**6. Computational models for radiopharmaceuticals**

Computer models in the pharmaceutical industry is used to discover new drugs, to optimize of chemical processes and to design clinical trials. Accurate computational estimation of the responses to the treatments and clinical profiles of administration is of great importance for patients [14–19]. Also, it is very useful to help clinicians decide on the most effective and least toxic treatment available options, and is significant for researchers to selecte for *in vitro* and *in vivo* studies [20–22]. In addition, the computational prediction of drug responses can substantially contribute to preclinical studies, as in silico drug screening model. These tools can help researchers in the selection of candidate compounds in their research, and can be used to improve efficiency in experimental planning and to reduce costs [23–25]. Especially, computational estimation of drug responses in cancer disease involves significant research challenges. It is a biological challenge since cancer is a very heterogeneous and multifactorial disease. Furthermore, increasing the need for data integration, new technical questions arise from multiple sources such as the adjustment and normalization of data from multiple sources [14]. In the Oak Ridge National Laboratory, Snyder [26] the first mathematical model of radiopharmaceuticals was realized. In this design, the fractions of energies and amount of gamma and X-rays emitted from radiopharmaceuticals in the targeting tissue were used by Monte Carlo method [26]. Computational model supports the development of radiolabeled complex synthesis and coordination chemistry of radiopharmaceuticals by providing a better understanding of the physicochemical properties of molecular imaging agents. Combining experimental studies with computational study helps to define structure-activity relationships of radiopharmaceuticals and facilitates the rational design of new generation radiopharmaceuticals with improved properties. Francisco et al. [27] used different computational simulators to investigate the therapeutic potential of various radionuclides. These simulators are:


Fast Monte Carlo damage formation simulator can be used to estimate the types of DNA damage and post-irradiation efficiency. This method allows multiple data analyzes with multiple irradiation estimates to be collected as soon as possible [28, 29].

Fast Monte Carlo excision repair simulator can be used to estimate the occurrence, repair of DNA and correct repair results by mutation [27].

**85**

*Computational Study of Radiopharmaceuticals DOI: http://dx.doi.org/10.5772/intechopen.85140*

model, and two lesion kinetics [28].

pharmaceutical kit.

chemical synthesis.

**7. Conclusion**

receptor by using molecular modeling.

dosimetry in nuclear medicine administration.

specific radionuclide treatments [27, 28].

the planning of *in vitro* and *in vivo* studies [29–33].

Virtual cell radiobiology simulator is a radiobiological model used to describe dose response relationship, damage production process and key repair mechanisms. These models often relate the dose rate to the cell's response to irradiation. Some of the current models include a repair-false-repair model, a fatal potentially fatal

In one study, the Monte Carlo method was used to assess the amount of radionuclide in animal models for preclinical testing of radiopharmaceuticals. In another study, this model predicted 3D images simulated with SPECT or PET for patient-

Computational methods allow quick and easy data collection. However, some models are based on available information and are evaluated according to this information and this imposes some restrictions. The used algorithms report an optimal ionization scenario to ionize the DNA in place of ineffective radionuclide biodistribution process that is obtained by *in vitro* and *in vivo* studies under difficult conditions. Thus, although the validation of the used simulators has been performed in selected irradiation scenarios and specific cellular populations, specifying these methods may be useful as a first step approach to large data sets to assist in

Kurniawan et al. [34] studied on radiopharmaceutical ligands and concrete examples of molecular docking. T3, 4BCPP is an imidazolylporphyrin derivative and has been used as a radioimaging agent for melanoma cancers. They designed new imidazolylporphyrin derivatives with better selectivity and higher affinity than T3, 4BCPP by using AutoDock 4.2. After that they develop a new radiopharmaceutical by using a radionuclide such as Technetium (Tc) for diagnostic and Rhenium (Re) for therapeutic purposes. They concluded that radiolabelled imidazolylporphyrin derivatives could be two potential candidate ligands for a melanoma radio-

Chen et al. [35] assessed the effects of structural modification on the interaction

of 125I-labeled iodo Hoechst ligands and DNA. Also, they designed new analogs with specified distances between the Auger-electron-emitting 125I atom and the DNA central axis by using computer-assisted molecular modeling software. This software program has been obtained the reactivity of newly designed radiolabelled molecules with their targeted DNA molecules by molecular modeling prior to their

El-Motaleb et al. [36] developed an easy method for radio iodination of propranolol with high percent labeling yield for the purpose of lung perfusion imaging. They used molecular modeling and docking studies to ensure the binding of the newly obtained radio iodination of propranolol to beta-2 adrenergic receptor and confirmed that radio iodination did not affect the binding of propranolol to beta-2

Computational methodology provides some advantages such as creating timedependent organ dose rate curves, making easier for researchers and clinicians to take the right choices. Also, these approaches give accurate and effective results and reduce the cost of research being useful to simulate complex situations before research, to avoid harmful/side effects. Furthermore, the radiopharmaceutical dosimetry estimates demonstrate large variation due to the patient's anatomical characteristics and computational model can be useful for obtaining personalized data. We believe that using these methods could enhance the personalization of

#### *Computational Study of Radiopharmaceuticals DOI: http://dx.doi.org/10.5772/intechopen.85140*

*Molecular Docking and Molecular Dynamics*

these critical factors and quality characteristics [13].

