Understanding and predicting the impact of critical dissolution variables for nifedipine immediate release capsules by multivariate data analysis

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.

Development and Validation of a Discriminatory Dissolution Model for an Immediately Release Dosage Form by DOE and Statistical Approaches

A discriminatory dissolution model was built through DOE with multivariate analysis of variance (MANOVA) and multiple linear regression (MLR) modeling to assess dissolution operational space for a highly water soluble immediate-release solid dosage drug product. The dissolution was utilized in the following five aspects: (1) understand the impact of individual variables and their interactions on dissolution performance through effect analysis; (2) explain the lack of discriminatory power of the initial dissolution condition used in early phase development by prediction profiler; (3) predict discriminatory dissolution operational space to differentiate photo degraded drug products from control with contour profiler analysis; (4) validate by the external experimental data acquired with the initial nondiscriminatory dissolution condition and the predicted discriminatory dissolution condition, followed by model independent statistical analysis (e.g., f2); and (5) establish correlation of the discriminatory dissolution with disintegration. The selected discriminatory dissolution method was validated by demonstrating accuracy, precision and linearity, specificity, repeatability, intermediate precision, stability, filter verification, and robustness.

Victory Prediction of Ladies Professional Golf Association Players: Influential Factors and Comparison of Prediction Models

This study aims to identify the most accurate prediction model for the possibility of victory from the annual average data of 25 seasons (1993–2017) of the Ladies Professional Golf Association (LPGA), and to determine the importance of the predicting factors. The four prediction models considered in this study were a decision tree, discriminant analysis, logistic regression, and artificial neural network analysis. The mean difference in the classification accuracy of these models was analyzed using SPSS 22.0 software (IBM Corp., Armonk, NY, USA) and the one-way analysis of variance (ANOVA). When the prediction was based on technical variables, the most important predicting variables for determining victory were greens in regulation (GIR) and putting average (PA) in all four prediction models. When the prediction was based on the output of the technical variables, the most important predicting variable for determining victory was birdies in all four prediction models. When the prediction was based on the season outcome, the most important predicting variables for determining victory were the top 10 finish% (T10) and official money. A significant mean difference in classification accuracy was observed while performing the one-way ANOVA, and the least significant difference post-hoc test showed that artificial neural network analysis exhibited higher accuracy than the other models, especially, for larger data sizes. From the results of this study, it can be inferred that the player who wants to win the LPGA should aim to increase GIR, reduce PA, and improve driving distance and accuracy through training to increase the birdies chance at each hole, which can lead to lower average strokes and increased possibility of being within T10.

Multivariate analysis in the development of bioequivalent tablets containing bicalutamide

The pharmaceutical industry has to tackle the explosion of high amounts of poorly soluble APIs. This phenomenon leads to numerous sophisticated solutions. These include the use of multifactorial data analysis identifying correlations between the components and dosage form properties, laboratory and production process parameters with respect to the API liberation Example of such API is bicalutamide. Improved liberation is achieved by particle size reduction. Laboratory batches, with different PSD of API, were filled into gelatinous capsules and consequently granulated for tablet compression. Comparative dissolution profiles with Casodex 150 mg (Astra Zeneca) were performed. The component analysis was used for the statistical evaluation of f₁ and f₂ factors and D(v,0.9) and D[4,3] parameters of PSD to identify optimal PSD values. Suitable PSD limits for API were statistically confirmed in laboratory and in commercial scale with respect to optimized tablet properties. The tablets were bioequivalent with originator (n = 20; 90% CI for ln AUC_0-120: 99.8-111.9%; 90% CI for ln c_max: 101.1-112.9%). In conclusion, the micronisation of the API is still an efficient and inexpensive method improving the bioavailability, although there are more complicated and expensive methods available. Statistical multifactorial methods improved the safety and reproducibility of production.

In Vivo Predictive Dissolution Testing of Montelukast Sodium Formulations Administered with Drinks and Soft Foods to Infants

In vitro dissolution testing conditions that reflect and predict in vivo drug product performance are advantageous, especially for the development of paediatric medicines, as clinical testing in this population is hindered by ethical and technical considerations. The aim of this study was to develop an in vivo predictive dissolution test in order to investigate the impact of medicine co-administration with soft food and drinks on the dissolution performance of a poorly soluble compound. Relevant in vitro dissolution conditions simulating the in vivo gastrointestinal environment of infants were used to establish in vitro-in vivo relationships with corresponding in vivo data. Dissolution studies of montelukast formulations were conducted with mini-paddle apparatus on a two-stage approach: infant fasted-state simulated gastric fluid (Pi-FaSSGF; for 1 h) followed by either infant fasted-state or infant fed-state simulated intestinal fluid (FaSSIF-V2 or Pi-FeSSIF, respectively; for 3 h). The dosing scenarios tested reflected in vivo paediatric administration practices: (i.) direct administration of formulation; (ii.) formulation co-administered with vehicles (formula, milk or applesauce). Drug dissolution was significantly affected by co-administration of the formulation with vehicles compared with after direct administration of the formulation. Montelukast dissolution from the granules was significantly higher under fed-state simulated intestinal conditions in comparison with the fasted state and was predictive of the in vivo performance when the granules are co-administered with milk. This study supports the potential utility of the in vitro biorelevant dissolution approach proposed to predict in vivo formulation performance after co-administration with vehicles, in the paediatric population.

Optimal Selection of Incoming Materials from the Inventory for Achieving the Target Drug Release Profile of High Drug Load Sustained-Release Matrix Tablet

In the pharmaceutical process, raw material (including APIs and excipients) variability can be delivered to the final product, and lead to batch-to-batch and lot-to-lot variances in its quality, finally impacting the efficacy of the drug. In this paper, the Panax notoginseng saponins (PNS) sustained-release matrix tablet was taken as the model formulation. Hydroxypropyl methylcellulose with the viscosity of 4000 mPa·s (HPMCK4M) from different vendors and batches were collected and their physical properties were characterized by the SeDeM methodology. The in-vitro dissolution profiles of active pharmaceutical ingredients (APIs) from matrix tablets made up of different batches HPMC K4M displayed significant variations. Multi-block partial least squares (MB-PLS) modeling results further demonstrated that physical properties of excipients played dominant roles in the drug release. In order to achieve the target drug release profile with respect to those far from the criteria, the optimal selection method of incoming materials from the available was established and validated. This study provided novel insights into the control of the input variability of the process and amplified the application of the SeDeM expert system, emphasizing the importance of the physical information of the raw materials in the drug manufacturing process.

Multivariate analysis as a method to understand variability in a complex excipient, and its contribution to formulation performance

A key part of the Risk Assessment of excipients is to understand how raw material variability could (or does) contribute to differences in performance of the drug product. Here we demonstrate an approach which achieves the necessary understanding for a complex, functional, excipient. Multivariate analysis (MVA) of the certificates of analysis of an ethylcellulose aqueous dispersion (Surelease) formulation revealed low overall variability of the properties of the systems. Review of the scores plot to highlight batches manufactured using the same ethylcellulose raw material in the formulation, indicated that these batches tend to be more closely related than other randomly selected batches. This variability could result in potential differences in the quality of drug product lots made from these batches. Manufacture of a model drug product from Surelease batches coated using different lots of starting material revealed small differences in the release of a model drug, which could be detected by certain model dependent dissolution modeling techniques, but they were not observed when using model-independent techniques. This illustrates that the techniques are suitable for detecting and understanding excipient variability, but that, in this case, the product was still robust.