Journal: Pharmaceuticals

Article Title: Physiologically Based Pharmacokinetic Modeling of Tofacitinib: Predicting Drug Exposure and Optimizing Dosage in Special Populations and Drug–Drug Interaction Scenarios

doi: 10.3390/ph18030425

Figure Lengend Snippet: The observed and model-predicted plasma concentration–time profiles of tofacitinib following oral administration ( a – o ) [ , , , , , ]. The red lines indicate median concentration data from model predictions and blue dots indicate reported concentrations from different studies. The goodness-of-fit plot for model-predicted tofacitinib concentrations ( p ). Different colors represent data from different simulations. Black and gray trend lines indicate 2-fold and 1.25-fold prediction errors, respectively. IR, immediate release. XR, extended release.

Article Snippet: The PBPK model of tofacitinib has previously been developed and validated in Simcyp ® [ ].

Techniques: Clinical Proteomics, Concentration Assay

Journal: Pharmaceuticals

Article Title: Physiologically Based Pharmacokinetic Modeling of Tofacitinib: Predicting Drug Exposure and Optimizing Dosage in Special Populations and Drug–Drug Interaction Scenarios

doi: 10.3390/ph18030425

Figure Lengend Snippet: The observed and model-predicted plasma concentration–time profiles of tofacitinib in pediatric patients ( a – d ) . The red lines indicate median concentration data from model predictions, while the shaded areas indicate a 5%-to-95% concentration range, and blue dots indicate reported concentrations from different studies. Box–whisker plots of pediatric patients at different cohorts after oral tofacitinib to compare AUC values ( e , f ).

Article Snippet: The PBPK model of tofacitinib has previously been developed and validated in Simcyp ® [ ].

Techniques: Clinical Proteomics, Concentration Assay, Whisker Assay

Journal: Pharmaceuticals

Article Title: Physiologically Based Pharmacokinetic Modeling of Tofacitinib: Predicting Drug Exposure and Optimizing Dosage in Special Populations and Drug–Drug Interaction Scenarios

doi: 10.3390/ph18030425

Figure Lengend Snippet: The observed and model-predicted plasma concentration–time profiles of tofacitinib in populations with hepatic impairment ( a – c ) . The red lines indicate median concentration data from model predictions, while the shaded areas indicate a 5%-to-95% concentration range, and blue dots indicate reported concentrations from different studies. The goodness-of-fit plot for model-predicted tofacitinib concentrations ( d ). Different colors represent data from different simulations. Black and gray trend lines indicate 2-fold and 1.25-fold prediction errors, respectively. Box–whisker plots of populations with different hepatic functions after oral tofacitinib to compare AUC values ( e , f ).

Article Snippet: The PBPK model of tofacitinib has previously been developed and validated in Simcyp ® [ ].

Techniques: Clinical Proteomics, Concentration Assay, Whisker Assay

Journal: Pharmaceuticals

Article Title: Physiologically Based Pharmacokinetic Modeling of Tofacitinib: Predicting Drug Exposure and Optimizing Dosage in Special Populations and Drug–Drug Interaction Scenarios

doi: 10.3390/ph18030425

Figure Lengend Snippet: The observed and model-predicted plasma concentration–time profiles of tofacitinib in populations with renal impairment ( a – d ) . The red lines indicate median concentration data from model predictions, while the shaded areas indicate a 5%-to-95% concentration range, and blue dots indicate reported concentrations from different studies. The goodness-of-fit plot for model-predicted tofacitinib concentrations ( e ). Different colors represent data from different simulations. Black and gray trend lines indicate 2-fold and 1.25-fold prediction errors, respectively. Box–whisker plots of populations with different renal functions after oral tofacitinib to compare AUC values ( f , g ).

Article Snippet: The PBPK model of tofacitinib has previously been developed and validated in Simcyp ® [ ].

