Black seed oil (BSO) has been used for various therapeutic purposes around the world since ancient eras. It is one of the most prominent oils used in nutraceutical formulations and daily consumption for its significant therapeutic value is common phenomena. The main aim of this study was to develop alginate-BSO beads as a controlled release system designed to control drug release in the gastrointestinal tract (GIT). Electrospray technology facilitates formulation of small and uniform beads with higher diffusion and swelling rates resulting in process performance improvement. The effect of different formulation and process variables was evaluated on the internal and external bead morphology, size, shape, encapsulation efficiency, swelling rate, in vitro drug release, release mechanism, ex vivo mucoadhesive strength and gastrointestinal tract qualitative and quantitative distribution. All the formulated beads showed small sizes of 0.58 ± 0.01 mm (F8) and spherical shape of 0.03 ± 0.00 mm. The coefficient of weight variation (%) ranged from 1.37 (F8) to 3.93 (F5) ng. All formulations (F1-F9) were studied in vitro for release characteristics and swelling behaviour, then the release data were fitted to various equations to determine the exponent (ns), swelling kinetic constant (ks), swelling rate (%/h), correlation coefficient (r2) and release kinetic mechanism. The oil encapsulation efficiency was almost complete at 90.13% ± 0.93% in dried beads. The maximum bead swelling rate showed 982.23 (F8, r2 = 0.996) in pH 6.8 and the drug release exceeded 90% in simulated gastrointestinal fluid (pH 6.8). Moreover, the beads were well distributed throughout various parts of the intestine. This designed formulation could possibly be advantageous in terms of increased bioavailability and targeted drug delivery to the intestine region and thus may find applications in some diseases like irritable bowel syndrome.
Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This development has been further accelerated with the increasing use of machine learning (ML), mainly deep learning (DL), and computing hardware and software advancements. As a result, initial doubts about the application of AI in drug discovery have been dispelled, leading to significant benefits in medicinal chemistry. At the same time, it is crucial to recognize that AI is still in its infancy and faces a few limitations that need to be addressed to harness its full potential in drug discovery. Some notable limitations are insufficient, unlabeled, and non-uniform data, the resemblance of some AI-generated molecules with existing molecules, unavailability of inadequate benchmarks, intellectual property rights (IPRs) related hurdles in data sharing, poor understanding of biology, focus on proxy data and ligands, lack of holistic methods to represent input (molecular structures) to prevent pre-processing of input molecules (feature engineering), etc. The major component in AI infrastructure is input data, as most of the successes of AI-driven efforts to improve drug discovery depend on the quality and quantity of data, used to train and test AI algorithms, besides a few other factors. Additionally, data-gulping DL approaches, without sufficient data, may collapse to live up to their promise. Current literature suggests a few methods, to certain extent, effectively handle low data for better output from the AI models in the context of drug discovery. These are transferring learning (TL), active learning (AL), single or one-shot learning (OSL), multi-task learning (MTL), data augmentation (DA), data synthesis (DS), etc. One different method, which enables sharing of proprietary data on a common platform (without compromising data privacy) to train ML model, is federated learning (FL). In this review, we compare and discuss these methods, their recent applications, and limitations while modeling small molecule data to get the improved output of AI methods in drug discovery. Article also sums up some other novel methods to handle inadequate data.
A standard protocol to develop type 1 diabetes in zebrafish is still uncertain due to unpredictable factors. In this study, an optimized protocol was developed and used to evaluate the anti-diabetic activity of Psychotria malayana leaf. The aims of this study were to develop a type 1 diabetic adult zebrafish model and to evaluate the anti-diabetic activity of the plant extract on the developed model. The ability of streptozotocin and alloxan at a different dose to elevate the blood glucose levels in zebrafish was evaluated. While the anti-diabetic activity of P. malayana aqueous extract was evaluated through analysis of blood glucose and LC-MS analysis fingerprinting. The results indicated that a single intraperitoneal injection of 300 mg/kg alloxan was the optimal dose to elevate the fasting blood glucose in zebrafish. Furthermore, the plant extract at 1, 2, and 3 g/kg significantly reduced blood glucose levels in the diabetic zebrafish. In addition, LC-MS-based fingerprinting indicated that 3 g/kg plant extract more effective than other doses. Phytosterols, sugar alcohols, sugar acid, free fatty acids, cyclitols, phenolics, and alkaloid were detected in the extract using GC-MS. In conclusion, P. malayana leaf aqueous extract showed anti-diabetic activity on the developed type 1 diabetic zebrafish model.