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Related Concept Videos

Transdermal Drug Delivery Systems01:18

Transdermal Drug Delivery Systems

Transdermal drug delivery systems (TDDS) enable the controlled release of drugs across the skin into systemic circulation. They are particularly advantageous for drugs with short half-lives or narrow therapeutic indices, as they maintain consistent plasma concentrations and reduce the risk of subtherapeutic or toxic levels.TDDS are categorized into monolithic, reservoir, and mixed systems. Monolithic systems embed the drug in a polymer matrix, where diffusion governs release. Reservoir systems...
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...
Modified-Release Drug Delivery Systems: Site-Targeted01:24

Modified-Release Drug Delivery Systems: Site-Targeted

Site-targeted drug delivery systems enhance therapeutic efficacy while minimizing systemic toxicity and treatment costs. Unlike conventional methods, these systems ensure precise drug delivery, improving bioavailability and reducing side effects. Targeted drug delivery is classified into three levels. First-order targeting directs drugs to the capillary beds of specific organs or tissues. Second-order targets specific cell types, such as tumor cells, using receptor-mediated interactions.
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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).

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Related Experiment Video

Updated: Jun 5, 2026

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers
18:57

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers

Published on: October 17, 2013

Advancing transdermal drug delivery using translational data and mechanistic based modeling.

John P Rose1, Nisha Shrestha1, Yogesh Prakash Patil2

  • 1Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA.

Drug Delivery and Translational Research
|June 3, 2026
PubMed
Summary

This study developed an in silico model to predict transdermal drug delivery success. The model accelerates the selection of drug candidates for transdermal drug delivery systems (TDDS) by simulating skin permeation.

Keywords:
diffusion coefficientmodelingpartition coefficientstratum corneumtransdermal delivery

Related Experiment Videos

Last Updated: Jun 5, 2026

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers
18:57

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers

Published on: October 17, 2013

Area of Science:

  • Pharmacology
  • Drug Delivery
  • Computational Modeling

Background:

  • Transdermal drug delivery systems (TDDS) offer patient benefits but face high attrition rates in early discovery.
  • Current predictive screening methods for transdermal candidates are limited, hindering efficient development.

Purpose of the Study:

  • To introduce a mechanistic, data-driven in silico model for accelerating transdermal drug candidate selection.
  • To improve the early feasibility assessment and optimization of molecules for TDDS.

Main Methods:

  • Developed a computational model simulating passive diffusive transport across the epidermis.
  • Focused on key parameters: partition coefficient (KSC/patch) and stratum corneum diffusion coefficient.
  • Validated the model using translational data from four approved transdermal drugs (fentanyl, rivastigmine, nicotine, lidocaine).

Main Results:

  • The in silico model demonstrated strong agreement with in vitro and in vivo data for marketed transdermal drugs.
  • The platform provides mechanistic insights into transport parameters and establishes in vitro/in vivo correlations.
  • Enabled thermodynamic rationale for observed relationships between drug properties and transdermal performance.

Conclusions:

  • The developed in silico model supports rational design and early assessment of transdermal drug candidates.
  • This approach enhances the efficient identification and optimization of molecules for pre-clinical and clinical translation.
  • Addresses the challenge of high attrition rates in transdermal drug discovery through predictive modeling.