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Certara L.P simcyp pbpk simulator v21
Key <t>PBPK</t> modelling steps and components of each clinical study used in model development, refinement, verification and application. BCRP, breast cancer resistance protein; DDI, drug–drug interaction; fu inc , fraction of unbound dersimelagon in in vitro system; K m , Michaelis constant; MAD, multiple ascending dose; OATP, organic anion transporting polypeptide; PBPK, physiologically based pharmacokinetic; PK, pharmacokinetic; QD, once daily; V max , maximum velocity of reaction; V ss , volume of distribution at steady state.
Simcyp Pbpk Simulator V21, supplied by Certara L.P, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/simcyp+v21/simcyp+simulator+v23/pmc11773107-133-6-10
Average 90 stars, based on 1 article reviews
simcyp pbpk simulator v21 - by Bioz Stars, 2026-09
90/100 stars

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1) Product Images from "Physiologically based pharmacokinetic modelling to predict potential drug–drug interactions of dersimelagon (MT‐7117)"

Article Title: Physiologically based pharmacokinetic modelling to predict potential drug–drug interactions of dersimelagon (MT‐7117)

Journal: British Journal of Clinical Pharmacology

doi: 10.1111/bcp.16271

Key PBPK modelling steps and components of each clinical study used in model development, refinement, verification and application. BCRP, breast cancer resistance protein; DDI, drug–drug interaction; fu inc , fraction of unbound dersimelagon in in vitro system; K m , Michaelis constant; MAD, multiple ascending dose; OATP, organic anion transporting polypeptide; PBPK, physiologically based pharmacokinetic; PK, pharmacokinetic; QD, once daily; V max , maximum velocity of reaction; V ss , volume of distribution at steady state.
Figure Legend Snippet: Key PBPK modelling steps and components of each clinical study used in model development, refinement, verification and application. BCRP, breast cancer resistance protein; DDI, drug–drug interaction; fu inc , fraction of unbound dersimelagon in in vitro system; K m , Michaelis constant; MAD, multiple ascending dose; OATP, organic anion transporting polypeptide; PBPK, physiologically based pharmacokinetic; PK, pharmacokinetic; QD, once daily; V max , maximum velocity of reaction; V ss , volume of distribution at steady state.

Techniques Used: In Vitro

Input parameters of the  physiologically based pharmacokinetic  model for dersimelagon.
Figure Legend Snippet: Input parameters of the physiologically based pharmacokinetic model for dersimelagon.

Techniques Used: Binding Assay, Software, Permeability, In Vitro, Inhibition

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Article Title: Model-informed repurposing of eliglustat for treatment and prophylaxis of Shiga toxin-producing Escherichia coli hemolytic-uremic syndrome (STEC-HUS) in children.
Article Snippet: PBPK simulations were conducted using the Simcyp® Simulator (version 21; Certara, Sheffield, UK).

Article Title: Establishing a physiologically based pharmacokinetic framework for aldehyde oxidase and dual aldehyde oxidase-CYP substrates.
Article Snippet: Development and refinement of PBPK models PBPK modeling and simulations were performed following a stepwise approach (Figure 1) using the Simcyp simulator (version 21; Certara, Sheffield, UK).

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 were developed using the Simcyp PBPK Simulator (version 22, Certara, Sheffield, UK) and the virtual healthy adult population.

Article Title: Inhibition of Neural Crest Cell Migration by Strobilurin Fungicides and Other Mitochondrial Toxicants
Article Snippet: A physiology-based kinetic (PBK) modeling was performed for picoxystrobin in the Simcyp Simulator V22 (Certara, UK) using a previously published approach [ ].

Software:

Article Title: Drug-Drug Interaction Potential of Mavacamten with Midazolam: Combined Results from Clinical and Model-Based Studies.
Article Snippet: .. Simulations Using a PBPK Model APBPKmodel was previously developed using Simcyp Simulator version 19 (Certara, Princeton, NJ) to investigate the impact of CYP inducers and inhibitors, and CYP2C19 phenotype on mavacamten exposure.23 In December 2021, an updated version of the PBPK software was released, Simcyp Simulator version 21 (Certara, Princeton, NJ), which implemented updates to the CYP2C19 characteristics in the adult population, including the values for hepatic and intestinal CYP2C19 enzyme abundance, changes to the existing UM population, and the addition of IM and RM phenotypes.24 The mavacamten model initially developed in Simcyp nloaded from https://accp1.onlinelibrary.w iley.com /doi/10.1002/jcph.6175 by M anipal A cadem y O f H igher E ducation, W iley O nline L ibrary on [25/12/2024]. .. 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 Simulator version 19 was updated in Simcyp Simulator version 21 by incorporating additional mechanistic absorption and distribution components (Simcyp Simulator M-ADAM), and refining enzymatic clearance pathways and DDI-related parameters.

