Study
Commercial ProductionHigh ImpactStrong effect

Passive Diffusion Method Achieves 3% Drug Loading in 3D Printed Filaments for Personalised Medicines

A passive diffusion loading method, guided by Hansen Solubility Parameters and filament stiffness, can effectively load drugs into printable filaments for 3D-printed personalised medicines, achieving up to 3% w/w drug loading.

Pharmaceutics · 2020

01

Key Findings

  • 01Passive diffusion is a viable method for loading drugs into filaments for FDM.
  • 02Hansen Solubility Parameters (HSP) and HSP distances (Ra) can predict optimal drug-solvent and solvent-filament combinations for high drug loading.
  • 03Filament stiffness and surface roughness significantly influence drug diffusion into filaments.
  • 04A predictive model using Support Vector Machine regression showed a strong correlation between Ra, filament stiffness, and drug loading.
  • 05Up to 3% w/w drug loading was achieved with a model BCS Class II drug (nifedipine).
02

Application

Design takeaway

When designing drug-loaded filaments for 3D printing, prioritise materials and processes that facilitate passive diffusion, guided by solubility parameters and mechanical properties like stiffness.

How to apply

When developing drug-eluting materials for additive manufacturing, use solubility parameter calculations to guide material selection and conduct stiffness measurements to predict drug loading capacity.

Project actions

  • 01Consider using solubility parameters to guide material selection for drug delivery projects.
  • 02Investigate how material properties like stiffness or surface texture affect the performance of your designed product.
03

Method & Evidence

AimTo develop and validate a passive diffusion method for loading drugs into filaments for Fused Deposition Modelling (FDM) of personalised solid dosage forms, optimizing material properties for successful diffusion and predicting drug loading.
MethodExperimental and computational modelling (Support Vector Machine regression).
ProcedureThe researchers utilized Hansen Solubility Parameters (HSP) to pre-screen optimal drug-solvent and solvent-filament combinations. They then experimentally loaded a model drug (nifedipine) into filaments using passive diffusion, investigating the role of surface roughness and stiffness. A predictive model based on Support Vector Machine regression was developed to correlate HSP distances and filament stiffness with drug loading. Finally, 3D printed tablets were fabricated and their dissolution characteristics and chemical stability were assessed.
ContextPharmaceutical manufacturing, personalised medicine, 3D printing.

Variables

IV["Hansen Solubility Parameters (HSP) of drug, solvent, and filament","Filament stiffness","Surface roughness"]
DV["Drug loading percentage (w/w)","Dissolution rate","Chemical stability"]
CV["Type of drug (BCS Class II, nifedipine)","Filament material (PVA-derived)","Printing parameters (temperature, speed, layer height)"]
04

Strengths & Limitations

Strengths

  • +Utilisation of a predictive modelling approach (SVM regression).
  • +Investigation of multiple material properties influencing drug loading.
  • +Assessment of final dosage form performance (dissolution, stability).

Limitations

The predictive model may not be universally applicable to all drug-excipient systems. The study focused on passive diffusion, and other loading methods might yield different results. The long-term stability and efficacy in real-world clinical settings were predicted rather than definitively proven.

Reliability & validity

Reliability could be enhanced by repeating the diffusion and loading measurements multiple times. Validity is supported by the use of established scientific principles (HSP) and a predictive model, as well as the comparison of 3D printed tablets to commercial forms.

Think critically

How might the limitations of passive diffusion loading, such as the time required or the potential for incomplete loading, be overcome in a design project aiming for rapid, on-demand manufacturing?

05

Design Principles

"Material selection and process design should be guided by predictive models that correlate physicochemical properties with drug loading efficiency for additive manufacturing of pharmaceuticals."

This research offers a practical and accessible method for incorporating active pharmaceutical ingredients into filaments used for 3D printing dosage forms. It addresses a key challenge in the development of personalised medicines by providing a predictable and efficient drug loading process, potentially streamlining clinical manufacturing.

