Baseline observation carry forward: reasoning, properties, and practical issues.

In some longitudinal drug studies, regulatory agencies suggest baseline observation carry forward (BOCF) as a method of handling patient dropout, despite the existence of many criticisms to BOCF. The reason for using BOCF is not clear to many users who either treat BOCF as an imputation method or consider BOCF to be "conservative" in the sense that it allows treatment effect to be evaluated with a severe penalty for dropouts. In this article we address the following questions and issues: What is the reason for using BOCF? Is BOCF a conservative approach to assessing drug efficacy? Is BOCF reasonable? If not, what are the alternatives? Our discussions are based on both theoretical and practical viewpoints.

Minimum significant ratio of selectivity ratios (MSRSR) and confidence in ratio of selectivity ratios (CRSR): quantitative measures for selectivity ratios obtained by screening assays.

Development of inhibitor compounds selective against undesirable targets is critical in drug discovery. Selectivity ratios for candidate compounds are evaluated by dividing potencies from two assays assessing the off-target and target. Because all potency measurements have underlying uncertainty, understanding error propagation is essential to interpreting selectivity data. Assay noise introduces ambiguity in the statistical significance of selectivity ratios, particularly at low replicate numbers when compounds are often prioritized for subsequent testing. The ability to differentiate potency results for any pair of compounds in one assay is evaluated using a metric called minimum significant ratio (MSR). Potency results of one compound tested in a pair of assays can be differentiated by the minimum significant selectivity ratio (MSSR). To differentiate selectivity ratios for any pair of compounds, we extend this concept by proposing two new parameters called the minimum significant ratio of selectivity ratios (MSRSR) and confidence in ratio of selectivity ratios (CRSR). Importantly, these tools can be used after a single selectivity measurement. We describe these methods and illustrate their usefulness using structure-activity relationship data from a Janus kinase inhibitor project, in which these tools informed a cogent retesting strategy and enabled rapid and objective decision making.

Collapsing mechanistic models: an application to dose selection for proof of concept of a selective irreversible antagonist.

When data fail to support fully mechanistic models, alternative modeling strategies must be pursued. Simpler, more empirical models or the fixing of various rate constants are necessary to avoid over-parameterization. Fitting empirical models can dilute information, limit interpretation, and cloud inference. Fixing rate constants requires external, relevant, and reliable information on the mechanism and can introduce subjectivity as well as complicate determining the validity of model extrapolation. Furthermore, both these methods ignore the possibility that failure of the data to support the mechanistic model could contain information about the pharmacodynamic process. If the pathway has processes with "fast" dynamics, these steps could collapse yielding parametrically simpler classes of models. The collapsed models would retain the mechanistic interpretation of the full model, which is crucial for performing substantive inference, while reducing the number of parameters to be estimated. These concepts are illustrated through their manifestations on the dose-effect relationship and ensuing dose selection for a proof of concept study. Specifically, a mechanistic model for a selective irreversible antagonist was posited and candidate classes of models were derived utilizing "fast dynamics" assumptions. Model assessment determined the rate-limiting step facilitating pertinent inference with respect to the mechanism. For comparison, inference using a more empirical modeling strategy is also presented. A general solution for the collapse of the typical PK-PD model differential equations is provided in Appendix A.