**1. Introduction**

154 Biomarker

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The World Health Organization, WHO, reported in 2010 that there were approximately 490 million (7%) of people suffering from mental disorders, such as abuse of alcohol and other substances, major depression, bipolar disorder, schizophrenia and dementia, recommending research the pathophysiology of these disorders in order to improve their understanding and to develop more efficient and cost-effective interventions (WHO 2001, 2005).

The diagnosis of mental disorders, should be based on objectively measurable parameters and, also, should help us to establish therapeutic guidelines, based on the etiology of the diseases, and their adaptation to each individual. The absence of an objective biological test for the diagnosis of mental disorders which would constitute the Gold Standard for this task, along with the frequent psychiatric comorbidity, heterogeneity and, polygenic and multifactorial etiology, requires the search for Biomarkers that facilitated the diagnosis and treatment of the psychiatric illnesses. The Convergent Functional Genomics has revealed that proteins, compounds of biochemical nature related to different physiological functions, genetic tests for diagnosis or, even, different functional tests such as, for example, the dexamethasone suppression test, DEX, used to study the function of the hypothalamicpituitary-adrenal axis, HPA, can be candidates (Avissar & Schreiber 2003; Kemperman 2007; Russell 2004).

The use of Biomarkers, that is, a physical characteristic, or a biochemical or biological parameter, objectively measurable and quantifiable, that report the evolution and condition of certain normal physiological processes or pathological, or response to a therapeutic intervention, pharmacological or otherwise, is relatively new but this field has gained much interest in recent decades among clinicians and researchers. A Biomarker, as a measurable biological entity that points the presence or absence of a disease, a toxin, a biological condition, a genetic pattern, or a therapeutic response to a drug, must be related quantitatively with the disease progression and/or therapy (http://www.gabanetwork.org; Biomarkers Definitions Working Group, BDWG, 2001).

Biomarkers, are utilized in psychiatry not only as tools that aid us in the diagnosis and treatment of various diseases, facilitating the use of therapies aimed at specific groups or individuals, but also, as Surrogate Markers or Clinical Surrogate Endpoints for PK, PD and

Biomarkers and Therapeutic Drug Monitoring in Psychiatry 157

1. Pharmacokinetic models, PK. They are used to describe control processes of drug concentration in biological fluids at any time after its administration, being absorption, distribution, metabolism and excretion, the main stages that determine the evolution of the drug concentration versus time, so the PK-models, determine the overall course of

2. Pharmacodynamic models, PD. They are used to describe the relationship between the drug concentration and/or active metabolites and the magnitude of the pharmacological effect obtained such as, eg, blood pressure, heart rate, etc (Geldof 2007;

3. Pharmacogenetic models, PG. They are used to describe the influence of interindividual genetic variations in the response to a drug in terms of efficacy and safety. They use genotype-phenotype, gene-concentration and gene-dose correlations for predicting the phenotype of a individual and provide support to achieve a optimal pharmacotherapy. As important part of the construction of these models, a Surrogate Marker or Biomarker is defined as measure that characterizes, in a strictly quantitative manner, the various processes and stages that occur between the administration of a drug and its pharmacological effect, and can be used in clinical practice for the individualization of drug therapy, from the viewpoint of dosage regimen calculation and therapeutic strategy. In fact, there is a growing interest in the use of biomarkers in drug development, as is reflected in the publication of numerous reviews and comments, appeared recently, on this topic. As well as, the recent increase in the number of publications about PK-PD models in journals of Clinical Pharmacology and Clinical Pharmacy, concerning to theoretical models and their implementation. Despite the continued increase in the number of articles, there are still, a large number of publications that contain pharmacokinetic and pharmacodynamic data without PK-PD modeling studies (Francheteau 1993; Geldof 2007; Mandem & Wada 1995;

Optimization of pharmacologic treatment is usually done by monitoring serum drug concentrations, in PK models, or by the direct or indirect pharmacological response, represented by Biomarkers or Surrogate Markers, in PD models, combined with

