Prediction accuracy of pharmacokinetic parameters is often assessed using prediction fold error, i.e., being within 2-, 3-, or n-fold of observed values. However, published studies disagree on which fold error represents an accurate prediction. In addition, "observed data" from only one clinical study are often used as the gold standard for in vitro to in vivo extrapolation (IVIVE) studies, despite data being subject to significant interstudy variability and subjective selection from various available reports. The current study involved analysis of published systemic clearance (CL) and volume of distribution at steady state (V ss ) values taken from over 200 clinical studies. These parameters were obtained for 17 different drugs after intravenous administration. Data were analyzed with emphasis on the appropriateness to use a parameter value from one particular clinical study to judge the performance of IVIVE and the ability of CL and V ss values obtained from one clinical study to "predict" the same values obtained in a different clinical study using the n-fold criteria for prediction accuracy. The twofold criteria method was of interest because it is widely used in IVIVE predictions. The analysis shows that in some cases the twofold criteria method is an unreasonable expectation when the observed data are obtained from studies with small sample size. A more reasonable approach would allow prediction criteria to include clinical study information such as sample size and the variance of the parameter of interest. A method is proposed that allows the "success" criteria to be linked to the measure of variation in the observed value.

This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

Purpose Clinical history data reported on test requisition forms (TRFs) for hereditary cancer multigene panel testing (MGPT) are routinely used by genetic testing laboratories. More recently, publications have incorporated TRF-based clinical data into studies exploring yield of testing by phenotype and estimating cancer risks for mutation carriers. We aimed to assess the quality of TRF data for patients undergoing MGPT. Patients and Methods Ten percent of patients who underwent hereditary cancer MGPT between January and June 2015 at a clinical laboratory were randomly selected. TRF-reported cancer diagnoses were evaluated for completeness and accuracy for probands and relatives using clinical documents such as pedigrees and chart notes as the comparison standard in cases where these documents were submitted after the time of test order. Results TRF-reported cancer sites and ages at diagnosis were complete for > 90.0% of proband cancer diagnoses overall, and the completion rate was even higher (> 96.0%) for breast, ovarian, colorectal, and uterine cancers. When reported, these data were accurate on TRFs for > 99.5% of proband cancer sites and > 97.5% of proband ages at diagnosis. Cancer site and age at diagnosis data were also complete on the TRF for the majority of cancers among first- and second-degree relatives. Completeness decreased as relation to the proband became more distant, whereas accuracy remained high across all degrees of relation. Conclusion Data collected as part of cancer genetic risk assessment is completely and accurately reported on TRFs for the majority of probands and their close relatives and is comparable to information directly obtained from clinic notes, particularly for breast and other cancers commonly associated with hereditary cancer syndromes.

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