design of experiments jmp

• Search Menu
• Sign in through your institution
• Advance Articles
• Author Guidelines
• Submission Site
• Open Access Policy
• Self-Archiving Policy
• Why publish with Series A?
• About the Journal of the Royal Statistical Society Series A: Statistics in Society
• About The Royal Statistical Society
• Editorial Board
• Advertising & Corporate Services
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

• < Previous

Introduction to Design of Experiments with JMP Examples

• Article contents
• Figures & tables
• Supplementary Data

S. E. Lazic, Introduction to Design of Experiments with JMP Examples, Journal of the Royal Statistical Society Series A: Statistics in Society , Volume 173, Issue 3, July 2010, Pages 692–693, https://doi.org/10.1111/j.1467-985X.2010.00646_5.x

• Permissions Icon Permissions

This book is written for practising engineers and researchers, and thus has a very applied focus; it would perhaps be less suitable for a course textbook, since problems and exercises are not included. All the examples are from situations that would be found in an industrial setting, especially manufacturing and production.

The book does a fair job of covering basic concepts and ideas and illustrates the results by using JMP software (from the SAS Institute). The book is not a step-by-step manual showing how to use JMP for the design of experiments, as there is a separation between ‘what to do conceptually’ and ‘how actually to do it in JMP’. Only in the final chapter (which is more like an appendix) are details given regarding implementation.

Regarding the structure of the book, one could question the logic of putting the chapter ‘Statistical concepts for designed experiments’ (Chapter 5) after ‘A three-factor designed experiment’ (Chapter 3) and ‘Four-factor full-factorial experiments’ (Chapter 4). Concepts such as populations versus samples, degrees of freedom and interactions should arguably be introduced before a full factorial analysis.

The style is very ‘choppy’: almost as if a PowerPoint presentation had been made into a book. Many pages contained excessive blank space, partly because of the size of the headings and subheadings, and often there is very little text under either. I was left with the impression that the authors started with a skeleton of topics that they wanted to discuss but did not quite flesh them out. Often there are several consecutive paragraphs that are only a sentence in length, and all relating to the same topic; these could have been combined into a single paragraph and would have made the ideas flow together more smoothly. It is thus not very dense with information and, if the book were to be reformatted in the style of CRC Press or Springer for example, it would be reduced in size by half. There were also many errors in referencing equations (more than expected given that it is the third edition), and values in the figures did not always correspond to values in the text or figure captions.

The book does describe concepts in an intuitive manner, and the chapter on optimal designs (Chapter 11) is particularly good in this regard. However, the topics were discussed somewhat superficially, and greater detail in many parts of the book would have been welcome.

Email alerts

Citing articles via.

• Recommend to Your Librarian
• Advertising & Corporate Services
• Journals Career Network
• Email Alerts

Affiliations

• Online ISSN 1467-985X
• Print ISSN 0964-1998
• Copyright © 2024 Royal Statistical Society
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Computers & Technology

Sorry, there was a problem.

Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet, or computer - no Kindle device required .

Read instantly on your browser with Kindle for Web.

Using your mobile phone camera - scan the code below and download the Kindle app.

Image Unavailable

• To view this video download Flash Player

Follow the author

Introduction to Design of Experiments with JMP Examples 3rd Edition

• ISBN-10 1599944227
• ISBN-13 978-1599944227
• Edition 3rd
• Publisher Sas Inst
• Publication date November 9, 2007
• Language English
• Dimensions 7.5 x 1 x 8.75 inches
• Print length 448 pages
• See all details

Editorial Reviews

About the author, product details.

• Publisher ‏ : ‎ Sas Inst; 3rd edition (November 9, 2007)
• Language ‏ : ‎ English
• Paperback ‏ : ‎ 448 pages
• ISBN-10 ‏ : ‎ 1599944227
• ISBN-13 ‏ : ‎ 978-1599944227
• Item Weight ‏ : ‎ 1.7 pounds
• Dimensions ‏ : ‎ 7.5 x 1 x 8.75 inches
• #1,397 in Mathematical & Statistical Software
• #5,254 in Computer Programming Languages
• #16,841 in Programming Languages (Books)

About the author

Jacques goupy.

Discover more of the author’s books, see similar authors, read author blogs and more

Customer reviews

Our goal is to make sure every review is trustworthy and useful. That's why we use both technology and human investigators to block fake reviews before customers ever see them.  Learn more

We block Amazon accounts that violate our community guidelines. We also block sellers who buy reviews and take legal actions against parties who provide these reviews.  Learn how to report

No customer reviews

• About Amazon
• Investor Relations
• Amazon Devices
• Amazon Science
• Sell products on Amazon
• Sell on Amazon Business
• Sell apps on Amazon
• Become an Affiliate
• Advertise Your Products
• Self-Publish with Us
• Host an Amazon Hub
• › See More Make Money with Us
• Amazon Business Card
• Shop with Points
• Reload Your Balance
• Amazon Currency Converter
• Amazon and COVID-19
• Your Account
• Your Orders
• Shipping Rates & Policies
• Returns & Replacements
• Manage Your Content and Devices
• Amazon Assistant

• Conditions of Use
• Privacy Notice
• Consumer Health Data Privacy Disclosure
• Your Ads Privacy Choices

Get full access to JMP 12 Design of Experiments Guide and 60K+ other titles, with a free 10-day trial of O'Reilly.

There are also live events, courses curated by job role, and more.

JMP 12 Design of Experiments Guide

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Book description

JMP 12 Design of Experiments Guide covers classic DOE designs (for example, full factorial, response surface, and mixture designs). Read about more flexible custom designs, which you generate to fit your particular experimental situation. Discover JMP’s definitive screening designs, an efficient way to identify important factor interactions using fewer runs than required by traditional designs. And read about creating designs that test systems where failures occur as a result of interactions among components or subsystems. The book also provides guidance on determining an appropriate sample size for your study.

