PK-Sim

Willmann S, Lippert J, Sevestre M, Solodenko J, Fois F, Schmitt W. PK-Sim a physiologically based pharmacokinetic whole-body model. Biosilico 2003 1(4) 121-4... 

PK-Map and PK-Sim (Bayer Technology Services, Wuppertal, Germany), that are based on the models described by Willman et al. [54], In these software packages, the intestinal permeability coefficient can be calculated using a compound s lipophilicity and molecular weight [52,54] and hence, no experimental permeability data is needed. Different to the model described by Willman et al. [54], the commercial prediction tools model the dissolution rate taking the particle size distribution of the solid particles into account (www.pk-sim.com). 

More recent PBPK software packages such as SIMCYP [www.simcyp.com], PK-Sim [www.pk-sim.com], GastroPlus [www.simulations-plus.com], and Cloe PK [www.cyprotex.com] offer similar bioavailability estimation. It is clear that these approaches require more data input than just molecular structure as in QSAR models. 

Lin, H.C. and Sheen, G.J., An approximation approach for making decisions in assessing the capability index C-pk from the subsamples. Comm. Stat. Sim. Comp., 34, 191, 2005. 

PK studies aim to monitor the concentration-time profiles of distinct drugs in the circulation by analysing plasma or serum. Therefore, sample preparation, chromatographic separation and mass spectrometric detection are typically optimized for a single analyte. Achieving best selectivity the MRM mode was used most frequently as summarized in Table 5. Only rare examples are found applying the simple SIM mode detection most often due to the use of SQ instruments. However, the LOQs for TA quantification were often quite similar being approximately 0.5 ng/ml (Table 5). 

The LME model of Section 4.2.1 and the NLME model of Section 4.2.2 both involved two random components measurement error and subject random effects. In this section we explore a two-level random effect hierarchy by introducing lO variability in the PK parameters Ka, Ke, and V), so that the subject s parameters may vary from period to period. Note that this is not a period effect, but rather an uncontrollable random variation in the subject s pharmacokinetics. The data frame dp2, incorporating lO random effects, is obtained by calling sim.dp.muit as follows ... 

The function sim.pkpd.muit simulates a clinical response R) on the basis of a PK/PD model. It incorporates a combined placebo (P) effect and a drug (D) effect. The placebo effect at time t is defined as... 

The data frame pkpdi is generated according to the PK-PD model above, using the following call to the function sim.pkpd.muit. Figure 4.2 shows a trellis display of the corresponding time profiles for the simulated PD response. 

The NONMEM control file provided in Appendix 25.2 (Model Sim) was used to simulate pain relief scores and remedication events according to the population PK/PD models described previously. The NONMEM control file provided in Appendix 25.5 (Model PR-i-RM) was used to estimate the parameters in the pain relief and remedication models, from the simulated pain relief data and PK parameter estimates for each individual. Separate NONMEM control files are provided for simulation and estimation, because the dependent variables are not identical for simulation and estimation. The dependent variables for simulation are pain relief scores and remedication events, whereas the dependent variables for estimation are the probabilities of observing these scores and events. The pain relief scores and remedication events are simulated by utilizing a uniform random variable to assign a pain relief score or remedication event, given the respective simulated probabilities. 

Fig. 6.6. SIMS imaging of potassium, sodium, calcium, and platinum in fractured freeze-dried renal epithelial LLC-PK-i cells treated with 6 xM cisplatin tor 4 h (panels a-d). Individual images of and Na were integrated on the CCD camera for 0.4 s and Ca" and i95pt+ for 2 min. Regions marked in the figure by arrows and numbers are individual cells measured and described in the text and in Table 6.1.

Nawarathna RLS, Choundhary PK (2013) Measuring agreement in method comparison studies with heteroscedastic measurements. Statist Med. doi 10.1002/sim.5955... 

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