Browsing Category
Machine Learning
Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development
Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical and clinical rewards. However, the manufacturing of ODFs does not adhere to contemporary paradigms of personalised, on-demand medicine, nor sustainable manufacturing.…
Read More...
Read More...
Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm
Lipid nanoparticle (LNP) is commonly used to deliver mRNA vaccines. Currently, LNP optimization primarily relies on screening ionizable lipids by traditional experiments which consumes intensive cost and time. Current study attempts to apply computational methods to accelerate the LNP development…
Read More...
Read More...
Machine learning predicts the effect of food on orally administered medicines
Food-mediated changes to drug absorption, termed the food effect, are hard to predict and can have significant implications for the safety and efficacy of oral drug products in patients. Mimicking the prandial states of the human gastrointestinal tract in preclinical studies is challenging, poorly…
Read More...
Read More...
Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota
Over 150 drugs are currently recognised as being susceptible to metabolism or bioaccumulation (together described as depletion) by gastrointestinal microorganisms; however, the true number is likely higher. Microbial drug depletion is often variable between and within individuals, depending on their…
Read More...
Read More...
Use of machine learning in prediction of granule particle size distribution and tablet tensile…
In the manufacturing of pharmaceutical Oral Solid Dosage (OSD) forms, Particle Size Distribution (PSD) and Tensile Strength (TS) are common in-process tests that are controlled in order to achieve the quality targets of the end-product. The Quality by Design (QbD) concept elaborates process…
Read More...
Read More...
Design of Biopharmaceutical Formulations Accelerated by Machine Learning
In addition to activity, successful biological drugs must exhibit a series of suitable developability properties, which depend on both protein sequence and buffer composition. In the context of this high-dimensional optimization problem, advanced algorithms from the domain of machine learning are…
Read More...
Read More...
Machine learning predicts 3D printing performance of over 900 drug delivery systems
三维印刷(3 dp)a transformative technology that is advancing pharmaceutical research by producing personalized drug products. However, advances made via 3DP have been slow due to the lengthy trial-and-error approach in optimization. Artificial intelligence (AI) is a technology…
Read More...
Read More...
Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated…
In response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy…
Read More...
Read More...
Recent Development in Pharmaceutical 3D Printing: A Bird’s Eye Perspective
Pharmaceutical product development is constantly witnessing advancements in the creation of novel delivery methods in order to enhance medication therapeutic effectiveness. Furthermore, 3D printing (3DP) has been utilized to produce medication delivery systems and biomedical equipment, resulting in…
Read More...
Read More...
Integrated in silico formulation design of self-emulsifying drug delivery systems
The drug formulation design of self-emulsifying drug delivery systems (SEDDS) often requires numerous experiments, which are time- and money-consuming. This research aimed to rationally design the SEDDS formulation by the integrated computational and experimental approaches. 4495 SEDDS formulation…
Read More...
Read More...