AIMC Topic: Pharmaceutical Preparations

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Quantitative analysis of excipient dominated drug formulations by Raman spectroscopy combined with deep learning.

Analytical methods : advancing methods and applications
Owing to the growing interest in the application of Raman spectroscopy for quantitative purposes in solid pharmaceutical preparations, an article on the identification of compositions in excipient dominated drugs based on Raman spectra is presented. ...

Analysis of Biological Screening Compounds with Single- or Multi-Target Activity via Diagnostic Machine Learning.

Biomolecules
Predicting compounds with single- and multi-target activity and exploring origins of compound specificity and promiscuity is of high interest for chemical biology and drug discovery. We present a large-scale analysis of compound promiscuity including...

Artificial Intelligence Predicts Drug Response.

Cancer discovery
A new artificial intelligence-based predictive modeling framework called DrugCell could accurately predict effective drugs and treatment combinations based on tumor genotype, according to a proof-of-concept analysis.

Development challenges of high concentration monoclonal antibody formulations.

Drug discovery today. Technologies
High concentration monoclonal antibody drug products represent a special segment of biopharmaceuticals. In contrast to other monoclonal antibody products, high concentration monoclonal antibodies are injected subcutaneously helping increase patient c...

Machine learning guided prediction of liquid chromatography-mass spectrometry ionization efficiency for genotoxic impurities in pharmaceutical products.

Journal of pharmaceutical and biomedical analysis
The limitation and control of genotoxic impurities (GTIs) has continued to receive attention from pharmaceutical companies and authorities for several decades. Because GTIs have the ability to damage deoxyribonucleic acid (DNA) and the potential to c...

MultiCon: A Semi-Supervised Approach for Predicting Drug Function from Chemical Structure Analysis.

Journal of chemical information and modeling
Semi-supervised learning has proved its efficacy in utilizing extensive unlabeled data to alleviate the use of a large amount of supervised data and improve model performance. Despite its tremendous potential, semi-supervised learning has yet to be i...

Single-Cell Techniques and Deep Learning in Predicting Drug Response.

Trends in pharmacological sciences
Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques...

Transfer learning enables prediction of CYP2D6 haplotype function.

PLoS computational biology
Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene whose protein product metabolizes more than 20% of clinically used drugs. Genetic variations in CYP2D6 are responsible for interindividual heterogeneity in drug response that can lead to drug ...

Biologically Informed Neural Networks Predict Drug Responses.

Cancer cell
Deep neural networks often achieve high predictive accuracy on biological problems, but it can be hard to contextualize how and explain why predictions are made. In this issue, Kuenzi et al. model the sensitivity of cancers to drugs using deep neural...