Modeling multiple dose

Analyze the problem. What data are available to solve the problem Given the data available can the problem be solved Has proper attention to study design and data collection been done to achieve the objective Question whether a model is even necessary. Perhaps a noncompartmental analysis of the data will suffice instead. If the goal is to model multiple-dose data from single dose data, then something simple like the superposition principle may be useful. 

As with all previous PK models, multiple dosing models require a number of inherent assumptions. It turns out that the assumptions required for multiple dosing models are all derived from or identical to the assumptions already made in earlier single-dose models. 

Dose-Response Extrapolation Models. A dose-response model is simply a hypothetical mathematical relationship between dose-rate and probability of response. For example, the simplest form of such a model asserts that probability of tumor initiation is a linear multiple of dose-rate (provided the dosage is well below the organism s acute effect threshold for the substance in question). In general, we will express dose-response models as follows ... 

Tariot PN, Cohen RM, Welkowitz JA, et al Multiple-dose arecohne infusions in Alzheimer s disease. Arch Gen Psychiatry 45 901-905, 1988 Taylor AE, Saint-Cyr JA, Lang AE Frontal lobe dysfunction in Parkinson s disease the cortical focus of neostriatal outflow. Brain 109 845-883, 1986 Taylor DP, Smith DW, Hyslop DK, et al Receptor binding and atypical antidepressant drug discovery, in Receptor Binding in Drug Research. Edited by O Brien RA. New York, Marcel Dekker, 1986, pp 151-165 Tejedor-Real P, Mico JA, Maldonado R, et al Effect of mixed (RB 38A) and selective (RB 38B) inhibitors of enkephalin degrading enzymes on a model of depression in the rat. Biol Psychiatry 34 100-107, 1993... 

Modeling to Predict Single- and Multiple-Dose Pharmacokinetic Profiles 98... 

Various PK parameters such as CL, Vd, F%, MRT, and T /2 can be determined using noncompartmental methods. These methods are based on the empirical determination of AUC and AUMC described above. Unlike compartmental models (see below), these calculation methods can be applied to any other models provided that the drug follows linear PK. However, a limitation of the noncompartmental method is that it cannot be used for the simulation of different plasma concentration-time profiles when there are alterations in dosing regimen or multiple dosing regimens are used. 

As previously discussed, compartmental models can be effectively used to project plasma concentrations that would be achieved following different dosage regimens and/or multiple dosing. However, for these projections to be accurate, the drug PK profile should follow first-order kinetics where various PK parameters such as CL, V,h T /2, and F% do not change with dose. 

M.C. Wacholtz, N. Minton, and W.J. Jusko. 2004. Pharmacokinetic and pharmacodynamic modeling of recombinant human erythropoietin after single and multiple doses in healthy volunteers. 

The statistical submodel characterizes the pharmacokinetic variability of the mAb and includes the influence of random - that is, not quantifiable or uncontrollable factors. If multiple doses of the antibody are administered, then three hierarchical components of random variability can be defined inter-individual variability inter-occasional variability and residual variability. Inter-individual variability quantifies the unexplained difference of the pharmacokinetic parameters between individuals. If data are available from different administrations to one patient, inter-occasional variability can be estimated as random variation of a pharmacokinetic parameter (for example, CL) between the different administration periods. For mAbs, this was first introduced in sibrotuzumab data analysis. In order to individualize therapy based on concentration measurements, it is a prerequisite that inter-occasional variability (variability within one patient at multiple administrations) is lower than inter-individual variability (variability between patients). Residual variability accounts for model misspecification, errors in documentation of the dosage regimen or blood sampling time points, assay variability, and other sources of error. 

Oral bioavailability of mibefradil is dose dependent and ranges from 37% to over 90% with doses of 10 mg or 160 mg, respectively. The plasma half-life is 17 to 25 hours after multiple doses, and it is more than 99% protein bound (15). The metabolism of mibefradil is mediated by two pathways esterase-catalyzed hydrolysis of the ester side chain to yield an alcohol metabolite and CYP3A4-catalyzed oxidation. After chronic dosing, the oxidative pathway becomes less important and the plasma level of the alcohol metabolite of mibefradil increases. In animal models, the pharmacological effect of the alcohol metabolite is about 10% compared to that of the parent compound. After metabolic inactivation, mibefradil is excreted into the bile (75%) and urine (25%), with less than 3% excreted unchanged in the urine. 

The interoccasion variability (IOV) or intraindividual variability [11] arises when a parameter of the model, e.g. CL, varies within a subject between study occasions. The term occasion can be defined arbitrarily, usually logical intervals for an occasion are chosen, e.g. each dosing interval in multiple dose studies or each treatment period of a cross-over study can be defined as an occasion. To assess the IOV of a specific parameter more than one measurement per individual has to be available per occasion. The IOV can be implemented in the random effect model as described in the following ... 

The pharmacokinetic evaluation of biopharmaceuticals is generally simplified by the usual metabolism of products to small peptides and to amino acids, and thus classical biotransformation and metabolism studies are rarely necessary. Routine studies to assess mass balance are not useful. However, both single- and multiple-dose toxicokinetic data are essential in safety pharmacology asessments, and these can be complicated by two factors (1) biphasic clearance with a saturable, initial, receptor-dependent clearance phase, which may cause nonlinearity in dose-exposure relationships and doseresponses [14] and (2) antibody production against an antigenic biopharmaceutical that can alter clearance or activity in more chronic repeat-dose safety studies in the preclinical model. 

If possible, these developability pharmacology studies should be conducted with dosing to steady state unless the candidate is to be used as a singledose therapeutic. The number of doses required to reach steady state depends on the compound s pharmacokinetic profile in the same animal model. These multiple-dose studies provide information on the frequency of dosing necessary to maximize the biological response. This is particularly important for compounds that inhibit an enzymatic system or are effective only during certain phases of a cell cycle. 

THERdbASE contains two major modules, namely a Database Module and a Model Base Module. The Database Module relates information from exposure, dose and risk-related data files, and contains information about the following population distributions, location/activity patterns, food-consumption patterns, agent properties, agent sources (use patterns), environmental agent concentrations, food contamination, physiological parameters, risk parameters and miscellaneous data files. The Model Base Module provides access to exposure dose and risk-related models. The specific models included with the software are as follows Model 101, subsetting activity pattern data Model 102, location patterns (simulated) Model 103, source (time application) Model 104, source (instantaneous application) Model 105, indoor air (two zones) Model 106, indoor air (n zones) Model 107, inhalation exposure (BEAM) Model 108, inhalation exposure (multiple chemicals) Model 109, dermal dose (film thickness) Model 110, dose scenario (inhalation/dermal) Model 201, soil exposure (dose assessment). 

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