Article Title: Heterotropic Allosteric Modulation of CYP3A4 In Vitro by Progesterone: Evidence for Improvement in Prediction of Time Dependent Inhibition for Macrolides.
Article Snippet: 226 words) Predictions of drug-drug interactions resulting from time-dependent inhibition (TDI) of CYP3A4 have consistently overestimated or mis-predicted (i.e. false positives) the interaction that is observed in vivo.. Recent findings demonstrated that the presence of the allosteric modulator progesterone (PGS) in the in vitro assay could alter the in vitro kinetics of CYP3A4 TDI with inhibitors that interact with the heme moiety, such as metabolic-intermediate complex (MIC) forming inhibitors.. The impact of the presence of 100 μM PGS on the TDI of molecules in the class of macrolides typically associated with MIC formation was investigated.

Article Title: Quantitative contributions of hepatic and renal organic cation transporters to the clinical pharmacokinetic cimetidine-metformin interaction
Article Snippet: .. A PBPK model was developed to investigate the DDI between metformin and cimetidine using Simcyp software (version 22.0, Certara, Sheffield, UK) to gain mechanistic insights into the contributions of hepatic OCT1 and renal OCT2 to the interaction. ..

Clinical Proteomics:

Article Title: Heterotropic Allosteric Modulation of CYP3A4 In Vitro by Progesterone: Evidence for Improvement in Prediction of Time Dependent Inhibition for Macrolides.
Article Snippet: 226 words) Predictions of drug-drug interactions resulting from time-dependent inhibition (TDI) of CYP3A4 have consistently overestimated or mis-predicted (i.e. false positives) the interaction that is observed in vivo.. Recent findings demonstrated that the presence of the allosteric modulator progesterone (PGS) in the in vitro assay could alter the in vitro kinetics of CYP3A4 TDI with inhibitors that interact with the heme moiety, such as metabolic-intermediate complex (MIC) forming inhibitors.. The impact of the presence of 100 μM PGS on the TDI of molecules in the class of macrolides typically associated with MIC formation was investigated.



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Key <t>PBPK</t> modelling steps and components of each clinical study used in model development, refinement, verification and application. BCRP, breast cancer resistance protein; DDI, drug–drug interaction; fu inc , fraction of unbound dersimelagon in in vitro system; K m , Michaelis constant; MAD, multiple ascending dose; OATP, organic anion transporting polypeptide; PBPK, physiologically based pharmacokinetic; PK, pharmacokinetic; QD, once daily; V max , maximum velocity of reaction; V ss , volume of distribution at steady state.
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Key PBPK modelling steps and components of each clinical study used in model development, refinement, verification and application. BCRP, breast cancer resistance protein; DDI, drug–drug interaction; fu inc , fraction of unbound dersimelagon in in vitro system; K m , Michaelis constant; MAD, multiple ascending dose; OATP, organic anion transporting polypeptide; PBPK, physiologically based pharmacokinetic; PK, pharmacokinetic; QD, once daily; V max , maximum velocity of reaction; V ss , volume of distribution at steady state.

Journal: British Journal of Clinical Pharmacology

Article Title: Physiologically based pharmacokinetic modelling to predict potential drug–drug interactions of dersimelagon (MT‐7117)

doi: 10.1111/bcp.16271

Figure Lengend Snippet: Key PBPK modelling steps and components of each clinical study used in model development, refinement, verification and application. BCRP, breast cancer resistance protein; DDI, drug–drug interaction; fu inc , fraction of unbound dersimelagon in in vitro system; K m , Michaelis constant; MAD, multiple ascending dose; OATP, organic anion transporting polypeptide; PBPK, physiologically based pharmacokinetic; PK, pharmacokinetic; QD, once daily; V max , maximum velocity of reaction; V ss , volume of distribution at steady state.

Article Snippet: All simulations were conducted using the Simcyp PBPK Simulator v21 (Certara), which integrates demographic, physiological, genomic and in vitro biochemical data to allow prediction of PK in virtual healthy populations.

Techniques: In Vitro

Input parameters of the  physiologically based pharmacokinetic  model for dersimelagon.

Journal: British Journal of Clinical Pharmacology

Article Title: Physiologically based pharmacokinetic modelling to predict potential drug–drug interactions of dersimelagon (MT‐7117)

doi: 10.1111/bcp.16271

Figure Lengend Snippet: Input parameters of the physiologically based pharmacokinetic model for dersimelagon.

Article Snippet: All simulations were conducted using the Simcyp PBPK Simulator v21 (Certara), which integrates demographic, physiological, genomic and in vitro biochemical data to allow prediction of PK in virtual healthy populations.

Techniques: Binding Assay, Software, Permeability, In Vitro, Inhibition