Population Modeling Model

Developing models based on the population data are called population modeling (pop modeling), where sparse sampling would be sufficient to obtain required data for modeling. Using population PK models, population kinetics parameters (volume of distribution (V), clearance (CL) etc.) and variability, as well as individual PK parameters can be obtained. Inter individual variability can be tested by adding them to the model as ‘covariates’. To assess drug effect such as adverse effect, biomarkers, change tumor burden or any similar biological outcome the PK model will be modified including a PD measurements. To test the drivers of specific PD response various PD descriptors are used – maximum concentration (Cmax) as fixed time point descriptor,

More than 90% of new molecules fail at phase I, and more than 50% molecules fail at expensive phase III. In oncology trials, modeling and simulation have multiple applications. New molecules effect on tumor would be first studied in in vitro assays and xenograft models. This data can be used as the input for model building and their analysis would provide information on potentially of several drug candidates and serve as a basis for the selection of doses and dosing schedule for early clinical studies. Data from clinical phases of study like tumor imaging data, survival outcomes would help to optimize the study by exposure-safety relationship, clinical response, dose optimization etc. (Bruno et.al, 2011; Venkatakrishnan et.al, 2015). To analyze an anticancer therapy by population modeling approach, tumor size modeling would be an appropriate tool and it has many advantages over ‘gold standartd’ RECIST evaluation. In modeling the data used as a continuous scale which helps to preserve relevant information and by population models efficacy of other treatment regimens than the one studied can be investigated. (Bruno et.al, 2011; Bender et.al,

METHODS