PK models can be non-parametric or compartimental. Non-parametric models provide an empirical description of the temporal evolution of drug concentration in terms of the maximum concentration, Cmax, and time needed to reach it, Tmax, and area under the concentration versus time curve, AUC. While compartmental models provide a description of concentration profile versus time of the drug in a body fluid compartment (Csajka &

Binary models PK-PD, PK-PG and PD-PG, as a result of the union of simple models, are aimed to find out the suitable therapeutic dose, body "clearance" and other kinetic parameters related to plasma drug concentration or pharmacological effect, establish an adequate dosage scheme to achieve the desired therapeutic goal, as well as know the time evolution of the pharmacological effect, using the appropiate model and the individual genotype of the different populations to which treatment is targeted, by using the following

pharmacogenetic and environmental characteristics, in PG models.

Vérotte 2006; Geldof 2007; Holford & Sheiner 1982; Perez-Urizar et al 2000).

Holford & Sheiner 1981; Rowland & Tozer 1995; Sheiner et al 1997) .

There are three unitary models for the therapeutic drug monitoring:

the process (Perez-Urizar 2000).

Zuideveld 2001).

mathematical tools:

PG modelling in drug response, and to investigate the biological mechanisms of action and the efficacy and safety profile of the target drug, being this last point developed along this chapter (Riggs 1990).

The search of a Biomarker begins with the selection of the groups of patients and controls, continous with the selection of the type of sample to use and, finally, the selection of the statistical analysis that allowed us to demonstrate differences between both groups, such as the multivariate analysis of cross-sectional data and multivariate correlation analysis of longitudinal data, so that, there was clear evidence that these Biomarkers are able to distinguish between controls and affected individuals. To complete the validation, we have to check this on a large group of patients following Food and Drugs Administration guidelines (FDA 2008). In the case of Biomarkers used to monitor pharmacological therapies, the choice and validation of these should be supported, in addition, on studies of the etiology of the disease being treated and mechanism of drug action, as well as provide data on its cost-effectiveness and its side effects (Kemperman 2007).

In the other hand, a Surrogate Marker, defined as a laboratory measurement, physical sign or symptom, is used in therapy as a direct measure of how patient feels, functions, or survives and is expected was able to predict the effect of a treatment, ie, it is a test that is used as measure of the effect of a given specific treatment. It is a candidate Biomarker if it can be validated, taking this atribute when the evidence has proven that the predicted effect induced by drugs or other therapy, on the Surrogate Marker, produces the outcomes desired on the clinical characteristic of interest, such as blood pressure, serum cholesterol, intraocular pressure, etc, while Clinical Surrogate Endpoint refers to the final desirable value that we want to achieve for a specific Surrogate Marker, which is related to the level of disease progression, intensity of a symptom or sign, or a laboratory test, that constitute the desired target and that reflect the expected clinical outcome (BDWG 2001).

To validate a Surrogate Marker as a Biomarker, we need to understand the biological relationship between the Surrogate Marker that predicts the desired clinical benefit and the clinical outcome achieved. For pharmacological treatments, we have to know, moreover, all therapeutic actions, if we want to conclude that the effect obtained on the Surrogate Marker will result in the beneficial clinical outcomes desired. (Buckley & Schatzberg 2009; Russell 2004).

In short, at the present time the biological causality of most psychiatric illnesses and the intimate mechanism of action of most psychotropic drugs are unknown. However, it is possible to modulate pharmacologically a large number of neuroreceptors, using this tool as target of drug therapies and utilizing to control of psychiatric disorders symptoms. We will review in this chapter, the role and potential use of Biomarkers and / or Surrogate Markers to optimize the pharmacological treatments of most common psychiatric illnesses, with emphasis on schizophrenia and depression, in order to use them in the dosage regimen calcualtion and choosing a particular therapeutic drug strategy (Noll 2006; Shaheen 2010).