Table of contents

• Documentation and Additional Resources
• Formatting Conventions
• JMP Documentation Library
• Discovering JMP
• Basic Analysis
• Essential Graphing
• Design of Experiments Guide
• Fitting Linear Models
• Specialized Models
• Multivariate Methods
• Quality and Process Methods
• Reliability and Survival Methods
• Consumer Research
• Scripting Guide
• JSL Syntax Reference
• Sample Data Tables
• Learn about Statistical and JSL Terms
• Learn JMP Tips and Tricks
• JMP User Community
• JMPer Cable
• JMP Books by Users
• The JMP Starter Window
• Overview of Design of Experiment Platforms
• Example and Key Concepts
• Overview of Experimental Design and the DOE Workflow
• Define the Study and Goals
• Define Responses and Factors
• Specify the Model
• Steps to Duplicate Results (Optional)
• Generate the Design
• Evaluate the Design
• Make the Table
• Run the Experiment
• Analyze the Data
• Effect Hierarchy
• Effect Heredity
• Effect Sparsity
• Center Points, Replicate Runs, and Testing
• Construct Designs That Meet Your Needs
• Overview of Custom Design
• Alias Terms
• Design Generation
• Design Evaluation
• Output Options
• Interpret the Full Model Results
• Reduce the Model
• Interpret the Reduced Model Results
• Optimize Factor Settings
• Lock a Factor Level
• Response Limits Column Property
• Factors Outline
• Factor Types
• Changes and Random Blocks
• Factor Column Properties
• Specify Linear Constraints
• Use Disallowed Combinations Filter
• Use Disallowed Combinations Script
• Save X Matrix
• Number of Starts
• Design Search Time
• Set Delta for Power
• Random Block Designs
• Split-Plot Designs
• Split-Split-Plot Designs
• Two-Way Split-Plot Designs
• Covariates with Hard-to-Change Levels
• Numbers of Whole Plots and Subplots
• D-Optimality
• Bayesian D-Optimality
• I-Optimality
• Bayesian I-Optimality
• Alias Optimality
• D-Efficiency
• Coordinate-Exchange Algorithm
• Perform Experiments That Meet Your Needs
• Design That Estimates Main Effects Only
• Design That Estimates All Two-Factor Interactions
• Design That Avoids Aliasing of Main Effects and Two-Factor Interactions
• Generating a Supersaturated Design
• Analyzing a Supersaturated Design Using the Screening Platform
• Analyzing a Supersaturated Design Using Stepwise Regression
• Design for Fixed Blocks
• Constructing a Response Surface Design
• Analyzing the Experimental Results
• Response Surface Design with Flexible Blocking
• I-Optimal Design
• D-Optimal Design
• Mixture Design with Nonmixture Factors
• Mixture of Mixtures Design
• Design with Fixed Covariates
• Design with Hard-to-Change Covariates
• Design with a Linear Time Trend
• Split-Plot Experiment
• Two-Way Split-Plot Experiment
• Overview of Definitive Screening Design
• Create the Design
• Analyze the Experimental Data
• Comparison with a Fractional Factorial Design
• Comparison of a Definitive Screening Design with a Plackett-Burman Design
• Orthogonal Blocking Options
• Add Blocks with Center Runs to Estimate Quadratic Effects
• Add Blocks without Extra Center Runs
• Definitive Screening Design Options
• Conference Matrices and the Number of Runs
• Definitive Screening Designs for Four or Fewer Factors
• Analysis of Experimental Data
• Overview of Screening Designs
• A Standard Design with Two Continuous Factors and One Categorical Factor
• A Standard Design for Five Continuous Factors
• Specifying Goal Types and Lower and Upper Limits
• Understanding Importance Weights
• Types of Factors
• Choose Screening Type
• Design List
• Display and Modify Design
• Aliasing of Effects
• Look at the Confounding Pattern
• Understanding Design Codes
• Changing the Coded Design
• Related Options
• Chi2 Efficiency
• Specify Output Options
• View the Design Table
• Creating a Plackett-Burman Design
• Creating a Main Effects Screening Design
• Analyze Data from Screening Experiments
• Overview of the Screening Platform
• An Example Comparing Screening and Fit Model
• Launch the Screening Platform
• Half Normal Plot
• The Actual-by-Predicted Plot
• The Scaled Estimates Report
• A Power Analysis
• Analyzing a Plackett-Burman Design
• Analyzing a Supersaturated Design
• Order of Effect Entry
• Screening as an Orthogonal Rotation
• Lenth’s Pseudo-Standard Error
• The Prediction Profiler
• A Response Surface Plot (Contour Profiler)
• Geometry of a Box-Behnken Design
• Enter Responses and Factors
• Box-Behnken Designs
• Central Composite Designs
• Specify Axial Value (Central Composite Designs Only)
• Analyze the Reactor Data
• Select Output Options
• Mixture Design Types
• Adding Effects to the Model
• Creating the Design
• Simplex Centroid Design Examples
• The Simplex Lattice Design
• An Extreme Vertices Example with Range Constraints
• An Extreme Vertices Example with Linear Constraints
• Extreme Vertices Method: How It Works
• The ABCD Design
• FFF Optimality Criterion
• Set Average Cluster Size
• Linear Constraints
• A Space Filling Example
• A Space Filling Example with a Linear Constraint
• Creating Ternary Plots
• Whole Model Tests and Analysis of Variance Reports
• Understanding Response Surface Reports
• Analyze the Mixture Model
• The Mixture Profiler
• A Ternary Plot of the Mixture Response Surface
• Detecting Component Interaction Failures
• Covering Arrays and Strength
• Load Factors
• Restrict Factor Level Combinations
• Specify Disallowed Combinations Using the Filter
• Specify Disallowed Combinations Using a Script
• Construct the Design Table
• Factors Table
• Editing the Factors Table
• Unsatisfiable Constraints
• Analysis Script
• Covering Array Options
• Algorithm for Optimize
• Unconstrained Design
• Constrained Design
• Create a Choice Design with No Prior Information
• Create an Example Choice Experiment
• Create a Choice Experiment for a Pilot Study
• Analyze the Pilot Study Data
• Design a Choice Experiment Using Prior Information
• Initial Choice Platform Analysis
• Find Unit Cost and Trade Off Costs with the Profiler
• Choice Design Options
• Overview of Space-Filling Designs
• Space Filling Design Methods
• Design Diagnostics
• Design Table
• Space Filling Design Options
• Creating a Sphere-Packing Design
• Visualizing the Sphere-Packing Design
• Creating a Latin Hypercube Design
• Visualizing the Latin Hypercube Design
• Uniform Designs
• Comparing Sphere-Packing, Latin Hypercube, and Uniform Methods
• Minimum Potential Designs
• Maximum Entropy Designs
• Gaussian Process IMSE Optimal Designs
• Constraints
• Creating and Viewing a Constrained Fast Flexible Filling Design
• Create the Sphere-Packing Design for the Borehole Data
• Results of the Borehole Experiment
• Designing Experiments for Accelerated Life Tests
• Overview of Accelerated Life Test Designs
• Using the ALT Design Platform
• Platform Options
• Using Nonlinear Fit to Find Prior Parameter Estimates
• Creating a Nonlinear Design with No Prior Data
• Identify the Response and Factor Column with Formula
• Set Up Factors and Parameters in the Nonlinear Design Dialog
• Enter the Number of Runs and Preview the Design
• Make Table or Augment the Table
• Advanced Options for the Nonlinear Designer
• The Taguchi Design Approach
• Detail the Response and Add Factors
• Choose Inner and Outer Array Designs
• Display Coded Design
• Make the Design Table
• Explore Properties of Your Design
• Overview of Evaluate Design
• Construct the Intended and Actual Designs
• Comparison of Intended and Actual Designs
• Evaluating Power Relative to a Specified Model
• Evaluate Design Launch Window
• Power Analysis Overview
• Power Analysis Details
• Power Analysis for Coffee Experiment
• Prediction Variance Profile
• Fraction of Design Space Plot
• Prediction Variance Surface
• Fractional Increase in CI Length
• Relative Std Error of Estimate
• Alias Matrix Examples
• Color Map Example
• D Efficiency
• G Efficiency
• A Efficiency
• Average Variance of Prediction
• Design Creation Time
• Evaluate Design Options
• Power for a Single Parameter
• Power for a Categorical Effect
• Relative Prediction Variance
• Analyze the Augmented Design
• Replicate a Design
• Add Center Points
• Creating a Foldover Design
• Adding Axial Points
• Space Filling
• Adding New Runs and Terms
• Save the Design (X) Matrix
• Modify the Design Criterion (D- or I- Optimality)
• Select the Number of Random Starts
• Specify the Sphere Radius Value
• Launching the Sample Size and Power Platform
• Power versus Sample Size Plot
• Power versus Difference Plot
• Sample Size and Power Animation for One Mean
• Plot of Power by Sample Size
• k-Sample Means
• One Sample Standard Deviation Example
• Actual Test Size
• One-Sample Proportion Window Specifications
• Two Sample Proportion Window Specifications
• Counts per Unit Example
• Sigma Quality Level Example
• Number of Defects Computation Example
• Understanding Column Properties Assigned by DOE
• Adding and Viewing Column Properties
• Response Limits Example
• Editing Response Limits
• Design Role Example
• Low and High Values
• Coding Column Property and Center Polynomials
• Coding Example
• Assigning Coding
• Mixture Example
• Factor Changes Example
• Value Ordering Example
• Assigning Value Ordering
• Value Labels Example
• RunsPerBlock Example
• ConstraintState Example
• The Model and Alias Matrices
• The Model Matrix
• Designs with Hard or Very Hard Factor Changes
• Designs with If Possible Effects

Product information

• Title: JMP 12 Design of Experiments Guide
• Author(s): SAS Institute
• Release date: March 2015
• Publisher(s): SAS Institute
• ISBN: 9781629594446

You might also like

Jmp 13 design of experiments guide.

by SAS Institute

The JMP 13 Design of Experiments Guide covers classic DOE designs (for example, full factorial, response …

JMP 11 Design of Experiments Guide

The JMP 11 Design of Experiments Guide covers classic DOE designs (for example, full factorial, response …

JMP 10 Design of Experiments Guide

The JMP 10 Design of Experiments Guide contains information about the JMP Design of Experiments (DOE) …

JMP 11 Essential Graphing

Start with JMP 11 Essential Graphing to find the ideal graph for your data. The book …

Don’t leave empty-handed

Get Mark Richards’s Software Architecture Patterns ebook to better understand how to design components—and how they should interact.

It’s yours, free.

Check it out now on O’Reilly

Dive in for free with a 10-day trial of the O’Reilly learning platform—then explore all the other resources our members count on to build skills and solve problems every day.

Your browser is not supported

Sorry but it looks as if your browser is out of date. To get the best experience using our site we recommend that you upgrade or switch browsers.

Find a solution

• Skip to main content
• Skip to navigation
• hot-topics Extras
• Newsletters
• Reading room

Tell us what you think. Take part in our reader survey

Celebrating twenty years

• Back to parent navigation item
• Collections
• Water and the environment
• Chemical bonding
• Antimicrobial resistance
• Energy storage and batteries
• AI and automation
• Sustainability
• Research culture
• Nobel prize
• Food science and cookery
• Plastics and polymers
• Periodic table
• Coronavirus
• More navigation items

Applications of modern Design of Experiments (DoE)

Sponsored by JMP

• No comments

Design of Experiments (DoE) is the most effective way to empirically learn about technologies when there are many variables or factors to consider.

In this fun, engaging and free three-part online training program on DoE, delivered by JMP, you will be introduced to DoE and discover how it enables scientists and engineers to develop better solutions faster.

In this workshop you will learn:

• The fundamentals of DoE – gain a deeper understanding of how your product/system/process performs
• How to build better, cheaper solutions – by exploiting the deeper knowledge gained you can determine how to achieve optimum performance, consistently and at a lower cost
• How to reduce total experimental effort by 50% (often more) – by determining the best and minimal set of experimental combinations needed to address your questions upfront

The best way to learn DoE is to get started in applying DoE. We learn by doing. It is a journey, the more we practice, the more we learn.

This series of three one-hour workshops will provide inspirational examples of the use of DoE in many aspects of bringing products to market – including product design/discovery/development, process development/scale-up/transfer and analytical method development. It will also provide the necessary know-how to get going with your first DoEs along with resources to use during your continuous learning journey.

Whether you are new to DoE or want a refresher, this workshop series will inspire you to get going with DoE.

Register now to view the recorded on-demand events

Register now

Programme benefits

The programme will provide you with a solid theoretical understanding of Design of Experiments, complemented by hands-on exercises that will give you experience using JMP’s statistical discovery software in support of your learning.

A supportive learning experience

The programme is led by JMP’s experts, with multiple facilitators on hand to answer questions and provide real-time assistance. Both the theory and practice of Design of Experiments will be explained and demonstrated – and then you’ll try it yourself using JMP’s popular statistical discovery software.

A powerful addition to your CV

Demonstrate a solid theoretical foundation in DoE as well as gaining valuable hands-on experience of using JMP’s statistical discovery software. Attend all three webinars to receive a certificate.

Add value to your own work

Using DoE will help you deliver results faster and more efficiently. By accelerating your R&D, and having the right tools to make critical resource decisions, you can save time and money, and get a much-needed edge in competitive markets.

Use and communicate data

Visualise experimental data in new ways to uncover fresh insights and give real impact to your presentations and communications with key stakeholders.

Registration

You can register for each part individually to suit your interests and availability. If you complete all three parts then you will receive a certificate for inclusion in your CPD record. You can also use attendance on this programme as supporting evidence for your Chartered Chemist (CChem) application.

Day 1: Introduction to design of experiments

What is DoE? From example case studies you will see why this is such an important tool for scientists and engineers.