Techniques: Clinical Proteomics, Concentration Assay, Whisker Assay

Journal: Pharmaceuticals

Article Title: Physiologically Based Pharmacokinetic Modeling of Tofacitinib: Predicting Drug Exposure and Optimizing Dosage in Special Populations and Drug–Drug Interaction Scenarios

doi: 10.3390/ph18030425

Figure Lengend Snippet: The observed and model-predicted plasma concentration–time profiles of tofacitinib with fluconazole, ketoconazole, and rifampicin ( a – c ) . The red and blue lines indicate median concentration data from model predictions in the absence and presence of perpetrators, while the shaded areas indicate a 5%-to-95% concentration range, and solid dots indicate reported concentrations from different studies. Box–whisker plots of populations in the absence and presence of perpetrators after oral tofacitinib to compare AUC values ( d – f ).

Article Snippet: The PBPK model of tofacitinib has previously been developed and validated in Simcyp ® [ ].

Techniques: Clinical Proteomics, Concentration Assay, Whisker Assay

Journal: Pharmaceuticals

Article Title: Physiologically Based Pharmacokinetic Modeling of Tofacitinib: Predicting Drug Exposure and Optimizing Dosage in Special Populations and Drug–Drug Interaction Scenarios

doi: 10.3390/ph18030425

Figure Lengend Snippet: Predicted and observed C max R and AUCR of tofacitinib in the presence of fluconazole, ketoconazole, and rifampicin.

Article Snippet: The PBPK model of tofacitinib has previously been developed and validated in Simcyp ® [ ].

Techniques:

Journal: Pharmaceuticals

Article Title: Physiologically Based Pharmacokinetic Modeling of Tofacitinib: Predicting Drug Exposure and Optimizing Dosage in Special Populations and Drug–Drug Interaction Scenarios

doi: 10.3390/ph18030425

Figure Lengend Snippet: Input compound parameters for the tofacitinib PBPK model.

Article Snippet: The PBPK model of tofacitinib has previously been developed and validated in Simcyp ® [ ].

Techniques: Clinical Proteomics, Molecular Weight, Solubility, Permeability, Formulation, Dissolution

Journal: Journal of Clinical Pharmacology

Article Title: Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug Exposure and Support Dosing Recommendations for Potential Drug‐Drug Interactions or in Special Populations: An Example Using Tofacitinib

doi: 10.1002/jcph.1679

Figure Lengend Snippet: Simcyp Input Parameters for the Tofacitinib PBPK Model

Article Snippet: The simulations of mild and moderate hepatic impairment using the tofacitinib PBPK model and the Simcyp Liver Cirrhosis CP‐A and CP‐B Population Files show a gradual increase in both C max and AUC values (predicted AUC ratios of ∼1.7 for CP‐A and ∼3.1 for CP‐B relative to healthy volunteers; Table ), which appeared to be overpredicted relative to the observed data (predicted hepatic impairment AUC ratios relative to observed hepatic impairment AUC ratios were ∼1.6 and ∼1.9 for CP‐A and CP‐B, respectively; Table ).

Techniques: Binding Assay, Molecular Weight, In Vitro, Recombinant

Journal: Journal of Clinical Pharmacology

Article Title: Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug Exposure and Support Dosing Recommendations for Potential Drug‐Drug Interactions or in Special Populations: An Example Using Tofacitinib

doi: 10.1002/jcph.1679

Figure Lengend Snippet: Arithmetic Mean (SD) Observed and Predicted Pharmacokinetics of Tofacitinib After (a) a Single Intravenous or Oral Dose in Healthy Volunteers and (b) After Multiple (14 Days) Oral Doses of Tofacitinib 15 mg Twice Daily in Healthy Volunteers

Article Snippet: The simulations of mild and moderate hepatic impairment using the tofacitinib PBPK model and the Simcyp Liver Cirrhosis CP‐A and CP‐B Population Files show a gradual increase in both C max and AUC values (predicted AUC ratios of ∼1.7 for CP‐A and ∼3.1 for CP‐B relative to healthy volunteers; Table ), which appeared to be overpredicted relative to the observed data (predicted hepatic impairment AUC ratios relative to observed hepatic impairment AUC ratios were ∼1.6 and ∼1.9 for CP‐A and CP‐B, respectively; Table ).

