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Clinical Application of Pharmacokinetics in Psychiatry

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Errors on medication administration records also can occur. For example, it might be indicated that a drug was given at a time other than the actual time it was received by the patient. Furthermore, incomplete drug delivery due to patient problems (e.g., infiltration of an IV fluid or clogging of a nasal cannula) can influence drug concentration measurements. Problems in sample collection can lead to unexpected drug concentration measurements. A blood sample may be drawn at the wrong time, or the time the sample was collected may be reported incorrectly.

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The reasons for studying higher doses, if deemed safe to do so, can be multi-faceted. Confirming safety at higher doses helps determine a margin of safety around the efficacious doses, and aids in determining the clinical relevance of any drug–drug- and drug-disease-interactions, or special population differences that may be elucidated later. Pharmacokinetics over a wider range leads to the ability to correlate drug effects (therapeutic or adverse) with drug exposure, and to characterize these relationships. The aforementioned safety margin also allows the drug sponsor some flexibility in determination of the final marketed dose if necessary. Phase 1 also includes specific studies designed to study special populations, such as the elderly, children, in people with hepatic or renal impairment. In these studies, pharmacokinetic endpoints are the primary goal, allowing relatively small studies (low numbers of subjects) to inform the future Phase 2 and 3 studies and marketing after approval. The results of these studies are reflected in the approved drug’s labeling, where warnings about the use in certain disease-states or dosage adjustments are communicated. In Phase 2 studies of clinical drug development, the objective is not only to determine that a drug continues to be safe but that it remains to be safe when used in patients with the disease it is intended for to treat

The information gathered in Phase 2 serves the dual purpose of studying safety and efficacy while providing proof to the sponsor that the drug is worthy of further development. The pivotal Phase 2 study for continuation of Phase 2 and/or starting Phase 3 is often called ‘Proof of Concept (POC).’ The value of PK measurements in Phase 2 adds another layer of understanding how the body processes the drug; these studies determine differences in PK data between categories of patients, namely those with the targeted disease and normal healthy volunteers.

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A limitation of this method is the a priori need for a rich and robust data set that may be used for building a POPPK model capable of representing an upper bound of the true magnitude of variability associated with the model parameters (Gilbert PL, Harris MJ, McAdams LA, Jeste DV (1995)). Such a data set may not exist during early clinical development, which may partially explain previous efforts to minimize the effect of nonadherence rather than to quantify it

However, it is possible that an ADHMET approach may be used after Phase 2a or in late clinical development (examples above) provided that adequate PK sampling had been conducted in earlier phases (Robinson D, Woerner MG, Alvir JM, Bilder R, Goldman R, Geisler S, Koreen A, (1999)). It should be noted that this metric is cumulative and is not representative of adherence with the exact time of dosing; however, while the exact time of dosing becomes more critical as the dosing interval exceeds the terminal half-life of the compound, oral therapies are commonly developed for once-daily dosing accompanied by appreciable accumulation to steady-state, where minor deviations in the dosing time may not drastically affect the average profile. Another limitation of the work is its partial dependence on observed PK variability for a given compound, which could be a potential limiting factor for detecting relationships from a sample size perspective (Liu X, Chen Y, Faries DE (2011)). Given that the aggregate adherence metric is a convolution of expected (normal) variability and adherence factors, it is not believed to be appropriate for indicating individual adherence levels. However, neither the magnitude of expected variability in the population, nor the lack of sensitivity at the individual level, are expected to effect the utility of the aggregate adherence metric for group comparisons—only the power to which a difference may be detected across those groups (Marder SR (2003)).

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In summary, pharmacokinetic interactions in particular are systematic. Knowledge of which enzymatic metabolic path is clinically relevant to the metabolization of a drug, whether it is the substrate of a drug transporter, and whether it inhibits or induces these proteins, makes it possible to predict pharmacokinetic interactions. Inhibitors of certain cytochrome P450 enzymes can influence the bioavailability of a whole group of drugs metabolized by the same enzyme, while inducers usually contribute to a loss of effectiveness

As a general principle, drugs that are metabolized more quickly and have a lower bioavailability carry a higher potential risk of interactions. Predicting pharmacodynamic interactions often requires a deeper understanding of the mechanisms of action; but here too a certain system can be recognized, just as for pharmacokinetic interactions. Electronic prescribing systems that can alert the user early on to possible interactions and can assist with drug selection and dosage are helpful.

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Marder SR (2003) Overview of partial compliance. J Clin Psychiatry 64(suppl 16):3–9

Gilbert PL, Harris MJ, McAdams LA, Jeste DV (1995) Neuroleptic withdrawal in schizophrenic patients. A review of the literature. Arch Gen Psychiatry 52(3):173–188

Liu X, Chen Y, Faries DE (2011) Adherence and persistence with branded antidepressants and generic SSRIs among managed care patients with major depressive disorder. Clinicoecon Outcomes Res 3:63–72.

Robinson D, Woerner MG, Alvir JM, Bilder R, Goldman R, Geisler S, Koreen A, Sheitman B, Chakos M, Mayerhoff D, Lieberman JA (1999) Predictors of relapse following response from a first episode of schizophrenia or schizoaffective disorder. Arch Gen Psychiatry 56(3):241–247

Subotnik KL, Nuechterlein KH, Ventura J, Gitlin MJ, Marder S, Mintz J, Hellemann GS, Thornton LA, Singh IR (2011) Risperidone nonadherence and return of positive symptoms in the early course of schizophrenia. Am J Psychiatry 168(3):286–292.

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