This session will include:

• The advantage of Design of Experiments over traditional experimental methods
• How DoE fits into your experimental plan
• The types of classical experimental designs with use-cases
• Hands-on exercises: Traditional optimisation & DoE

If you want to learn more:

• Day 1 homework instructions
• Homework exercise: Traditional optimisation.jmp
• Homework exercise: DoE.jmp

Day 2: Introduction to Modern DoE

The modern state of DoE is a rich landscape of tools and capabilities to help an experimenter with everything from screening to developing predictive models. This session will introduce the state-of-the-art capabilities modern DoE has to offer.

• How Optimal DoE relates to Classical DoE
• How to create an Optimal Design and which optimality strategy to use when
• How to leverage modern screening designs to identify important factors more efficiently
• Hands-on exercise: Homework
• Homework exercise: Day 2.jmp

Day 3: Applications of Modern DoE

Join us for a session that will show the amazing things you can do with modern DoE methods through a series of case studies covering topics from manufacturing to spectroscopy.

• How to design and analyse a DoE where the response is a curve
• How Functional Data Analysis is used to analyse curves
• How Functional Data Analysis is combined with a DoE model to meet the goals of the experiment

Download your free trial software

This programme includes hands-on exercises each day, which you can complete on your own computer using JMP’s statistical discovery software. You will need to download and activate a free trial of this software. Once activated this trial will last for 30 days. We recommend you download and activate your free trial two weeks prior to the start of the programme. You can get your free trial direct from JMP’s website .

Meet your JMP experts

Scott allen.

Scott Allen is a Senior systems engineer at JMP, Statistical Discovery, where he supports customers in the Ohio Valley region of the United States. Allen has over 14 years of experience in product and process development experience in the fields of catalysis, chemistry, polymers, and sustainable technologies. In his role as an R&D and product development leader, he was a strong advocate for data-driven decision making and the use of statistics and Design of Experiments (DoE) to accelerate innovation and product development. In his current role, he enjoys helping customers explore their data and develop their capabilities in DoE and predictive modeling. Allen received his PhD in chemistry from Cornell University and a Bachelor of science degree in chemistry from Virginia Commonwealth University.

Michael Anderson

Michael Anderson is a Systems engineer at JMP, Statistical Discovery. In his current role, Anderson enjoys helping customers find hidden insights in data that result in quantifiable improvement. Before joining JMP, he worked in a variety of roles in high-tech manufacturing, encompassing measurement statistics, Lean Six Sigma, and cost and defect reduction. Anderson holds a PhD in materials chemistry from the University of Oregon and has achieved certification as a Lean Six Sigma Black Belt.

Andrea Coombs

Andrea Coombs supports customer development and sales in the Ohio Valley region as Senior systems engineer for JMP. Coombs has over 20 years of experience in applied analytics in the life science industry. Coombs spent the first eight years of her career at Michigan State University, where she conducted agricultural research using Design of Experiments (DoE) and statistical modelling. Coombs later joined Emergent BioSolutions, where she held a variety of roles, gaining wide-ranging expertise in vaccine manufacturing from process development to GMP commercial manufacturing using Quality by Design (QbD) approaches including DoE, continuous process verification and process and analytical technology. Leading a team of manufacturing science and technology scientists, she also served as lead on several continuous improvement projects and manufacturing investigations, and oversaw an at-scale pilot plant. Prior to joining JMP, Coombs served as Senior industry consultant for SAS.

• Analytical chemistry
• data analytics
• data driven approaches
• Design of Experiments
• On-demand webinars

Related articles

Workshop: Making better data-based decisions with statistical modelling techniques

Workshop: Your guide to solving complex problems by mastering Design of Experiments

Building organisational consensus around the tools of digital chemistry

Workshop: Getting started with Design of Experiments

Speed up process development by making more from your offline and online data

Unlock the potential of definitive screening designs to drive your projects forward

No comments yet, only registered users can comment on this article., more webinars.

LGBTQ+ in Stem: Using data to foster inclusion – Part 2

By Chemistry World

LGBTQ+ in Stem: Using data to foster inclusion – Part 1

Expanding AI and next generation simulation tools to accelerate scientific discovery

Sponsored by Microsoft

The science of CBD: exploring safe limits

It’s a gas – with author Mark Miodownik

Empowering voices: Advancing social mobility in the chemical sciences

• Contributors
• Terms of use
• Accessibility
• Permissions
• This website collects cookies to deliver a better user experience. See how this site uses cookies .
• This website collects cookies to deliver a better user experience. Do not sell my personal data .
• Este site coleta cookies para oferecer uma melhor experiência ao usuário. Veja como este site usa cookies .

Site powered by Webvision Cloud

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Biomedicines
• PMC10604315

Quality by Design (QbD) and Design of Experiments (DOE) as a Strategy for Tuning Lipid Nanoparticle Formulations for RNA Delivery

Associated data.

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

The successful development of nonviral delivery systems for nucleic acids has been reported extensively over the past years. Increasingly employed to improve the delivery efficiency and therapeutic efficacy of RNA are lipid nanoparticles (LNPs). Many of the various critical formulation parameters can affect the quality attributes and effectiveness of these nano-formulations. Therefore, the systematic drug development approach (QbD) and multivariate design and statistical analysis (DOE) can be very helpful and recommended for the optimization of the composition and production of RNA–LNPs. This review addresses the concepts and applications of QbD and/or DOE for the development of lipid nanoparticles for the delivery of different types of RNA, reporting examples published in the ten recent years presenting the latest trends and regulatory requirements as well as the modern mathematical and statistical design methods. As the topic explored in this review is a novel approach, the full QbD has been described in only a few papers, and a few refer only to some aspects of QbD. In contrast, the DOE approach has been used in most of the optimization works. Different approaches and innovations in DOE have been observed. Traditional statistical tests and modeling (ANOVA, regression analysis) are slowly being replaced by artificial intelligence and machine learning methods.

1. Introduction

In recent years, important developments in the pharmaceutical industry have occurred. Modern systems have been developed to ensure product quality in manufacturing information, quality management systems, and risk management [ 1 , 2 , 3 ]. These tools allow drug manufacturers to detect, analyze, correct, and prevent problems while continuously improving the drug manufacturing process [ 4 , 5 , 6 ]. Supporting this modernization in the rules used to manage drug manufacturing and product quality, in 2002, the FDA introduced the regulatory environment of the Current Good Manufacturing Practices (cGMP) [ 7 , 8 ].

After the publication of the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use considerations (ICH) guidelines Q8 (R2), Q9, Q10, and Q11 [ 9 , 10 , 11 , 12 ], regulatory agencies (Food and Drug Administration—FDA and European Medicines Agency—EMA) approved the Quality by Design (QbD) approach. The ICH Q8 guideline recommended the integration of the “quality by design (QbD)” concept into the pharmaceutical industry [ 9 ].

The development of safe and efficient new drugs is a long, difficult, and expensive process. Using the new QbD approach, which ensures that the quality is provided by manufacturing instead of by-product testing, decreases the costs of product optimization. QbD studies may provide some income to the drug manufacturers and benefits for patient safety—ensuring that they will obtain higher-quality drugs in a shorter time.

As defined by ICH Q8 [ 9 ], QbD is a systematic drug development approach based on predefined goals, an understanding of the product and processes, and sound science and quality risk management.

In recent decades, research related to nanosystems has started a technological revolution in medicines, especially in RNA delivery. Some researchers apply QbD in the development of drug RNA-LNP systems [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ].

This review summarized the QbD concepts and applications to the development of pharmaceutical products containing RNA-LNPs. The second section describes the concepts of QbD, pharmaceutical QbD implementation, risk assessment, design of experiment (DoE), and process analytical technology (PAT). Afterward, the use of the QbD and DOE approach in the development of lipid nanoparticles is shown through examples of works published in the last 10 years.

2. Quality by Design Concept

Generally, the steps for implementing QbD in the development of pharmaceutical products are similar and include ( Figure 1 ) the use of risk assessment to identify risk parameters, defining the main quality target product profile (QTTP), identifying parameters that influence the process performance, carrying out a DOE and definition of dependency of critical quality attributes (CQAs) to critical material attributes (CMAs) and critical process parameters (CPPs), defining a process design space that originates a final product with the desired QTPP, developing a risk control strategy to identify the causes of variability, and continuous monitoring and improving the manufacturing process [ 2 , 3 , 4 , 8 , 9 , 30 ].

Scheme of quality by design for a development pharmaceutical product.

2.1. Quality Target Product Profile (QTTP)

A quality target product profile (QTPP) consists of quantitative support for clinical safety and efficiency, and it makes a foundation to design and optimize a formulation or manufacturing process. The QTPP is a set of the attributes and characteristics of a product that determine its quality. According to the FDA, the QTPP is more focused on the chemical, manufacturing, and control stages of development [ 7 , 9 ].

The QTPP defines the quantitative characteristic of the drug and refers to specifications such as the dosage form, application way, packing, appearance, and diagnosis. Examples of QTPP are clinical use, administration route, therapeutic dosage, pharmaceutical dosage form, drug delivery system, packing container, factors affecting pharmacokinetic parameters, and quality criteria of the final product, such as stability during storage, sterility, and drug release. The QTPP only includes the product properties related to the patient. QTPP served as a guide for the optimization of formulation and process parameters to ensure that CQAs were within the desired range [ 1 , 2 , 3 , 4 , 8 , 30 ].

2.2. Critical Quality Attributes (CQA)

CQAs are physical, chemical, biological, or microbiological attributes/characteristics that should be controlled to ensure product quality [ 9 ]. CQAs are parameters that affect QTPP and are critical to product quality. These are generally related to the selection of correct amounts of excipients and drugs [ 1 , 2 , 3 , 4 , 8 , 30 ].

2.3. Critical Process Parameters (CPP)

CPPs are production parameters that affect CQAs and should be controlled. For example, parameters that interfere with the quality of the final product should be monitored during the production process. A parameter represents a measured or calculated characteristic of a system or process. These are parameters of the manufacturing system and are usually properties of materials or processes that affect production, such as temperature and composition.

