Journal: CPT: Pharmacometrics & Systems Pharmacology

Article Title: Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling

doi: 10.1002/psp4.12805

Figure Lengend Snippet: Summary of key input parameters for the pemigatinib physiologically based pharmacokinetic model

Article Snippet: In addition, the pemigatinib PBPK model was further validated by simulation of pemigatinib PK at 6, 9, 13.5, and 20 mg q.d. doses using the Simcyp virtual cancer population, with the study design matching the corresponding phase I study in adults with advanced malignancies.

Techniques: Molecular Weight, Solubility

Journal: CPT: Pharmacometrics & Systems Pharmacology

Article Title: Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling

doi: 10.1002/psp4.12805

Figure Lengend Snippet: Simulated and observed mean plasma concentration–time profiles of pemigatinib: (a) single oral dose of 4.5 mg pemigatinib in the absence of itraconazole, (b) single oral dose of 13.5 mg pemigatinib in the absence of rifampin, (c) multiple oral dose (6–20 mg) of pemigatinib in patients with cancer. Simulated mean = red solid line; simulated 5% and 95% = red dashed line; observed = black circle

Article Snippet: In addition, the pemigatinib PBPK model was further validated by simulation of pemigatinib PK at 6, 9, 13.5, and 20 mg q.d. doses using the Simcyp virtual cancer population, with the study design matching the corresponding phase I study in adults with advanced malignancies.

Techniques: Clinical Proteomics, Concentration Assay

Journal: CPT: Pharmacometrics & Systems Pharmacology

Article Title: Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling

doi: 10.1002/psp4.12805

Figure Lengend Snippet: Predicted and observed pemigatinib exposures (geometric mean) for single oral doses of 4.5 or 13.5 mg pemigatinib in healthy volunteers and multiple oral doses of pemigatinib in patients with cancer

Article Snippet: In addition, the pemigatinib PBPK model was further validated by simulation of pemigatinib PK at 6, 9, 13.5, and 20 mg q.d. doses using the Simcyp virtual cancer population, with the study design matching the corresponding phase I study in adults with advanced malignancies.

Techniques:

Journal: CPT: Pharmacometrics & Systems Pharmacology

Article Title: Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling

doi: 10.1002/psp4.12805

Figure Lengend Snippet: Predicted and observed pemigatinib C max and AUC ratios following a single oral dose of 9 mg pemigatinib tablets with and without itraconazole or rifampin administration

Article Snippet: In addition, the pemigatinib PBPK model was further validated by simulation of pemigatinib PK at 6, 9, 13.5, and 20 mg q.d. doses using the Simcyp virtual cancer population, with the study design matching the corresponding phase I study in adults with advanced malignancies.

Techniques:

Journal: CPT: Pharmacometrics & Systems Pharmacology

Article Title: Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling

doi: 10.1002/psp4.12805

Figure Lengend Snippet: Simulated and observed plasma concentration–time profiles of a single oral dose of 9 mg pemigatinib when coadministered with (a) itraconazole (200 mg once daily [q.d.]) and (b) rifampin (600 mg q.d.)

Article Snippet: In addition, the pemigatinib PBPK model was further validated by simulation of pemigatinib PK at 6, 9, 13.5, and 20 mg q.d. doses using the Simcyp virtual cancer population, with the study design matching the corresponding phase I study in adults with advanced malignancies.

Techniques: Clinical Proteomics, Concentration Assay

Journal: CPT: Pharmacometrics & Systems Pharmacology

Article Title: Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling

doi: 10.1002/psp4.12805

Figure Lengend Snippet: Simulated pemigatinib drug–drug interactions with various CYP3A4 inhibitors or inducers

Article Snippet: In addition, the pemigatinib PBPK model was further validated by simulation of pemigatinib PK at 6, 9, 13.5, and 20 mg q.d. doses using the Simcyp virtual cancer population, with the study design matching the corresponding phase I study in adults with advanced malignancies.

Techniques: Inhibition

Journal: CPT: Pharmacometrics & Systems Pharmacology

Article Title: Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling

doi: 10.1002/psp4.12805

Figure Lengend Snippet: Observed and simulated AUC and C max ratios of a single dose of 13.5 mg pemigatinib with various cytochrome P450 3A4 inhibitors and inducers. AUC, area under the plasma drug concentration–time curve; C max , maximum plasma drug concentration; CI, confidence interval; DDI, drug–drug interaction

Article Snippet: In addition, the pemigatinib PBPK model was further validated by simulation of pemigatinib PK at 6, 9, 13.5, and 20 mg q.d. doses using the Simcyp virtual cancer population, with the study design matching the corresponding phase I study in adults with advanced malignancies.

