Assessing goodness-of-fit for evaluation of dose-proportionality.

For the clinical development of a new drug, the determination of dose-proportionality is an essential part of the pharmacokinetic evaluations, which may provide early indications of non-linear pharmacokinetics and may help to identify sub-populations with divergent clearances. Prior to making any conclusions regarding dose-proportionality, the goodness-of-fit of the model must be assessed to evaluate the model performance. We propose the use of simulation-based visual predictive checks to improve the validity of dose-proportionality conclusions for complex designs. We provide an illustrative example and include a table to facilitate review by regulatory authorities.

Two doses of sclerostin antibody in cynomolgus monkeys increases bone formation, bone mineral density, and bone strength.

The development of bone-rebuilding anabolic agents for treating bone-related conditions has been a long-standing goal. Genetic studies in humans and mice have shown that the secreted protein sclerostin is a key negative regulator of bone formation. More recently, administration of sclerostin-neutralizing monoclonal antibodies in rodent studies has shown that pharmacologic inhibition of sclerostin results in increased bone formation, bone mass, and bone strength. To explore the effects of sclerostin inhibition in primates, we administered a humanized sclerostin-neutralizing monoclonal antibody (Scl-AbIV) to gonad-intact female cynomolgus monkeys. Two once-monthly subcutaneous injections of Scl-AbIV were administered at three dose levels (3, 10, and 30 mg/kg), with study termination at 2 months. Scl-AbIV treatment had clear anabolic effects, with marked dose-dependent increases in bone formation on trabecular, periosteal, endocortical, and intracortical surfaces. Bone densitometry showed that the increases in bone formation with Scl-AbIV treatment resulted in significant increases in bone mineral content (BMC) and/or bone mineral density (BMD) at several skeletal sites (ie, femoral neck, radial metaphysis, and tibial metaphysis). These increases, expressed as percent changes from baseline were 11 to 29 percentage points higher than those found in the vehicle-treated group. Additionally, significant increases in trabecular thickness and bone strength were found at the lumbar vertebrae in the highest-dose group. Taken together, the marked bone-building effects achieved in this short-term monkey study suggest that sclerostin inhibition represents a promising new therapeutic approach for medical conditions where increases in bone formation might be desirable, such as in fracture healing and osteoporosis.

Ectomycorrhizal community structure in a healthy and a Phytophthora-infected chestnut (Castanea sativa Mill.) stand in central Italy.

Ink disease caused by Phytophthora cambivora is a major disease of sweet chestnut (Castanea sativa). In two C. sativa stands in central Italy, one (Montesanti) that is infected with P. cambivora and the trees showing symptoms of ink disease and another healthy stand (Puzzella), the ectomycorrhizal (ECM) community structure was investigated. On the roots of the surviving trees of the diseased stand, 29 different ECM species were determined compared to 23 in the healthy stand. Eleven ECM species were common to both stands; however, a number of species were unique to one of the stands. Cenococcum geophilum was dominant at both sites, but the percentage colonisation was much higher at Montesanti (40.8%) compared to Puzzella (27.2%). There was a switch in species from Russula vesca, Russula lepida and Russula azurea at Puzzella to Russula nigricans, R. lepida and Russula delica at Montesanti. Both R. vesca and R. azurea were found only at the Puzzella site. At the diseased site, the ECMs formed had a smaller root tip diameter, and the ECM at the healthy site had more abundant extramatrical hyphae.

Sample size calculation for the Power Model for dose proportionality studies.

There are several approaches to assess or demonstrate pharmacokinetic dose proportionality. One statistical method is the traditional ANOVA model, where dose proportionality is evaluated using the bioequivalence limits. A more informative method is the mixed effects Power Model, where dose proportionality is assessed using a decision rule for the estimated slope. Here we propose analytical derivations of sample sizes for various designs (including crossover, incomplete block and parallel group designs) to be analysed according to the Power Model.

Combining estimates from multiple early studies to obtain estimates of response: using shrinkage estimates to obtain estimates of response.

Development of anti-cancer therapies usually involve small to moderate size studies to provide initial estimates of response rates before initiating larger studies to better quantify response. These early trials often each contain a single tumor type, possibly using other stratification factors. Response rate for a given tumor type is routinely reported as the percentage of patients meeting a clinical criteria (e.g. tumor shrinkage), without any regard to response in the other studies. These estimates (called maximum likelihood estimates or MLEs) on average approximate the true value, but have variances that are usually large, especially for small to moderate size studies. The approach presented here is offered as a way to improve overall estimation of response rates when several small trials are considered by reducing the total uncertainty.The shrinkage estimators considered here (James-Stein/empirical Bayes and hierarchical Bayes) are alternatives that use information from all studies to provide potentially better estimates for each study. While these estimates introduce a small bias, they have a considerably smaller variance, and thus tend to be better in terms of total mean squared error. These procedures provide a better view of drug performance in that group of tumor types as a whole, as opposed to estimating each response rate individually without consideration of the others. In technical terms, the vector of estimated response rates is nearer the vector of true values, on average, than the vector of the usual unbiased MLEs applied to such trials.