2.4. Critical Material Attributes (CMAs)

CMAs are properties of materials that must reach adequate limits to guarantee the quality of excipients, drugs, and other materials used during the process.

It is important to understand the difference between CQAs (output data) and CMAs (input data) during development. For example, CQAs of an intermediate may become CMAs of an intermediate in the next production step. Moreover, based on the QbD approach, CMAs and CPPs may differ in the defined design space without significantly interfering with CQAs. Additionally, following the ICH guideline “Quality Risk Management” (ICH Q9), the identification of possible causes of process variability and effective risk analysis is of key importance during process optimization. This procedure can be performed in the initial or final steps, repeating or redefining it as needed. Thus, the quality of the final product is verified by the previous experience in the determination of CQAs, CPPs, and risk assessment [ 8 , 10 , 31 ].

2.5. Risk Assessment

Risk assessment is used to identify what can go wrong, the likelihood of it going wrong, and the consequences. According to ICH Q9, risk control is related to procedures that should be adopted to reduce risks [ 10 ]. This guidance provides examples of commonly used risk management tools. At this stage, it is necessary to decide which risks can be accepted and which should be minimized. Analysis of variance (ANOVA) or multiple regression analysis is used for risk assessment based on experimental data. A mathematical equation for the relationship between variables can be derived using multiple regression analysis. While using ANOVA, the statistical significance of the influence of each factor and interaction effects is assessed [ 31 ]. In the final stage, the results should be monitored using a risk assessment, taking into account previous knowledge and experience. Identifying QTPP, CQA, and CPP requires prior experience and knowledge as various risk assessment tools are used, including risk filtering, Ishikawa diagram, and failure mode and effects analysis [ 4 , 10 ].

2.6. Design Space (DS)

The design space (DS) has an important place in the pharmaceutical industry. The design space defines the multivariate functional relationships between CQA and CPP and includes their relationships. These relationships can be found by applying risk assessment, design of experiments (DOE), and modeling.

The design space is specific to a unit operation or a single manufacturing process and defines operating process parameters (such as moisture ratio) that are known to affect product quality. DS can also be considered as a link between CQA and CPP [ 32 ]. It is a way to show the development of understanding of a process, and the benefits of creating a design space are obvious. However, one of the challenges for using DS effectively is the cost of creating it. The most important point in developing the design space is to demonstrate or determine that the unclassified parameters excluded from the DOE are non-critical process parameters and, therefore, do not interact. Therefore, DOE screening can be used to identify interactions between process parameters. In the absence of interaction, single-variable intervals can be directly added to the design space as non-critical parameters.

2.7. Control Strategy

A control strategy is a very important step to secure the process performance and product quality. It should be carefully planned based on product and process information [ 11 ]. In the QbD approach, a control strategy is performed during product development and allows a deeper understanding of the product and process. Control strategy options with QbD are easier for areas requiring more time and specialty knowledge by delivering more information than the standard approach. In this novelty approach to pharmaceutical quality, the finished product quality is built by identifying and controlling an optimal range of formulation and manufacturing variables. Finished products are finally tested to confirm the product quality [ 33 ].

The development of an effective control strategy should be risk-based to ensure that product quality requirements are met. The development of the control strategy starts with the QTPP. The first studies are focused on the characterization of the active ingredient and important physical, chemical, biological, and microbiological properties of the formulation. The development of the process is also defined in this phase. For example, if the active ingredient has low solubility in water, the immediate-release tablet form must ensure sufficient dissolution of the drug. Toxicity studies performed during the early stages provide a baseline assessment of the impurity profile of the active ingredient. Understanding impurity generation and removal, at which stages elements of the control strategy will take place, and the development of acceptance criteria and methods to be included in the specifications will provide an important point of view. Although a control strategy should be developed using these steps, it can also be developed using the quality risk management principles defined in ICH Q9 [ 10 , 33 ].

During production, the continuous control strategy enables understanding and stabilization of the process. It is very important to modify the pharmaceutical quality system for supporting to the use of a QbD approach in the control strategy. In the commercial manufacturing process, it should be made possible to update, modify, or continuously improve the control strategy. When product information, documents, and operating procedures must be changed, the pharmaceutical quality system should provide the appropriate technical and administrative audits required to obtain the necessary reviews and approvals [ 33 ].

3. QbD and DOE in the Development of the RNA-LNP System

QbD implementation in the development of pharmaceutical products containing RNA-LNPs is closely connected to the structure ( Figure 2 ) and the main therapeutic goal of the nanosystem.

Scheme of the hypothetical structure of an RNA-LNP system (adapted from [ 13 ]).

The definition of the main steps of the QbD approach is similar to different published research (see Table 1 ) and includes generally the following parameters [ 14 ]:

• QTPP: safety and efficacy.
• CQAs: z-average, PDI, zeta potential, encapsulation efficiency, loading efficiency, stability, shelf life, storage conditions, and lipids contents.
• CMAs: N/P ratio, lipids type, non-toxicity, biodegradability.
• Critical process parameters (CPPs): temperature, microfluidics, filtration.

Optimization of RNA-LNP system information about optimizing variables and the tested range was described in the Section DOE Approach—Design Models, Variables, and Range.

In the DOE, a wide range of different statistical approaches and modeling methods were used.

The following table shows examples of recent studies using the QbD and/or DOE approach to optimize formulations containing RNA-LNPs ( Table 1 ). Particular information about optimized variables and their tested range applied in the DOE approach was described in Section DOE Approach—Design Models, Variables, and Range. More detailed results of these papers are shown in Supplementary Materials (Table S1) .

DOE Approach—Design Models, Variables, and Range

In the first paper listed in Table 1 , Cun J. et al. [ 34 ] applied 2 (5−1) fractional factorial design (FFD)—five input variables with a center point (each on the three levels): inner water phase and the oil phase volume ratio (0.1; 0.3; 0.5), poly(DL-lactide-glycolide acid) (PLGA) concentration (20.0; 40.0; 60.0 mg/mL), sonication time for the primary emulsification (30.0; 60.0; 90.0 s), siRNA load (35.9; 62.8; 89.7 µg), acetylated bovine serum albumin (Ac-BSA) content in the inner water phase (0.0; 200; 400 mg). Optimized variables were encapsulation efficiency (%EE) of the siRNA, obtained in the range of 2.01% to 51.18%, and particle size in the range of 218.3 nm (standard deviation, SD ( n = 3) = 0.7 nm) to 257.9 nm (SD = 2.7 nm). Optimal values of significant input parameters are volume ratio 0.46, PLGA concentration 40.7 mg/mL, siRNA load 39 µg, acetylated bovine serum albumin (Ac-BSA) content in the inner water phase 400 µg for low siRNA load (770 ng/mg nanoparticles, SD = 33 ng/mg nanoparticles), volume ratio 0.40, PLGA concentration 42.4 mg/mL, siRNA load 90 µg, acetylated bovine serum albumin (Ac-BSA) content in the inner water phase 400 µg for high siRNA load (2192 ng/mg nanoparticles, SD = 115 ng/mg nanoparticles). The optimal values of %EE were equal to 64.35% (SD = 2.78%) for low siRNA load and 70.63% (SD = 5.75%) for high siRNA load. The optimal average particle size was equal to about 260 nm both for low and high siRNA load. The zeta potentials, additionally measured, were all negative in the range of −45.5 mV to −37.5 mV.

Chen D. et al. [ 35 ] optimized siRNA-LNP in terms of the structure of used cationic lipids. The matrix of 70 different compounds of cationic lipids was tested—5 different hydrocarbon chain lengths (C8–C14) and 14 different amine headgroups. Optimized output parameters were: hydrodynamic diameter of particles (obtained in the range of 50 to 130 nm), total siRNA concentration, the percentage of particle-entrapped siRNA and luciferase silencing in vitro, and factor VII silencing in vivo (C57BL/6 mice). The lipids composition of LNPs was 2.0 mg/mL of cationic lipid, 0.28 mg/mL of DSPC, 0.52 mg/mL of cholesterol, and 0.13 mg/mL of mPEG2000-DMG. The concentration of polynucleotide was 0.4 mg/mL in 10 mM citrate buffer, pH 3.0. The dose of LNPs for in vivo factor VII delivery was 1.0 mg/kg (siRNA/body weight), and volume injection was less than 200 μL into the animal. Two main dependencies were identified from the LNPs test: siRNA delivery efficiencies in vitro and in vivo for different amine headgroups are a function of the hydrocarbon chain length, the potency of in vitro did not change significantly with hydrocarbon chain length, the in vivo potency of LNPs decreased with increasing hydrophobicity of the cationic lipids. In addition, based on this optimized approach, seven novel materials with in vivo gene silencing potencies of >90% at a dose of 1.0 mg/kg in mice were discovered.

In turn, Li et al. [ 36 ] carried out optimization of lipid-like nanoparticles (LLNs) composed with different derivatives of N 1, N 3, N 5-tris(2-aminoethyl)benzene-1,3,5-tri carboxamide (TT) for mRNA delivery. In the first step, the correlation analysis was performed to choose the most optimal type of TT (seven different compounds were tested). The output variables were: particle size (obtained in the range of 99 nm (SD = 2 nm) to 178 nm (SD = 1 nm), polydispersity index (PDI < 0.2), zeta potential (all nanoparticles were positively charged), entrapment efficiency of mRNA (15–82%), delivery efficiency, and cytotoxicity of TT2-TT8 LLNs-FLuc mRNA in Hep3B cells. At a dose of 1.2 μg/mL of luciferase mRNA, LLNs with TT3 as components characterized statistically significantly higher expression of the firefly luciferase compared to other TT LLNs. A significant positive correlation was observed between transfection efficiency and entrapment efficiency. No significant correlation was noticed between transfection efficiency and particle size, surface charge, and cell viability. In the next steps, the content of other ingredients of TT3-LLNs was optimized. Two separate orthogonal experimental designs, L16(4 4 ), were performed for different ranges of tested parameters. Four input variables on four levels were considered. The first design concerned the TT3 mole ratio (30; 40; 50; 60), dioleoyl phosphatidylethanolamine (DOPE) mole ratio (1.25; 2.5; 5.0; 10.0), cholesterol mole ratio (18.5; 28.5; 38.5; 48.5), DMG-PEG2000 mole ratio (0.75; 1.5; 3.0; 6.0), and for the second, the TT3 mole ratio (10; 20; 30; 40), DOPE mole ratio (10.0; 20.0; 30.0; 40.0), cholesterol (Chol) mole ratio (25; 30; 35; 40), DMG-PEG2000 mole ratio (0.0; 0.03; 0.15; 0.75). In both cases, the mRNA delivery efficiency expressed as a relative intensity of luminescence was used as an output variable. In the first optimization, the efficiency was obtained in the range of 20 to 4663, and in the second, obtained in the range of 1917 to 101,863, depending on the composition of TT3-LLNs. The optimal composition of the formulation was TT3/DOPE/Chol/DMG-PEG2000: 20/30/40/0, but the particle size of these LLNs, with a low ratio of DMG-PEG2000, increased dramatically 5 h after. Whereas for formulation TT3/DOPE/Chol/DMG-PEG2000: 20/30/40/0.75 were stable for a minimum of 2 weeks.

In the next paper, Kauffman et al. [ 37 ] carried out the optimization of the composition of LNPs Erythropoietin(EPO)-mRNA-loaded with C12-200. Six input variables were considered: C12-200:mRNA weight ratio, phospholipid type and phospholipid, C12-200, cholesterol, and PEG molar contents. The output variables were encapsulation efficiency (%EE), particle size, polydispersity index (PDI), and EPO serum concentration. In the first step, library A was built with a definitive screening design (14 experiments). The most optimal phospholipid type was chosen as a matrix of qualitative variables: tail group (DS, DO) and head group (PC, PE). Quantitative variables were on the three levels: C12-200:mRNA weight ratio (2.5; 5.0; 7.5), C12-200 %mol (40; 50; 60), phospholipid %mol (4; 10; 16), PEG %mol (0.5; 1.5; 2.5), and cholesterol %mol was added to 100% (21.5–55.50). DSPC and DOPE were selected for further optimization. Within library B, Taguchi fractional factorial screening design (18 experiments) was performed with the following input variables: C12-200:mRNA weight ratio (7.5, 10.0, 12.5), C12-200 %mol (30, 35, 40), phospholipid %mol (16, 22, 28), PEG %mol (2.5, 3.0), and cholesterol %mol (from 28.5 to 51.5). The DOPE phospholipid was selected as the most optimal. In the final step (library C, 6 experiments), the C12-200:mRNA weight ratio was optimized in the range of 5.0; 7.5; 10.0; 15.0; 20.0; 25.0. The optimal composition of the formulation was 10:1 C12-200:mRNA weight ratio, 35% C12-200, 16% DOPE, 46.5% cholesterol, and 2.5% C14-PEG2000. The average efficacy with 15 µg total EPO mRNA injection in vivo was obtained at 7065 ± 513 ng/µL. The particle size of optimal LNP was 102 nm, PDI: 0.158, encapsulation efficiency: 43%, pKa: 6.9, and zeta potential: −5.0 mV.

Next, Thanki et al. [ 15 ] tested the lipidoids as the lipid component of siRNA-loaded lipid-polymer hybrid nanoparticles (LPNs). They checked whether replacing the cationic lipids with lipidoids improved safety and was more efficacious (efficient gene silencing at lower doses). The initial experiments of one factor at a time were performed to identify criticality influencing the overall quality attributes of the LPNs: the lipidoid content and siRNA: lipidoid ratio. A 17-run design of an experiment with an I-optimal approach was performed to systematically assess the effect of lipidoid content % (in the range of 10–20) and weight ratio of lipidoid:siRNA (from 10:1 to 20:1) on physicochemical properties (hydrodynamic size, zeta potential, and siRNA encapsulation/loading) and the biological performance (in vitro gene silencing and cell viability). The response surface methodology was applied to the identification of optimal operating space (OOS). The optimal lipidoid-modified LPNs showed more than 50 times higher in vitro gene silencing at well-tolerated doses and about twice the increase in siRNA loading than the LPNs with cationic lipid dioleyltrimethylammonium propane (DOTAP). Another work by Thanki and co-authors [ 16 ] described the optimization of LPNs composition prepared of cationic lipidoid (L5) and poly(DL-lactic-co-glycolic acid) (PLGA) or delivery of an antisense oligonucleotide (ASO) mediating splice correction of a luciferase gene transcript (Luc-ASO). Critical formulation variables and their levels were identified in one-factor-at-a-time (OFAT) experiments. Multilevel factorial design (25 experiments) was performed for critical, independent variables with three levels: L5 content (10–20% ( w / w )), L5:Luc-ASO weight ratio (in the range of 10:1 to 30:1), and molecular weight of PLGA (10–50 kDa). The following output variables were considered: Z-average, PDI, zeta potential, encapsulation efficiency, loading, and biological responses (i.e., in vitro splice correction efficiency and cell viability). Quadratic and an I-optimal model were performed, and the obtained responses were subjected to model fitting using analysis of variance (ANOVA). The best model was selected on the basis of statistical analysis. The process validation was performed by preparing and testing the five formulations with composition within the design space (range of 14–17% ( w / w ) L5 content and L5:Luc-ASO ratios from 11:1 to 21:1).

In turn, Blakney et al. [ 38 ] presented the optimization of self-amplifying mRNA (saRNA) lipid nanoparticle composition in human skin explants. Full factorial design was performed for five input variables: complexing lipid type (C12-200 (ionizable), dimethyldioctadecylammonium bromide [(DDA), cationic], 1,2-dioleoyl-3-trimethylammonium-propane [(DOTAP), cationic] and cephalin (zwitterionic)), total lipid to RNA ratio ( w / w ) (1:1, 4:1, 18:1, 90:1), lipid concentration (high (7.5 mg/mL), medium, low), particle concentration (high 10 9 , medium 10 8 , low 10 7 particles/mL), and cationic lipid to zwitterionic lipid ratio (0.1:1, 1:1, 10:1). The luciferase expression in human skin explants after 10 days was the output variable. Cephalin lipid had the strongest effect. Cephalin LNPs with a ratio of total lipids to RNA of 18:1 ( w / w ), low lipid concentration, and medium particle concentration yielded a 7-fold increase in luciferase expression over the original formulation (with C12-200 lipid).

In the next paper, Hashiba et al. [ 39 ] described the optimization of liver-targeted mRNA-loaded LNPs prepared with pH-sensitive cationic lipids that had been previously designed for siRNA delivery. The first 3 4 × 2 2 definitive screening design (DSD, 18 formulations) was performed (library A). The input variables were qualitative factors: cationic lipid (CL) type (CL4H6, CL15H6) and phospholipid (PL) type (DSPC, DOPE) and quantitative factors: mRNA: lipid ratio (18.3–36.7), mol% of CL (40–60), mol% of PL (5–25), and mol% of PEG-DMG (0.5–2.5). The second step, a 3 4 × 2 1 Taguchi fractional factorial design (FFD, 18 formulations), was performed (Library B). The input variables were the qualitative factor phospholipid (PL) type (DSPC, ESM—egg sphingomyelin) and quantitative factors: mol% of CL (50–70), mol% of PL (5–15), and mol% of PEG-DMG (0.5–2.5). The cationic lipid (CL) type was fixed as a CL4H6, and the mRNA/lipid ratio was fixed as 18.3. Output variables were gene expression in the liver (Nluc expression was measured 24 h after the injection), physicochemical properties (LNP diameter and polydispersity index (PdI)), and liver-specificity (gene expression in off-target organs (spleen, lung, and kidney)). Detailed results are described in Supplementary Table S1 .

The work of Lokras et al. [ 17 ] presented an approach to optimization of the intracellular delivery of therapeutic anti-inflammatory TNF-a siRNA-loaded LNPs to activated macrophages. The formulation design space was defined based on the I-optimal design for three independent formulation batches and three technical replicates. Input variables were L5 content (15; 20; 25% ( w / w )) and L5:TNF-a siRNA weight ratio (5.0:1; 7.5:1; 10.0:1; 15.0:1). Output variables were PDI, zeta potential (mV), encapsulation efficiency (%), TNF-a siRNA loading (μg siRNA/mg LPNs), and fold reduction in IC50 value for TNF-a gene silencing. The optimal operating space was defined: L5 content of 15%, L5:TNF-a siRNA weight ratio of 13.5:1, and 25%, 15.0:1 respectively.

The next paper, published by Zheng et al. [ 18 ], deals with the optimization of lipid-like nanoparticles (LLNs) on the expression of luciferase mRNA in the liver containing three new cholesterol derivatives Chol-PEG400-self peptide (Chol-PEG400-SP), Chol-PEG400-Mannose (Chol-PEG400-Man), and Chol-PEG2000-W5R4K (Chol-PEG2000-WRK)) to achieve the liver-targeting delivery of mRNA. The central composite design (CCD) model (20 experiments), a multi-factor, five-level experimental design, which is formed by adding extreme points and center points based on a two-level factorial design, was investigated. Input variables were the molar ratio of the Chol-PEG400-SP (from 1.0 to 7.5%), Chol-PEG400-Man (from 1.0 to 10.0%), and Chol-PEG2000-WRK (from 1.0 to 7.5%). The output variable was in vivo Luc expression of livers measured by the IVIS (Balb/c mice i.v. injection)—bioluminescence.