Techniques: Clinical Proteomics, Concentration Assay

Journal: The AAPS Journal

Article Title: Physiologically Based Pharmacokinetic Predictions of Tramadol Exposure Throughout Pediatric Life: an Analysis of the Different Clearance Contributors with Emphasis on CYP2D6 Maturation

doi: 10.1208/s12248-015-9803-z

Figure Lengend Snippet: A one-compartment linear disposition model was used to fit the parent drug. An additional compartment for the ODT metabolite was linked to the central compartment by its formation clearance (CLP2M). Urinary excretion of tramadol (CLPR) was estimated based on the total amount of tramadol found in urine (UR). ODT moiety-related clearance (CLM) and volume (VM) were estimated based on the ODT plasma concentrations and total ODT amount found in urine (UR); CLPO other clearance parent

Article Snippet: An intravenous tramadol retrograde PBPK model was set up in Simcyp® using adult clearance values, qualified for CYP2D6, CYP3A4, CYP2B6, and renal contributions.

Techniques: Clinical Proteomics

Journal: The AAPS Journal

Article Title: Physiologically Based Pharmacokinetic Predictions of Tramadol Exposure Throughout Pediatric Life: an Analysis of the Different Clearance Contributors with Emphasis on CYP2D6 Maturation

doi: 10.1208/s12248-015-9803-z

Figure Lengend Snippet: Criteria by which CYP contributions in the retrograde model are assessed. CYP2D6 involvement was estimated by comparing hepatic clearance fold increase from PM to EM between predictions and in vivo. CYP2B6-CYP3A4 involvement was estimated by comparing AUCinduced/AUCcontrol ratios between predictions and observations

Article Snippet: An intravenous tramadol retrograde PBPK model was set up in Simcyp® using adult clearance values, qualified for CYP2D6, CYP3A4, CYP2B6, and renal contributions.

Techniques: In Vivo, Control

Journal: The AAPS Journal

Article Title: Physiologically Based Pharmacokinetic Predictions of Tramadol Exposure Throughout Pediatric Life: an Analysis of the Different Clearance Contributors with Emphasis on CYP2D6 Maturation

doi: 10.1208/s12248-015-9803-z

Figure Lengend Snippet: DDI clinical trial simulation of tramadol and rifampicin to assess the CYP2B6-3A4 contribution. The figure depicts the results of 100 trial simulations mimicking the original trial, when CYP2B6-3A4 contributions are assumed to be 10–42%, respectively. Black horizontal lines represent the in vivo observed ratio of AUC geo means (solid) and 90% confidence limits (dashed). The solid red line represents the average ratio of simulated AUC geo means. Since the solid black line still is in the 90% confidence region (grayed area) of the simulated ratio, we assume that CYP2B6 contribution should be between 0 and 10%

Article Snippet: An intravenous tramadol retrograde PBPK model was set up in Simcyp® using adult clearance values, qualified for CYP2D6, CYP3A4, CYP2B6, and renal contributions.

Techniques: In Vivo

Journal: The AAPS Journal

Article Title: Physiologically Based Pharmacokinetic Predictions of Tramadol Exposure Throughout Pediatric Life: an Analysis of the Different Clearance Contributors with Emphasis on CYP2D6 Maturation

doi: 10.1208/s12248-015-9803-z

Figure Lengend Snippet: Fractional Activities of Pediatric Tramadol Metabolism (Relative to Adult Activity) for the Formation of ODT and NDT Are Provided, Together with Those for the Probe Substrates DEX and MDZ Representative of CYP2D6 and CYP3A4 Activity, Respectively

Article Snippet: An intravenous tramadol retrograde PBPK model was set up in Simcyp® using adult clearance values, qualified for CYP2D6, CYP3A4, CYP2B6, and renal contributions.

Techniques: Activity Assay

Journal: The AAPS Journal

Article Title: Physiologically Based Pharmacokinetic Predictions of Tramadol Exposure Throughout Pediatric Life: an Analysis of the Different Clearance Contributors with Emphasis on CYP2D6 Maturation

doi: 10.1208/s12248-015-9803-z

Figure Lengend Snippet: Maturation of total tramadol clearance as a function of PMA (left) and body weight (right). PBPK predictions, represented as blue dots, are compared to in vivo maturation functions: Hill model (19) (black), exponential model (20) (red), and WinNonlin fits (light brown)

Article Snippet: An intravenous tramadol retrograde PBPK model was set up in Simcyp® using adult clearance values, qualified for CYP2D6, CYP3A4, CYP2B6, and renal contributions.

