**4.8.1 Classification**

It is important to know that if the desired compound is CNS active or not. To do this a border value should be defined for the scaled dependent variable. Different data sets have been used for these models:


These models are applicable for screening studies (primary steps of CNS drug development) where the goal is to select the possible CNS active compounds from large compound libraries and in advanced steps of CNS drug studies where the possible reasons of efficacy failure are investigated. Different classification methods have been developed until now using different algorithms and descriptors. The review of these studies showed that the methods were more successful for CNS+ and BBB+ compounds than CNS- and BBB- ones. One reason for this approach is raised from efflux pumps which efflux some structurally suitable compounds from brain. Considering the efflux system substrates during method development will improve the prediction accuracy for these compounds. It should be noted that there is a difference between BBB+ and CNS+, since a drug could be penetrated into brain without measurable biological effect. However in some modelling studies these data were mixed up. It seems that in order to develop more accurate classifiers, some physiological properties of brain such as the extent of non-specific protein and tissue binding, the concentration of the target protein and specific receptors in the brain should be considered.

#### **4.8.2 Permeability prediction (The rate and extent of penetration)**

The log*BB*, *Kp,uu* (for exposure extent studies) and log*PS* (for rate studies) have been frequently used to develop prediction models. The multiple linear regression and least square methods are among the most studied models providing simple and interpretable equations.

Detailed review of these equations could be found in the literature (Garg et al., 2008; Klon, 2009; Mehdipour & Hamidi, 2009; Shayanfar et al., 2011). The descriptors used for rules of five (Table 3) studies originally comprised from these equations and at least one of these descriptors or similar descriptors which provide relevant information can be found in these equations. In this regard, most of the time, medicinal chemists use the same descriptors to check the new data set or new methods. Lipophilicity descriptors, size and shape descriptors, ionization states of compounds, and polar surface area descriptors proved to

and their significances, the inter correlation between them should be checked and just one of the highly correlated descriptors should be kept in multiple linear regression methods, while this is not a problem for partial least square or principal component analyses. There are different methods for descriptor selection and more information can be found in the literature. It is better to keep the penetration mechanisms and approved relationships in

As soon as the descriptors selected or provided in mechanistic approach, the model should be developed according to the purpose of the modelling. The *in silico* methods developed for

It is important to know that if the desired compound is CNS active or not. To do this a border value should be defined for the scaled dependent variable. Different data sets have

These models are applicable for screening studies (primary steps of CNS drug development) where the goal is to select the possible CNS active compounds from large compound libraries and in advanced steps of CNS drug studies where the possible reasons of efficacy failure are investigated. Different classification methods have been developed until now using different algorithms and descriptors. The review of these studies showed that the methods were more successful for CNS+ and BBB+ compounds than CNS- and BBB- ones. One reason for this approach is raised from efflux pumps which efflux some structurally suitable compounds from brain. Considering the efflux system substrates during method development will improve the prediction accuracy for these compounds. It should be noted that there is a difference between BBB+ and CNS+, since a drug could be penetrated into brain without measurable biological effect. However in some modelling studies these data were mixed up. It seems that in order to develop more accurate classifiers, some physiological properties of brain such as the extent of non-specific protein and tissue binding, the concentration of the target

The log*BB*, *Kp,uu* (for exposure extent studies) and log*PS* (for rate studies) have been frequently used to develop prediction models. The multiple linear regression and least square methods are among the most studied models providing simple and interpretable

Detailed review of these equations could be found in the literature (Garg et al., 2008; Klon, 2009; Mehdipour & Hamidi, 2009; Shayanfar et al., 2011). The descriptors used for rules of five (Table 3) studies originally comprised from these equations and at least one of these descriptors or similar descriptors which provide relevant information can be found in these equations. In this regard, most of the time, medicinal chemists use the same descriptors to check the new data set or new methods. Lipophilicity descriptors, size and shape descriptors, ionization states of compounds, and polar surface area descriptors proved to

mind in this step and avoids complete statistical methods.

**4.8 Method development** 

been used for these models: - logBB data (BBB+/-),

**4.8.1 Classification** 

equations.

following purposes in CNS drug studies:


protein and specific receptors in the brain should be considered.