Van de Berg and co-authors [ 19 ], in turn, described the bioprocess model development of rapid RNA vaccine production against emerging infectious diseases. The model parameters were fitted to a subset of 51 experimental samples from a statistical DoE dataset obtained from lab-scale saRNA synthesis experiments using wild-type nucleotide triphosphate (NTP). Thirty-three samples correspond to the RNA yield at 0.04 M NTP and 1 × 10 −8 M of T7 RNA Polymerase (T7RNAP) vs. 11 concentrations of Mg ranging from 0.025 to 0.125 M after 2, 4, and 6 h. Moreover, 12 samples correspond to the RNA yield at 0.04 M NTP and 0.075 M Mg for 1.25 × 10 −9 , 2.5 × 10 −9 , 5 × 10 −9 , and 1 × 10 −8 M of T7RNAP after 2, 4, and 6 h. six samples correspond to the RNA yield after 2 h at 0.02, 0.04, and 0.08 M NTP at 0.075 and 0.14 M Mg. Optimal values of input variables were NTP concentration 40.8 mM (optimization range 0.01–0.06M), T7RNAP concentration 1.5 × 10 −8 M (optimization range 0.5 × 10 −8 –1.5 × 10 −8 ), Mg concentration 85 mM (optimization 0.01–0.09 M), and reaction time: after 2, 4, and 6 h. The optimal output variables were the yield in a bioreactor: 4.34 g × L −1 , and the cost of T7RNAP and NTPs per g of RNA: 2740 USD × g −1 .

In the paper by Fan et al. [ 20 ], the optimization of LNPs loaded with antisense oligonucleotides (ASOs), formulated by an automated solvent-injection method using a robotic liquid handler (e.g., muscular dystrophy Duchenne’s) was performed. In the first step, optimization of the phase mixing process using a robotic TECAN liquid handler was carried out. Input variables were investigated as an injection speed: 0.1, 0.5, or 0.9 mL/s at 10 mixing repeats, and the ethanol-to-buffer injection at a speed of 0.5 or 0.9 mL/s followed by 10 or 20 mixing repeats. Output variables were particle size, polydispersity, and encapsulation efficiency (calculated based on free ASO-1 measured by OD260). The second step involved the high throughput screening (HTS) workflow for ASO-loaded LNP formulations (96 samples). Input variables were two levels of total lipid concentrations (1 mM; 2 mM), four levels of ASO loading controlled by N/P ratios (0.5; 1; 2; 5), and four levels of the PEGylated lipid (DSPE-PEG2000) content (0; 1.5; 3; 5 %mol of total 2 mM lipids). Output variables were particle size (obtained in the range of 45–145 nm), polydispersity %PD (10–50), and ASO encapsulation efficiency measured by absorbance at 260 nm. We observed that 5 mol% of DSPE-PEG2000 and an N/P ratio ≥ 1 would produce optimal LNP formulations with a homogeneous and stable particle size as well as high ASO loading. Other detailed results are summarized in Table S1 .

Terada et al. [ 40 ] described the DOE approach to find the preparation conditions and their relationship with the properties of LNP-siRNA. The design included three levels of the three-factor Box–Behnken design: low, center, and high, and three center samples (15 preparation). The influence of the input variables: lipid concentration (10, 20, 30 mM), flow rate ratio (FRR) (1, 3, 5 ( v / v )), and total flow rate (TFR) (1, 2, 3 mL/min) on the output parameters: the particle size, polydispersity index (PDI), and siRNA entrapment, was tested. The charge ratio (N/P) in the prepared LNP-siRNA systems was fixed at 3. LNPs were prepared using hydrogenated soy phosphatidylcholine (HSPC), cholesterol (Chol), and 1,2-distearoyl-sn-glycero-3-phosphoethanol amine- N -[methoxy-(polyethylene glycol)-2000] (ammonium salt) (PEG-DSPE). In addition, 1,2-dioleoyl-3-dimethylammonium propane (DODAP) was used which has a reported apparent pKa of 6.5. The optimal lipid composition was DODAP/Chol/HSPC/PEG-DSPE (50/10/39/1 %mol).

The next authors, Bevers et al. [ 21 ], optimized mRNA-LNP compositions to achieve high-magnitude tumor-specific CD8 T cell antitumor immunotherapy by engaging the splenic immune cells. A Roquemore hybrid design was used to select the formulations to be tested (11 different experimental conditions). Each condition was applied in triplicate (3 mice /treatment), and each lipid’s molar percentage takes five values. The experimental protocol was carried out for three different choices of PEG-lipid chemistry (DMG-PEG2000, DSG-PEG2000, and DSPE-PEG1000) and different lipid molar ratios (10, 20, and 30 mM). The highest %E7-specfic T cell response was investigated as an output variable. The optimal LNPs composition were: DMG-PEG2000: ionizable lipid 56.5%, DOPE 5.25%, cholesterol 37.75%, PEG-lipid 0.5%, and DSG-PEG2000: ionizable lipid 64.4%, DOPE 8%, cholesterol 27.1%, PEG-lipid 0.5%. More detailed relationships between variables are presented in Table S1 .

Karl et al. [ 22 ], in turn, proposed a holistic workflow for lipid nanoparticle (LNP) formulation optimization for in vivo gene delivery systems. They used designed mixture-process experiments and a self-validated ensemble model (SVEM) (23 runs, LNP batches were tested). The generally used input variables were listed and presented on the fishbone diagram, e.g., ionizable lipid type (H101, H102, H103), ionizable lipid molar content (10–60%), helper lipid type, helper lipid molar content (10–60%), PEG lipid type, PEG lipid molar content (10–50%), N/P ratio (6–14), buffer concentration, and flow rate (1–3 mL/min). Output variables were maximum potency, minimum particle size, minimum PDI (polydispersity index), maximum % encapsulation, and minimum side effects—such as body weight loss in in vivo studies. Exemplary optimal LNP formulation candidates were described in Table S1 .

The paper by Schmidt et al. [ 23 ] refers to a general approach to process automation and control strategy by QbD in total continuous mRNA manufacturing platforms (mRNA)-based vaccines. The work contains very detailed information about all aspects of LNP-mRNA formation, process parameters, and product composition based on risk analysis. The authors presented, e.g., Ishikawa diagrams to identify all possible sources of variability. Different multivariate optimization prediction was comparable: Ordinary least squares (OLS) regression, partial least squares (PLS) regression, and neural network (NN) regression. Full factorial design and one factor at a time (OFAT) analysis were also used. The optimal process and product parameters are listed in Table S1 .

In the next paper, Toma et al. [ 24 ] presented a general QbD approach for the development of miRNA nonviral vectors for genetic material delivery in cancer therapy. The DOE is defined generally as providing better results with a minimum number of experiments and evaluating CMAs and CPPs to obtain a product meeting the QTPP.

Young and co-authors [ 25 ] described that optimization of LNP composition drives the delivery of mRNA to the placenta. In the DOE approach, a factorial design study was performed. The definitive screening design (DSD) was used to create a mini-library of 18 chemically unique LNPs (A1–A18). A combination of three-level continuous and two-level categorical factors to identify linear and quadratic effects was used: ionizable lipid type (C12-200, DLin-MC3-DMA), phospholipid type (DSPC, DOPE), ionizable lipid molar percentage (25–45%), phospholipid molar percentage (10–22%), DMPE-PEG molar percentage (1.5–3.5%), and cholesterol molar percentage (add up to 100%). Output variables were hydrodynamic diameter (obtained in the range of 72.2–171.5 nm), polydispersity index (0.120–0.317), mRNA encapsulation efficiency (35.6–83.2%), transfection efficiency, and apparent pKa (TNS assay) (5.298 to 7.111). Optimal LNP was formulation A10 with the following composition and characterization: 35% C12-200, 10% DOPE, 1.5% PEG, 53.5% cholesterol; 130.2 nm hydrodynamic diameter, 0.064 PDI, 56.5% EE, 6.607 pKa.

The next paper, by Nag et al. [ 26 ], presented a novel approach to size regulation of LNPs using the combined effects of buffer exchanger pH and sonication time. Input variables were: pH of buffer exchanger (4.0–5.0), sonication time (0–100 s), buffer dialysis type (50 mM HEPES pH 6.7, 50 mM HEPES/50 mM sodium acetate pH 6.7, PBS pH 7.2, PBS pH 7.4), and % of dialysis buffer (75–100%). Optimized parameters were LNP size (nm) and polydispersity index (PDI). Optimal values of LNP diameter were 60–180 nm (±10 nm) and PDI ≤ 0.200.

DOE approach to the optimization of lipid nanoparticles for self-amplifying mRNA (saRNA) expression and cellular activation was presented by Ly et al. [ 27 ]. The first optimization was performed based on a definitive screening design (DSD). A 7 three-level matrix was built with two two-level qualitative factors and one three-level qualitative factor (iteration A, 26 formulations). Input variables were: N/P ratio (5; 10; 15), phospholipid type: DOPE; DSPC phospholipid content (10; 15; 20 mol %), ionizable lipid type (pKa) (DLin-MC3-DMA (6.4); ALC-0315 (6.09); SM-102 (6.75)), ionizable lipid content (30; 40; 50 %mol), DMG-PEG-2000 content (0; 1.25; 2.5 %mol) total flow rate during preparation (2; 9; 16 mL/min), ambient temperature during formulation (4; 20; 37 °C), aqueous-phase pH (3; 5; 7), and RNA type (mRNA; saRNA). Output variables were size, PDI, EE%, charge, % filled particles, and % full RNA transcripts. Apart from standard statistical methods such as variance analysis (ANOVA), linear regression, and Spearman’s correlation, Python’s scikit-learn module, statsmodels package, and the seaborn library were also used. The second optimization was performed based on a Box–Behnken design (BBD). Three three-level and 1 three-level quantitative factor (iteration B, 26 formulation) were tested. Input variables were phospholipid content (12.5; 15; 17.5 %mol), ionizable lipid type (pKa) (DLin-MC3-DMA (6.4); ALC-0315 (6.09); SM-102 (6.75)), ionizable lipid content (35; 40; 45 %mol), and aqueous-phase pH (4; 5; 6). The N/P ratio was fixed at 10, DOPE was used as a phospholipid, DMG-PEG-2000 content was fixed at 1.25 %mol, the total flow rate was fixed at 16 mL/min, and ambient temperature during formulation was fixed at 20 °C). Output variables were size, PDI, EE%, charge, % filled particles, % full RNA transcripts, protein expression, and cellular activation. Response surface modeling was carried out using second-order ordinary least square (OLS) regression based on the scikit-learn module and statsmodels package. A Box–Cox transformation of response variables was used to improve model accuracy. The optimization of simultaneous responses was realized by the desirability function, and optimal operating conditions were obtained using the SciPy 1.0 library and Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization algorithm.

