Blinded sample size re-estimation for recurrent event data with time trends.

The use of an internal pilot study for blinded sample size re-estimation (BSSR) allows to reduce uncertainty on the appropriate sample size compared with conventional fixed sample size designs. Recently BSSR procedures for recurrent event data were proposed and investigated. These approaches assume treatment-specific constant event rates that might not always be appropriate as found in relapsing multiple sclerosis. On the basis of a proportional intensity frailty model, we propose methods for BSSR in situations where a time trend of the event rates is present. For the sample size planning and the final analysis standard negative binomial methods can be used, as long as the patient follow-up time is approximately equal in the treatment groups. To re-estimate the sample size at interim, however, a full likelihood analysis is necessary. Operating characteristics such as rejection probabilities and sample size distribution are evaluated in a simulation study motivated by a systematic review in relapsing multiple sclerosis. The key factors affecting the operating characteristics are the study duration and the length of the recruitment period. The proposed procedure for BSSR controls the type I error rate and maintains the desired power against misspecifications of the nuisance parameters.

A Bayesian adaptive dose selection procedure with an overdispersed count endpoint.

Adaptive trial designs can considerably improve upon traditional designs, by modifying design aspects of the ongoing trial, like early stopping, adding, or dropping doses, or changing the sample size. In the present work, we propose a two-stage Bayesian adaptive design for a Phase IIb study aimed at selecting the lowest effective dose for Phase III. In this setting, efficacy has been proved for a high dose in a Phase IIa proof-of-concept study, but the existence of a lower but still effective dose is investigated before the scheduled Phase III starts. In the first stage, we randomize patients to placebo, maximal tolerated dose, and one or more additional doses within the dose range. Based on an interim analysis, we either stop the study for futility or success or continue the study to the second stage, where newly recruited patients are allocated to placebo, some fairly high dose, and one additional dose chosen based on interim data. At the interim analysis, we use the criteria based on the predictive probability of success to decide on whether to stop or to continue the trial and, in the latter case, which dose to select for the second stage. Finally, we will select a dose as lowest effective dose for Phase III either at the end of the first stage or at the end of the second stage. We evaluate the operating characteristics of the procedure via simulations and present the results for several scenarios, comparing the performance of the proposed procedure to those of the non-adaptive design.

Ethnic sensitivity study of fingolimod in white and Asian subjects.

OBJECTIVE: The authors compared the pharmacokinetics and pharmacological effects of the immunomodulator fingolimod in healthy white and Asian subjects for potential ethnic differences. METHODS: White and Asian (Japanese) healthy subjects were demographically matched for sex, age and weight. Subjects received single 1.25 mg doses of fingolimod (6 ethnic pairs), 2.5 mg (7 pairs), 5 mg (6 pairs) or 5 mg/day for 7 days (6 pairs). The pharmacokinetics of fingolimod, major metabolites, peripheral blood lymphocyte counts and heart rate were characterized over 1 month after single-dose and 2 months after multiple-dose administration. RESULTS: There were no clinically relevant differences in the fingolimod dose Cmax or dose AUC relationships between Asian subjects (slopes 0.84 and 1.05) versus white subjects (slopes 1.13 and 1.26) after single-dose administration. During multiple-dose administration, there were no clinically relevant interethnic differences in fingolimod accumulation ratios (6.6 +/- 0.4 for whites, 7.0 +/- 0.7 for Asians), area under the concentration-time curve (390 +/- 73 versus 382 +/- 106 ng x h/ml), or elimination half-life (7.4 +/- 0.8 versus 7.9 +/- 2.0 days). The acute decrease in lymphocyte counts after single- and multiple-dose fingolimod were similar in the two ethnic groups. The lymphocyte recovery rate to baseline after a 5 mg single dose and 5 mg/day multiple dose was reduced by 36 and 15% in Asian subjects compared with white subjects. The transient, acute decrease in heart rate after the first dose of fingolimod and the subsequent return to baseline was similar in the two ethnic groups. CONCLUSION: There were no marked differences between healthy white and Asian subjects in fingolimod single-dose and multiple-dose pharmacokinetics, lymphocyte trafficking and heart rate responses.

