates of ETS at week 8 as well as the baseline prognostic factors and PFS was tested in multivariate log-logistic models. Model performance was evaluated using simulations of PFS distributions and hazard ratio (HR) across treatments for the two studies. In addition, an external validation was conducted using data from an independent Phase II RCC study. The relationship between expected HR of an investigational treatment vs. sunitinib and the differences in ETS was simulated. RESULTS: A model with a nonlinear ETS-PFS link was qualified to predict PFS distribution by ETS quartiles as well as to predict HRs of sunitinib vs. interferon-alpha and axitinib vs. sorafenib. The model also performed well in simulations of an independent study of axitinib (external validation). The simulations suggested that if a new investigational treatment could further reduce the week 8 ETS by 30 % compared with sunitinib, an expected HR [95 % predictive interval] of the new treatment vs. sunitinib would be 0.59 [0.46, 0.79]. CONCLUSION: A model has been developed that uses early changes in tumor size to predict the HR for PFS differences between treatment arms for first-line mRCC. Such a model may have utility in predicting the outcome of ongoing studies (e.g., as part of interim futility analyses), supporting early decision making and future study design for investigational agents in development for this indication.

Modeling and simulations relating overall survival to tumor growth inhibition in renal cell carcinoma patients.

PURPOSE: To assess the link between tumor growth inhibition (TGI) and overall survival (OS) based on historical renal cell carcinoma (RCC) data. To illustrate how simulations can help to identify TGI thresholds based on target OS benefit [i.e., hazard ratio (HR) compared with standard of care] to support new drug development in RCC. METHODS: Tumor size (TS) data were modeled from 2552 patients with first-line or refractory RCC who received temsirolimus, interferon, sunitinib, sorafenib or axitinib in 10 Phase II or Phase III studies. Three model-based TGI metrics estimates [early tumor shrinkage (ETS) at week 8, 10 or 12, time to tumor growth (TTG) and growth rate] as well as baseline prognostic factors were tested in multivariate lognormal models of OS. Model performance was evaluated by posterior predictive check of the OS distributions and hazard ratio across treatments. RESULTS: TTG was the best TGI metric to predict OS. However, week 8 ETS had a satisfactory performance and was employed in order to maximize clinical utilization. The week 8 ETS to OS model was then used to simulate clinically relevant ETS thresholds for future Phase II studies with investigational treatments. CONCLUSIONS: The published OS model and resultant simulations can be leveraged to support Phase II design and predict expected OS and HR (based on early observed TGI data obtained in Phase II or Phase III studies), thereby informing important mRCC development decisions, e.g., Go/No Go and dose regimen selection.

A Model-Based Meta-analysis to Compare Efficacy and Tolerability of Tramadol and Tapentadol for the Treatment of Chronic Non-Malignant Pain.

INTRODUCTION: Pain is a major symptom in many medical conditions which can be relieved thanks to analgesics. The goal of this work was to present an indirect comparison of efficacy and tolerability profiles of two analgesics, tramadol and tapentadol, in patients with chronic non-malignant pain. METHODS: In the absence of a head-to-head comparison between these two opioid drugs, model-based meta-analyses were used to characterize the pain intensity time dynamics and evaluate the proportions of most frequent adverse events (constipation, nausea, vomiting, dizziness, and somnolence) and drop-outs (due to adverse event, as well as due to lack of efficacy) in each treatment group. Using these models, the investigational treatments were compared on the basis of Monte Carlo simulation outcomes. RESULTS: Data were extracted from 45 Phase II and Phase III studies representing a total of 81 treatment arms, i.e., approximately 13,000 patients. The pain intensity model shows, that after having adjusted for differences in baseline pain intensity and placebo effects, tramadol 300 mg once daily (qd) was slightly more effective in reducing pain than tapentadol 100-250 mg twice daily (bid), with a 46% change from baseline for the former versus 36% for the latter. From a tolerability standpoint, both drugs showed, as expected, increased risks of adverse events compared to placebo. Yet, tapentadol was associated with slightly lower risks of constipation, and nausea than tramadol. CONCLUSION: Overall, the analysis showed that the benefit-risk profiles of tramadol 300 mg qd and tapentadol 100-250 mg bid were approximately even. The amount of data to characterize dose-response relationships was sufficient only in the tramadol group; public access to tapentadol efficacy and tolerability readouts across a wide dose range in chronic non-malignant pain would allow a comparison of therapeutic indices, a straight quantitation of the benefit-risk ratio. Knowing that their side-effects have been identified as potential hindrance to prescription, a broad and open access to clinical trial data in this indication is encouraged in order to facilitate the evaluation of the opiate analgesics clinical utility.

