Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though pharmacologically...
Pharmacokinetic–Pharmacodynamic Relationship: Model Components01:14

Pharmacokinetic–Pharmacodynamic Relationship: Model Components

Pharmacokinetic-pharmacodynamic (PK–PD) modeling is essential in drug development and clinical pharmacology. It provides a quantitative framework to predict drug behavior and response over time. This approach integrates pharmacokinetics (PK), which describes the drug's absorption, distribution, metabolism, and excretion, with pharmacodynamics (PD), which characterizes the drug’s biological effects and mechanisms of action.The disposition kinetics of a drug determine its plasma...
Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

Solid dosage forms such as tablets and capsules undergo rigorous manufacturing processes to ensure stability and effectiveness. Their dissolution and absorption properties are influenced significantly by the choice of excipients (inactive ingredients that serve various roles in the formulation), and the methodology applied during production. The manufacturing parameters, such as compression force and granulation techniques, significantly affect dissolution rates. Elevated compression forces...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
Pharmacokinetic–Pharmacodynamic Relationship: Problems01:24

Pharmacokinetic–Pharmacodynamic Relationship: Problems

The empirical approach to drug therapy optimization relies on correlating pharmacological response with administered dosage. Such an approach can be costly, time-consuming, and often yields poor correlation due to variables like formulation factors and drug elimination characteristics. A more precise approach correlates response with plasma drug concentration or the amount of drug in the body, rather than dosage. This is achieved through pharmacokinetic-pharmacodynamic (PK/PD) modeling, which...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Understanding Discordance between In Vitro Dissolution, Local Gut and Systemic Bioequivalence of Budesonide in Healthy and Crohn's Disease Patients through PBPK Modeling.

Pharmaceutics·2023
Same author

Physiologically based pharmacokinetic modeling for development and applications of a virtual celiac disease population using felodipine as a model drug.

CPT: pharmacometrics & systems pharmacology·2023
Same author

Non-specific binding of compounds in <i>in vitro</i> metabolism assays: a comparison of microsomal and hepatocyte binding in different species and an assessment of the accuracy of prediction models.

Xenobiotica; the fate of foreign compounds in biological systems·2022
Same author

Population PBPK modeling using parametric and nonparametric methods of the Simcyp Simulator, and Bayesian samplers.

CPT: pharmacometrics & systems pharmacology·2022
Same author

Mechanistic PBPK Modelling to Predict the Advantage of the Salt Form of a Drug When Dosed with Acid Reducing Agents.

Pharmaceutics·2021

Related Experiment Video

Updated: Jun 17, 2026

Anionic Polymerization of an Amphiphilic Copolymer for Preparation of Block Copolymer Micelles Stabilized by &#960;-&#960; Stacking Interactions
10:53

Anionic Polymerization of an Amphiphilic Copolymer for Preparation of Block Copolymer Micelles Stabilized by π-π Stacking Interactions

Published on: October 10, 2016

Mechanistic PBPK Modeling of pH-Modifying Excipient Effects to Guide Formulation Design for Mitigating pH-Mediated

Siri Kalyan Chirumamilla1, David B Turner1

  • 1Certara Predictive Technologies, Certara UK Limited, Level 2-Acero, 1 Concourse Way, Sheffield S1 2BJ, United Kingdom.

Molecular Pharmaceutics
|June 16, 2026
PubMed
Summary

A new mechanistic model predicts how acidulants in drug formulations affect drug solubility and mitigate drug-drug interactions (DDIs). This approach improves predictions for formulation development and DDI management.

Keywords:
PBBM, PBPKSimcyp simulatoracidulantspH-modifying excipientssurface pH, pH-DDI

More Related Videos

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Dissolving Microneedle Array Patches Manufactured By Solvent Casting Technique and Essential Characterization of Microneedle-Based Biomedical Devices
08:26

Dissolving Microneedle Array Patches Manufactured By Solvent Casting Technique and Essential Characterization of Microneedle-Based Biomedical Devices

Published on: January 30, 2026

Related Experiment Videos

Last Updated: Jun 17, 2026

Anionic Polymerization of an Amphiphilic Copolymer for Preparation of Block Copolymer Micelles Stabilized by &#960;-&#960; Stacking Interactions
10:53

Anionic Polymerization of an Amphiphilic Copolymer for Preparation of Block Copolymer Micelles Stabilized by π-π Stacking Interactions

Published on: October 10, 2016

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Dissolving Microneedle Array Patches Manufactured By Solvent Casting Technique and Essential Characterization of Microneedle-Based Biomedical Devices
08:26

Dissolving Microneedle Array Patches Manufactured By Solvent Casting Technique and Essential Characterization of Microneedle-Based Biomedical Devices

Published on: January 30, 2026

Area of Science:

  • Pharmacokinetics and Drug Metabolism
  • Pharmaceutical Formulation
  • Computational Chemistry

Background:

  • Acidic excipients (acidulants) enhance drug solubility and stability, particularly for basic drugs.
  • Acidulants are increasingly used to manage pH-mediated drug-drug interactions (DDIs).
  • Current selection of excipients relies on empirical methods with limited predictive modeling.

Purpose of the Study:

  • To develop and validate a mechanistic mathematical model for predicting excipient effects on pH.
  • To assess the model's ability to predict in vitro dissolution and in vivo pharmacokinetics (PK).
  • To evaluate the model's utility in predicting acidulant effectiveness for DDI mitigation.

Main Methods:

  • Developed a first-principles mechanistic mathematical model implemented in the Simcyp Simulator.
  • Predicted effects of tartaric and succinic acid on in vitro dissolution and in vivo PK of entrectinib and palbociclib.
  • Accounted for drug self-buffering properties in the presence of excipients and buffers.

Main Results:

  • The model accurately predicted increased in vitro dissolution rates with acidulant use.
  • Acidulants successfully mitigated pH-mediated DDIs in clinical PK studies.
  • The model successfully recovered human PK data with and without acidulants in DDI studies.

Conclusions:

  • The developed mechanistic model effectively predicts acidulant impact on drug dissolution and PK.
  • This model aids in predicting the efficacy of acidulants for pH-DDI mitigation.
  • Supports informed formulation development and DDI management strategies.