Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development.

Four non-small-cell lung cancer (NSCLC) registration trials were utilized to develop models linking survival to risk factors and changes in tumor size during treatment. The purpose was to leverage existing quantitative knowledge to facilitate future development of anti-NSCLC drugs. Eleven risk factors were screened using a Cox model. A mixed exponential decay and linear growth model was utilized for modeling tumor size. Survival times were described in a parametric model. Eastern Cooperative Oncology Group (ECOG) score and baseline tumor size were consistent prognostic factors of survival. Tumor size was well described by the mixed model. The parametric survival model includes ECOG score, baseline tumor size, and week 8 tumor size change as predictors of survival duration. The change in tumor size at week 8 allows early assessment of the activity of an experimental regimen. The survival model and the tumor model will be beneficial for early screening of candidate drugs, simulating NSCLC trials, and optimizing trial designs.

A dynamic model of hand-and-foot syndrome in patients receiving capecitabine.

For the purpose of developing a longitudinal model to predict hand-and-foot syndrome (HFS) dynamics in patients receiving capecitabine, data from two large phase III studies were used. Of 595 patients in the capecitabine arms, 400 patients were randomly selected to build the model, and the other 195 were assigned for model validation. A score for risk of developing HFS was modeled using the proportional odds model, a sigmoidal maximum effect model driven by capecitabine accumulation as estimated through a kinetic-pharmacodynamic model and a Markov process. The lower the calculated creatinine clearance value at inclusion, the higher was the risk of HFS. Model validation was performed by visual and statistical predictive checks. The predictive dynamic model of HFS in patients receiving capecitabine allows the prediction of toxicity risk based on cumulative capecitabine dose and previous HFS grade. This dose-toxicity model will be useful in developing Bayesian individual treatment adaptations and may be of use in the clinic.

Overview of model-building strategies in population PK/PD analyses: 2002-2004 literature survey.

AIMS: A descriptive survey of published population pharmacokinetic and/or pharmacodynamic (PK/PD) analyses from 2002 to 2004 was conducted and an evaluation made of how model building was performed and reported. METHODS: We selected 324 articles in Pubmed using defined keywords. A data abstraction form (DAF) was then built comprising two parts: general characteristics including article identification, context of the analysis, description of clinical studies from which the data arose, and model building, including description of the processes of modelling. The papers were examined by two readers, who extracted the relevant information and transmitted it directly to a MySQL database, from which descriptive statistical analysis was performed. RESULTS: Most published papers concerned patients with severe pathology and therapeutic classes suffering from narrow therapeutic index and/or high PK/PD variability. Most of the time, modelling was performed for descriptive purposes, with rich rather than sparse data and using NONMEM software. PK and PD models were rarely complex (one or two compartments for PK; E(max) for PD models). Covariate testing was frequently performed and essentially based on the likelihood ratio test. Based on a minimal list of items that should systematically be found in a population PK-PD analysis, it was found that only 39% and 8.5% of the PK and PD analyses, respectively, published from 2002 to 2004 provided sufficient detail to support the model-building methodology. CONCLUSIONS: This survey allowed an efficient description of recent published population analyses, but also revealed deficiencies in reporting information on model building.

Stepwise modulation of neurokinin-3 and neurokinin-2 receptor affinity and selectivity in quinoline tachykinin receptor antagonists.

A stepwise chemical modification from human neurokinin-3 receptor (hNK-3R)-selective antagonists to potent and combined hNK-3R and hNK-2R antagonists using the same 2-phenylquinoline template is described. Docking studies with 3-D models of the hNK-3 and hNK-2 receptors were used to drive the chemical design and speed up the identification of potent and combined antagonsits at both receptors. (S)-(+)-N-(1-Cyclohexylethyl)-3-[(4-morpholin-4-yl)piperidin-1-yl]methyl-2-phenylquinoline-4-carboxamide (compound 25, SB-400238: hNK-3R binding affinity, K(i) = 0.8 nM; hNK-2R binding affinity, K(i) = 0.8 nM) emerged as the best example in this approach. Further studies led to the identification of (S)-(+)-N-(1,2,2-trimethylpropyl)-3-[(4-piperidin-1-yl)piperidin-1-yl]methyl-2-phenylquinoline-4-carboxamide (compound 28, SB-414240: hNK-3R binding affinity, K(i) = 193 nM; hNK-2R binding affinity, K(i) = 1.0 nM) as the first hNK-2R-selective antagonist belonging to the 2-phenylquinoline chemical class. Since some members of this chemical series showed a significant binding affinity for the human mu-opioid receptor (hMOR), docking studies were also conducted on a 3-D model of the hMOR, resulting in the identification of a viable chemical strategy to avoid any significant micro-opioid component. Compounds 25 and 28 are therefore suitable pharmacological tools in the tachykinin area to elucidate further the pathophysiological role of NK-3 and NK-2 receptors and the therapeutic potential of selective NK-2 (28) or combined NK-3 and NK-2 (25) receptor antagonists.