Prediction of overall survival or progression free survival by disease control rate at week 8 is independent of ethnicity: Western versus Chinese patients with first-line non-small cell lung cancer treated with chemotherapy with or without bevacizumab.

Categorizations of best response observed at week 8 (between week 3 and 14) of first-line treatment in two studies of bevacizumab plus chemotherapy in Western (878 patients) and Chinese (198 patients) patients with non-small cell lung cancer were assessed together with baseline prognostic factors in multivariate parametric models to predict overall survival (OS) and progression free survival (PFS). Predictive performances of the models were assessed by simulating multiple replicates of the studies. Disease control rate (DCR) was the best response categorization to predict OS and PFS. In the OS model, DCR fully captured bevacizumab effect. For PFS, DCR did not fully capture bevacizumab treatment effect. The models adequately predicted OS and PFS distributions in each arm as well as bevacizumab hazard ratio (HR) for OS and PFS, for example, in Western patients (model prediction [95% prediction interval]: 0.84 [0.71-0.98] vs. observed: 0.77 for OS and 0.59 [0.49-0.72] vs. observed: 0.58 for PFS). Covariates in the models captured endpoint differences seen in Chinese patients. There was no impact of Chinese ethnicity on the DCR relationship to OS or PFS. DCR predicted OS benefit with bevacizumab in first-line NSCLC patients. Western data can be used to inform design of studies in Chinese patients.

Evaluation of tumor-size response metrics to predict overall survival in Western and Chinese patients with first-line metastatic colorectal cancer.

PURPOSE: To assess new metrics of tumor-size response to predict overall survival (OS) in colorectal cancer (CRC) in Western and Chinese patients. PATIENTS AND METHODS: Various metrics of tumor-size response were estimated using longitudinal tumor size models and data from two phase III studies that compared bevacizumab plus chemotherapy versus chemotherapy as first-line therapy in Western (n = 923) and Chinese (n = 203) patients with CRC. Baseline prognostic factors and tumor-size metrics estimates were assessed in multivariate models to predict OS. Predictive performances of the models were assessed by simulating multiple replicas of the phase III studies. RESULTS: Time to tumor growth (TTG) was the best metric to predict OS. TTG fully captured bevacizumab effect. Chinese ethnicity had no impact on OS or on the TTG-OS relationships. The model correctly predicted OS distributions in each arm as well as bevacizumab hazard ratio (model prediction, 0.75 v 0.68 observed in Western patients; 95% prediction interval, 0.62 to 0.91). CONCLUSION: TTG captured therapeutic benefit with bevacizumab in first-line CRC patients. Chinese ethnicity had no impact. Longitudinal tumor size data coupled with model-based approaches may offer a powerful alternative in the design and analysis of early clinical studies.

The c-di-GMP phosphodiesterase VmpA absent in Escherichia coli K12 strains affects motility and biofilm formation in the enterohemorrhagic O157:H7 serotype.

