The Observed Correlation between in vivo Clinical Pharmacokinetic Parameters and in vitro Potency of VEGFR-2 Inhibitors

Free Drug Theory - No Longer Just a Hypothesis?

The Free Drug Hypothesis is a well-established concept within the scientific lexicon pervading many areas of Drug Discovery and Development, and yet it is poorly defined by virtue of many variations appearing in the literature. Clearly, unbound drug is in dynamic equilibrium with respect to absorption, distribution, metabolism, elimination, and indeed, interaction with the desired pharmacological target. Binding interactions be they specific (e.g. high affinity) or nonspecific (e.g. lower affinity/higher capacity) are governed by the same fundamental physicochemical tenets including Hill-Langmuir Isotherms, the Law of Mass Action and Drug Receptor Theory. With this in mind, it is time to recognise a more coherent version and consider it the Free Drug Theory and a hypothesis no longer. Today, we have the experimental and modelling capabilities, pharmacological knowledge, and an improved understanding of unbound drug distribution (e.g. Kp_uu) to raise the bar on our understanding and analysis of experimental data. The burden of proof should be to rule out mechanistic possibilities and/or experimental error before jumping to the conclusion that any observations contradict these fundamentals.

Strategy for the Prediction of Steady-State Exposure of Digoxin to Determine Drug-Drug Interaction Potential of Digoxin With Other Drugs in Digitalization Therapy

Digoxin, a narrow therapeutic index drug, is widely used in congestive heart failure. However, the digitalization therapy involves dose titration and can exhibit drug-drug interaction. Ctrough versus area under the plasma concentration versus time curve in a dosing interval of 24 hours (AUC0-24h) and Cmax versus AUC0-24h for digoxin were established by linear regression. The predictions of digoxin AUC0-24h values were performed using published Ctrough or Cmax with appropriate regression lines. The fold difference, defined as the quotient of the observed/predicted AUC0-24h values, was evaluated. The mean square error and root mean square error, correlation coefficient (r), and goodness of the fold prediction were used to evaluate the models. Both Ctrough versus AUC0-24h (r = 0.9215) and Cmax versus AUC0-24h models for digoxin (r = 0.7781) showed strong correlations. Approximately 93.8% of the predicted digoxin AUC0-24h values were within 0.76-fold to 1.25-fold difference for Ctrough model. In sharp contrast, the Cmax model showed larger variability with only 51.6% of AUC0-24h predictions within 0.76-1.25-fold difference. The r value for observed versus predicted AUC0-24h for Ctrough (r = 0.9551; n = 177; P < 0.001) was superior to the Cmax (r = 0.6134; n = 275; P < 0.001) model. The mean square error and root mean square error (%) for the Ctrough model were 11.95% and 16.2% as compared to 67.17% and 42.3% obtained for the Cmax model. Simple linear regression models for Ctrough/Cmax versus AUC0-24h were derived for digoxin. On the basis of statistical evaluation, Ctrough was superior to Cmax model for the prediction of digoxin AUC0-24h and can be potentially used in a prospective setting for predicting drug-drug interaction or lack of it.

Interspecies scaling of urinary excretory amounts of nine drugs belonging to different therapeutic areas with diverse chemical structures - accurate prediction of the human urinary excretory amounts

1. The human urinary excretory amounts of total drug (parent + metabolites) were predicted for nine drugs with diverse chemical structures using simple allometry. The drugs used for scaling were cephapirin, olanzapine, labetolol, carisbamate, voriconazole, tofacitinib, nevirapine, ropinirole, and cyclindole. 2. The traditional allometric scaling was attempted using Y = aW^b relationship. The corresponding predicted urinary amounts were converted into % recovery by using appropriate human dose. Appropriate statistical tests comprising of fold-difference (predicted/observed values) and error calculations (MAE and RMSE) were performed. 3. The interspecies scaling of all nine drugs tested showed excellent correlation (r > 0.9672). The predictions for eight out of nine drugs (exception was cephaphirin) were contained within 0.80-1.25 fold-differences. The MAE and RMSE were within ± 18% and 14.64%, respectively. 4. The present work supported the potential application of prospective allometry scaling to predict the urinary excretory amounts of the total drug and gauge any issues for the renal handling of the total drug.

Applicability of a Single Time Point Strategy for the Prediction of Area Under the Concentration Curve of Linezolid in Patients: Superiority of Ctrough- over Cmax-Derived Linear Regression Models

Background and Objectives

Linezolid, a oxazolidinone, was the first in class to be approved for the treatment of bacterial infections arising from both susceptible and resistant strains of Gram-positive bacteria. Since overt exposure of linezolid may precipitate serious toxicity issues, therapeutic drug monitoring (TDM) may be required in certain situations, especially in patients who are prescribed other co-medications.

Methods

Using appropriate oral pharmacokinetic data (single dose and steady state) for linezolid, both maximum plasma drug concentration (C_max) versus area under the plasma concentration–time curve (AUC) and minimum plasma drug concentration (C_min) versus AUC relationship was established by linear regression models. The predictions of the AUC values were performed using published mean/median C_max or C_min data and appropriate regression lines. The quotient of observed and predicted values rendered fold difference calculation. The mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), and the goodness of the AUC fold prediction were used to evaluate the two models.

Results

The C_max versus AUC and trough plasma concentration (C_trough) versus AUC models displayed excellent correlation, with r values of >0.9760. However, linezolid AUC values were predicted to be within the narrower boundary of 0.76 to 1.5-fold by a higher percentage by the C_trough (78.3 %) versus C_max model (48.2 %). The C_trough model showed superior correlation of predicted versus observed values and RMSE (r = 0.9031; 28.54 %, respectively) compared with the C_max model (r = 0.5824; 61.34 %, respectively).

Conclusions

A single time point strategy of using C_trough level is possible as a prospective tool to measure the AUC of linezolid in the patient population.

Prediction of area under the concentration-time curve for lopinavir from peak or trough lopinavir concentrations in patients receiving lopinavir-ritonavir therapy

PURPOSE

A "single time point" strategy for predicting the area under the concentration-time curve (AUC) for lopinavir in patients receiving ritonavir-boosted lopinavir therapy was investigated.

METHODS

Linear regression equations describing the relationships of lopinavir peak and trough concentrations to lopinavir AUC values were established using pharmacokinetic data from published studies of patients or healthy subjects receiving lopinavir and ritonavir at standard dosages. The resulting "trough-AUC model" and "peak-AUC model" were used to predict lopinavir AUC values in the evaluated study populations (total n = 479); those values were then compared with reported AUC values.

RESULTS

Lopinavir peak or trough concentrations were strongly correlated with lopinavir AUC values (r = 0.9947 and r = 0.9541, respectively). For about 94% of calculations using the peak-AUC model and 87% of calculations using the trough-AUC model, differences between predicted and observed AUC values were in the range of 0.76-1.5 fold; the associated r values were 0.9514 (p < 0.001) and 0.9345 (p < 0.001), respectively. The mean absolute predictive error was less than 6% with the use of either the peak-AUC model or the trough-AUC model, with corresponding values for root-mean-square error of 17.6% and 23.5%, respectively.

CONCLUSION

Equations incorporating lopinavir peak and trough concentrations were found to satisfactorily predict lopinavir AUC values in data sets describing patients receiving lopinavir with ritonavir boosting. Variability in predictions was higher with use of the trough-AUC model.

Interspecies scaling of excretory amounts using allometry - retrospective analysis with rifapentine, aztreonam, carumonam, pefloxacin, miloxacin, trovafloxacin, doripenem, imipenem, cefozopran, ceftazidime, linezolid for urinary excretion and rifapentine, cabotegravir, and dolutegravir for fecal excretion

1. Interspecies allometry scaling for prediction of human excretory amounts in urine or feces was performed for numerous antibacterials. Antibacterials used for urinary scaling were: rifapentine, pefloxacin, trovafloxacin (Gr1/low; <10%); miloxacin, linezolid, PNU-142300 (Gr2/medium; 10-40%); aztreonam, carumonam, cefozopran, doripenem, imipenem, and ceftazidime (Gr3/high; >50%). Rifapentine, cabotegravir, and dolutegravir was used for fecal scaling (high; >50%). 2. The employment of allometry equation: Y = aW(b) enabled scaling of urine/fecal amounts from animal species. Corresponding predicted amounts were converted into % recovery by considering the respective human dose. Comparison of predicted/observed values enabled fold difference and error calculations (mean absolute error [MAE] and root mean square error [RMSE]). Comparisons were made for urinary/fecal data; and qualitative assessment was made amongst Gr1/Gr2/Gr3 for urine. 3. Average correlation coefficient for the allometry scaling was >0.995. Excretory amount predictions were largely within 0.75- to 1.5-fold differences. Average MAE and RMSE were within ±22% and 23%, respectively. Although robust predictions were achieved for higher urinary/fecal excretion (>50%), interspecies scaling was applicable for low/medium excretory drugs. 4. Based on the data, interspecies scaling of urine or fecal excretory amounts may be potentially used as a tool to understand the significance of either urinary or fecal routes of elimination in humans in early development.

Differences in the Prediction of Area Under the Curve for a Protease Inhibitor Using Trough Versus Peak Concentration: Assessment Using Published Pharmacokinetic Data for Indinavir

In the present day antiretroviral therapy, Ctrough is a key tool for efficacy assessment. The present work explored the feasibility of using Ctrough or Cmax in the area under the concentration-time curve (AUC) prediction of indinavir. A simple unweighted linear regression model was developed to describe the relationship between Cmax versus AUC (r = 0.8101, P < 0.001) and Ctrough versus AUC (r = 0.8127, P < 0.001) for indinavir. The regression lines were used to predict the AUC values from literature Cmax or Ctrough data of indinavir in HIV and healthy subjects. The fold difference, defined as the quotient of the observed and predicted AUC values, was evaluated along with statistical comparison, including root mean square error (RMSE) prediction for the 2 models. The correlation between Cmax versus AUC and Ctrough versus AUC was established. Majority of the predicted values for Cmax versus AUC were within 0.75- to 1.5-fold differences. However, the Ctrough versus AUC model showed larger variability with approximately one-third of the predictions within 0.75- to 1.5-fold differences. The r value and %RMSE for observed versus predicted AUC for Ctrough (r = 0.5925, n = 65, P < 0.001, and RMSE: 67%) were inferior to the Cmax (r = 0.8773, n = 86, P < 0.001, and RMSE: 46%). In conclusion, Cmax versus AUC and Ctrough versus AUC relationships were established for indinavir showing the utility of a single concentration time point for therapeutic drug monitoring purpose. The Cmax model for indinavir may be more relevant for AUC prediction as determined by the statistical criteria.