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Simcyp piperaquine pbpk model
Overview of the workflow for physiologically‐based pharmacokinetic modeling of <t>piperaquine</t> for a pregnant population using an individualized profile (‘virtual twin’) approach.
Piperaquine Pbpk Model, supplied by Simcyp, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/pbpk+models/model+pbpk+piperaquine/pmc13163144-22-1-10
Average 86 stars, based on 1 article reviews
piperaquine pbpk model - by Bioz Stars, 2026-10
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1) Product Images from "Physiologically‐Based Pharmacokinetic Modeling to Investigate Piperaquine Exposure in Pregnant Women Using an Individualized Profile Approach"

Article Title: Physiologically‐Based Pharmacokinetic Modeling to Investigate Piperaquine Exposure in Pregnant Women Using an Individualized Profile Approach

Journal: Clinical and Translational Science

doi: 10.1111/cts.70589

Overview of the workflow for physiologically‐based pharmacokinetic modeling of piperaquine for a pregnant population using an individualized profile (‘virtual twin’) approach.
Figure Legend Snippet: Overview of the workflow for physiologically‐based pharmacokinetic modeling of piperaquine for a pregnant population using an individualized profile (‘virtual twin’) approach.

Techniques Used:

Individual piperaquine normalized AUC ratios ( R AUC / Dose ) for the first dose (blue solid line) and last dose (blue dotted line) in (a) Sudanese pregnant women, second trimester; (b) Sudanese pregnant women, third trimester; (c) Thai pregnant women, second trimester; (d) Thai pregnant women, third trimester. Internal gray line: R AUC = 0 , middle gray line: R AUC = 1 , and external gray line: R AUC = 2 . ID, identification number for the women included in the clinical trials. ID = patient identification number.
Figure Legend Snippet: Individual piperaquine normalized AUC ratios ( R AUC / Dose ) for the first dose (blue solid line) and last dose (blue dotted line) in (a) Sudanese pregnant women, second trimester; (b) Sudanese pregnant women, third trimester; (c) Thai pregnant women, second trimester; (d) Thai pregnant women, third trimester. Internal gray line: R AUC = 0 , middle gray line: R AUC = 1 , and external gray line: R AUC = 2 . ID, identification number for the women included in the clinical trials. ID = patient identification number.

Techniques Used: Clinical Proteomics

Mean plasma concentration–time plots for piperaquine in plasma in representative individualized profiles compared to observed data for (a) a Sudanese pregnant woman (second trimester, patient ID7), and (b) a Thai pregnant woman (second trimester, patient ID6). The shaded area is 95% prediction interval.
Figure Legend Snippet: Mean plasma concentration–time plots for piperaquine in plasma in representative individualized profiles compared to observed data for (a) a Sudanese pregnant woman (second trimester, patient ID7), and (b) a Thai pregnant woman (second trimester, patient ID6). The shaded area is 95% prediction interval.

Techniques Used: Clinical Proteomics, Concentration Assay

Predicted vs. observed piperaquine clearance up to Day 7 in (a) Sudanese pregnant women, and (b) Thai pregnant women. The central line is the median, the box represents the interquartile range, the cross is the mean, and the whiskers are the lowest and highest values.
Figure Legend Snippet: Predicted vs. observed piperaquine clearance up to Day 7 in (a) Sudanese pregnant women, and (b) Thai pregnant women. The central line is the median, the box represents the interquartile range, the cross is the mean, and the whiskers are the lowest and highest values.

Techniques Used:

Piperaquine concentrations on Day 7 for (a) individual predicted vs. observed plasma concentrations and predictive performance assessed by (b) prediction error (PE), and (c) relative difference (RD). The central line is the median, the box represents the interquartile range, the cross is the mean, and the whiskers are the lowest and highest values. All the predicted concentrations were normalized by the actual mg/kg dose administered to the patient. T2 = second trimester; T3 = third trimester.
Figure Legend Snippet: Piperaquine concentrations on Day 7 for (a) individual predicted vs. observed plasma concentrations and predictive performance assessed by (b) prediction error (PE), and (c) relative difference (RD). The central line is the median, the box represents the interquartile range, the cross is the mean, and the whiskers are the lowest and highest values. All the predicted concentrations were normalized by the actual mg/kg dose administered to the patient. T2 = second trimester; T3 = third trimester.

