Plasma drug concentrations nonlinear pharmacokinetics

The approach involves a semimechanistic or mechanistic model that describes the joint probability of the time of remedication and the pain relief score (which is related to plasma drug concentrations). This joint probability can be written as the product of the conditional probability of the time of remedication, given the level of pain relief and the probability of the pain relief score. First, a population pharmacokinetic (PK) model is developed using the nonlinear mixed effects modeling approach (16-19) (see also Chapters 10 and 14 of this book). With this approach both population (average) and random (inter- and intraindividual) effects parameters are estimated. When the PK model is linked to an effect (pharmacodynamic (PD) model), the effect site concentration (C ) as defined by Sheiner et al. (20) can be obtained. The effect site concentration is useful in linking dose to pain relief and subsequently to the decision to remedicate. 

The original proposal of the approach, supported by a Monte Carlo simulation study [36], has been further validated with both pre-clinical [38, 39] and clinical studies [40]. It has been shown to be robust and accurate, and is not highly dependent on the models used to fit the data. The method can give poor estimates of absorption or bioavailability in two sets of circumstances (i) when the compound shows nonlinear pharmacokinetics, which may happen when the plasma protein binding is nonlinear, or when the compound has cardiovascular activity that changes blood flow in a concentration-dependent manner or (ii) when the rate of absorption is slow, and hence flip-flop kinetics are observed, i.e., when the apparent terminal half-life is governed by the rate of drug input. 

Fluvoxamine, fluoxetine, and paroxetine have nonlinear pharmacokinetics, which means that dose increases lead to disproportionately greater increases in plasma drug levels (25). In contrast, citalopram and sertraline have linear pharmacokinetics. For these reasons, dose increases with fluvoxamine, fluoxetine, and paroxetine can lead to greater than proportional increases in concentration-dependent effects such as serotonin-mediated adverse effects (e.g., nausea) and inhibition of specific CYP enzymes. 

Nonlinear pharmacokinetics. Nonlinear pharmacokinetics simply means that the relationship between dose and Cp is not directly proportional for all doses. In nonlinear pharmacokinetics, drug concentration does not scale in direct proportion to dose (also known as dose-dependent kinetics). One classic drug example of nonlinear pharmacokinetics is the anticonvulsant drug phenytoin.38 Clinicians have learned to dose pheny-toin carefully in amounts greater than 300 mg/day above this point, most individuals will have dramatically increased phenytoin plasma levels in response to small changes in the input dose. 

To discuss nonlinear pharmacokinetics, it is first useful to consider linear pharmacokinetics, Thus, for a drug that does not exceed the metabolic capability of the removal process ([S] Km), the clearance is given by Vmax/ Km. For a given steady-state plasma concentration (Css), the dosage rate (DR) required is given by... 

It can be seen from Equation 9.25 that there is a linear relationship between the plasma concentration and dosage of drug required to maintain it (linear pharmacokinetics). For example, for a drug with a clearance of 10 L h, a dosage rate of 30 pg h 1 is required for a Css of 3 pg L If an increase in the steady-state concentration to 6 pg L 1 is required, a concentration of 60 pg h 1 would have to be administered. In the case of nonlinear pharmacokinetics, [S] is not << Km and the relationship between DR and Css is given by... 

Linear pharmacokinetics within the investigated rate of delivery (dose/time units) of the drug to the body, i.e., the plasma concentration-time profile, should be identical for different doses after correction for dose. The most common reason for nonlinear pharmacokinetics is dose-dependent first-pass metabolism. This phenomenon does not just occur at high doses, but has also been shown for the slow delivery rates obtained by ER formulations. 

Perhaps a better approach than this arbitrary dose-driven escalation is pharmacokinetic or drug concentration-driven dose escalation. This requires flexibility in protocol design, rapid assay of plasma samples, and pharmacokinetic analysis of results between dosing escalations. However, there are many advantages with this approach. Issues related to bioavailabihty or nonlinear pharmacokinetics are immediately apparent and can be readily addressed. Selecting subsequent doses based on pharmacokinetic data from previous doses increases both safety and efficiency fewer dose escalation steps are usually required. Another potential advantage is better correlation with safety and pharmacological effects. 

In spite of its limitations, the ACAT model combined with modeling of saturable processes has become a powerful tool in the study of oral absorption and pharmacokinetics. To our knowledge, it is the only tool that can translate in vitro data from early drug discovery experiments all the way to plasma concentration profiles and nonlinear dose-relationship predictions. As more experimental data become available, we believe that the model will become more comprehensive and its predictive capabilities will be further enhanced. 

As mentioned in Chapter 4, experiments have determined that the distribution of ASOs into tissues is nonlinear. This revelation invalidates the above BAV equation in that it is dependent on linear pharmacokinetics and the principle of superposition. A way to circumvent this problem is to decrease the drag input function (i.e., systemic presentation of the ASO) such that ASO plasma concentrations are maintained below the level at which saturation, and thus nonlinearity of the distribution processes, occurs. Drug administration by SC rather than IV administration has a reduced drug input rate and can produce such a scenario. The corresponding plasma-derived data are then suitable for the determination of absolute bioavailability - consistent with linear pharmacokinetic principles and the following equation ... 

The diversity of the SSRIs is evident not only in their chemical structures, but also in their pharmacokinetic profiles. Fluoxetine has an elimination half-fife of 2 to 3 days (4 to 5 days with multiple dosing). The single-dose hah-hfe of norfluoxetine, the active metabolite, is 7 to 9 days. Paroxetine and sertrahne have half-lives of approximately 24 hours. Unlike paroxetine, sertraline has an active metabolite, but the metabohte contributes minimally to the pharmacologic effects. Escitalopram has a half-life of approximately 30 hours. Peak plasma concentrations of citalopram are observed within 2 to 4 hours after dosing, and the elimination half-life is about 30 hours. The SSRIs, with the exception of fluvoxamine, escitalopram, and citalopram, are extensively bound to plasma proteins (94% to 99%). The SSRIs are extensively distributed to the tissues, and aU, with the possible exception of citalopram, may have a nonlinear pattern of drug accumulation with long-term administration. ... 

An integrated summary and analysis is to be provided on all data that pertain to dose-response or drug candidate plasma concentration-response relationships of effectiveness and thus to contribute to dose selection, dosing interval, and dosage duration. Relevant data from nonclinical and clinical studies should be referenced and, where appropriate, summarized to illustrate further and describe these relationships. Any identified deviations (e.g., nonlinearity of pharmacokinetics, delayed effects, tolerance, enzyme induction) from relatively simple relationships should be discussed. Also, any evidence of differences in the relationships that result from the age, gender, race, disease status, or other factors of the patients should be described. How the potential for these deviations and differences were evaluated, even if no differences were found, should be described. 

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