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Selection of In Vivo Predictive Dissolution Media Using Drug Substance and Physiological Properties.

ABSTRACT: The rate and extent of drug dissolution in the gastrointestinal (GI) tract are highly dependent upon drug physicochemical properties and GI fluid properties. Biorelevant dissolution media (BDM), which aim to facilitate in vitro prediction of in vivo dissolution performance, have evolved with our understanding of GI physiology. However, BDM with a variety of properties and compositions are available, making the choice of dissolution medium challenging. In this tutorial, we describe a simple and quantitative methodology for selecting practical, yet physiologically relevant BDM representative of fasted humans for evaluating dissolution of immediate release formulations. Specifically, this methodology describes selection of pH, buffer species, and concentration and evaluates the importance of including bile salts and phospholipids in the BDM based upon drug substance log D, pK_a, and intrinsic solubility. The methodology is based upon a mechanistic understanding of how three main factors affect dissolution, including (1) drug ionization at gastrointestinal pH, (2) alteration of surface pH by charged drug species, and (3) drug solubilization in mixed lipidic aggregates comprising bile salts and phospholipids. Assessment of this methodology through testing and comparison with literature reports showed that the recommendations correctly identified when a biorelevant buffer capacity or the addition of bile salts and phospholipids to the medium would appreciably change the drug dissolution profile. This methodology can enable informed decisions about when a time, complexity, and/or cost-saving buffer is expected to lead to physiologically meaningful in vitro dissolution testing, versus when a more complex buffer would be required.

SUBMITTER: Mudie DM

PROVIDER: S-EPMC6985051 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Selection of In Vivo Predictive Dissolution Media Using Drug Substance and Physiological Properties.

Mudie Deanna M DM Samiei Nasim N Marshall Derrick J DJ Amidon Gregory E GE Bergström Christel A S CAS

The AAPS journal 20200127 2

The rate and extent of drug dissolution in the gastrointestinal (GI) tract are highly dependent upon drug physicochemical properties and GI fluid properties. Biorelevant dissolution media (BDM), which aim to facilitate in vitro prediction of in vivo dissolution performance, have evolved with our understanding of GI physiology. However, BDM with a variety of properties and compositions are available, making the choice of dissolution medium challenging. In this tutorial, we describe a simple and q ...[more]

PMID: 31989343

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Project description:Objective: Opioids are a class of drugs that are known for their use as pain relievers. They bind to opioid receptors on nerve cells in the brain and the nervous system to mitigate pain. Addiction is one of the chronic and primary adverse events of prolonged usage of opioids. They may also cause psychological disorders, muscle pain, depression, anxiety attacks etc. In this study, we present a collection of predictive models to identify patients at risk of opioid abuse and mortality by using their prescription histories. Also, we discover particularly threatening drug-drug interactions in the context of opioid usage. Methods and Materials: Using a publicly available dataset from MIMIC-III, two models were trained, Logistic Regression with L2 regularization (baseline) and Extreme Gradient Boosting (enhanced model), to classify the patients of interest into two categories based on their susceptibility to opioid abuse. We've also used K-Means clustering, an unsupervised algorithm, to explore drug-drug interactions that might be of concern. Results: The baseline model for classifying patients susceptible to opioid abuse has an F1 score of 76.64% (accuracy 77.16%) while the enhanced model has an F1 score of 94.45% (accuracy 94.35%). These models can be used as a preliminary step towards inferring the causal effect of opioid usage and can help monitor the prescription practices to minimize the opioid abuse. Discussion and Conclusion: Results suggest that the enhanced model provides a promising approach in preemptive identification of patients at risk for opioid abuse. By discovering and correlating the patterns contributing to opioid overdose or abuse among a variety of patients, machine learning models can be used as an efficient tool to help uncover the existing gaps and/or fraudulent practices in prescription writing. To quote an example of one such incidental finding, our study discovered that insulin might possibly be interacting with opioids in an unfavourable way leading to complications in diabetic patients. This indicates that diabetic patients under long term opioid usage might need to take increased amounts of insulin to make it more effective. This observation backs up prior research studies done on a similar aspect. To increase the translational value of our work, the predictive models and the associated software code are made available under the MIT License.

Dataset Information

Selection of In Vivo Predictive Dissolution Media Using Drug Substance and Physiological Properties.

Publications

Selection of In Vivo Predictive Dissolution Media Using Drug Substance and Physiological Properties.

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