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Background: There is a critical need for novel primary endpoints designed to detect early and subtle changes in cognition in clinical trials targeting the asymptomatic (preclinical) phase of Alzheimer’s disease (AD). The Alzheimer’s Prevention Initiative (API) Generation Program, conducted in cognitively unimpaired individuals at risk of developing AD (e.g., enriched by the apolipoprotein E (APOE) genotype), used novel dual primary endpoints based on 1) time to event (TTE) – with an event defined as diagnosis of mild cognitive impairment (MCI) due to AD and/or dementia due to AD – and 2) change from baseline to Month 60 in the API preclinical composite cognitive (APCC) test score. Methods: Historical observational data from three sources were used to fit models to describe the TTE and the longitudinal APCC decline, both in people who do and do not progress to MCI or dementia due to AD. Clinical endpoints were simulated based on the TTE and APCC models to assess the performance of the dual endpoints versus each of the two single endpoints, with the selected treatment effect ranging from a hazard ratio (HR) of 0.60 (40% risk reduction) to 1 (no effect). Results: A Weibull model was selected for TTE, and power and linear models were selected to describe the APCC score for progressors and non-progressors, respectively. Derived effect sizes in terms of reduction of the APCC change from baseline to Year 5 were low (0.186 for HR=0.67). The power for the APCC alone was consistently lower compared to the power of TTE alone (58% [APCC] vs 84% [TTE] for HR=0.67). Also, the overall power was higher for the 80%/20% distribution (82%) of the family-wise type-1 error rate (alpha) between TTE and APCC compared to 20%/80% (74%). Conclusions: Dual endpoints including TTE and a measure of cognitive decline perform better than the cognitive decline measure as a single primary endpoint in a cognitively unimpaired population at risk of AD (based on the APOEgenotype). Clinical trials in this population, however, need to be large, include older age, and have a long follow-up period of at least 5 years to be able to detect treatment effects.

Pasireotide (SOM230, Signifor®) is a somatostatin analog approved in a subcutaneous formulation for the treatment of Cushing’s disease. This analysis characterizes the population pharmacokinetics (PopPK) of subcutaneous pasireotide jointly in healthy volunteers (HVs) and Cushing’s disease patients (CDPs), evaluating the effects of age, body size, and population on pasireotide pharmacokinetics. The analysis dataset included five phase I studies and one each from phase II and phase III. A three-compartment, linear structural pharmacokinetic model was used. Models were specified a priori that varied in the relationship between HVs and CDPs, and the model with the lowest value of the Bayes Information Criterion (BIC) was selected. It was then used to illustrate various features of pasireotide pharmacokinetics. In the final model, the estimated values of apparent clearance (CL/F), central volume of distribution, and deep peripheral volume of distribution of pasireotide in CDP were 59, 43, and 225% those of HVs at the same age and body size. Clearance increased with body size and decreased with age similarly for CDPs and HVs. The estimated CL/F for a typical CDP (40 years old, lean body weight [LBW] 49 kg) was 3.72 L/h, and for a typical HV (29 years old, LBW 61 kg) was 7.96 L/h. The model was judged adequate by visual predictive checks and diagnostic plots separately for HVs and CDPs and can be used for simulations for deriving exposure–response metrics for pharmacokinetic/pharmacodynamic analyses.

Clinical outcomes are often described as events: death, stroke, epileptic seizure, multiple sclerosis lesions, recurrence of cancer, disease progression, pain, infection and bacterial/viral eradication, severe toxic adverse effect, resistance to treatment, etc. They may be quantified as time-to-event, counts of events per time interval (rates), their severity grade, or a combination of these. Such data are discrete and require specific modelling structures and methods. This article references the most common modelling approaches for categorical, count and time-to-event data, and reviews examples of such models applied in the analysis of pharmacodynamic data. Modelling is useful for identification of influential factors related to the clinical outcome, characterization and quantification of their impact, for making better informed predictions and clinical decisions, assessments of efficacy of therapeutic interventions, optimizing the individual treatments and drug development studies.

A three-part license expansion for omalizumab (Xolair(®)), humanized anti-IgE antibody, was recently made in Japan for paediatric use, additional higher doses and revised dosing frequency in allergic asthma. The dosing level and frequency of omalizumab are guided by a dosing table based on the total serum IgE and bodyweight. Nonlinear mixed-effect pharmacokinetic (PK) and pharmacodynamic (PD) modeling and simulation techniques described the binding between omalizumab and its target IgE. The population PKPD analysis was conducted using data from the nine studies included originally in the European application of dosing table expansion together with three Japanese clinical studies to assess the influence of the ethnicity. Statistically significant differences between the ethnic groups were detected. These were small, within or close to bioequivalence criteria. The model described the primary pharmacology in Caucasian and Japanese patients, both adult and paediatric, with simulations showing that the interplay between the clearance, volume and binding affinity parameters was such that there was no clinical impact of the Japanese ethnic differences on either drug PK or free IgE suppression and hence the required posology.