Comparison of artificial neural networks (ANN) with classical modelling techniques using different experimental designs and data from a galenical study on a solid dosage form.

Artificial Neural Networks (ANN) methodology was used to analyse experimental data from a tabletting study and compared both graphically and numerically to classical modelling techniques (i.e. Response surface methodology, RSM). The aim of this investigation was to describe quantitatively the degree of data fitting achieved and the robustness of the developed models using two types of experimental design (i.e. a statistical, highly organised design and a randomised design). To compare goodness of fit, the R(2) coefficient was used, whereas for the robustness of the models the R(2) coefficient of an independent validation data set was computed. Comparable results were achieved for both ANN and RSM methodology when using the statistical plan. However, the robustness of the models when developed based on a randomised plan was clearly better for the ANN methodology. Based on the results of this study, it appears that the ANN methodology is much less sensitive to the organisational level of a trial design and is therefore better adapted to the data analysis of the results of historical or poorly organised trials. All tablet properties determined were largely influenced by the dwell time during compression as well as by concentration of silica aerogel and magnesium stearate, whereas the other factors showed very much weaker effects.

Advantages of Artificial Neural Networks (ANNs) as alternative modelling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage form.

Artificial Neural Networks (ANN) methodology was used to assess experimental data from a tablet compression study showing highly non-linear relationships (i.e. measurements of ejection forces) and compared to classical modelling technique (i.e. Response Surface Methodology, RSM). These kinds of relationships are known to be difficult to model using classical methods. The aim of this investigation was to quantitatively describe the achieved degree of data fitting and predicting abilities of the developed models. The comparison between the ANN and RSM was carried out both graphically and numerically. For comparing the goodness of fit, all data were used, whereas for the goodness of prediction the data were split into a learning and a validation data set. Better results were achieved for the model using ANN methodology with regard to data fitting and predicting ability. All determined ejection properties were mainly influenced by the concentration of magnesium stearate and silica aerogel, whereas the other factors showed very much lower effects. Important relationships could be recognised from the ANN model only, whereas the RSM model ignored them. The ANN methodology represents a useful alternative to classical modelling techniques when applied to variable data sets presenting non-linear relationships.

Pitfalls of artificial neural networks (ANN) modelling technique for data sets containing outlier measurements using a study on mixture properties of a direct compressed dosage form.

An application of the Artificial Neural Networks (ANN) methodology was investigated using experimental data from a mixture properties study and compared to classical modelling technique (i.e. Response Surface Methodology, RSM) both graphically and numerically. The aim of this investigation was to quantitatively describe the achieved degree of data fitting and robustness of the developed models. For comparing the goodness of fit, the R2 coefficient was used, whereas for the robustness of the models an outlier measurement was integrated in the data set. Comparable results were achieved for both ANN- and RSM methodologies for data fitting. The robustness of the models towards outliers was clearly better for the RSM methodology. All determined mixture properties were mainly influenced by the concentration of silica aerogel, whereas the other factors showed very much lower effects. For that reason the physical properties of this excipient (e.g. its specific surface area) are of importance for the behaviour of the mixtures.

Effects of benazepril in the treatment of feline hypertrophic cardiomyopathy Results of a prospective, open-label, multicenter clinical trial.

OBJECTIVES: To evaluate the efficacy of benazepril on clinical signs and echocardiographic parameters in cats with primary hypertrophic cardiomyopathy (HCM). BACKGROUND: ACE-inhibitors have positive effects in man with HCM, and contribute to a reduction of myocardial hypertrophy. Addition of an ACE-inhibitor to the standard treatment of HCM in cats may have beneficial effects. METHODS: A total of 32 cats which were either asymptomatic or in stabilised congestive heart failure (ISACHC* class Ib, II or IIIa) were included in a one-year, prospective, open-label, clinical trial in 5 centres in Switzerland. 28 of these cats were allocated to one of two treatment groups: 1) standard therapy (ST) alone (n=9), consisting of a long-acting formulation of diltiazem (6-9 mg/kg sid) and optional acetylsalicylic acid (50 mg twice weekly, or 2) the same ST plus benazepril (0.33 - 0.75 mg/kg sid, n=19). RESULTS: Cats treated with benazepril showed a statistically significant decrease (mean +/- SEM, 0.11 +/- 0.03 mm/month, p = 0.002) in the left ventricular wall thickness (LVWD) from baseline, while no change (increase of 0.02 +/- 0.04 mm/month, p=0.66) was observed in cats on ST alone. Differences in LVWD between the two groups reached statistical significance (p=0.02). Benazepril treated cats showed more improvement in clinical signs (20-53%) than cats receiving ST alone (0-20%) but differences between the groups were not statistically significant (p>0.1). No change in septal thickness (IVSD) or left atrial to aortic root ratio (LA/Ao) was observed in either group. CONCLUSIONS: Benazepril had some beneficial effects on clinical signs and cardiac remodelling in cats with HCM and was well tolerated. These results, however, need to be confirmed in additional controlled studies. * ISACHC classification is described in the previous paper (Bench-study).

Blinded sample size reestimation with negative binomial counts in superiority and non-inferiority trials.

BACKGROUND: In the planning of clinical trials with count outcomes such as the number of exacerbations in chronic obstructive pulmonary disease (COPD) often considerable uncertainty exists with regard to the overall event rate and the level of overdispersion which are both crucial for sample size calculations. OBJECTIVES: To develop a sample size reestimation strategy that maintains the blinding of the trial, controls the type I error rate and is robust against misspecification of the nuisance parameters in the planning phase in that the actual power is close to the target. METHODS: The operation characteristics of the developed sample size reestimation procedure are investigated in a Monte Carlo simulation study. RESULTS: Estimators of the overall event rate and the overdispersion parameter that do not require unblinding can be used to effectively adjust the sample size without inflating the type I error rate while providing power values close to the target. CONCLUSIONS: If only little information is available regarding the size of the overall event rate and the overdispersion parameter in the design phase of a trial, we recommend the use of a design with sample size reestimation as the one suggested here. Trials in COPD are expected to benefit from the proposed sample size reestimation strategy.

Pharmacokinetics and pharmacodynamics of the novel daily rivastigmine transdermal patch compared with twice-daily capsules in Alzheimer's disease patients.

A transdermal patch has been developed for the cholinesterase inhibitor rivastigmine. This study investigated the pharmacokinetics and pharmacodynamics of rivastigmine and NAP226-90, and compared drug exposure between patch and capsule administrations. This was an open-label, parallel-group study in Alzheimer's disease patients randomized to receive either capsule (1.5-6 mg Q12H, i.e., 3-12 mg/day) or patch (5-20 cm2) in ascending doses through four 14-day periods. The patch showed lower Cmax (ca. 30% lower at 20 cm2, 19.5 versus 29.3 ng/ml), longer tmax (8.0 versus 1.0 h), and greater AUC (ca. 1.8-fold at 20 cm2, 345 versus 191 ng x h/ml) compared with the 6 mg Q12H capsule dose, with markedly less fluctuation between peak and trough plasma levels (80% at 20 cm2 versus 620% at 1.5 mg Q12H). Plasma butyrylcholinesterase inhibition rose slowly after patch administration, whereas two distinct peaks were seen after capsule administration. Average exposure with the 10 cm2 patch was comparable to the highest capsule dose (6 mg Q12H, i.e., 12 mg/day).

Population pharmacokinetics of imatinib mesylate in patients with chronic-phase chronic myeloid leukaemia: results of a phase III study.

AIMS: This study was designed to investigate the biochemical and physiological covariates or comedications that affect the pharmacokinetics of imatinib mesylate in patients with chronic-phase chronic myeloid leukaemia (CP CML). METHODS: Pharmacokinetic data were analyzed in 371 patients receiving 400 mg imatinib once daily during a phase III trial of imatinib vs interferon-alfa plus cytarabine for the treatment of newly diagnosed CP CML. Covariates included age, weight, sex, ethnicity, haemoglobin (Hb) concentration, white blood cell (WBC) count, liver function, and creatinine concentration. Blood samples for imatinib analysis were taken on treatment days 1 and 29. Nonlinear mixed effects modelling was used for the population pharmacokinetic analysis. RESULTS: Population mean estimates (95% confidence interval) at day 1 for apparent clearance (CL) and apparent volume of distribution (V) of imatinib were 14 (13-15) l h(-1) and 252 (237-267) l, respectively. Modelling suggested that CL decreased by 4 (3-5) l h(-1) from day 1 to day 29, whereas V remained unchanged. Interindividual variability in CL and V was 32% and 31%, respectively. Weight, Hb, and WBC count demonstrated small effects on CL and V. Doubling body weight or Hb or halving the WBC count was associated with a 12%, 86% and 8% increase in CL, respectively, and a 32%, 60% and 5% increase in V, respectively. Comedications showed no clear effects on imatinib CL. CONCLUSIONS: Population covariates and coadministered drugs minimally affected imatinib pharmacokinetics in newly diagnosed CP CML patients.

Basic concepts of artificial neural networks (ANN) modeling in the application to pharmaceutical development.

Artificial neural networks (ANN) methodology is a new modeling method that has not been broadly applied to pharmaceutical sciences up to now. The aim of this paper is to give a detailed description of the associating networks as well as a description of less well-known networks (i.e., feature-extracting and nonadaptive networks) and their scope of application in pharmaceutical sciences. The descriptions include the historical origin and the basic concepts behind the computing. ANN are based on the attempt to model the neural networks of the brain. Learning algorithms for associating ANN use mathematical procedures usually derived from the gradient descent method whereas feature-extracting ANN map multidimensional input data sets onto two-dimensional spaces. Nonadaptive ANN map data sets and are able to reconstruct their patterns when presented with corrupted or noisy samples. Associating networks can typically be applied in the pharmaceutical field as an alternative to traditional response surface methodology, feature-extracting networks as alternative to principal component analysis, and nonadaptive networks for image recognition. Based on these abilities, the potential application fields of the ANN methodology in the pharmaceutical sciences is broad, ranging from clinical pharmacy through biopharmacy, drug and dosage form design, to interpretation of analytical data. The few applications presented in the pharmaceutical technology area seem promising and should be investigated in more detail.

Application of artificial neural networks (ANN) in the development of solid dosage forms.

The application of ANN in pharmaceutical development has been assessed using theoretical as well as typical pharmaceutical technology examples. The aim was to quantitatively describe the achieved data fitting and predicting abilities of the models developed with a view to using ANN in the development of solid dosage forms. The comparison between the ANN and a traditional statistical (i.e., response surface methodology, RSM) modeling technique was carried out using the squared correlation coefficient R2. Using a highly nonlinear arbitrary function the ANN models showed better fitting (R2 = 0.931 vs. R2 = 0.424) as well as predicting (R2 = 0.810 vs. R2 = 0.547) abilities. Experimental data from a tablet compression study were fitted using two types of ANN models (i.e., multilayer perceptrons and a hybrid network composed of a self-organising feature map joined to a multilayer perception). The achieved data fitting was comparable for the three methods (MLP R2 = 0.911, SOFM-MLP R2 = 0.850, and RSM R2 = 0.897). ANN methodology represents a promising modeling technique when applied to pharmaceutical technology data sets.