**6. Computational models for radiopharmaceuticals**

• Fast Monte Carlo damage formation simulator.

Fast Monte Carlo damage formation simulator can be used to estimate the types of DNA damage and post-irradiation efficiency. This method allows multiple data analyzes with multiple irradiation estimates to be collected as soon as

Fast Monte Carlo excision repair simulator can be used to estimate the occur-

rence, repair of DNA and correct repair results by mutation [27].

• Fast Monte Carlo excision repair simulator.

• Virtual Cell Radiobiology Simulator [27].

Each additional experiments and sample analysis performed to product development in the pharmaceutical industry means that spending a lot of money, time and labor loss. The selection, implementation and interpretation of the appropriate factorial design that serves to reach the result accurately and rapidly is very important. Selecting the appropriate experimental design ensures that development studies are completed with a small number of trials. In order to optimize the process and formulation, mathematical models are described that best relationship between

Computer models in the pharmaceutical industry is used to discover new drugs, to optimize of chemical processes and to design clinical trials. Accurate computational estimation of the responses to the treatments and clinical profiles of administration is of great importance for patients [14–19]. Also, it is very useful to help clinicians decide on the most effective and least toxic treatment available options, and is significant for researchers to selecte for *in vitro* and *in vivo* studies [20–22]. In addition, the computational prediction of drug responses can substantially contribute to preclinical studies, as in silico drug screening model. These tools can help researchers in the selection of candidate compounds in their research, and can be used to improve efficiency in experimental planning and to reduce costs [23–25]. Especially, computational estimation of drug responses in cancer disease involves significant research challenges. It is a biological challenge since cancer is a very heterogeneous and multifactorial disease. Furthermore, increasing the need for data integration, new technical questions arise from multiple sources such as the adjustment and normalization of data from multiple sources [14]. In the Oak Ridge National Laboratory, Snyder [26] the first mathematical model of radiopharmaceuticals was realized. In this design, the fractions of energies and amount of gamma and X-rays emitted from radiopharmaceuticals in the targeting tissue were used by Monte Carlo method [26]. Computational model supports the development of radiolabeled complex synthesis and coordination chemistry of radiopharmaceuticals by providing a better understanding of the physicochemical properties of molecular imaging agents. Combining experimental studies with computational study helps to define structure-activity relationships of radiopharmaceuticals and facilitates the rational design of new generation radiopharmaceuticals with improved properties. Francisco et al. [27] used different computational simulators to investigate the therapeutic potential of various radionuclides. These

**84**

simulators are:

possible [28, 29].

Virtual cell radiobiology simulator is a radiobiological model used to describe dose response relationship, damage production process and key repair mechanisms. These models often relate the dose rate to the cell's response to irradiation. Some of the current models include a repair-false-repair model, a fatal potentially fatal model, and two lesion kinetics [28].

In one study, the Monte Carlo method was used to assess the amount of radionuclide in animal models for preclinical testing of radiopharmaceuticals. In another study, this model predicted 3D images simulated with SPECT or PET for patientspecific radionuclide treatments [27, 28].

Computational methods allow quick and easy data collection. However, some models are based on available information and are evaluated according to this information and this imposes some restrictions. The used algorithms report an optimal ionization scenario to ionize the DNA in place of ineffective radionuclide biodistribution process that is obtained by *in vitro* and *in vivo* studies under difficult conditions. Thus, although the validation of the used simulators has been performed in selected irradiation scenarios and specific cellular populations, specifying these methods may be useful as a first step approach to large data sets to assist in the planning of *in vitro* and *in vivo* studies [29–33].

Kurniawan et al. [34] studied on radiopharmaceutical ligands and concrete examples of molecular docking. T3, 4BCPP is an imidazolylporphyrin derivative and has been used as a radioimaging agent for melanoma cancers. They designed new imidazolylporphyrin derivatives with better selectivity and higher affinity than T3, 4BCPP by using AutoDock 4.2. After that they develop a new radiopharmaceutical by using a radionuclide such as Technetium (Tc) for diagnostic and Rhenium (Re) for therapeutic purposes. They concluded that radiolabelled imidazolylporphyrin derivatives could be two potential candidate ligands for a melanoma radiopharmaceutical kit.

Chen et al. [35] assessed the effects of structural modification on the interaction of 125I-labeled iodo Hoechst ligands and DNA. Also, they designed new analogs with specified distances between the Auger-electron-emitting 125I atom and the DNA central axis by using computer-assisted molecular modeling software. This software program has been obtained the reactivity of newly designed radiolabelled molecules with their targeted DNA molecules by molecular modeling prior to their chemical synthesis.

El-Motaleb et al. [36] developed an easy method for radio iodination of propranolol with high percent labeling yield for the purpose of lung perfusion imaging. They used molecular modeling and docking studies to ensure the binding of the newly obtained radio iodination of propranolol to beta-2 adrenergic receptor and confirmed that radio iodination did not affect the binding of propranolol to beta-2 receptor by using molecular modeling.
