Modeling of Pharmacokinetic Systems Using Stochastic Deconvolution

Applications of bio-predictive dissolution tools for the development of solid oral dosage forms: Current industry experience

Development and optimization of orally administered drug products often require bio-predictive tools to help with informing formulation and manufacturing decisions. Reliable bio-predictive dissolution toolkits not only allow rational development of target formulations without having to conduct excessive in vivo studies but also help in detecting critical material attributes (CMAs), critical formulation variables (CFVs), or critical process parameters (CPPs) that could impact a drug's in vivo performance. To provide early insights for scientists on the development of a bio-predictive method for drug product development, this review summarizes current phase-appropriate bio-predictive dissolution approaches applicable to address typical concerns on solubility-limited absorption, food effect, achlorhydria, development of extended-release formulation, clinically relevant specification, and biowaiver. The selection of an in vitro method which can capture the key rate-limiting step(s) of the in vivo dissolution and/or absorption is considered to have a better chance to produce a meaningful in vitro-in vivo correlation (IVIVC) or in vitro-in vivo relationship (IVIVR).

Administration in fed state but not controlled release in the colon increases oral bioavailability of DF030263, a promising drug candidate for chronic lymphocytic leukemia

For treatment of chronic cancers, the oral administration route is preferred as it provides numerous advantages over other delivery routes. However, these benefits of oral chemotherapy can be limited due to unfavorable pharmacokinetics. Accordingly, pharmacokinetic development of chemotherapeutic agents is crucial to the improvement of cancer treatment. In this study, assessment and optimization of biopharmaceutical properties of a promising drug candidate for cyclin-dependent kinase 9 (CDK9) inhibitor (DF030263) was performed to promote oral delivery. Oral bioavailability of DF030263 in fasted rats was 23.8%, and a distinct double-peak phenomenon was observed. A two-site absorption windows mechanism was proposed as a possible explanation to the phenomenon. The two-site absorption window hypothesis was supported by in vitro solubility assays in biorelevant fluids with different pH levels, as well as by in silico simulation by GastroPlus™. Controlled release to the colon was conducted in rats in order to exploit the colonic absorption window but did not improve the oral bioavailability. On the other hand, oral administration at postprandial conditions in rats (performed based on the high in vitro solubility in fed state simulated fluid and reduced pH-dependency) resulted in an almost 3-fold increase in bioavailability to 63.6%. In conclusion, this study demonstrates an efficient in vitro-in vivo-in silico drug development approach for improving the oral bioavailability of DF030263, a promising candidate for the treatment of chronic lymphocytic leukemia.

Evaluating In Vivo-In Vitro Correlation Using a Bayesian Approach

A Bayesian approach with frequentist validity has been developed to support inferences derived from a "Level A" in vivo-in vitro correlation (IVIVC). Irrespective of whether the in vivo data reflect in vivo dissolution or absorption, the IVIVC is typically assessed using a linear regression model. Confidence intervals are generally used to describe the uncertainty around the model. While the confidence intervals can describe population-level variability, it does not address the individual-level variability. Thus, there remains an inability to define a range of individual-level drug concentration-time profiles across a population based upon the "Level A" predictions. This individual-level prediction is distinct from what can be accomplished by a traditional linear regression approach where the focus of the statistical assessment is at a marginal rather than an individual level. The objective of this study is to develop a hierarchical Bayesian method for evaluation of IVIVC, incorporating both the individual- and population-level variability, and to use this method to derive Bayesian tolerance intervals with matching priors that have frequentist validity in evaluating an IVIVC. In so doing, we can now generate population profiles that incorporate not only variability in subject pharmacokinetics but also the variability in the in vivo product performance.

Stochastic nonlinear mixed effects: a metformin case study

In nonlinear mixed effect (NLME) modeling, the intra-individual variability is a collection of errors due to assay sensitivity, dosing, sampling, as well as model misspecification. Utilizing stochastic differential equations (SDE) within the NLME framework allows the decoupling of the measurement errors from the model misspecification. This leads the SDE approach to be a novel tool for model refinement. Using Metformin clinical pharmacokinetic (PK) data, the process of model development through the use of SDEs in population PK modeling was done to study the dynamics of absorption rate. A base model was constructed and then refined by using the system noise terms of the SDEs to track model parameters and model misspecification. This provides the unique advantage of making no underlying assumptions about the structural model for the absorption process while quantifying insufficiencies in the current model. This article focuses on implementing the extended Kalman filter and unscented Kalman filter in an NLME framework for parameter estimation and model development, comparing the methodologies, and illustrating their challenges and utility. The Kalman filter algorithms were successfully implemented in NLME models using MATLAB with run time differences between the ODE and SDE methods comparable to the differences found by Kakhi for their stochastic deconvolution.