Enterohemorrhagic Escherichia coli (EHEC) O157:H7 is a foodborne pathogen that resists the acidic gastric environment, colonizes the gut epithelium, and causes hemorrhagic colitis and hemolytic-uremic syndrome, especially in children. The genomic island OI-47 of E. coli O157:H7 contains a gene, z1528, encoding an EAL-domain protein potentially involved in c-di-GMP hydrolysis that is absent in non-pathogenic E. coli. This gene, designated vmpA, is co-transcribed with ycdT, which is present in non pathogenic E. coli and encodes a diguanylate cyclase involved in c-di-GMP synthesis. To test for vmpA function, we constructed a vmpA knockout mutant. We also overexpressed vmpA, purified the VmpA protein and assayed for its activity in vitro. We found that VmpA possesses c-di-GMP phosphodiesterase activity and that the vmpA mutation results in increased biofilm formation, and reduced swimming motility, which is consistent with the function determined in vitro. Unexpectedly, suppressor mutations arise frequently in the vmpA background suggesting that VmpA plays an important regulatory role in E. coli O157:H7. These findings represent an example of remarkable flexibility in the organization of c-di-GMP signaling pathways in closely related species.

Population pharmacokinetic/pharmacodynamic modeling for the time course of tumor shrinkage by motesanib in thyroid cancer patients.

OBJECTIVE: To develop a population pharmacokinetic/pharmacodynamic model describing the relationship between motesanib exposure and tumor response in a phase 2 study of motesanib in patients with advanced differentiated thyroid cancer or medullary thyroid cancer. METHODS: Data from patients (n = 184) who received motesanib 125 mg once daily were used for population pharmacokinetic/pharmacodynamic modeling. Motesanib concentrations were fitted to a 2-compartment population pharmacokinetic model. Observed change in tumor size was the drug response measure for the pharmacodynamic model. Exposure measures in the pharmacokinetic/pharmacodynamic model included dose, plasma concentration profile, or steady-state area under the concentration versus time curve (AUC( ss )). A longitudinal exposure-tumor response model of drug effect on tumor growth dynamics was used. RESULTS: Motesanib oral clearance in patients with medullary thyroid cancer was 67% higher than in patients with differentiated thyroid cancer patients (73.7 vs. 44 L/h). Patients' disease type (medullary thyroid cancer vs. differentiated thyroid cancer) was the most important covariate for explaining interpatient variability in clearance. The objective response rates were 14 versus 2% for differentiated thyroid cancer and medullary thyroid cancer, respectively. Motesanib exposure measures (AUC( ss ) or concentration profile) were better predictors of tumor response than motesanib dose. The estimated motesanib concentration yielding tumor stasis (1.9 ng/mL) was lower than the observed trough concentrations in differentiated thyroid cancer and medullary thyroid cancer patients. CONCLUSIONS: Differences in motesanib pharmacokinetics likely explain the difference in tumor response observed between differentiated thyroid cancer and medullary thyroid cancer patients. The population pharmacokinetic/pharmacodynamic model provides a tool for predicting tumor response to the drug to support the dosing regimen of motesanib in thyroid cancer patients.

Development of a modeling framework to simulate efficacy endpoints for motesanib in patients with thyroid cancer.

PURPOSE: To develop a modeling framework that simulates clinical endpoints (objective response rate and progression-free survival) to support development of motesanib. The framework was evaluated using results from a phase 2 study of motesanib in thyroid cancer. METHODS: Models of probability and duration of dose modifications and overall survival were developed using data from 93 patients with differentiated thyroid cancer and 91 patients with medullary thyroid cancer, who received motesanib 125 mg once daily. The models, combined with previously developed population pharmacokinetic and tumor growth inhibition models, were assessed in predicting dose intensity, tumor size over time, objective response rate, and progression-free survival. Dose-response simulations were performed in patients with differentiated thyroid cancer. RESULTS: The predicted objective response rate and median progression-free survival in patients with differentiated thyroid cancer was 15.0% (95% prediction interval, 7.5%-23.7%) and 40 weeks (95% prediction interval, 32-49 weeks), respectively, compared with the observed objective response rate of 14.0% and median progression-free survival of 40 weeks. The simulated median objective response rate increased with motesanib starting dose from 13.5% at 100 mg once daily to 38.0% at 250 mg once daily. However, simulated median progression-free survival was independent of starting dose, ranging from 40.5 weeks (95% prediction interval, 38.6-46.9 weeks) at 100 mg once daily to 40.0 weeks (95% prediction interval, 38.6-46.8 weeks) at 250 mg once daily. CONCLUSIONS: Dose-response simulations confirmed the appropriateness of 125-mg once-daily dosing; no clinically relevant improvement in progression-free survival would be obtained by dose intensification. This modeling framework represents an important tool to simulate clinical response and support clinical development decisions.