06

What This Means for Your Design

This study found a way to get drugs into the plastic 'ink' used for 3D printing medicine. By understanding how well the drug dissolves in the plastic and how stiff the plastic is, they could predict how much drug would get loaded. They managed to get a good amount of drug in, and the 3D printed pills worked well.

How to use in your project

  • 1.Reference this study when discussing material selection for drug delivery systems or additive manufacturing processes.
  • 2.Use the concept of solubility parameters as a theoretical framework for predicting material compatibility in your design project.
07

Add to My Project

08

Quick Cite

(2020). Personalised 3D Printed Medicines: Optimising Material Properties for Successful Passive Diffusion Loading of Filaments for Fused Deposition Modelling of Solid Dosage Forms. Pharmaceutics. https://doi.org/10.3390/pharmaceutics12040345 Retrieved from https://designdex.org/study/11df30fc-f8c6-4548-a438-9588c8f76cf5/passive-diffusion-method-achieves-3-drug-loading-in-3d-printed-filaments-for-personalised-medicines

Paragraph starter

The development of personalised medicines through additive manufacturing presents unique material challenges. Research by Cerda et al. (2020) demonstrated that passive diffusion, guided by Hansen Solubility Parameters and filament stiffness, can effectively load drugs into filaments for Fused Deposition Modelling. This approach achieved significant drug loading (up to 3% w/w) and resulted in 3D printed dosage forms with favourable dissolution and stability characteristics, suggesting a viable pathway for clinical implementation.

09

Source

Pharmaceutics

Personalised 3D Printed Medicines: Optimising Material Properties for Successful Passive Diffusion Loading of Filaments for Fused Deposition Modelling of Solid Dosage Forms

journal · 2020

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Questions about this research

What does the research say about passive diffusion method achieves 3% drug loading in 3d printed filaments for personalised medicines?
When designing drug-loaded filaments for 3D printing, prioritise materials and processes that facilitate passive diffusion, guided by solubility parameters and mechanical properties like stiffness. Evidence: Pharmaceutics (2020).
Why does "Passive Diffusion Method Achieves 3% Drug Loading in 3D Printed Filaments for Personalised Medicines" matter for design?
This research offers a practical and accessible method for incorporating active pharmaceutical ingredients into filaments used for 3D printing dosage forms. It addresses a key challenge in the development of personalised medicines by providing a predictable and efficient drug loading process, potentially streamlining clinical manufacturing.
How can designers apply this research?
When designing drug-loaded filaments for 3D printing, prioritise materials and processes that facilitate passive diffusion, guided by solubility parameters and mechanical properties like stiffness.
What were the main findings?
Passive diffusion is a viable method for loading drugs into filaments for FDM.. Hansen Solubility Parameters (HSP) and HSP distances (Ra) can predict optimal drug-solvent and solvent-filament combinations for high drug loading.. Filament stiffness and surface roughness significantly influence drug diffusion into filaments.. A predictive model using Support Vector Machine regression showed a strong correlation between Ra, filament stiffness, and drug loading.
What research method was used?
Experimental and computational modelling (Support Vector Machine regression)..
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2020 journal from Pharmaceutics.
What should I do differently in my next project?
When developing drug-eluting materials for additive manufacturing, use solubility parameter calculations to guide material selection and conduct stiffness measurements to predict drug loading capacity.
What are the limitations?
The study used a model drug (nifedipine) and a specific filament material; further research is needed to validate the method across a wider range of drugs and excipients. The long-term clinical efficacy and regulatory aspects of 3D printed personalised medicines require further investigation.
Is there evidence that passive diffusion affects design outcomes?
A new method using passive diffusion and material property analysis allows for effective drug loading into 3D printing filaments, enabling the creation of stable, personalised medicines with controlled drug release. This research offers a practical and accessible method for incorporating active pharmaceutical ingredien Source: Pharmaceutics (2020).
Where does this drug loading research apply?
Pharmaceutical manufacturing, personalised medicine, 3D printing. It sits within commercial production research on designdex.org.

Related research topics

passive diffusion design research · evidence on passive diffusion · does passive diffusion improve design outcomes · drug loading studies for designers · passive diffusion and drug loading findings · commercial production research evidence