Techniques Used: Clinical Proteomics

Related Articles

Drug discovery:

Article Title: Exploring the Impact of Developmental Clearance Saturation on Propylene Glycol Exposure in Adults and Term Neonates Using Physiologically Based Pharmacokinetic Modeling.
Article Snippet: .. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense PG Physiologically Based Pharmacokinetic (PBPK) Model Development The PBPK models used for predicting the pharmacokinetics of PG in adults and term neonates were developed in Simcyp (Simcyp Ltd., a Certara company, Sheffield, UK, Version 20). .. The Simcyp simulator has built-in virtual pediatric and adult populations which have been previously validated.26–28 Published physiological and biochemical parameters of pediatrics and adults populations29,30 were used to develop the respective virtual populations in the Simcyp simulator and the interindividual variabilities in those parameters as expected in the real population were incorporated in the virtual human models.31 To account for the impact of age-related changes in physiology and biochemistry on the PK of PG in neonates, the built-in neonatal age group of the virtual pediatric population within the simulator was used for simulations of PG PK.

Recombinant:

Article Title: PBPK Modelling for Drugs Cleared by Non-CYP Enzymes: State-of-the-Art and Future Perspectives.
Article Snippet: Physiologically-based pharmacokinetic (PBPK) modeling has become the established method for predicting human pharmacokinetics (PK) and drug-drug interactions (DDI).. The number of drugs cleared by non-CYP enzyme metabolism has increased steadily and to date, there is no consolidated overview of PBPK modeling for drugs cleared by non-CYP enzymes.. This review aims to describe the state-of-the-art PBPK modeling for drugs cleared via non-CYP enzymes, to identify successful strategies, to describe gaps and to provide suggestions to overcome them.

In Vitro:

Article Title: PBPK Modelling for Drugs Cleared by Non-CYP Enzymes: State-of-the-Art and Future Perspectives.
Article Snippet: Physiologically-based pharmacokinetic (PBPK) modeling has become the established method for predicting human pharmacokinetics (PK) and drug-drug interactions (DDI).. The number of drugs cleared by non-CYP enzyme metabolism has increased steadily and to date, there is no consolidated overview of PBPK modeling for drugs cleared by non-CYP enzymes.. This review aims to describe the state-of-the-art PBPK modeling for drugs cleared via non-CYP enzymes, to identify successful strategies, to describe gaps and to provide suggestions to overcome them.

Article Title: Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective.
Article Snippet: .. Hence building confidence in PBPK modeling to predict DDIs and inform drug labeling of CYP3A substrates offers an alternative approach for drug development.5,13,14 This strategy was recently used successfully for vonoprazan, where instead of conducting a strong inducer clinical DDI study, PBPK simulations were used to inform drug labeling.15,16 PBPK models for rifampin are available in several software platforms (e.g., Simcyp, PK- Sim, and GastroPlus).17–19 For the Simcyp model, iterative optimizations have been made to improve the known underprediction of induction magnitude.17 Factors implicated in the underprediction include variability of in vitro and in vivo data, limited validation of substrate models, rifampin pleiotropic effects, differing in vitro–in vivo extrapolation (IVIVE) scaling approaches, and clinical study design.17,20 In addition to modeling the pleiotropic effects of rifampin, appropriate parameterization of substrate disposition pathways is critical for the development of high- fidelity PBPK models to predict induction DDIs. ..

other:

Article Title: Physiologically Based Pharmacokinetic Modeling of Cannabidiol, Delta‐9‐Tetrahydrocannabinol, and Their Metabolites in Healthy Adults After Administration by Multiple Routes
Article Snippet: PBPK models for these precipitants were applied from the Simcyp library or published models.

Labeling:

Article Title: Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective.
Article Snippet: .. Hence building confidence in PBPK modeling to predict DDIs and inform drug labeling of CYP3A substrates offers an alternative approach for drug development.5,13,14 This strategy was recently used successfully for vonoprazan, where instead of conducting a strong inducer clinical DDI study, PBPK simulations were used to inform drug labeling.15,16 PBPK models for rifampin are available in several software platforms (e.g., Simcyp, PK- Sim, and GastroPlus).17–19 For the Simcyp model, iterative optimizations have been made to improve the known underprediction of induction magnitude.17 Factors implicated in the underprediction include variability of in vitro and in vivo data, limited validation of substrate models, rifampin pleiotropic effects, differing in vitro–in vivo extrapolation (IVIVE) scaling approaches, and clinical study design.17,20 In addition to modeling the pleiotropic effects of rifampin, appropriate parameterization of substrate disposition pathways is critical for the development of high- fidelity PBPK models to predict induction DDIs. ..

Software:

Article Title: Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective.
Article Snippet: .. Hence building confidence in PBPK modeling to predict DDIs and inform drug labeling of CYP3A substrates offers an alternative approach for drug development.5,13,14 This strategy was recently used successfully for vonoprazan, where instead of conducting a strong inducer clinical DDI study, PBPK simulations were used to inform drug labeling.15,16 PBPK models for rifampin are available in several software platforms (e.g., Simcyp, PK- Sim, and GastroPlus).17–19 For the Simcyp model, iterative optimizations have been made to improve the known underprediction of induction magnitude.17 Factors implicated in the underprediction include variability of in vitro and in vivo data, limited validation of substrate models, rifampin pleiotropic effects, differing in vitro–in vivo extrapolation (IVIVE) scaling approaches, and clinical study design.17,20 In addition to modeling the pleiotropic effects of rifampin, appropriate parameterization of substrate disposition pathways is critical for the development of high- fidelity PBPK models to predict induction DDIs. ..