Techniques: Drug discovery

Journal: Journal of Clinical Pharmacology

Article Title: Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug Exposure and Support Dosing Recommendations for Potential Drug‐Drug Interactions or in Special Populations: An Example Using Tofacitinib

doi: 10.1002/jcph.1679

Figure Lengend Snippet: Observed and predicted mean (with upper and lower 95% confidence limits) plasma concentration‐versus‐time profiles of tofacitinib after (A) a single intravenous infusion of 10 mg (infusion time, 0.5 hours) and (B) a single oral dose of 10 mg. CI, confidence interval; IV, intravenous; PO, oral.

Article Snippet: The simulations of mild and moderate hepatic impairment using the tofacitinib PBPK model and the Simcyp Liver Cirrhosis CP‐A and CP‐B Population Files show a gradual increase in both C max and AUC values (predicted AUC ratios of ∼1.7 for CP‐A and ∼3.1 for CP‐B relative to healthy volunteers; Table ), which appeared to be overpredicted relative to the observed data (predicted hepatic impairment AUC ratios relative to observed hepatic impairment AUC ratios were ∼1.6 and ∼1.9 for CP‐A and CP‐B, respectively; Table ).

Techniques: Clinical Proteomics, Concentration Assay

Journal: Journal of Clinical Pharmacology

Article Title: Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug Exposure and Support Dosing Recommendations for Potential Drug‐Drug Interactions or in Special Populations: An Example Using Tofacitinib

doi: 10.1002/jcph.1679

Figure Lengend Snippet: Simcyp and Clinical Assessments for Tofacitinib as a Victim of Drug‐Drug Interactions

Article Snippet: The simulations of mild and moderate hepatic impairment using the tofacitinib PBPK model and the Simcyp Liver Cirrhosis CP‐A and CP‐B Population Files show a gradual increase in both C max and AUC values (predicted AUC ratios of ∼1.7 for CP‐A and ∼3.1 for CP‐B relative to healthy volunteers; Table ), which appeared to be overpredicted relative to the observed data (predicted hepatic impairment AUC ratios relative to observed hepatic impairment AUC ratios were ∼1.6 and ∼1.9 for CP‐A and CP‐B, respectively; Table ).

Techniques: Inhibition

Journal: Journal of Clinical Pharmacology

Article Title: Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug Exposure and Support Dosing Recommendations for Potential Drug‐Drug Interactions or in Special Populations: An Example Using Tofacitinib

doi: 10.1002/jcph.1679

Figure Lengend Snippet: Simulation and Clinical Assessments for the Impact of Renal and Hepatic Impairment on Tofacitinib Pharmacokinetics

Article Snippet: The simulations of mild and moderate hepatic impairment using the tofacitinib PBPK model and the Simcyp Liver Cirrhosis CP‐A and CP‐B Population Files show a gradual increase in both C max and AUC values (predicted AUC ratios of ∼1.7 for CP‐A and ∼3.1 for CP‐B relative to healthy volunteers; Table ), which appeared to be overpredicted relative to the observed data (predicted hepatic impairment AUC ratios relative to observed hepatic impairment AUC ratios were ∼1.6 and ∼1.9 for CP‐A and CP‐B, respectively; Table ).

Techniques:

Journal: Journal of Clinical Pharmacology

Article Title: Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug Exposure and Support Dosing Recommendations for Potential Drug‐Drug Interactions or in Special Populations: An Example Using Tofacitinib

doi: 10.1002/jcph.1679

Figure Lengend Snippet: Simcyp Input Parameters for the Tofacitinib PBPK Model

Article Snippet: To further evaluate the tofacitinib Simcyp model in other DDI scenarios under which active renal efflux transporters may be inhibited, the hypothetical effect of complete inhibition of active renal secretion was evaluated using this model. Renal clearance attributed to passive filtration was calculated as follows: passive filtration = GFR × f u,p = 125 mL/min (7.5 L/h) × 0.61 = 4.6 L/h.

