O. Spjuth

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

This paper reviews methodological advancements in Cell Painting over a decade, highlighting improvements in protocols, feature extraction, and machine learning applications for biological insights.

From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability

This study introduces BioMorph space, which integrates Cell Painting and Cell Health data to improve the interpretability of morphological features and reveal compound mechanisms of action.

Prediction of the Human Pharmacokinetics of 30 Modern Antibiotics Using the ANDROMEDA Software

The paper uses ANDROMEDA software to predict human pharmacokinetics of 30 antibiotics, showing high accuracy and validating its use for drugs with complex pharmacokinetic properties.

Application of the ANDROMEDA Software for Prediction of the Human Pharmacokinetics of Modern Anticancer Drugs

The paper uses ANDROMEDA to predict human pharmacokinetics of 12 anticancer drugs, showing high accuracy and consistency with previous studies.

Predicting the Influence of Fat Food Intake on the Absorption and Systemic Exposure of Small Drugs using ANDROMEDA by Prosilico Software

This paper evaluates ANDROMEDA's ability to predict fat food-drug interactions using molecular structure and dose, comparing it to PBPK models and showing similar accuracy with lower error and broader applicability.

Predicting Gastrointestinal Absorption of Prodrugs and their Drugs with the ANDROMEDA by Prosilico Software

The paper uses ANDROMEDA software to predict absorption properties of prodrugs and their drugs, finding similar average passive absorption and dissolution potential, with some prodrugs showing higher absorption than their drugs.

Validation of predicted conformal intervals for prediction of human clinical pharmacokinetics

The paper validates conformal prediction intervals for human pharmacokinetic parameters using ANDROMEDA software, showing 78% of observed data within 70% confidence intervals.

ANDROMEDA by Prosilico Software Successfully Predicts Human Clinical Pharmacokinetics of 300 Drugs Out of Reach for In Vitro Methods

The paper predicts human clinical pharmacokinetics for 300 drugs that are not measurable by in vitro methods using the ANDROMEDA software, with predictions aligning with in vivo data.

Prediction and Classification of the Uptake and Disposition of Antidepressants and New CNS-Active Drugs in the Human Brain using the ANDROMEDA by Prosilico Software and Brainavailability-Matrix

The paper predicts brain uptake and disposition of antidepressants and CNS drugs using ANDROMEDA software and a Brainavailability-Matrix, showing high brainavailability and efflux are common in modern CNS drugs.

Prediction of Biopharmaceutical Characteristics of PROTACs using the ANDROMEDA by Prosilico Software

The paper uses ANDROMEDA software to predict in vivo dissolution and absorption of PROTACs, showing limited absorption and moderate correlation between in vitro solubility and predicted in vivo dissolution.

Using the ANDROMEDA by Prosilico Software for Prediction of the Human Pharmacokinetics of 4 Compounds of Natural Origin - Colistin, Curucumin, UCN-01 and Voclosporin

The paper evaluates the ANDROMEDA by Prosilico software's ability to predict human pharmacokinetics for four natural compounds, finding mostly accurate predictions with notable errors for UCN-01.

Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects

The paper uses Cell Painting to assess combination effects of BPA, CTAB, and DBTDL on human cell lines, showing dose- and combination-dependent morphological changes.

An Open-Source Modular Framework for Automated Pipetting and Imaging Applications

This paper presents an open-source framework for automated pipetting and imaging using affordable tools, enabling reproducible and customizable lab experiments.

A phenomics approach for in vitro antiviral drug discovery

This paper introduces a phenomics approach combining Cell Painting and antibody-based detection to screen antiviral drugs in infected cells, showing effective compounds reverse morphological changes.

Deep learning models for lipid-nanoparticle-based drug delivery

The paper uses deep learning models to predict GFP expression in HepG2 cells exposed to lipid-nanoparticle-based mRNA therapeutics, showing improved performance with temporal dynamics modeling.