Complete genome sequence of Crohn's disease-associated adherent-invasive E. coli strain LF82.

BACKGROUND: Ileal lesions of Crohn's disease (CD) patients are abnormally colonized by pathogenic adherent-invasive Escherichia coli (AIEC) able to invade and to replicate within intestinal epithelial cells and macrophages. PRINCIPAL FINDINGS: We report here the complete genome sequence of E. coli LF82, the reference strain of adherent-invasive E. coli associated with ileal Crohn's disease. The LF82 genome of 4,881,487 bp total size contains a circular chromosome with a size of 4,773,108 bp and a plasmid of 108,379 bp. The analysis of predicted coding sequences (CDSs) within the LF82 flexible genome indicated that this genome is close to the avian pathogenic strain APEC_01, meningitis-associated strain S88 and urinary-isolated strain UTI89 with regards to flexible genome and single nucleotide polymorphisms in various virulence factors. Interestingly, we observed that strains LF82 and UTI89 adhered at a similar level to Intestine-407 cells and that like LF82, APEC_01 and UTI89 were highly invasive. However, A1EC strain LF82 had an intermediate killer phenotype compared to APEC-01 and UTI89 and the LF82 genome does not harbour most of specific virulence genes from ExPEC. LF82 genome has evolved from those of ExPEC B2 strains by the acquisition of Salmonella and Yersinia isolated or clustered genes or CDSs located on pLF82 plasmid and at various loci on the chromosome. CONCLUSION: LF82 genome analysis indicated that a number of genes, gene clusters and pathoadaptative mutations which have been acquired may play a role in virulence of AIEC strain LF82.

Role of decreased levels of Fis histone-like protein in Crohn's disease-associated adherent invasive Escherichia coli LF82 bacteria interacting with intestinal epithelial cells.

The interaction of Crohn's disease (CD)-associated adherent-invasive Escherichia coli (AIEC) strain LF82 with intestinal epithelial cells depends on surface appendages, such as type 1 pili and flagella. Histone-like proteins operate as global regulators to control the expression of these virulence factors. We evaluated the role of histone-like proteins in AIEC reference strain LF82 during infection of intestinal epithelial cells, Intestine-407, and observed that the fis mRNA level was decreased. The role of Fis in AIEC LF82 was determined by studying the phenotype of an LF82 fis::Km mutant. This was the first mutant of strain LF82 that has been described thus far that is unable to express flagellin but still able to produce type 1 pili. The cyclic-di-GMP pathway linking flagella and type 1 pilus expression is not involved in Fis-mediated regulation, and we identified in the present study Fis-binding sites located upstream of the fimE gene and in the intergenic region between fimB and nanC of the fim operon encoding type 1 pili. The major consequence of decreased Fis expression in AIEC bacteria in contact with host cells is a direct downregulation of fimE expression, leading to the preferential ON phase of the fimS element. Thus, by maintaining type 1 pilus expression, AIEC bacteria, which interact with the gut mucosa, have greater ability to colonize and to induce inflammation in CD patients.

Model-based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumor dynamics.

