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

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.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

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.

Oral delivery of posaconazole-loaded phospholipid-based nanoformulation: Preparation and optimization using design of experiments, machine learning, and TOPSIS

Phospholipid-based nanosystems show promising potentials for oral administration of hydrophobic drugs. The study introduced a novel approach to optimize posaconazole-loaded phospholipid-based nanoformulation using the design of experiments, machine learning, and Technique for Order of Preference by Similarity to the Ideal Solution. These approaches were used to investigate the impact of various variables on the encapsulation efficiency (EE), particle size, and polydispersity index (PDI). The optimized formulation, with %EE of ∼ 74 %, demonstrated a particle size and PDI of 107.7 nm and 0.174, respectively. The oral pharmacokinetic profiles of the posaconazole suspension, empty nanoformulation + drug suspension, and drug-loaded nanoformulation were evaluated. The nanoformulation significantly increased maximum plasma concentration and the area under the drug plasma concentration-time curve (∼3.9- and 6.2-fold, respectively) and could be administered without regard to meals. MTT and histopathological examinations were carried out to evaluate the safety of the nanoformulation and results exhibited no significant toxicity. Lymphatic transport was found to be the main mechanism of oral delivery. Caco-2 cell studies demonstrated that the mechanism of delivery was not based on an increase in cellular uptake. Our study represents a promising strategy for the development of phospholipid-based nanoformulations as efficient and safe oral delivery systems.