More Than a Gut Feeling─A Combination of Physiologically Driven Dissolution and Pharmacokinetic Modeling as a Tool for Understanding Human Gastric Motility.

In vivo studies of formulation performance with in vitro and/or in silico simulations are often limited by significant gaps in our knowledge of the interaction between administered dosage forms and the human gastrointestinal tract. This work presents a novel approach for the investigation of gastric motility influence on dosage form performance, by combining biopredictive dissolution tests in an innovative PhysioCell apparatus with mechanistic physiology-based pharmacokinetic modeling. The methodology was based on the pharmacokinetic data from a large (n = 118) cohort of healthy volunteers who ingested a capsule containing a highly soluble and rapidly absorbed drug under fasted conditions. The developed dissolution tests included biorelevant media, varied fluid flows, and mechanical stress events of physiological timing and intensity. The dissolution results were used as inputs for pharmacokinetic modeling that led to the deduction of five patterns of gastric motility and their prevalence in the studied population. As these patterns significantly influenced the observed pharmacokinetic profiles, the proposed methodology is potentially useful to other in vitro-in vivo predictions involving immediate-release oral dosage forms.

In Vitro Simulation of the Fasted Gastric Conditions and Their Variability to Elucidate Contrasting Properties of the Marketed Dabigatran Etexilate Pellet-Filled Capsules and Loose Pellets.

Variability of the gastrointestinal tract is rarely reflected in in vitro test protocols but often turns out to be crucial for the oral dosage form performance. In this study, we present a generation method of dissolution profiles accounting for the variability of fasted gastric conditions. The workflow featured 20 biopredictive tests within the physiological variability. The experimental array was constructed with the use of the design of experiments, based on three parameters: gastric pH and timings of the intragastric stress event and gastric emptying. Then, the resulting dissolution profiles served as a training data set for the dissolution process modeling with the machine learning algorithms. This allowed us to generate individual dissolution profiles under a customizable gastric pH and motility patterns. For the first time ever, we used the method to successfully elucidate dissolution properties of two dosage forms: pellet-filled capsules and bare pellets of the marketed dabigatran etexilate product Pradaxa. We showed that the dissolution of capsules was triggered by mechanical stresses and thus was characterized by higher variability and a longer dissolution onset than observed for pellets. Hence, we proved the applicability of the method for the in vitro and in silico characterization of immediate-release dosage forms and, potentially, for the improvement of in vitro-in vivo extrapolation.

An extension of biorelevant fed-state dissolution tests to clinical pharmacokinetics - A study on gastrointestinal factors influencing rivaroxaban exposure and efficacy in atrial fibrillation patients.

A direct oral anticoagulant rivaroxaban fails to prevent stroke and systemic embolism in one-to-several percent of patients with nonvalvular atrial fibrillation (NVAF), but the reasons are unknown. The study used semi-mechanistic in vitro-in vivo prediction (IVIVP) modeling to explore the reasons for ineffective thrombosis prevention in NVAF patients. Steady-state drug concentrations in plasma were measured at 0 h (Ctrough), 3 h (C3h), and 12 h post-dosing in thirty-four patients treated with 20 mg rivaroxaban daily. The clinical data were compared against "virtual twins" generated with a novel IVIVP model that combined drug dissolution modeling, mechanistic description of gastric drug transit, and population pharmacokinetics defining the variability of drug disposition. The nonresponders had significantly lower C3h and Ctrough than the responders (p < 0.001) and the covariates included in the population pharmacokinetic submodel did not fully explain this difference. Simulations involving varied gastrointestinal parameters in the "virtual twins" revealed that lower small intestinal effective permeability (Peff), rather than a slower stomach emptying rate, could explain low rivaroxaban exposure in the nonresponders. IVIVP modeling was effectively used for exploring pharmacotherapy failure. Low Peff, found as a major determinant of ineffective rivaroxaban treatment, encourages further research to find (pato)physiological factors influencing suboptimal absorption.

A Rational Approach to Predicting Immediate Release Formulation Behavior in Multiple Gastric Motility Patterns: A Combination of a Biorelevant Apparatus, Design of Experiments, and Machine Learning.

Gastric mechanical stress often impacts drug dissolution from solid oral dosage forms, but in vitro experiments cannot recreate the substantial variability of gastric motility in a reasonable time. This study, for the first time, combines a novel dissolution apparatus with the design of experiments (DoE) and machine learning (ML) to overcome this obstacle. The workflow involves the testing of soft gelatin capsules in a set of fasted-state biorelevant dissolution experiments created with DoE. The dissolution results are used by an ML algorithm to build the classification model of the capsule's opening in response to intragastric stress (IS) within the physiological space of timing and magnitude. Next, a random forest algorithm is used to model the further drug dissolution. The predictive power of the two ML models is verified with independent dissolution tests, and they outperform a polynomial-based DoE model. Moreover, the developed tool reasonably simulates over 50 dissolution profiles under varying IS conditions. Hence, we prove that our method can be utilized for the simulation of dissolution profiles related to the multiplicity of individual gastric motility patterns. In perspective, the developed workflow can improve virtual bioequivalence trials and the patient-centric development of immediate-release oral dosage forms.