Objective:To evaluate effects of siponimod on response to T-cell–dependent (influenza) and T-cell–independent (pneumococcal polysaccharide vaccine [PPV-23]) vaccinations in healthy participants.Methods:In this double-blind, placebo-controlled, parallel-group study, each participant underwent a 7-week treatment period and received intramuscular injections of influenza and PPV-23 vaccines (day 21). Participants were randomized to 4 treatment groups (N = 30 each) and received placebo or siponimod 2 mg once daily in concomitant, interrupted, or preceding fashion. Individual response to vaccination was defined by a ≥4-fold (influenza) antibody titer increase and by a ≥2-fold increase in serotype-specific immunoglobulin (Ig) G concentrations (PPV-23) on day 28 vs baseline. Responder rates were compared using noninferiority analysis.Results:Mean influenza titers were similar to placebo in the preceding and interrupted groups but lower in the concomitant group. The proportion of participants with influenza titers ≥40 four weeks after vaccination (seroprotection) was similar to placebo across all groups and antigens. In each treatment group, response criteria were met for 3 of 4 antigens including H1N1 and H3N2. A noninferior response was determined in the context of preceding treatment but not interrupted or concomitant treatment. Regarding PPV-23, approximately 90%–100% of participants exhibited a ≥2-fold increase in IgG concentrations vs baseline. Noninferior responder rates were determined for each siponimod treatment group.Conclusions:Siponimod treatment had no relevant effect on antibody response to PPV-23. European Medicines Agency response criteria were essentially met for influenza, but titers were lower on concomitant treatment. Overall, these data suggest that siponimod has limited effect on the efficacy of vaccinations with neoantigens.Classification of evidence:This study provides Class II evidence that in healthy persons, siponimod had limited effect on the immune response following influenza or pneumococcal vaccinations.

Anticancer drugs often show a narrow therapeutic index and high inter-patient variability, which can lead to the need to adjust doses individually during the treatment. One approach to doing this is to use individual model predictions. Such methods have been proposed to target-specific drug concentrations or blood cell count, both of which are continuous variables. However, many toxic effects are evaluated on a categorical scale. This article presents a novel approach to dose adjustments for reducing a graded toxicity while maintaining efficacy, applied to hand-and-foot syndrome (HFS) induced by capecitabine. A mixed-effects proportional odds Markov model relating capecitabine doses to HFS grades was individually adjusted at the end of each treatment cycle (3 weeks) by estimating subject-specific parameters by Bayesian MAP technique. It was then used to predict the risk of intolerable (grade ≥ 2) toxicity over the next treatment cycle and determine the next dose accordingly, targeting a predefined tolerable risk. Proof of concept was given by simulating virtual clinical trials, where the standard dose reductions and the prediction-based adaptations were compared, and where the therapeutic effect was simulated using a colorectal tumor inhibition model. A sensitivity analysis was carried out to test various specifications of prediction-based adaptation. Individualized dose adaptation might reduce the average duration of intolerable HFS by 10 days as compared to the standard reductions (3.8 weeks vs. 5.2 weeks; 27% relative reduction) without compromising antitumor efficacy (both responder rates were 49%). A clinical trial comparing the two methods should include 350 patients per arm to achieve at least 90% power to show a difference in grade ≥2 HFS duration at an alpha level of 0.05. These results indicate that individual prediction-based dose adaptation based on ordinal data may be feasible and beneficial.

The objective of this work was to investigate the factors influencing the quality of empirical Bayes estimates (EBEs) of individual random effects of a mixed-effects Markov model for ordered categorical data. It was motivated by an attempt to develop a model-based dose adaptation tool for clinical use in colorectal cancer patients receiving capecitabine, which induces severe hand-and-foot syndrome (HFS) toxicity in more than a half of the patients. This simulation-based study employed a published mixed-effects model for HFS. The quality of EBEs was assessed in terms of accuracy and precision, as well as shrinkage. Three optimization algorithms were compared: simplex, quasi-Newton and adaptive random search. The investigated factors were amount of data per patient, distribution of categories within patients, magnitude of the inter-individual variability, and values of the effect model parameters. The main factors affecting the quality of EBEs were the values of parameters governing the dose-response relationship and the within-subject distribution of categories. For the chosen HFS toxicity model, the accuracy and precision of EBEs were rather low, and therefore the feasibility of their use for individual model-based dose adaptation seemed limited.