Predictive Drug Release Modeling Across Dissolution Apparatuses I and II using Computational Fluid Dynamics

Predictive dissolution modeling across USP apparatuses I, II, and III

Dissolution testing provides in vitro drug release characterization and serves a critical role in the development of solid oral dosage forms. The most common dissolution apparatuses are the USP apparatuses I and II, for which in silico tools have been previously developed for predictive dissolution modeling (PDM). While apparatuses I and II serve the greater volume of projects, apparatus III offers higher agitation levels and multivessel capabilities, which is critical for certain projects, and the physics of which have not been previously characterized. To mitigate that knowledge gap, the present work characterizes the transport physics and thermodynamics of dissolution apparatus III, such that a 1-D model is established and validated which scales release kinetics with agitation level across apparatuses I, II, and III. The resulting PDM is calibrated with at least two dissolution experiments at different agitation levels, for a particular formulation-medium combination, after which release kinetics are predicted within the design spaces of the three apparatuses. Calibration data can come from experiments using a single apparatus or different apparatuses, while still predicting across all three apparatuses. Erosion-based formulations are used for validation. Additionally, apparatus III vessel residence time analysis is demonstrated.

Optimizing carry-over in automated dissolution system dissoBOT for paracetamol and diclofenac sodium analysis

In this work, an automated dissolution system (dissoBOT) was used for dissolution testing for the first time. Carry-over (CO) of the dissoBOT was determined for paracetamol (PA) and diclofenac sodium (DS), which are active pharmaceutical ingredients (APIs). Initially, partial method validation of the UV-VIS spectrophotometry method for PA and DS determination was performed by defining the limit of detection (LOD), the limit of quantification (LOQ), linear concentration range, accuracy, and precision. The LODs and LOQs were less than 0.01 mg/L for both APIs. The determined linear concentration ranges were from 1.00 mg/L to 30.00 mg/L for PA and from 0.50 mg/L to 3.50 mg/L for DS (the square of the correlation coefficient was greater than 0.9990, and the quality coefficient was less than 1.00 % for both APIs). The accuracy of the method was evaluated by calculating the recovery (Re) of the solutions of standards with known concentrations. The method for both APIs was deemed to be accurate (the average Re for PA and DS were 99.81 % and 101.43 %, respectively). Precision was evaluated by calculating the relative standard deviation (RSD). The method for PA and DS was deemed to be precise, as the RSD value for PA was 0.13 %, and for DS was 0.38 %. The volume (V) of the washing medium in both cleaning cycles performed by the dissoBOT system, as well as the medium dispensing V, were established, where the medium dispensing V was in accordance with the United States Pharmacopeia requirements. The CO of the dissoBOT system, using tap water as the washing medium, was determined to be less than 1.00 % for both APIs. The CO values for one cleaning cycle of the sampling station with a V of 2 mL was in the range of 1.24-1.54 %, for V of 5 mL was in the range of 0.78-0.93 %, and for V of 10 mL was in the range of 0.27-0.36 %. In addition, the CO of the dissoBOT, when employing two cleaning cycles of the sampling station (each V of 10 mL) was reduced (CO <0.20 %). Finally, the dissoBOT was successfully employed for the dissolution PA and DS tables.

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

Modeling and validation of drug release kinetics using hybrid method for prediction of drug efficiency and novel formulations

This paper presents a thorough examination for drug release from a polymeric matrix to improve understanding of drug release behavior for tissue regeneration. A comprehensive model was developed utilizing mass transfer and machine learning (ML). In the machine learning section, three distinct regression models, namely, Decision Tree Regression (DTR), Passive Aggressive Regression (PAR), and Quadratic Polynomial Regression (QPR) applied to a comprehensive dataset of drug release. The dataset includes r(m) and z(m) inputs, with corresponding concentration of solute in the matrix (C) as response. The primary objective is to assess and compare the predictive performance of these models in finding the correlation between input parameters and chemical concentrations. The hyper-parameter optimization process is executed using Sequential Model-Based Optimization (SMBO), ensuring the robustness of the models in handling the complexity of the controlled drug release. The Decision Tree Regression model exhibits outstanding predictive accuracy, with an R2 score of 0.99887, RMSE of 9.0092E-06, MAE of 3.51486E-06, and a Max Error of 6.87000E-05. This exceptional performance underscores the model's capability to discern intricate patterns within the drug release dataset. The Passive Aggressive Regression model, while displaying a slightly lower R2 score of 0.94652, demonstrates commendable predictive capabilities with an RMSE of 6.0438E-05, MAE of 4.82782E-05, and a Max Error of 2.36600E-04. The model's effectiveness in capturing non-linear relationships within the dataset is evident. The Quadratic Polynomial Regression model, designed to accommodate quadratic relationships, yields a noteworthy R2 score of 0.95382, along with an RMSE of 5.6655E-05, MAE of 4.49198E-05, and a Max Error of 1.86375E-04. These results affirm the model's proficiency in capturing the inherent complexities of the drug release system.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.