Techniques: In Vivo

Journal: The AAPS Journal

Article Title: Physiologically Based Pharmacokinetic Predictions of Tramadol Exposure Throughout Pediatric Life: an Analysis of the Different Clearance Contributors with Emphasis on CYP2D6 Maturation

doi: 10.1208/s12248-015-9803-z

Figure Lengend Snippet: Maturation of CYP2D6 tramadol clearance as a function of PMA (left) and body weight (right). PBPK predictions, represented as blue dots, are compared to in vivo maturation functions: Hill model (19) (black), exponential model (20) (red), and WinNonlin fits (light brown)

Article Snippet: An intravenous tramadol retrograde PBPK model was set up in Simcyp® using adult clearance values, qualified for CYP2D6, CYP3A4, CYP2B6, and renal contributions.

Techniques: In Vivo

Journal: The AAPS Journal

Article Title: Physiologically Based Pharmacokinetic Predictions of Tramadol Exposure Throughout Pediatric Life: an Analysis of the Different Clearance Contributors with Emphasis on CYP2D6 Maturation

doi: 10.1208/s12248-015-9803-z

Figure Lengend Snippet: Maturation of renal tramadol clearance as a function of PMA (left) and body weight (right). PBPK predictions, represented as blue dots, are compared to WinNonlin fits (light brown)

Article Snippet: An intravenous tramadol retrograde PBPK model was set up in Simcyp® using adult clearance values, qualified for CYP2D6, CYP3A4, CYP2B6, and renal contributions.

Techniques:

Journal: The AAPS Journal

Article Title: Physiologically Based Pharmacokinetic Predictions of Tramadol Exposure Throughout Pediatric Life: an Analysis of the Different Clearance Contributors with Emphasis on CYP2D6 Maturation

doi: 10.1208/s12248-015-9803-z

Figure Lengend Snippet: Maturation of total tramadol clearance as a function of PMA. Comparing the PBPK predictions (blue dots) to the in vivo Hill maturation function (red dots) reveals that the largest underprediction in total clearance is manifested between 2 and 13 years (100–650 weeks) of age

Article Snippet: An intravenous tramadol retrograde PBPK model was set up in Simcyp® using adult clearance values, qualified for CYP2D6, CYP3A4, CYP2B6, and renal contributions.

Techniques: In Vivo

Journal: The AAPS Journal

Article Title: Physiologically Based Pharmacokinetic Predictions of Tramadol Exposure Throughout Pediatric Life: an Analysis of the Different Clearance Contributors with Emphasis on CYP2D6 Maturation

doi: 10.1208/s12248-015-9803-z

Figure Lengend Snippet: Maturation of CYP2D6 tramadol clearance as a function of PMA. Comparing the PBPK predictions (blue dots) to the in vivo Hill maturation function (red dots) reveals that the largest underprediction in CYP2D6 clearance is manifested between 2 and 13 years (100–650 weeks) of age

Article Snippet: An intravenous tramadol retrograde PBPK model was set up in Simcyp® using adult clearance values, qualified for CYP2D6, CYP3A4, CYP2B6, and renal contributions.

Techniques: In Vivo

Journal: EFSA Journal

Article Title: Statement on the toxicological properties and maximum residue levels of acetamiprid and its metabolites

doi: 10.2903/j.efsa.2024.8759

Figure Lengend Snippet: Overview of identified major uncertainties related to metabolism and excretion processes of the available PBK model for acetamiprid.

Article Snippet: However, as the combination of these different uncertain model parameters will affect the PBK model predictions for the internal dosimetry and urinary levels of both acetamiprid and IM‐2‐1, i.e. the critical PBK model outcomes that are required to answer the assessment question 1b, the WG considered that a translation of the available PBK model in Simcyp to a similar PBK model in PK‐Sim would not result in a PBK model that is sufficiently robust to answer the assessment question 1b of the mandate.

Techniques: Radioactivity, Selection, In Vivo

Journal: EFSA Journal

Article Title: Statement on the toxicological properties and maximum residue levels of acetamiprid and its metabolites

doi: 10.2903/j.efsa.2024.8759

Figure Lengend Snippet: Schematic overview of interrelation of identified uncertainties related to metabolism and excretion processes of acetamiprid and IM‐2‐1 in the available PBK model for acetamiprid. The PBK model parameter values chosen for the kinetic processes related to U1, U2 and U4 impact on predicted internal and urinary acetamiprid concentrations (U6). The PBK model parameter values chosen for the kinetic processes related to U1–U5 impact on predicted internal and urinary IM‐2‐1 concentrations (U7).

Article Snippet: However, as the combination of these different uncertain model parameters will affect the PBK model predictions for the internal dosimetry and urinary levels of both acetamiprid and IM‐2‐1, i.e. the critical PBK model outcomes that are required to answer the assessment question 1b, the WG considered that a translation of the available PBK model in Simcyp to a similar PBK model in PK‐Sim would not result in a PBK model that is sufficiently robust to answer the assessment question 1b of the mandate.

Techniques: