Neuro-fuzzy modeling of ibuprofen-sustained release from tablets based on different cellulose derivatives

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.

Evaluation of Pharmacokinetics and Safety with Bioequivalence of Ibuprofen Sustained-Release Capsules of Two Formulations, in Chinese Healthy Volunteers: Bioequivalence Study

Purpose

Ibuprofen is the first of the nonsteroidal anti-inflammatory drug (NSAID) to be used in the clinic. Our aim was to explore the pharmacokinetics (PK), bioequivalence, food effect, and safety of oral ibuprofen sustained-release capsules by two sponsors in healthy volunteers (HVs).

Methods

Two separate randomized, open-label, single-dose, crossover-design studies were conducted: a fasting study (n = 24) and a fed study (n = 24). In each study, HVs were 1:1 divided into two groups (T-R and R-T) and received 0.3-g/capsule ibuprofen with a 3-day washout. The plasma was collected for up to 24 hours at the time point after dosing on Day 1/Day 4. The plasma concentrations of ibuprofen were measured using an HPLC-MS/MS method, and PK parameters were determined by noncompartmental methods.

Results

Forty-eight healthy volunteers were enrolled. In fasting subjects, the maximum plasma concentration (C_max, mean ± SD) was 14.86±3.19 μg/mL at 5.0 (4.0, 7.0) hours (median [min, max]) for sponsor T, and 13.88±2.60 μg/mL at 4.5 (3.0, 8.0) hours for sponsor R. In fed subjects, C_max was 21.31±4.08 μg/mL at 5.6 (4.3, 10.0) hours for sponsor T, and 19.77±3.36 μg/mL at 6.0 (2.0, 8.0) hours for sponsor R. All 90% confidence intervals (CIs) for C_max, AUC_0-t, and AUC_0-∞ were within the bioequivalence bounds (80-125%) both fasting and fed studies.

Conclusion

Ibuprofen is well tolerated and has a favorable safety profile. In both fasting and fed study, there were no serious AEs, or AEs leading to withdrawal. Bioequivalence is achieved under fasting and fed conditions, supporting the demonstration of biosimilarity.

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

The main objective of this study was to create a mathematical tool that could be used with experimental data to predict the rheological flow behavior of functionalized xanthan gum according to the types of chemical groups grafted onto its backbone. Different rheological and physicochemical analyses were applied to assess six derivatives synthesized via the etherification of xanthan gum by hydrophobic benzylation with benzyl chloride and carboxymethylation with monochloroacetic acid at three (regent/polymer) ratios R equal to 2.4 and 6. Results from the FTIR study verified that xanthan gum had been modified. The degree of substitution (DS) values varying between 0.2 and 2.9 for carboxymethylxanthan gum derivatives were found to be higher than that of hydrophobically modified benzyl xanthan gum for which the DS ranged from 0.5 to 1. The molecular weights of all the derivatives were found to be less than that of xanthan gum for the two types of derivatives, decreasing further as the degree of substitution (DS) increased. However, the benzyl xanthan gum derivatives presented higher molecular weights varying between 1,373,146 (g/mol) and 1,262,227 (g/mol) than carboxymethylxanthan gum derivatives (1,326,722-1,015,544) (g/mol). A shear-thinning behavior was observed in the derivatives, and the derivatives' viscosity was found to decrease with increasing DS. The second objective of this research was to create an ANN model to predict one of the rheological properties (the apparent viscosity). The significance of the ANN model (R² = 0.99998 and MSE = 5.95 × 10^-3) was validated by comparing experimental results with the predicted ones. The results showed that the model was an efficient tool for predicting rheological flow behavior.

Formulation of Modified-Release Bilayer Tablets of Atorvastatin and Ezetimibe: An In-Vitro and In-Vivo Analysis

The objective of this work was to formulate co-loaded bilayer tablets containing ezetimibe (EZB) and atorvastatin (ATC). ATC loaded in the immediate-release (IR) layer is an HMG CoA reductase inhibitor, while EZB, added in the sustained-release (SR) layer, is a lipid-lowering agent. This study was conducted to evaluate the effects of polymer on the formulation and characterization of bilayer tablets, as well as the therapeutic impact of the concurrent use of both drugs having a sequential release pattern. To obtain the optimized results, four different formulations with variable compositions were developed and evaluated for different parameters. The drug release studies were carried out using a type II dissolution apparatus, using phosphate buffer solution (PBS) of 1.2 pH for IR of EZB for an initial 2 h, followed by 24 h studies for ATC in PBS 6.8 pH. The IR layer showed rapid drug release (96%) in 2 h, while 80% of the ATC was released in 24 h from the SR layer. Locally obtained, 6-week-old female albino rats were selected for in vivo studies. Both preventive and curative models were applied to check the effects of the drug combination on the lipid profile, atherosclerosis and physiology of different organs. Studies have shown that the administration of both drugs with different release patterns has a better therapeutic effect (p < 0.05), both in preventing and in curing hyperlipidemia. Conclusively, through the sequential release of ATC and EZB, a better therapeutic response could be obtained.