The paper by Mendonca et al. [ 28 ] presented a review of LNP delivery of different types of nucleic acids: siRNA, mRNA, and pDNA. Stricted DOE was not described, but a lot of important variables that affected the effectiveness of LNP formulations were listed ( Table S1 ).

The last presented work by Bastogne et al. [ 29 ] concerns the optimization of cationic nano-lipid for siRNA transfection. DOE was performed based on a D-optimal mixture design (36 formulations were tested). Input variables were DOTAP proportion (%) in the LNP content, the concentration of PEG surfactant (%), the lecithin proportion (%), and LNP size (small, medium, large). Output variables were safety attributes related to the LNP stability (unstable or stable states, 0 or 1), siRNA transfection rate (PC3-GFP) (min 30%), and PDI. Response surface equations, a class of polynomial models, are used to describe the links between the input and output variables. Bayesian estimation method, the posterior predictive check, and the leave-one-out cross-correlation were used to evaluate the model prediction.

4. Discussion

Optimization of lipid-based RNA formulations is essential to achieve reproducible quality of pharmaceutical products in terms of efficacy and safety; therefore, the application of QbD and DOE appears to be a very useful tool for the robust development of this complex RNA-LNP system. The performance of LNPs is strongly influenced by the chemical structure of each component, the interactions between them, and the physicochemical properties of the final formulation.

The most important characteristics of RNA-LNPs are zeta potential, size and particle size distribution, shape, morphology, encapsulation efficiency, cellular uptake, and transfection efficiency. The chemical structure of lipids, lipid concentration, and lipid molar ratio were the most significant factors regulating formulation average and gene expression [ 15 , 16 , 28 , 38 , 39 ]. Cationic lipids for gene transfer have become a major research tool for the transfer of genetic material into cells, and there is great potential for progress in this direction [ 20 ].

Particle size was retained as one parameter of QTPP as it is widely known to affect pharmacokinetics, tissue distribution, tissue extravasation, uptake and/or accumulation in clearance organs [ 15 ]. The particle size of formulation LNPs depends most on the composition of lipids. In general, the presence of PEG improved particle stability and reduced size but hindered delivery efficiency [ 27 , 29 , 36 , 37 ]. Particle size and PDI decrease with increasing RNA: ionizable lipid ratio due to higher RNA concentrations and concomitantly reduced amount of ionizable lipid [ 15 , 16 ].

Another parameter characteristic of RNA-LNP is the zeta potential, which also depends on the lipid type and concentration. In general, QTPP for zeta potential was set to >0 mV because nanoparticles with positive zeta potential enhanced interactions with the plasma membrane. Positively charged nanoparticles can encapsulate negatively charged genetic material by electrostatic interaction [ 24 ]. Depending on the composition and especially the modifications of the end groups (acid, amine, or esters), LNP particles can have a negative [ 20 , 22 , 25 ] or a positive charge [ 15 , 17 , 24 ]. Zeta potential increased proportionally as a function of ionizable lipid content and RNA: ionizable lipid ratio [ 15 , 16 , 17 ]. Zeta potential showed a significant correlation with transfection efficiency [ 27 , 28 , 36 ].

Another important CQA is entrapment efficiency, measured as an encapsulation efficiency. Encapsulation efficiency is more dependent on the LPN preparation process rather than independent variables [ 17 , 26 ]. No statistically significant differences in encapsulation efficiency were observed with particle size and PDI [ 15 ] but increased with higher RNA: ionizable lipid ratio [ 15 , 17 , 27 , 37 ].

All of the above particle properties contribute to biological efficacy but are not always the same in vitro and in vivo studies. For example, the efficacy of lipoplex increased with increasing hydrocarbon chain length, whereas the in vitro efficacy of LNP did not change significantly [ 35 ]. In vivo, the efficacy of LNP decreased with increasing hydrophobicity of cationic lipids [ 35 ]. The presence of the phospholipid DOPE was generally the strongest predictor of in vivo efficacy [ 36 , 37 , 39 ]. The specific combination of ionizable lipid and phospholipid in the LNP design provides high transfection efficiency in vitro [ 25 ].

The DOE approach has been used in most of the optimization works; however, the full QbD approach has been described in only a few papers [ 19 , 23 , 24 , 25 , 28 ], and a few articles refer only to some aspects of QbD—the definition of QTPP and CQA [ 15 , 16 , 17 , 20 , 27 ].

We have seen different approaches and innovations in DOE and statistical analysis. Traditional statistical tests and modeling based on ANOVA and regression analysis are slowly being replaced by artificial intelligence and machine learning methods, e.g., neural networks [ 23 ]. From a methodological point of view, the most interesting new development in pharmacy is the development of self-validated ensemble models (SVEM), especially with the connection of mixture design [ 22 ].

5. Conclusions

Over the past 10 years, many research articles have described the development of studies of RNA-loaded lipid nanoparticles for various clinical purposes, e.g., prophylaxis of infectious diseases, treatment of rare diseases, and gene, cancer, and protein replacement therapy. In addition, the coronavirus disease 2019 (COVID-19) pandemic and the emergence of safe and effective RNA vaccines have brought RNA technology to the forefront of medical innovation. Due to the mechanism of action, the therapeutic scope of RNA technology is wide, and the manufacturing processes are versatile. Different products could be produced using the same raw materials, consumables, equipment, unit operations, and analytical methods. In parallel with the development of new drug delivery methods, the QbD approach is becoming increasingly widespread in pharmaceutical manufacturing.

This review could be very useful for researchers and pharmaceutical manufacturers to apply the quality by design approach to the development of lipid nanosystems loaded with different types of RNA, following the latest trends and regulatory requirements and using modern mathematical and statistical design methods.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biomedicines11102752/s1 , Table S1: Optimization of RNA-LNP system using the QbD and/or DOE approach—summary and results.

Funding Statement

This research was co-funded by Celon Pharma S.A. and the Medical Research No. 2021/ABM/05/00005 entitled “Development of Innovative Therapeutic Solutions using RNA technology (TransformRNA—mRNA Therapeutics generation platform)”.

Author Contributions

Conceptualization, L.G.-B. and W.M.; methodology, L.G.-B. and D.A.S.; software, L.G.-B.; formal analysis, L.G.-B. and W.M.; investigation L.G.-B. and D.A.S.; resources, D.A.S. and K.D. and M.W.; data curation, W.M.; writing—original draft preparation, L.G.-B.; writing—review and editing, L.G.-B. and W.M.; visualization, L.G.-B.; supervision, W.M. and K.D.; project administration, K.D.; funding acquisition, K.D. and M.W. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: All contributors to this work at the time of their direct involvement in the project were the full-time employees of Celon Pharma S.A. M. Wieczorek is the CEO of Celon Pharma S.A. Some of the authors are the shareholders of Celon Pharma S.A.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Design of Experiments

Designed experiments for more understanding and maximum impact..

• Understand cause and effect using the power of statistically designed experiments -- even when you have limited resources.
• Design efficient experiments to meet your real-world constraints, process limitations and budget with the Custom Designer.
• Use a Definitive Screening Design to help you untangle important effects when you have many factors to consider.

Design of Experiments With JMP: Highlighted Features

Textbook designs (classical designs).

• Full factorial
• Fractional factorial
• Response surface (RSM)
• Blocking designs (RCBD, CBD, IBD, RIBD)
• Split-plot design
• Strip-plot design
• Mixture design

Optimal Designs 

• Custom design
• Optimality criteria
• Constrained designs

Screening Designs

• Definitive screening

Robust Designs 

• Taguchi array
• Taguchi design
• Noise factor

DOE Concepts  (DOE Techniques)

• Randomization
• Replication
• Center points
• Sequential experimentation
• Augment design 

Special Purpose Designs 

• Space filling
• Covering array PRO
• Nonlinear design

Evaluate Design

• Design comparison
• Design explorer
• Design diagnostics

From the beginning, we were able to quantify how much value DOE adds for us. We were using DOE in several internal cases and we were able to actually quantify our savings of at least 50% of the time and resources. In some cases it was even 70%.

Pilar Gomez Jimenez Principal Scientist, Johnson Matthey

The best discoveries start with JMP

• Try JMP free
• Buy JMP now

Featured Resources

Additional resources

JMP ® Analytic Capabilities

See everything that JMP ® can do for you and your organization, from data access and cleaning, to exploration and visualization, all the way through sharing and communicating your results.

All capabilities

Analytic workflow

Ready to take the next step?

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Design and experiment of a film-drilling potato seeder with eccentric coupling.

