Designed Experiments for Biotech and Pharma

Designed Experiments for Biotech and Pharma

Gain expertise in the intricacies of designing experiments within the context of biotech and pharma through this specialized workshop of design expert training. By doing so, you will significantly expedite your research and development efforts. Uncover the ways in which tools for design of experiments (DOE) contribute to the establishment of streamlined and dependable processes, as well as the production of exceptional products. Each concept is exemplified through industry-specific case studies, providing a comprehensive understanding. Contact us for the Design Expert training for biotech and pharma industry.

This design expert training for biotech and pharma workshop covers all the practical aspects of DOE needed to improve your product development, as well as optimize R&D and manufacturing processes. You will learn how to:

  • Utilize the benefits of multifactor experimentation
  • Secure success through a systematic four-step process in planning Design of Experiments (DOE)
  • Evaluate statistical soundness using ANOVA and diagnostic techniques
  • Unearth concealed interrelationships
  • Take advantage of resource-efficient fractional designs for initial screening or comprehensive characterization
  • Employ precision-focused tools to appropriately scale designs, avoiding excess complexity or oversimplification
  • Implement Response Surface Methods (RSM) for fine-tuning process efficiency
  • Devise optimal formulations through intricate multicomponent mixture designs
  • Harness the full potential of predictive models at your disposal
  • Fine-tune numerous outcomes to pinpoint the optimal configuration


  • Familiarity with fundamental statistics (mean and standard deviation) and experience with elementary comparative experiments (such as a two-sample t-test) are prerequisites.
  • A foundational understanding of the Design-Expert software for biotech and pharma is expected.
Topics Included:

Introduction to Factorial Design

Factorial designs are primarily utilized to assess the significance of factors within a process. This can involve screening to pinpoint essential factors from an array of possibilities or examining how established factors interact and independently impact the process. Such designs frequently serve as initial stages for delving into more intricate response surface modeling.

  • A factorial design is a form of structured experiment that enables the examination of how multiple factors can influence a response.
  • Conducting experiments by simultaneously varying levels of all factors allows the study of factor interactions.
  • A factorial design with center points helps assess curvature in response surfaces, albeit limited to the center point.
  • Fitted values can be computed only at corner and center points, preventing the creation of a contour plot.
  • Modeling curvature across the entire response surface requires quadratic terms, achievable through a response surface design.
  • Augmenting a factorial design with axial points enables the creation of a central composite response surface design.

Blocking and Fractional Factorials

A fractional design refers to an experimental arrangement where researchers execute only a specific subset or "fraction" of the complete runs found in a full factorial design:

  • Fractional factorial designs suitable for resource constraints or numerous factors
  • Utilize fewer runs compared to full factorial designs
  • Subset of full factorial design, leading to confounding of main effects and 2-way interactions
  • Higher-order interactions intertwined and indistinguishable
  • Often assumptions made about negligible higher-order effects
  • Gather data on main effects and low-order interactions with fewer runs.

Understanding aliasing is a fundamental aspect of designing fractional-factorial experiments effectively and drawing meaningful conclusions from the obtained results.

  • Aliasing in fractional-factorial design: inability to estimate all effects due to fewer unique combinations than full-factorial design.
  • Alias structure: determines combination of effects in the design.
  • Understanding aliasing basics helps researchers choose suitable designs for experimental goals.

Blocking is a method employed to mathematically eliminate the variation introduced by identifiable changes occurring during the experiment's progression.

  • Instances requiring blocking include the use of distinct raw material batches or the experiment's duration spanning multiple shifts or days.
  • Such changes might cause shifts in response data, which blocking counteracts, essentially "normalizing" the data.
  • Design-Expert presents multiple blocking options, contingent on the number of runs chosen.
  • The default option of 1 block signifies "no blocking."

Introduction to Response Surface Design

Apply Analyze Response Surface Design to capture data curvature and determine optimal settings.

  • Typically used post-factorial or fractional factorial experiments.
  • Comes after identifying key process factors.

Response Surface Methodology

Response Surface Methodology (RSM) is a statistical and mathematical approach used within the context of Design of Experiments (DOE). It's employed to optimize and improve processes, products, or systems by systematically exploring the relationships between multiple input variables and one or more response variables.

  • Beneficial for nonlinear relationships and potential interactions between input and response variables.
  • Facilitates the discovery of optimal conditions, reducing the need for extensive trial-and-error.
  • Efficiently conserves time, resources, and effort in experimentation.
  • Applied across diverse domains including engineering, manufacturing, chemistry, and process optimization.

Constrained Response Surface

Constrained Response Surface methodology is valuable when practical limitations are critical in the optimization process. It's applicable in various fields such as engineering, manufacturing, product design, and process optimization, where finding the best possible outcome while adhering to real-world constraints is essential.

  • Modeling technique for optimizing processes or systems while factoring in limitations on input variables.
  • An extension of Response Surface Methodology (RSM) that considers constraints during optimization.
  • Addresses specific restrictions or limitations imposed on input variables.
  • Incorporates practical considerations into the optimization process.
  • Valuable for achieving optimal outcomes within real-world constraints.

Quality by Design “QbD”

Quality by Design is:

  • Comprehensive, proactive, and risk-aware scientific approach to product development
  • Thoughtful design process spanning from product inception to market release
  • Thorough comprehension of the interplay between product characteristics, processes, and performance

Introduction to Mixture Design

The analysis of a mixture design is based on the same principle as linear regression.

  • Employ Analyze Mixture Design for designed experiment analysis
  • Suitable when response hinges on mixture component proportions, e.g., bread dough ingredients
  • Incorporate process variables and total mixture quantities in analysis

Mixture models are easily interpretable only when all mixture components span from 0 to the total value within the design. However, many mixture designs encompass more restricted spaces.

  • Mixture (Scheffé) polynomials: Scheffé models were uniquely formulated to effectively manage the inherent limitations present in mixture designs.
  • Simplex-Lattice designs: This design differs from a simplex-centroid design by having enough points to estimate a full cubic model.
    • Applicable for 2 to 30 components
    • Simplex-lattice design with degree m comprises m+1 equally spaced values (0 to 1) per component
    • Fractions range for m = 2: 0, 1/2, 1
    • Fractions range for m = 3: 0, 1/3, 2/3, 1
    • Includes pure components and intermediary points for degree m estimation

Combining a Mixture with a Process Factor (amount)

Design Expert software provides designs that incorporate both mixture components and process factors, whether they are numeric or categorical. In this training tutorial, you'll explore the distinctive capabilities of Design Expert for designing and analyzing mixture-process experiments, focusing on the following investigations:

  • Fish patties (background)
  • Fish patties (optimal design)