Innovations In Clinical Neuroscience

ISCTM Supplement 2015

A peer-reviewed, evidence-based journal for clinicians in the field of neuroscience

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Innovations in CLINICAL NEUROSCIENCE [ V O L U M E 1 2 , N U M B E R 3 – 4 , S U P P L E M E N T A , M A R C H – A P R I L 2 0 1 5 ] 28S tests, and demographic variables (e.g., age, gender, race) (Figure 1). Among the different roles served by biomarkers are included predictive, prognostic and surrogate properties. Predictive biomarkers are useful for identifying patient subgroups that respond differentially, either for benefit or for risk. Prognostic biomarkers are useful in predicting outcome for subgroups, independent of disease or treatment (e.g., cardiovascular risk profile). Surrogate biomarkers (endpoints) can potentially serve as substitutes for clinical endpoints (e.g., blood pressure or cholesterol). Genomic biomarkers have long been known as predictors of pharmacokinetic differences among individuals that lead to differences in plasma drug exposure. Genetically polymorphic cytochrome P450 (CYP) enzymes are associated with differences in plasma levels, resulting in differences in efficacy and safety. Several such biomarkers are reflected in labeling for certain compounds, including some psychiatric drugs (e.g., CYP2B6, CYP2C9, CYP2C19 and CYP2D6). Other biomarkers serve as predictors of pharmacodynamic response. One example on the efficacy side is the nonpsychiatric biologic Herceptin (trastuzumab). The Her-2 gene expresses a cell surface receptor TABLE 1. Biomarker types (not mutually exclusive) 1. Surrogate markers/disease markers: are closely linked to the disease process and correlate with the patient's well-being. According to the biomarkers definition working group a definition was proposed as follows: " A surrogate end point (or surrogate marker) is a biomarker used as a substitute for a clinical end point; a surrogate end point is expected to predict clinical benefit, harm, or lack of benefit or harm." 4 Feasibility: Due to the absence of a disease concept in psychiatric indications the likelihood to define a surrogate marker in psychiatric diseases is low. Current practical value: low, but with huge potential (RDoc-initiative). 2. Endophenotypes: Hereditary, do not show state like properties. (As a cautionary, several markers share state and trait-like properties [i.e., may exist as a risk factor, but become more pronounced during an acute episode]). Thus, these markers cannot be used as surrogate markers for any given treatment, but may serve as predictive markers in the context of personalized medicine (i.e. for patient stratification and selection). Feasibility: Many endophenotypes have been characterized: Evoked potential changes in schizophrenia (P50, mismatch negativity); REM- sleep changes in depression; Neuroendocrine abnormalities in depression. 3. Genetic markers: are hereditary. May serve as predictive markers for treatment response and therefore for patient selection. Effect size for a single polymorphism is generally low in polygenetic diseases. Examples for more prominent genes are polymorphisms of the genes for FKBP5 5,6 and angiotensin converting enzyme in depression; 7 Monoamine-oxidase and folate related genes in schizophrenia. Feasibility: technical feasibility is high. Clear regulatory path. 4. Diagnostic/prognostic markers for pre-treatment characterization of patients, related to the natural course of a disease process. Prognostic markers can be derived from genetic markers or endophenotypes, i.e. can be trait markers. However, also state markers can be used for the differentiation of patient subtypes as long as there is stability for recurring episodes. Examples are the stability of atypical features in recurring episodes of depression. The key aspect of these parameters is that they can be used for patient stratification into a biomarker positive and a biomarker negative group (i.e., act as predictive markers [see below]). In that case differential efficacy of a given compound between these subgroups provides the biological and regulatory validation of this differentiation. Feasibility: depends on the marker. It becomes higher with technical progress (i.e., more defined companion diagnostics). 5. Predictive markers for pre-treatment characterization of patients in the context of a specific treatment. The connection to the specific treatment differentiates this type of marker from the prognostic marker, which is more generally linked to the disease process. Importantly, prognostic markers can be predictive markers for a specific treatment as well (see above). Predictive markers are used for patient stratification into a biomarker positive and a biomarker negative group. Again, differential efficacy of a given compound between these subgroups provides the biological and regulatory validation of this differentiation. To exemplify the difference between a prognostic marker and a predictive marker the following may be considered: the occurrence of sleep disturbances (too much or too less sleep) is related to the risk to develop a depression (i.e., is prognostic for depression risk, independent of treatment). Treatment with a monoaminergic antidepressant in an efficacious dose leads to REM-sleep suppression, i.e. REM-sleep suppression with an antidepressant is predictive for response with such an antidepressant. 8 Feasibility: again, depends on the marker. It becomes higher with technical progress, i.e. defined companion diagnostics. 6. Markers of target engagement: are related to the mode of action of the compound and less to the disease biology. Maybe used for an individualized dose finding in a given patient. These markers could be utilized as early response markers: the absence of an early response of the marker would make a true (pharmacology driven) response unlikely. In addition they can serve to identify unresponsive patients and assist the decision of an early treatment discontinuation. Examples are changes of sleep-EEG parameters (REM sleep suppression of most antidepressant) or pupillometry changes for noradrenergic compounds. In other words, sufficient target engagement can be regarded as predictive for response as long as the given compound has the desired therapeutic property. Feasibility: From a scientific perspective the easiest to achieve, as the marker depends mainly on the property of the compound. Examples in other therapeutic areas include EEG measures for the treatment of epilepsy or blood glucose for the treatment of diabetes.

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