In Vivo:

Article Title: Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective.
Article Snippet: .. Hence building confidence in PBPK modeling to predict DDIs and inform drug labeling of CYP3A substrates offers an alternative approach for drug development.5,13,14 This strategy was recently used successfully for vonoprazan, where instead of conducting a strong inducer clinical DDI study, PBPK simulations were used to inform drug labeling.15,16 PBPK models for rifampin are available in several software platforms (e.g., Simcyp, PK- Sim, and GastroPlus).17–19 For the Simcyp model, iterative optimizations have been made to improve the known underprediction of induction magnitude.17 Factors implicated in the underprediction include variability of in vitro and in vivo data, limited validation of substrate models, rifampin pleiotropic effects, differing in vitro–in vivo extrapolation (IVIVE) scaling approaches, and clinical study design.17,20 In addition to modeling the pleiotropic effects of rifampin, appropriate parameterization of substrate disposition pathways is critical for the development of high- fidelity PBPK models to predict induction DDIs. ..

Biomarker Discovery:

Article Title: Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective.
Article Snippet: .. Hence building confidence in PBPK modeling to predict DDIs and inform drug labeling of CYP3A substrates offers an alternative approach for drug development.5,13,14 This strategy was recently used successfully for vonoprazan, where instead of conducting a strong inducer clinical DDI study, PBPK simulations were used to inform drug labeling.15,16 PBPK models for rifampin are available in several software platforms (e.g., Simcyp, PK- Sim, and GastroPlus).17–19 For the Simcyp model, iterative optimizations have been made to improve the known underprediction of induction magnitude.17 Factors implicated in the underprediction include variability of in vitro and in vivo data, limited validation of substrate models, rifampin pleiotropic effects, differing in vitro–in vivo extrapolation (IVIVE) scaling approaches, and clinical study design.17,20 In addition to modeling the pleiotropic effects of rifampin, appropriate parameterization of substrate disposition pathways is critical for the development of high- fidelity PBPK models to predict induction DDIs. ..



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Overview of the workflow for physiologically‐based pharmacokinetic modeling of piperaquine for a pregnant population using an individualized profile (‘virtual twin’) approach.

Journal: Clinical and Translational Science

Article Title: Physiologically‐Based Pharmacokinetic Modeling to Investigate Piperaquine Exposure in Pregnant Women Using an Individualized Profile Approach

doi: 10.1111/cts.70589

Figure Lengend Snippet: Overview of the workflow for physiologically‐based pharmacokinetic modeling of piperaquine for a pregnant population using an individualized profile (‘virtual twin’) approach.

Article Snippet: The piperaquine PBPK model was previously developed and validated within Simcyp for a healthy non‐pregnant population.

Techniques:

Individual piperaquine normalized AUC ratios ( R AUC / Dose ) for the first dose (blue solid line) and last dose (blue dotted line) in (a) Sudanese pregnant women, second trimester; (b) Sudanese pregnant women, third trimester; (c) Thai pregnant women, second trimester; (d) Thai pregnant women, third trimester. Internal gray line: R AUC = 0 , middle gray line: R AUC = 1 , and external gray line: R AUC = 2 . ID, identification number for the women included in the clinical trials. ID = patient identification number.

Journal: Clinical and Translational Science

Article Title: Physiologically‐Based Pharmacokinetic Modeling to Investigate Piperaquine Exposure in Pregnant Women Using an Individualized Profile Approach

doi: 10.1111/cts.70589

Figure Lengend Snippet: Individual piperaquine normalized AUC ratios ( R AUC / Dose ) for the first dose (blue solid line) and last dose (blue dotted line) in (a) Sudanese pregnant women, second trimester; (b) Sudanese pregnant women, third trimester; (c) Thai pregnant women, second trimester; (d) Thai pregnant women, third trimester. Internal gray line: R AUC = 0 , middle gray line: R AUC = 1 , and external gray line: R AUC = 2 . ID, identification number for the women included in the clinical trials. ID = patient identification number.

Article Snippet: The piperaquine PBPK model was previously developed and validated within Simcyp for a healthy non‐pregnant population.

Techniques: Clinical Proteomics

Mean plasma concentration–time plots for piperaquine in plasma in representative individualized profiles compared to observed data for (a) a Sudanese pregnant woman (second trimester, patient ID7), and (b) a Thai pregnant woman (second trimester, patient ID6). The shaded area is 95% prediction interval.