**4.8.2 Permeability prediction (The rate and extent of penetration)** 

have effect on BBB permeation. The complexity of BBB permeation encouraged scientists to check non linear methods applicability in this field and some exponential linear equations and neural networks have been successfully developed. Although neural networks provided more accurate predictions in comparison with linear ones, their interpretation and reproducibility are in question and their usefulness for developing universal models which can be applicable for chemists have not been approved yet. In fact the best model for a chemist is a model which is able to answer him/her what is the possible modification for desired property improvement and the un-interpretable models are not able to answer this question. Because of this, using less accurate but well defined models are preferred to complicate but accurate ones.

The studies of unbound fraction of the drug in brain (*Kp,uu*) showed that the previously accepted trend of permeation (higher permeation for more lipophilic compounds) which was raised from log*BB* and log*PS* studies are not the same for unbound fraction, and lipophilicity have inverse relation with it. These findings showed that the absolute values for the effective descriptors are not suitable and a balanced range of descriptors should be defined for them (Lavenskij et al., 2010).

#### **4.9 Validation**

In order to check the sensitivity, specificity, prediction capability, reproducibility, error margins and chance correlations for the developed models, some validation statistics should be provided and using these parameters researchers will be able to make decision on selecting or rejecting a model in comparison with others. The details of these parameters and their usefulness for evaluating the model have been reviewed. For classification methods the lower failure in localization of compounds (both positive and negative) is better and for predictive models the higher correlation coefficients (both for training and test sets and cross validation sets), lower prediction errors (less than about 1 log unit deviation and relative mean squared errors less than 0.3)and lower correlation coefficients (e.g. <0.2) for Y randomized data sets are acceptable. These parameters are not absolute and it would be possible to accept a low quality model in the absence of the better one.

#### **4.10 Prediction using commercial software**

Using the developed models, some software has been developed in order to calculate the BBB permeation or P-gp binding affinity which can be used for estimation of compound permeation. These predictions are included in the most of the ADME prediction software which could be found on internet.

#### **5. Conclusion**

The importance of BBB for reaching CNS drugs to their targets and also undesired penetration of non CNS drugs to avoid their CNS side effects are briefly discussed. Short review of measurement methods of drug's penetration to CNS is presented along with a summary of computational aspects used for modelling purposes.

The molecular and cellular properties of BBB have been reviewed and the role of its compartments in the regulating of drugs and xenobiotics penetration to the brain has been discussed. Working as a regulatory interface BBB is able to work as a physical and physiological barrier which prevents peripheral drugs to penetrate the brain and reduce

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#### **6. Acknowledgment**

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**2** 

*USA* 

**Diagnostic Accuracy and Interpretation of** 

Amadeo Pesce1, Cameron West1, Kathy Egan-City1 and William Clarke2

Pain is a complex disease. The complexities and co-morbidities of this disease include depression, anxiety, addiction, and other psychological diagnoses that lead to difficulties in management and aberrant behavior such as not taking medications as prescribed, taking additional medications, or illicit drugs. In the effort to provide the highest standard of care for their patients, pain physicians are required to continually assess patients for addiction and, if necessary, refer them to addictionologists for additional treatment (Chou et al., 2009).

In this chapter we will refer to pain patients as those persons being treated with chronic opioid therapy for non-cancer-related pain. It is this patient population that has been associated with opiate abuse and diversion, and therefore monitoring these patients for drug use in a manner analogous to therapeutic drug monitoring is necessary. One of the most frequent complaints by patients seeing pain physicians is back pain, which is often associated with failed back surgery (Manchikanti et al., 2004; Michna et al., 2007). Currently opiate medications are one of the treatments of choice used by physicians to provide pain relief. These medications can induce euphoria as well as pain relief; because of this, opiates are frequently abused by this population, as well as the general population (National Survey on Drug Use and Health: Detailed Tables - Prevalence Estimates, Standard Errors, P Values, and Sample Sizes, 1995-2006; Webster & Dove, 2007). Additionally, these medications are associated with physical as well as psychological dependence and can pose addiction risks

One of the treatments of choice for chronic pain involves strong medications such as opioids, as well as additional or adjuvant medications (Chou et al., 2009; Trescot et al., 2006). Side effects of opioids include sedation, dizziness, nausea, vomiting, and constipation. Living day to day with any or all of these symptoms is challenging at the least and is compounded by the underlying pain these patients suffer from. Naturally, patients often

**1. Introduction** 

**1.1 Chronic opioid therapy** 

(Webster & Dove, 2007).

**1.2 Pain treatment** 

 **Urine Drug Testing for Pain Patients:** 

 **An Evidence-Based Approach** 

*1Millennium Research Institute, 2Johns Hopkins School of Medicine,* 