PURPOSE: We developed a drug-disease simulation model to predict antitumor response and overall survival in phase III studies from longitudinal tumor size data in phase II trials. METHODS: We developed a longitudinal exposure-response tumor-growth inhibition (TGI) model of drug effect (and resistance) using phase II data of capecitabine (n = 34) and historical phase III data of fluorouracil (FU; n = 252) in colorectal cancer (CRC); and we developed a parametric survival model that related change in tumor size and patient characteristics to survival time using historical phase III data (n = 245). The models were validated in simulation of antitumor response and survival in an independent phase III study (n = 1,000 replicates) of capecitabine versus FU in CRC. RESULTS: The TGI model provided a good fit of longitudinal tumor size data. A lognormal distribution best described the survival time, and baseline tumor size and change in tumor size from baseline at week 7 were predictors (P < .00001). Predicted change of tumor size and survival time distributions in the phase III study for both capecitabine and FU were consistent with observed values, for example, 431 days (90% prediction interval, 362 to 514 days) versus 401 days observed for survival in the capecitabine arm. A modest survival improvement of 39 days (90% prediction interval, -21 to 110 days) versus 35 days observed was predicted for capecitabine. CONCLUSION: The modeling framework successfully predicted survival in a phase III trial on the basis of capecitabine phase II data in CRC. It is a useful tool to support end-of-phase II decisions and design of phase III studies.

The flagellar sigma factor FliA regulates adhesion and invasion of Crohn disease-associated Escherichia coli via a cyclic dimeric GMP-dependent pathway.

The invasion of intestinal epithelial cells by the Crohn disease-associated adherent-invasive Escherichia coli (AIEC) strain LF82 depends on surface appendages, such as type 1 pili and flagella. The absence of flagella in the AIEC strain LF82 results in a concomitant loss of type 1 pili. Here, we show that flagellar regulators, transcriptional activator FlhD(2)C(2), and sigma factor FliA are involved in the coordination of flagellar and type 1 pili synthesis. In the deletion mutants lacking these regulators, type 1 pili synthesis, adhesion, and invasion were severely decreased. FliA expressed alone in trans was sufficient to restore these defects in both the LF82-DeltaflhD and LF82-DeltafliA mutants. We related the loss of type 1 pili to the decreased expression of the FliA-dependent yhjH gene in the LF82-DeltafliA mutant. YhjH is an EAL domain phosphodiesterase involved in degradation of the bacterial second messenger cyclic dimeric GMP (c-di-GMP). Increased expression of either yhjH or an alternative c-di-GMP phosphodiesterase, yahA, partially restored type 1 pili synthesis, adhesion, and invasion in the LF82-DeltafliA mutant. Deletion of the GGDEF domain diguanylate cyclase gene, yaiC, involved in c-di-GMP synthesis in the LF82-DeltafliA mutant also partially restored these defects, whereas overexpression of the c-di-GMP receptor YcgR had the opposite effect. These findings show that in the AIEC strain LF82, FliA is a key regulatory component linking flagellar and type 1 pili synthesis and that its effect on type 1 pili is mediated, at least in part, via a c-di-GMP-dependent pathway.

Modeling and simulation of sexual activity daily diary data of patients with female sexual arousal disorder treated with sildenafil citrate (Viagra).

PURPOSE: To develop a model to explore the dose-response of sildenafil citrate in patients with female sexual arousal disorder (FSAD) based on telephone sexual activity daily diary (TSADD) data obtained in double-blind, placebo controlled clinical studies. MATERIALS: Data were available on 614 patients with FSAD. A parametric model (Weibull distribution) was developed to describe the probability density function of the time between sexual events. Orgasm satisfaction scores and overall sexual satisfaction scores were simultaneously modeled as ordered categorical variables. Simulations were performed to evaluate the expected clinical response in patients with FSAD. RESULTS: The expected time between sexual events was approximately 3.5 days. Satisfaction scores increased with time to achieve a plateau after 3 to 4 weeks on treatment. The expected probability of satisfying orgasm (score of 3 and higher) ranged from 34.7% for placebo to 41.6% for 100 mg sildenafil citrate. Treatment effect (difference from placebo) was 6.9% for 100 mg sildenafil citrate, ranging from 0.6 to 24.7% for testosterone levels of 0.1 to 4.0 pg/ml. The treatment effect in postmenopausal women was larger than in premenopausal women. CONCLUSION: A modeling and simulation framework to support drug development in FSAD was developed. Sildenafil citrate demonstrated a dose-dependent effect in patients with FSAD.