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Updated: Jun 29, 2026

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
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Enabling lead optimization with a focus on optimal dose selection by utilizing multi-parametric data visualization

Jörg Berghausen1, Ganesh Rajaraman2, Sujal Deshmukh2

  • 1Novartis Biomedical Research, Pharmacokinetic Sciences, Basel, 4002, Switzerland.

European Journal of Medicinal Chemistry
|June 27, 2026
PubMed
Summary
This summary is machine-generated.

Optimizing drug properties requires balancing absorption, distribution, metabolism, excretion (ADME), and pharmacokinetic (PK) parameters. This study presents an integrated framework using modeling and visualization for systematic PK optimization and dose estimation in drug development.

Keywords:
ADMEDiscoveryDoseIn silicoIn vitroModelsPK

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Area of Science:

  • Drug Discovery and Development
  • Pharmacokinetics and Drug Metabolism
  • Computational Chemistry and Modeling

Background:

  • Optimizing oral drug candidates necessitates balancing absorption, distribution, metabolism, and excretion (ADME) and pharmacokinetic (PK) properties.
  • Traditional methods often focused on individual ADME parameters, potentially discarding viable drug candidates with otherwise balanced profiles.
  • A shift towards integrated approaches, centered on dose and PK, is needed for effective drug development.

Purpose of the Study:

  • To present a framework for integrating ADME/PK principles with predictive modeling and visualizations.
  • To enable systematic optimization of PK properties and dose estimation early in the drug discovery process.
  • To support the identification of clinical candidates by balancing compound properties.

Main Methods:

  • Utilizing a one-compartmental model for integrated PK optimization.
  • Employing visualizations of PK parameter combinations against project-specific thresholds.
  • Integrating basic ADME/PK principles with model predictions and experimental results.

Main Results:

  • Demonstrated how combined property profiles influence early dose estimation and PK parameters.
  • Provided a systematic approach to PK and dose optimization.
  • Facilitated informed decision-making in lead optimization and candidate selection.

Conclusions:

  • An integrated framework enhances the systematic optimization of PK properties and dose.
  • Visualizations and modeling aid in balancing compound profiles for drug development.
  • This approach supports early ideation and efficient integration of experimental data for identifying clinical candidates.