Journal: Clinical and Translational Science

Article Title: Physiologically‐Based Pharmacokinetic Modeling to Investigate Piperaquine Exposure in Pregnant Women Using an Individualized Profile Approach

doi: 10.1111/cts.70589

Figure Lengend Snippet: Mean plasma concentration–time plots for piperaquine in plasma in representative individualized profiles compared to observed data for (a) a Sudanese pregnant woman (second trimester, patient ID7), and (b) a Thai pregnant woman (second trimester, patient ID6). The shaded area is 95% prediction interval.

Article Snippet: The piperaquine PBPK model was previously developed and validated within Simcyp for a healthy non‐pregnant population.

Techniques: Clinical Proteomics, Concentration Assay

Predicted vs. observed piperaquine clearance up to Day 7 in (a) Sudanese pregnant women, and (b) Thai pregnant women. The central line is the median, the box represents the interquartile range, the cross is the mean, and the whiskers are the lowest and highest values.

Journal: Clinical and Translational Science

Article Title: Physiologically‐Based Pharmacokinetic Modeling to Investigate Piperaquine Exposure in Pregnant Women Using an Individualized Profile Approach

doi: 10.1111/cts.70589

Figure Lengend Snippet: Predicted vs. observed piperaquine clearance up to Day 7 in (a) Sudanese pregnant women, and (b) Thai pregnant women. The central line is the median, the box represents the interquartile range, the cross is the mean, and the whiskers are the lowest and highest values.

Article Snippet: The piperaquine PBPK model was previously developed and validated within Simcyp for a healthy non‐pregnant population.

Techniques:

Piperaquine concentrations on Day 7 for (a) individual predicted vs. observed plasma concentrations and predictive performance assessed by (b) prediction error (PE), and (c) relative difference (RD). The central line is the median, the box represents the interquartile range, the cross is the mean, and the whiskers are the lowest and highest values. All the predicted concentrations were normalized by the actual mg/kg dose administered to the patient. T2 = second trimester; T3 = third trimester.

Journal: Clinical and Translational Science

Article Title: Physiologically‐Based Pharmacokinetic Modeling to Investigate Piperaquine Exposure in Pregnant Women Using an Individualized Profile Approach

doi: 10.1111/cts.70589

Figure Lengend Snippet: Piperaquine concentrations on Day 7 for (a) individual predicted vs. observed plasma concentrations and predictive performance assessed by (b) prediction error (PE), and (c) relative difference (RD). The central line is the median, the box represents the interquartile range, the cross is the mean, and the whiskers are the lowest and highest values. All the predicted concentrations were normalized by the actual mg/kg dose administered to the patient. T2 = second trimester; T3 = third trimester.

Article Snippet: The piperaquine PBPK model was previously developed and validated within Simcyp for a healthy non‐pregnant population.

Techniques: Clinical Proteomics

OSP Extension Module concept. PK‐Sim models are composed of individual Building Blocks ( https://docs.open‐systems‐pharmacology.org/v12/open‐systems‐pharmacology‐suite/modules‐philsophy‐building‐blocks ) separating information used for model building into dedicated groups. Building Blocks are combined to generate a model, they can be reused and combined to create different models. When transferring a PK‐SIM simulation to MoBi, model Building Blocks are further organized into a module. Modules represent either a full‐scale PBPK model imported from PK‐Sim (blue rectangle), or extension modules (orange rectangles) that represent model modifications or adaptations such as disease populations, custom administration routes, altered or new special structures that represent tissues or any other modification of the underlaying Building Blocks. Extension Modules provide a standardized, systematic, and reproducible way to efficiently develop and share complex models or model modifications across different projects and modeling scientists.

Journal: CPT: Pharmacometrics & Systems Pharmacology

Article Title: Open Systems Pharmacology Community Conference ( OSP ‐ CC ) Proceedings 2025

doi: 10.1002/psp4.70217

Figure Lengend Snippet: OSP Extension Module concept. PK‐Sim models are composed of individual Building Blocks ( https://docs.open‐systems‐pharmacology.org/v12/open‐systems‐pharmacology‐suite/modules‐philsophy‐building‐blocks ) separating information used for model building into dedicated groups. Building Blocks are combined to generate a model, they can be reused and combined to create different models. When transferring a PK‐SIM simulation to MoBi, model Building Blocks are further organized into a module. Modules represent either a full‐scale PBPK model imported from PK‐Sim (blue rectangle), or extension modules (orange rectangles) that represent model modifications or adaptations such as disease populations, custom administration routes, altered or new special structures that represent tissues or any other modification of the underlaying Building Blocks. Extension Modules provide a standardized, systematic, and reproducible way to efficiently develop and share complex models or model modifications across different projects and modeling scientists.

Article Snippet: Systematic development of PBPK models to support candidate selection and accelerate drug discovery , Grégori Gerebtzoff (Novartis).

Techniques: Transferring, Modification