Techniques: Binding Assay, Molecular Weight, In Vitro, Recombinant

Journal: Journal of Clinical Pharmacology

Article Title: Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug Exposure and Support Dosing Recommendations for Potential Drug‐Drug Interactions or in Special Populations: An Example Using Tofacitinib

doi: 10.1002/jcph.1679

Figure Lengend Snippet: Arithmetic Mean (SD) Observed and Predicted Pharmacokinetics of Tofacitinib After (a) a Single Intravenous or Oral Dose in Healthy Volunteers and (b) After Multiple (14 Days) Oral Doses of Tofacitinib 15 mg Twice Daily in Healthy Volunteers

Article Snippet: To further evaluate the tofacitinib Simcyp model in other DDI scenarios under which active renal efflux transporters may be inhibited, the hypothetical effect of complete inhibition of active renal secretion was evaluated using this model. Renal clearance attributed to passive filtration was calculated as follows: passive filtration = GFR × f u,p = 125 mL/min (7.5 L/h) × 0.61 = 4.6 L/h.

Techniques: Drug discovery

Journal: Journal of Clinical Pharmacology

Article Title: Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug Exposure and Support Dosing Recommendations for Potential Drug‐Drug Interactions or in Special Populations: An Example Using Tofacitinib

doi: 10.1002/jcph.1679

Figure Lengend Snippet: Observed and predicted mean (with upper and lower 95% confidence limits) plasma concentration‐versus‐time profiles of tofacitinib after (A) a single intravenous infusion of 10 mg (infusion time, 0.5 hours) and (B) a single oral dose of 10 mg. CI, confidence interval; IV, intravenous; PO, oral.

Article Snippet: To further evaluate the tofacitinib Simcyp model in other DDI scenarios under which active renal efflux transporters may be inhibited, the hypothetical effect of complete inhibition of active renal secretion was evaluated using this model. Renal clearance attributed to passive filtration was calculated as follows: passive filtration = GFR × f u,p = 125 mL/min (7.5 L/h) × 0.61 = 4.6 L/h.

Techniques: Clinical Proteomics, Concentration Assay

Journal: Journal of Clinical Pharmacology

Article Title: Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug Exposure and Support Dosing Recommendations for Potential Drug‐Drug Interactions or in Special Populations: An Example Using Tofacitinib

doi: 10.1002/jcph.1679

Figure Lengend Snippet: Simcyp and Clinical Assessments for Tofacitinib as a Victim of Drug‐Drug Interactions

Article Snippet: To further evaluate the tofacitinib Simcyp model in other DDI scenarios under which active renal efflux transporters may be inhibited, the hypothetical effect of complete inhibition of active renal secretion was evaluated using this model. Renal clearance attributed to passive filtration was calculated as follows: passive filtration = GFR × f u,p = 125 mL/min (7.5 L/h) × 0.61 = 4.6 L/h.

Techniques: Inhibition

Journal: Journal of Clinical Pharmacology

Article Title: Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug Exposure and Support Dosing Recommendations for Potential Drug‐Drug Interactions or in Special Populations: An Example Using Tofacitinib

doi: 10.1002/jcph.1679

Figure Lengend Snippet: Simulation and Clinical Assessments for the Impact of Renal and Hepatic Impairment on Tofacitinib Pharmacokinetics

Article Snippet: To further evaluate the tofacitinib Simcyp model in other DDI scenarios under which active renal efflux transporters may be inhibited, the hypothetical effect of complete inhibition of active renal secretion was evaluated using this model. Renal clearance attributed to passive filtration was calculated as follows: passive filtration = GFR × f u,p = 125 mL/min (7.5 L/h) × 0.61 = 4.6 L/h.

Techniques: