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Write a note on model construction. (10 marks)……………………………………………………

Modeling is building representations of things in the ‘real word’ and allowing ideas to be investigated.

The main steps involved in model construction are:

1. Defining the Objective

2. Gathering the Data

3. Data Management

4. Preparing the Data for Modelling

5. Transforming and Optimizing Parameters

6. Model validation

7. Implementing and Maintaining the Model

Step 1: Defining the Objective
The first step in any modeling process is defining the objective. Simulation should only be used
if an objective can be clearly stated and it is determined that simulation is the most suitable tool
for achieving the objective.

Step 2: Gathering the Data

Data gathering/collection consists of the processes of collecting reliable, control and
administrative data from literature, In-silico predicted or experimentally determined, and they are
optimized (if needed), highlighting diversity in the approaches to build a drug specific absorption

Simulation can be conveniently used in either single Simulation or virtual Trial mode (enables
incorporation of inter-subject variability in the model).

Accurate, actionable, accessible data is the key criteria of any successful model. The data
collection models can be of the following types:

● Raw data based data collection model

● Message based

Step 3: Data Management

Data management is the work that involves planning development implementation and
administration of systems for the acquisition, storage and retrieval of data.

Some data collection and managed system have been developed to enable data collection and
management of several trials being conducted.

This presents security issues, which secure access to data associated with the trial while restricting
them from accessing data of other trials.




Step 4: Preparing the Data for Modelling

Data preparation is the process of cleaning and transforming raw data prior to processing and
analysis. It is an important step prior to processing and often involves reformatting data, making
corrections to data and the combining of data sets to enrich data.

Step 5: Transforming and Optimizing Parameters

Determining the best fit is essential to good model performance. Binning and transforming
independent variables to insure the best fit with the dependent variable.

A model parameter is a variable that is internal to the model and whose value can be estimated
from the given data. They are required by the model when making predictions. Their values define
the skill of the model.

The parameter optimization method is used to obtain the optimal solution with minimum
computation and time.

In this method, the input of each variable is varied with other parameters remaining constant and
the effect on the design objective is observed.

It aims to evaluate the effect of different values of input variables on a system and finding the
optimal value for input variables in terms of system outcomes.

Step 6: Validating the Model

Validation demonstrates that the model is a reasonable representation of the actual system: that it
reproduces system behaviour with enough fidelity to satisfy analysis objectives. There are three
aspects which should be considered during model validation:

● assumptions

● input parameter values and distributions

● output values and conclusions

Validation is an interactive process that takes place throughout the development of a model. The
validation of simulation model starts after functional specifications have been documented and
initial model development has been completed.

A model should be verified and validated due to the following reasons:

–Models are increasingly used to solve problems and aid in decision making.

–They are approximate imitation of real-world system and they never exactly imitate the real-
world system.

A true test of model performance is how well it performs on data from a different time period.

Step 7: Implementing and Maintaining the Model:



Effective implementation is a combination of business intelligence and well designed

Example: –

A GI simulation of nimesulide oral absorption

A model is constructed, in this Experimentally determined intrinsic solubility and human jejunal
permeability was used as the input value.

The input solubility and permeability values were optimized to match the in vivo data by keeping Drug
particle radius and all other parameters fixed.

The simulation results of nimesulide namely plasma concentration-time profiles, absorption and
dissolution profiles, and the predicted and in vivo observed PK parameters for the model was obtained.

According to obtained data the percentage prediction errors for CMax and area under the curve (AUC)
values are less than 10%, and largest deviation was observed for tmax.However even the tmax is within the

The interpretation of the obtained data indicates that the constructed model is more realistic, significant
and it reflect nimesulide in vivo absorption.