vBioE2 / Blog: Recent posts

DESCRIPTION
The purpose of the current project is the development of a potentially open-source platform that would provide the capability to conduct virtual bioequivalence trial simulations. Ideally, this user-friendly platform will provide the user with many user-defined capabilities.
Algorithm implementation for population pharmacokinetic analysis: The current project describes the marriage between PBPK modeling and population-based statistical analysis. The latter is necessary for parameter estimation when individual-level data is available. A critical aspect of this coupling is the implementation of an algorithm that would allow the performance of population-based statistical analysis. Although novelty is encouraged in regards to the algorithm, proposals could entertain approaches such as: a. Implementing the non-linear mixed effects theory, b. Maximum log-likelihood algorithms (stiff, non-stiff Ordinary Differential Equation solving methods with linearization), c. Exact maximum likelihood with Expectation-Maximization algorithms (no linearization), d. Introducing a Bayesian population PBPK approach (Markov Chain Monte Carlo), e. Nonparametric methods.
methods to evaluate the robustness of the proposed algorithm in terms of bias to initial estimates, precision, accuracy, convergence rate, and computation time, in full-body PBPK models of low, intermediate, and high complexity using simulated and real (rich and sparse) datasets.
PBPK model structure to meet the needs and requirements of developing a generic drug product that can be administered via an oral or non-oral route to healthy volunteers or special populations. Features of the developed models may include:
a. capability to administer a dosage form in the gut or through any other tissue/organ (ie. skin, lung etc.) captured in the model.
b. capability to mechanistically describe the administration of different dosage forms that could include, but not be limited to: immediate release or extended release tablets or capsules, solutions or suspensions, aerosols, creams/ointments/emulsions/transdermal delivery systems.
c. Capability to account for and mechanistically describe the potential impact of formulation critical quality attributes on in vivo drug product performance by incorporating them into the model. These formulation characteristics could include, but not be limited to: content uniformity, particle size and particle size distribution, viscosity and rheology characterization parameters and in vitro dissolution/drug release characterization, aerodynamic and adhesion properties.
d. Capability to extrapolate model outcomes from one population to another by modifying physiology (system-related) parameters in the PBPK model. Most of bioequivalence studies are conducted in healthy volunteers who are not the target population. Therefore, the capability of simulating a virtual bioequivalence trial is advantageous in that it can provide an insight on the in vivo drug performance in patients to whom the drug product is intended to be administered and in special populations such as the elderly or pediatrics for whom bioequivalence studies are not always feasible to conduct.
e. Capability to simulate different study designs that include, but are not limited to: crossover, parallel, (fully) replicated study design, single and multiple (steady state) dose studies.
It desirable to be able, within the platform, to validate/qualify the previously developed PBPK models by utilizing appropriate datasets retrieved from independent and accredited literature sources, by utilizing in house data if available or by designing and conducting studies that would allow the generation of the necessary experimental datasets.
Appropriate datasets that capture the subpopulation, study design and dosage form characteristics that were incorporated in the models are expected to be utilized for model validation/qualification.
Virtual bioequivalence studies can be simulated on the developed platform to determine whether generic drug products that have been shown to not be bioequivalent to their innovators (positive control) or whether generic drug products that have been shown not to be bioequivalent to their innovators (negative control) are deemed not bioequivalent or bioequivalent, respectively, based on model output.