**4. In silico models**

It is generally recognized that drug discovery and development are time and resources consuming. There is an ever-growing effort to apply computational power to the combined, chemical and biological space? in order to streamline drug design, development and optimization. Computer-aided or in silico design is being utilized to expedite and facilitate identification, optimize the absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles and avoid safety issues. In silico modeling significantly minimizes the time and resource requirements of synthesis and biological testing. The aim is to enrich the group of molecules with the desired properties (active, drug-like, lead-like) and to eliminate those exhibiting unwanted properties (inactive, reactive, toxic, weak ADMET/pharmacokinetic profile).

The result is a compounds library and, by virtual screening using in silico methods, the number of molecules to be tested forward by experimental means, is considerably reduced. Structure-based library design is prejudiced by structural requirements for specific activity on a particular target and needs prior information of the target structure (e.g., X-ray or nuclear magnetic resonance). The goal is to select existing compound from libraries or to design compounds with threedimensional complementarity (i.e., shape, size and physicochemical properties) to the target-binding site. New approaches can directly guide the design of virtual combinatorial libraries, which are first screened in silico for targeting complementarity, thus reducing the number of compounds will have to be synthesized and tested *in vitro*.

The "leading" compound has the desired pharmacolological or biological activity and represents the starting point to design other molecules with improuved properties/chemical parameters in terms of efficacy and pharmcokinetic profile, better candidates for future chemical synthesis and trials. When leading molecules have been identified, they have to be optimized in terms of potency, selectivity, pharmacokinetics and toxicology before they can become candidates for drug development. The early analysis in this respect is becoming common practice because the high overall attrition rate in drug discovery is affected the identification of compounds. Traditionally, therapeutics have been small molecules that fall within the Lipinski's rule of five [9]. From this point of view, hydrogen bonds, log P value, penetration into the targeting side can be mentioned. If the hydrogen bond donors are <5, the hydrogen receptors are <10, the relative molecular weight is <500 g/mol and the lipophilicity (log P) is <5, the compound will probably be orally bioavailable. The concepts of virtual library and virtual browsing have become an integral part of pioneering discoveries. In silico approaches significantly contribute to early pharmaceutical research and are especially important in target and lead discovery. The need has been clearly recognized and is expected to improve efficiency of drug design for timely adaptation and application of in silico models in pharmaceutical research [10]. European policy for the evaluation of chemicals (REACH: Registration, Evaluation, and Authorization of Chemicals) has been a strong advocate of alternative in silico methods of predictive evaluation of chemical toxicity in order to minimize animal testing and conserve time and resources [11].

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developed.

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

**5. Factorial Design for Drug Delivery**

with a smaller number of attempts can be used.

dent variables and the independent variables that affect them.

and Plackett-Burman design [12].

Design of Experiments (DoE) is defined as a planning strategy that will be carried out to obtain the information from the collected data effectively. It is a structural method used to determine the relationship between different factors (independent variable, input, process parameter, formulation component, etc.,) and their responses (dependent variable, response variable, output, product quality feature, etc.,). Also, this model is a mathematical model that correlates all the relevant factors and the results obtained against these factors. The results can be interpreted, predicted and the design space can be determined by optimization with this mathematical model. Systematic DoE approaches have advantages such as less experimental study, easier problem identification and prevention, any active agent adjuvant interaction and product performance to guarantee an effective formula-

tion, and process optimization for good results in the scale up process.

An ideal drug form design should be depended on the understanding of the physicochemical and mechanical transformations of the materials that will eventually turn into the desired product. However, due to the diversity and complexity of the drug components, it is usually not fully understood. Factorial design with a systematic approach the product and production process can be understood in depth. In this way, a development approach can be provided which takes into account the variability of the inputs and other risks that may arise against product quality. It is very important to obtain the basic knowledge of the study in order to produce as much information as possible with the right modeling. The statistical analysis is the first stage of experimental work before optimizing the formulation. Simple models are used in statistical screening. For example, linear models with only the main factor effects, or linear models, including binary interactions. In this way, the factors that have the most effect on the outputs are determined with the least number of tests possible. Factors with little or no significant effect can be displayed. In addition, by decreasing the number of factors, optimization design

The choice of DoE should be based on the number and type of factors to be investigated. For example, if the goal is only to reduce the number of factors and find a few factors that have the highest effect on the outputs, and if there are too many factors to be investigated, then the statistical elimination by constructing linear models can be selected. The results consist in lowering the number of attempts and evaluate only the main factor effects. The most preferred statistical screening methods in drug formulations are two-level partial full factorial design

After statistical screening, response surface modeling (RSM) designs are started. The number of factors should be reduced by the statistical elimination design before the RSM design, so that the number of trials is not high and the statistical significance is important/relevant or other synonym and strong prediction models can be established. RSM is an approach where statistical and mathematical techniques are used together for the development and optimization of pharmaceutical processes. Includes modeling techniques used to determine the relationship between depen-

RSM designs for drug formulations allow us to understand the relationship between factors and response variables, as well as factor interactions (synergistic effect of two or more factors), quadratic effects, and cubic terms. In this way, the optimum value ranges of the factors are provided. Process problems can be solved. Robust processes that are less sensitive to process variability can be

*Molecular Docking and Molecular Dynamics*

ADMET/pharmacokinetic profile).

**4. In silico models**

tested *in vitro*.

In summary, computational models can be used to simulate complex situations prior to testing in reality, allowing us to make these inevitable mistakes and helping us to

It is generally recognized that drug discovery and development are time and resources consuming. There is an ever-growing effort to apply computational power to the combined, chemical and biological space? in order to streamline drug design, development and optimization. Computer-aided or in silico design is being utilized to expedite and facilitate identification, optimize the absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles and avoid safety issues. In silico modeling significantly minimizes the time and resource requirements of synthesis and biological testing. The aim is to enrich the group of molecules with the desired properties (active, drug-like, lead-like) and to eliminate those exhibiting unwanted properties (inactive, reactive, toxic, weak

The result is a compounds library and, by virtual screening using in silico methods, the number of molecules to be tested forward by experimental means, is considerably reduced. Structure-based library design is prejudiced by structural requirements for specific activity on a particular target and needs prior information of the target structure (e.g., X-ray or nuclear magnetic resonance). The goal is to select existing compound from libraries or to design compounds with threedimensional complementarity (i.e., shape, size and physicochemical properties) to the target-binding site. New approaches can directly guide the design of virtual combinatorial libraries, which are first screened in silico for targeting complementarity, thus reducing the number of compounds will have to be synthesized and

The "leading" compound has the desired pharmacolological or biological activity and represents the starting point to design other molecules with improuved properties/chemical parameters in terms of efficacy and pharmcokinetic profile, better candidates for future chemical synthesis and trials. When leading molecules have been identified, they have to be optimized in terms of potency, selectivity, pharmacokinetics and toxicology before they can become candidates for drug development. The early analysis in this respect is becoming common practice because the high overall attrition rate in drug discovery is affected the identification of compounds. Traditionally, therapeutics have been small molecules that fall within the Lipinski's rule of five [9]. From this point of view, hydrogen bonds, log P value, penetration into the targeting side can be mentioned. If the hydrogen bond donors are <5, the hydrogen receptors are <10, the relative molecular weight is <500 g/mol and the lipophilicity (log P) is <5, the compound will probably be orally bioavailable. The concepts of virtual library and virtual browsing have become an integral part of pioneering discoveries. In silico approaches significantly contribute to early pharmaceutical research and are especially important in target and lead discovery. The need has been clearly recognized and is expected to improve efficiency of drug design for timely adaptation and application of in silico models in pharmaceutical research [10]. European policy for the evaluation of chemicals (REACH: Registration, Evaluation, and Authorization of Chemicals) has been a strong advocate of alternative in silico methods of predictive evaluation of chemical toxicity in order to minimize animal testing and conserve time and

successfully avoid their deleterious impacts of new proposed drugs.

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resources [11].
