Pharmacokinetics input

Ultimately, a select few compounds with combinations of the best functional groups were compared in various animal efficacy and PK/PD models. On the basis of these studies, 27 was chosen as the next development candidate, ABT-263, which was later renamed navitoclax [73]. As is summarized in Fig. 6, at the former nitro and phenyl positions, both potency and various pharmacokinetic inputs were improved, while the morpholine sacrificed some of the gained potency for a more critical further improvement in oral absorption. The end result is a compound with very similar activity to ABT-737, but a much improved pharmacokinetic profile. 

Design of a controUed release dosage form requires sufficient knowledge of both the desired therapy to specify a target plasma level and the pharmacokinetics. The desired dmg input rate from a zero order system may be calculated by ... 

An attempt to estimate human daily impact of N nitroso compounds is shown in Table I. The apparent intake from food of preformed nitrosamines is comparatively low, at least in these surveys of a Western diet in England (3). The Intake directly to the respiratory tract from smoking could be somewhat larger. However, if the blood levels reported are confirmed as correct, then inputs of up to 700 meg per day of at least N nitrosodimethylamine (NDMA) may be calculated, based on pharmacokinetic considerations of data obtained in animals and extrapolated to man. It should be emphasized that no information is available at present on nitrosamide intake or in vivo formation, largely because of analytical limitations. 

In practice, one will seek to obtain an estimate of the elimination constant kp and the plasma volume of distribution Vp by means of a single intravenous injection. These pharmacokinetic parameters are then used in the determination of the required dose D in the reservoir and the input rate constant k (i.e. the drip rate or the pump flow) in order to obtain an optimal steady state plasma concentration... 

KR Godfrey, RP Jones, RF Brown. Identifiable pharmacokinetic models The role of extra inputs and measurements. J Pharmacokin Biopharm 8 633-648, 1980. 

Interroute extrapolation. The values for pharmacokinetic variables in the Leggett Model are independent of the route of exposure. Based on the description of the inputs to the model provided by Leggett (1993), lead intake from different exposure routes is defined as a total lead intake from all routes of exposure. 

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. 

H. K., Uncertainties in physiologically based pharmacokinetic models caused by several input parameters, bit. Arch. Occup. Environ. Health 1999, 72, 247— 254. 

In an early application of in silico approaches to predict human VD, Ritschel and coworkers described an approach using artificial neural networks (ANN), in this case for VDp [34]. However, this was not a truly in silico-only approach as the ANN that yielded accurate predictions of human VD required animal pharmacokinetic data as input parameters, along with in vitro data (protein binding and logP). 

Prototype selection is never wisely made based solely on in vitro dissolution data. This is because the resultant plasma concentration-time profiles are dependent not only on this input rate, but also on the pharmacokinetics of the particular drug. This is illustrated in Figure 2. 

C(t) modeled according to two-compartment model with zero-order and first-order absorption Pharmacokinetic/pharmacodynamic relationship modeled using Hill model with first-order absorption. Modeled parameters matched experimental parameters when bicompartmental model with zero-order input was used. Linear PKs, anticlockwise hysteresis loop established for all doses studied. Apomorphine and growth hormone concentration predicted with good accuracy... 

Pharmacokinetic/pharmacodynamic evaluated using indirect response modeling with inhibition of input... 

A major challenge in VLS is the appropriate treatment of ionization and tau-tomerization statuses in the input data. Currently there are no software that is able to deal the required corrections automatically [47]. Another utmost dispute remains the simultaneous optimization of both binding affinity and pharmacokinetic properties [39, 46, 48]. 

Using human physiology and in vitro data and clinically relevant input parameters (i.e., metabolic stability in human hepatocytes and microsomes as well as prothrombin time measured in various batches of human plasma) the model was extended to human. This human PK/PD model was then used to investigate the impact of the various physicochemical, pharmacokinetic and pharmacodynamic properties on the anticoagulant profile (i.e., prothrombin time) expected in man. 

In this project, compound A from a potential lead series was a neutral compound of MW 314 with low aqueous solubility (Systemic clearance, volume and AUC following a 0.5mg/kg intravenous dose to rats were well predicted (within twofold) from scaled microsomal clearance and in silica prediction of pKa, logP and unbound fraction in plasma. Figure 10.3a shows the predicted oral profile compared to the observed data from two rats dosed orally at 2mg/kg. The additional inputs for the oral prediction were the Caco-2 permeability and measured human fed-state simulated intestinal fluid (FeSSIF, 92(tg/mL). The oral pharmacokinetic parameters Tmax. Cmax. AUC and bioavailability were well predicted. Simulation of higher doses of compound A predicted absorption-limited... 

Overall, this study indicated that generic simulation of pharmacokinetics at the lead optimization stage could be useful to predict differences in pharmacokinetic parameters of threefold or more based upon minimal measured input data. Fine discrimination of pharmacokinetics (less than twofold) should not be expected due to the uncertainty in the input data at the early stages. It is also apparent that verification of simulations with in vivo data for a few compounds of each new compound class was required to allow an assessment of the error in prediction and to identify invalid model assumptions. 

As you read through each of the factors that may modify pharmacokinetics, work out for yourself what may happen to drug input, distribution and loss, and therefore to the plasma concentration of drugs affected by these factors. 

FIGURE 4.1 Schematic representation of the interrelationship between drug input (dosage), pharmacokinetics (concentration), pharmacodynamics, and clinical effects. 

The first two sections of Chapter 5 give a practical introduction to dynamic models and their numerical solution. In addition to some classical methods, an efficient procedure is presented for solving systems of stiff differential equations frequently encountered in chemistry and biology. Sensitivity analysis of dynamic models and their reduction based on quasy-steady-state approximation are discussed. The second central problem of this chapter is estimating parameters in ordinary differential equations. An efficient short-cut method designed specifically for PC s is presented and applied to parameter estimation, numerical deconvolution and input determination. Application examples concern enzyme kinetics and pharmacokinetic compartmental modelling. 

In fact, with simple input functions common in pharmacokinetic applications (e.g., impulse or step function), the columns of the observation matrix X created from the integrals in (5.69) tend to be linearly dependent, resulting in ill - conditioned estimation problems. As discussed in the next section, this method is, however, excellent for input identification. 

Another non - parametric approach is deconvolution by discrete Fourier transformation with built - in windowing. The samples obtained in pharmacokinetic applications are, however, usually short with non - equidistant sample time points. Therefore, a variety of parametric deconvolution methods have been proposed (refs. 20, 21, 26, 28). In these methods an input of known form depending on unknown parameters is assumed, and the model response predicted by the convolution integral (5.66) is fitted to the data. 

D.J. Cutler, Numerical deconvolution by least squares Use of prescribed input functions, J. Pharmacokinetics and Biopharm., 6 (1978) 227-242. 

The patient s recorded time history of dosing can be used as input to pharmacokinetic (PK) models, to project the time history of drug concentra-... 

A two-compartment open linear model has been described for the pharmacokinetic profile of cocaine after intravenous administration.14 The distribution phase after cocaine administration is rapid and the elimination half-life estimated as 31 to 82 min.14 Cone9 fitted data to a two-compartment model with bolus input and first-order elimination for the intravenous and smoked routes. For the intranasal route, data were fitted to a two-compartment model with first-order absorption and first-order elimination. The average elimination half-life (tx 2 3) was 244 min after intravenous administration, 272 min after smoked administration, and 299 min after intranasal administration. 

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