Efficient Prediction of In Vitro Piroxicam Release and Diffusion From Topical Films Based on Biopolymers Using Deep Learning Models and Generative Adversarial Networks

The purpose of this study was to simultaneously predict the drug release and skin permeation of Piroxicam (PX) topical films based on Chitosan (CTS), Xanthan gum (XG) and its Carboxymethyl derivatives (CMXs) as matrix systems. These films were prepared by the solvent casting method, using Tween 80 (T80) as a permeation enhancer. All of the prepared films were assessed for their physicochemical parameters, their in vitro drug release and ex vivo skin permeation studies. Moreover, deep learning models and machine learning models were applied to predict the drug release and permeation rates. The results indicated that all of the films exhibited good consistency and physicochemical properties. Furthermore, it was noticed that when T80 was used in the optimal formulation (F8) based on CTS-CMX3, a satisfactory drug release pattern was found where 99.97% of PX was released and an amount of 1.18 mg/cm² was permeated after 48 h. Moreover, Generative Adversarial Network (GAN) efficiently enhanced the performance of deep learning models and DNN was chosen as the best predictive approach with MSE values equal to 0.00098 and 0.00182 for the drug release and permeation kinetics, respectively. DNN precisely predicted PX dissolution profiles with f₂ values equal to 99.99 for all the formulations.

Design of multi-particulate "Dome matrix" with sustained-release melatonin and delayed-release caffeine for jet lag treatment

Multi-particulate Dome matrix with sustained-release melatonin and delayed-release caffeine was designed to restore jet lag sleep-wake cycle. The polymeric pellets were produced using extrusion-spheronization technique and fluid-bed coated when applicable. The compact and Dome module were produced by compressing pellets with cushioning agent. Dome matrix was assembly of modules with pre-determined compact formulation and drug release characteristics. The physicochemical and in vivo pharmacokinetics of delivery systems were examined. Melatonin loaded alginate/chitosan-less matrix exhibited full drug release within 8 h gastrointestinal transit with low viscosity hydroxypropymethylcellulose as cushioning agent. The cushioning agent reduced burst drug release and omission of alginate-chitosan enabled full drug release. Delayed-release alginate-chitosan caffeine matrix was not attainable through polymer coating due to premature coat detachment. Admixing of cushioning agent high viscosity hydroxypropylmethylcellulose and high viscosity ethylcellulose (9:1 wt ratio) with coat-free caffeine loaded particulates introduced delayed-release response via hydroxypropylmethylcellulose swelled in early dissolution phase and ethylcellulose sustained matrix hydrophobicity at prolonged phase. The caffeine was released substantially in colonic fluid in response to matrix polymers being degraded by rat colonic content. Dome matrix with dual drug release kinetics and modulated pharmacokinetics is produced to introduce melatonin-induced sleep phase then caffeine-stimulated wake phase.

Microenvironmental pH-modified Amisulpride-Labrasol matrix tablets: development, optimization and in vivo pharmacokinetic study

Amisulpride (AMS) is atypical antipsychotic with a weak basic nature (pKa 9.37), which results in low solubility in the high pH of the intestine. It is also recognized as a substrate of P-glycoprotein efflux pump. Both factors lead to its low oral bioavailability (48%). The daily dose of AMS is between 200 and 1200 mg to be taken in divided doses which compromise patient compliance. Therefore, controlled release formulation of AMS is of clinical significance. AMS was formulated into matrix tablets containing Labrasol, P-glycoprotein efflux inhibitor, and a penetration enhancer, using direct compression technique. The tablets were prepared according to 2¹·4¹ factorial design using two polymers, namely, HPMC and Carbopol 934 at four concentrations (20%, 30%, 40%, 50%). Percentage AMS released after 2 h (Q_2hr%) and 8 h (Q_8hr%) were chosen as dependent variables. Two acidic pH modifiers (fumaric acid and tartaric acid) at two levels (15% and 30%) were incorporated in the tablet according to 2² factorial design. All formulae with acidic pH modifier had similarity factor (f₂) ≥ 50 proving the pH independent release of AMS. The pharmacokinetic study in rabbits revealed 30% enhancement of the oral absorption AMS imparted by the pH-modified matrix tablet containing Labrasol. Graphical abstract.

Optimizing the Extraction and Encapsulation of Mucilage from Brasenia Schreberi

The mucilage from Brasenia schreberi (BS) exhibits various biological activities, including antialgal, antibacterial, soluble-fiber properties, and excellent lubricating behavior. Thus, the extraction and wide use of mucilage in the food industry are crucial. In this study, the high-speed shear-assisted extraction of mucilage from BS was optimized by using response surface methodology (RSM). The optimal extraction conditions were as follows: Extraction temperature of 82 °C, extraction time of 113 min, liquid-solid ratio of 47 mL/g, and shear speed of 10,000 rpm. Under these conditions, the actual yield of BS mucilage was 71.67%, which highly matched the yield (73.44%) predicted by the regression model. Then, the BS mucilage extract was powdered to prepare the capsule, and the excipients of the capsule were screened using a single-factor test to improve the disintegration property and flowability. The final capsule formulation, which consisted of: 39% BS mucilage powder (60 meshes); 50% microcrystalline cellulose (60 meshes) as the filler; both 10% sodium starch glycolate and PVPP XL-10 (3:1, 60 meshes) as the disintegrant; both 1% colloidal silicon dioxide and sodium stearyl fumarate (1:1, 100 meshes) as the glidant by weight; were used for preparing the weights of a 320 mg/grain of capsule with 154.7 ± 0.95 mg/g polysaccharide content. Overall, the optimized extraction process had a high extraction rate for BS mucilage and the capsule formulation was designed reasonably.