DDDPlus™...The industry’s only in vitro dissolution software for the formulation scientist.
What is DDDPlus?
DDDPlus (Dose Disintegration and Dissolution Plus) is an advanced technology computer program that models and simulates the in vitro dissolution of active pharmaceutical ingredients (API) and formulation excipients dosed as powders, tablets, capsules, and swellable or non-swellable polymer matrices under various experimental conditions.
Dissolution rate is a critical parameter of pharmaceutical dosage forms because the API needs to be dissolved before it can be absorbed. In vitro dissolution testing is important to screen formulations during development and to ensure batch-to-batch quality control during production. Throughout the world, more than 40 years of research have been devoted to characterizing the biopharmaceutical properties of drugs. Several guidelines have been published and all pharmacopoeias include a description of dissolution testing.
During drug development, in vitro dissolution testing is an important tool for evaluating candidate formulations and for understanding possible risks related to specific gastrointestinal factors, potential for dose dumping, food effects on bioavailability, and interaction with excipients. Today, dissolution studies are the most frequently used tools in the development, characterization, and utilization process of both immediate and controlled-release formulations.
Dissolution, in the simplest sense, can be defined as the sequence by which a solid solute enters into a solution in the presence of a solvent. We can define the dissolution rate as the amount of ingredient in a solid dosage form dissolved in unit time under particular conditions.
A DDDPlus simulation is essentially the numerical integration of a set of differential equations that coordinate well-characterized physical actions that occur during dissolution, including but not limited to changes in particle size distributions for both active and excipient ingredients, as well as changes in microclimate (surface) and medium bulk pH as formulation constituents dissolve.
DDDPlus allows you to select from one of 5 mathematical models and 5 dosage forms used to describe the dissolution of a
single ingredient. The mathematical models for the in vitro dissolution simulation account for the effects of:
Physicochemical properties of the formulation ingredients under study: pKa’s, solubility, diffusion coefficient, and density.
Manufacturing properties for immediate release dosage forms.
Particle size distribution for each of the formulation ingredients.
Different flow patterns and fluid velocities for each experimental apparatus.
Interactions between the active ingredient and formulation excipients.
Microclimate pH-dependence of solubility and dissolution/precipitation.
Micelle-facilitated dissolution through the incorporation of surfactants in the media.
In spite of its sophistication, DDDPlus is relatively easy for someone with a background in formulation and chemistry to learn and use. DDDPlus incorporates an intuitive and modern graphical user interface that enables rapid and smooth transition from setting up inputs to evaluating results.
Outputs are displayed with immediate on-screen text and graphics for single simulations, and can be saved to Microsoft Excel-compatible tab-delimited ASCII text files for both single and multiple simulations. Extended analyses through Parameter Sensitivity Analyses and Virtual Trials provide insight into the probable behaviors of formulations under varying conditions and can guide experimental efforts to focus precious resources where they will do the most good.
How Can DDDPlus be Used?
You can use the Optimization Module within DDDPlus to quickly build your own formulation-specific models based on your own data. The term “model” here refers to a specific set of one or more fitted parameters that determine the rate of dissolution under varying conditions during an experiment. Variables such as amounts of active pharmaceutical ingredient (API) and excipients, and particle size distributions for each, are accounted for in the dissolution equations. The Optimization Module enables DDDPlus to calibrate itself to a specific data set using a variety of user-selected parameters and optimization criteria. The formulation-specific models so derived can then be used to estimate the likely changes in dissolution while varying excipient content, particle size distribution, and experimental parameters. For example, you may have a set of dissolution data for an active ingredient that was formulated with a certain type of solubilizer. By calibrating the “solubilizer effect” coefficient for one solubilizer amount, you can then estimate the effects of differing amounts of the same solubilizer on the active ingredient’s dissolution.
Because DDDPlus allows the user to adjust a wide variety of physicochemical and experimental parameters (e.g., instrument speed, medium volume, buffer recipes), studies can be run to afford the researcher an insight into the likely dissolution behaviors as a result of a variety of excipient content and formulation changes (e.g., particle size distributions, amount of API, amount of excipients, and tablet compression force) under a variety of experimental conditions.
The Parameter Sensitivity Analysis feature in DDDPlus enables the user to assess the sensitivity of predicted dissolution to critical input parameters. For example, if a particular ingredient indicates a relatively high sensitivity of dissolution to estimated solubility but a relatively low sensitivity to the estimated standard deviation of the particle size distribution, you would know that it is important to accurately measure solubility by experiment, but that a reasonable estimate for the standard deviation might be sufficient. You would also know that improvements to solubility could have a substantial payoff, while improving standard deviation would not. If the solubility used in the simulation was actually determined by experiment (not an in silico estimate), and the predicted dissolution was poor, you would know to direct efforts toward improving the dissolution rate in spite of the solubility of the API (e.g., by reducing particle size, changing to a salt formulation, or adding rate-enhancing excipients).
NEW! Virtual Trials
The Virtual Trials feature in DDDPlus runs a series of simulations with different simulated dissolution experiments, each of which is described by a random sample of formulation and experimental parameters to mimic the variances expected with actual formulations or experimental setups. This powerful capability allows you to assess the combined effects of variations in formulation and experimental variables on the in vitro dissolution profiles, helping to establish critical dissolution specifications to meet certain regulatory guidelines.
DDDPlus Screen Shots
With DDDPlus, you can add as many ingredients to the formulation as you would like. This is done through the Formulation Composition window shown below (where carbamazepine is the active ingredient dosed with PVP acting as a solubilizer):
Once you have entered the formulation information, the environment in which the dissolution experiment will take place needs to be specified. This is done on the Experimental Setup tab shown below:
During the simulation, a dynamic calculation of microclimate and bulk pH occurs as the ingredients in the formulation dissolve. With DDDPlus, you can create a buffer with a specific pH. This is done in the window below:
Once you have entered the required information, you can run your simulation and view the output of many different variables. An example as to the plotting options for DDDPlus is shown below:
In addition to running simulations of in vitro dissolution experiments, you can also run a Parameter Sensitivity Analysis (PSA), where a particular parameter is varied in order to see how “sensitive” it is to the ingredients’ total percent dissolved. An example output plot at the end of a PSA run is shown below:
A new feature added to DDDPlus is the Virtual Trials mode. Virtual Trials enables you to see how much variability should be expected if a particular experiment was run many times. In real life, running the same experiment many times results in different outcomes each time, because there are many variables in both the dosage form and experiment that cannot be held perfectly constant. An example output plot at the end of a Virtual Trials run is show below: