Advancing pharmaceutical Intelligence via computationally Prognosticating the in-vitro parameters of fast disintegration tablets using Machine Learning models.

The field of Machine Learning (ML) has garnered significant attention, particularly in healthcare for predicting disease severity. Recently, the pharmaceutical sector has also adopted ML techniques in various stages of drug development. Tablets are the most common pharmaceutical formulations, with their efficacy influenced by the physicochemical properties of active ingredients, in-process parameters, and formulation components.

Development and comparison of machine learning models for in-vitro drug permeation prediction from microneedle patch.

The field of machine learning (ML) is advancing to a larger extent and finding its applications across numerous fields. ML has the potential to optimize the development process of microneedle patch by predicting the drug release pattern prior to its fabrication and production. The early predictions could not only assist the in-vitro and in-vivo experimentation of drug release but also conserve materials, reduce cost, and save time.

Advancements in microneedle fabrication techniques: artificial intelligence assisted 3D-printing technology.

Microneedles (MNs) are micron-scale needles that are a painless alternative to injections for delivering drugs through the skin. MNs find applications as biosensing devices and could serve as real-time diagnosis tools. There have been numerous fabrication techniques employed for producing quality MN-based systems, prominent among them is the three-dimensional (3D) printing.