1. Introduction

2. overall structure and technical parameters, 2.1. whole machine structure, 2.2. working principle, 2.3. technical requirements, 3. the design of the seeder’s main working components, 3.1. seeding system, 3.2. eccentric coupling device, 3.2.1. simulation and motion characteristic analysis of eccentric coupling mechanism, 3.2.2. analysis of the soil entry by the eccentric coupling, 3.2.3. hole size analysis, 3.3. hole-drilling and seeding device, 3.3.1. constraint analysis of hole-drilling and seeding device, 3.3.2. design of the opening and closing mechanism of the seeding valve, 3.4. simulation analysis of hole formation process, 3.5. device for soil covering of membrane, 3.5.1. analysis of soil coverage by rotary tillers, 3.5.2. analysis of cutting area of rotary tiller blades, 3.6. transmission system, 3.7. simulation of the plastic film breaking and hole formation by the hole seeding device, 3.7.1. smoothed particle hydrodynamics model, 3.7.2. simulation model establishment, 3.7.3. soil-cutting simulation results, 4. field trials, 4.1. test conditions and methods, 4.2. experimental results and analysis, 5. conclusions, author contributions, institutional review board statement, data availability statement, conflicts of interest.

• Xia, M. Glimo launches a new traction potato planter. Agric. Mach. Mark. 2024 , 53. [ Google Scholar ]
• Zhang, T. Design and experiment of combined micro-potatoes precision seed metering device with pneumatic scoop. Shandong Agric. Univ. 2022 , 2024 , 53. [ Google Scholar ]
• Li, P.; Tian, B.; Sun, W.; Zhang, H.; Liu, X.; Li, H. Development Status and Prospects of Potato Precision Planting Technology. Agric. Equip. Veh. Eng. 2024 , 62 , 29–33. [ Google Scholar ]
• Zhao, S. Development of 2BSL-2 Potato Ridge Planter ; Mongolia Autonomous Region; Inner Mongolia Agricultural University Agricultural Engineering Complete Equipment Research Institute: Huhhot, China, 2003. [ Google Scholar ]
• Zhao, J.; Zhang, Y.; Shen, L. Design of 2CMF-2 Potato Planter. Mod. Agric. 2009 , 4 , 37. [ Google Scholar ]
• Li, C.; Feng, Y.; Kan, Z.; Liang, R. Development of a single row hanging potato fertilizer planting machine. Jiangsu Agric. Sci. 2013 , 41 , 369–371. [ Google Scholar ]
• Liu, W.; He, J.; Li, H.; Li, X.; Liu, P. Design and Experiment of Vibration arranging based seeder for potato microseed. Trans. Chin. Soc. Agric. Mach. 2019 , 50 , 70–80. [ Google Scholar ]
• Niu, K.; Fan, Y.; Luo, M.; Liu, Y.; Lv, C.; Fang, X. Design and Experiment of potato metering device with double-deck seed tank. Trans. Chin. Soc. Agric. Eng. 2016 , 32 , 32–39. [ Google Scholar ]
• Yang, Y. Design and Experimental Research on Air-Suction. Master’s Thesis, Northeast Agricultural University, Harbin, China, 2017. [ Google Scholar ]
• Hou, J.; Liu, W.; Zhang, W.; Lv, J.; Lv, Z.; Zhang, Z. Design of Pneumatic cup seed-metering device for Potato. Trans. Chin. Soc. Agric. Eng. 2018 , 34 , 18–28. [ Google Scholar ]
• Wang, G.; Sun, W.; Zhang, H. Research on a kind of seeding-monitoring and compensating control system for potato planter without additional seed-metering channel. J. Comput. Electron. Agric. 2020 , 177 , 105681. [ Google Scholar ] [ CrossRef ]
• Sun, W.; Wang, G.; Wu, J. Design and experiment on loss sowing testing and compensation system of spoon-chain potato metering device. J. Trans. Chin. Soc. Agric. Eng. 2016 , 32 , 8–15. [ Google Scholar ] [ CrossRef ]
• Wang, G.; Sun, W. Development of a kind of potato loss sowing detection and compensation device. J. Res. Agric. Mod. 2016 , 37 , 1008–1014. [ Google Scholar ]
• Shi, L.; Wu, J.; Zhao, W.; Sun, W.; Zhang, F.; Sun, B. Establishment and parameter verification of farmland soil model in uniaxial compression based on discrete element method. J. China Agric. Univ. 2015 , 20 , 174–182. [ Google Scholar ]
• Niu, L.; Zhou, L.; Yuan, Y. Design and experiment on automatic compensation system of spoon-chain potato metering device. J. Trans. Chin. Soc. Agric. Mach. 2016 , 47 , 76–83. [ Google Scholar ]
• Wang, G.; Sun, W.; Chen, D. Realization of an integrated seeding and compensating potato planter based on one-way clutch. J. Int. J. Agric. Biol. Eng. 2020 , 13 , 79–87. [ Google Scholar ] [ CrossRef ]
• Lv, J.; Yang, Y.; Li, Z. Design and experiment of cup-belt type potato seed-metering device. J. Trans. Chin. Soc. Agric. Eng. 2016 , 32 , 17–25. [ Google Scholar ]
• Dai, F.; Xin, S.; Zhao, W.; Liu, F.; Xin, B.; Ma, M. Design and experiment of combined potato planting machine for covering soil on top of full surface. J. Trans. Chin. Soc. Agric. Mach. 2017 , 48 , 76–83. [ Google Scholar ]
• Wang, Y.; Yang, Z.; Li, X.; Yang, H.; Kou, M. Design and experiment of vertical submerged corn hole seeder based on parallel four-bar linkage mechanism. J. Chin. Agric. Mech. 2022 , 43 , 18–24. [ Google Scholar ] [ CrossRef ]
• Cui, R.; Jian, S.; Xv, W.; Ma, J.; Wang, X. Design and test of garlic upright plant. J. Agric. Mech. Res. 2016 , 38 , 201–205. [ Google Scholar ] [ CrossRef ]
• Cui, R.; Zhang, H.; Xv, W. Discussion on the present situation and development of garlic mechanized production in China. J. Agric. Mech. Res. 2015 , 37 , 83–86. [ Google Scholar ] [ CrossRef ]
• Shi, F.; Sun, S.; Deng, X.; Wang, J. Trajectory analysis of planting device of eccentric disc and finger clip pattern seedling planting machine. J. Hunan Agric. Univ. 2012 , 38 , 333–336. [ Google Scholar ] [ CrossRef ]
• Cui, R.; Wang, X.; Xin, J.; Sun, L.; Wu, C. Desing and test of arc duck-billed garlic seed planter. J. Int. J. Agric. Biol. Eng. 2022 , 53 , 120–130. [ Google Scholar ]
• Sun, W.; Liu, X.; Wang, H.; Zhang, H.; Wu, J.; Yang, X.; Wang, G. Design and test of double crank multi-rod hill-drop potato planter on plastic film. J. Trans. Chin. Soc. Agric. Eng. 2018 , 34 , 34–42. (In Chinese) [ Google Scholar ] [ CrossRef ]
• Li, Y.; Liu, M.; Duan, R. Design of complete machine and key components of potato planter. J. Agric. Eng. 2022 , 12 , 95–102. [ Google Scholar ] [ CrossRef ]
• Fang, H.; Ji, C.; Farman, A.; Guo, J.; Zhang, Q.; Chaudhry, A. Analysis of soil dynamic behavior during rotary tillage based on distinct element method. J. Trans. Chin. Soc. Agric. Mach. 2016 , 47 , 22–28. [ Google Scholar ]
• Tan, H.; Xv, L.; Ma, S.; Niu, C.; Yan, C.; Shen, C. Design and experiment of scraper organic fertilizer strip spreading and rotary tillage mixed fertilizer applicator. Trans. Chin. Soc. Agric. Mach. 2022 , 11 , 163–175. [ Google Scholar ]
• Liao, Q.; Du, W.; Zhang, Q.; Lin, J.; Chen, Z.; Zhang, J. Design and experiment of cigar tobacco adjustable seed-plot ridging and film spreading machine. Trans. Chin. Soc. Agric. Mach. 2022 , 11 , 116–126. [ Google Scholar ]
• Sun, J.; Liu, Q.; Luo, P.; Yang, F.; Liu, Z.; Wang, Z. Soil erosion law of contour rotary tillage of hillside tractor on sloping land. J. Trans. Chin. Soc. Agric. Mach. 2022 , 53 , 44–56. [ Google Scholar ]
• Chen, B.; Chen, X.; Chen, X.; Zhu, J.; Yu, Q.; Miao, Y.; Liu, Y. Design and experiment of crawler self-propelled ridging and fertilization combined machine for strawberry. J. Chin. Agric. Mech. 2022 , 43 , 55–60. [ Google Scholar ]
• Shi, B.; Sun, W.; Zhao, Z. Design and experiment of the combined machine for transplanting outcrop of codonopsis with micro ridge covered with film. J. Appl. Sci. 2023 , 13 , 9149. [ Google Scholar ] [ CrossRef ]
• Jing, W.; Wang, S.; Qian, H. Simulation analysis of cutting soil by weeding claws between plants in paddy field based on LS-DYNA. J. Agric. Mech. Res. 2023 , 45 , 203–209. [ Google Scholar ] [ CrossRef ]
• GB/T 25417-2010 ; Potato planting machine-Specifications. Standards of China: Beijing, China, 2010.
• NY/T 1415-2007 ; Technical Specification for Quality Evaluation of Potato Planter. Standards of China: Beijing, China, 2007.
• NY/T 987-2006 ; Operation Quality of Film Mulching Hole Seeder. Standards of China: Beijing, China, 2006.

Share and Cite

Pei, L.; Sun, W.; Wang, J.; Simionescu, P.A. Design and Experiment of a Film-Drilling Potato Seeder with Eccentric Coupling. Agriculture 2024 , 14 , 933. https://doi.org/10.3390/agriculture14060933

Pei L, Sun W, Wang J, Simionescu PA. Design and Experiment of a Film-Drilling Potato Seeder with Eccentric Coupling. Agriculture . 2024; 14(6):933. https://doi.org/10.3390/agriculture14060933

Pei, Liangqi, Wei Sun, Juanling Wang, and Petru A. Simionescu. 2024. "Design and Experiment of a Film-Drilling Potato Seeder with Eccentric Coupling" Agriculture 14, no. 6: 933. https://doi.org/10.3390/agriculture14060933

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals