broadly applicable way to estimate key safety metrics (Cmax and AUC) reasonably well. Big thanks to Zeynep Edizcan, Stephan Schaller, and Lars Kuepfer for the excellent collaboration that made this work possible.

whether this is due to greater variability in the underlying PK data and clinical study protocols, or because dermal absorption is inherently more complex. Still, we conclude that despite the challenges in capturing absorption dynamics, the method offers a pragmatic and...

One key aspect of the study is a systematic comparison of high-throughput PK prediction accuracy for dermal versus oral exposure. We found that dermal administration is more challenging to predict, though it’s unclear...

the most reliable systemic PK predictions of 52 substances. With the best strategy, 75% of Cmax and 75% of AUC values fell within a tenfold range of observed human plasma data.

be applied across pharmaceuticals, cosmetics, and industrial chemicals. We ran 14,336 PBK simulations using the mechanistic skin permeation model of the Open Systems Pharmacology Suite to identify which combinations of LogP, solubility, and other parameters yield...

In this work, we present a fully in silico HT-PBK workflow that predicts systemic exposure after dermal application using only QSAR-derived input parameters. It’s designed for early-stage use cases where no compound-specific in vitro or formulation data are available, and can...

Our new preprint on high-throughput PBK modelling for #dermal exposure is now available 📄👇 “High-throughput PBK modelling for dermal exposure: a pragmatic approach to predict systemic pharmacokinetics”

#DILI #InSilicoToxicology #NAMs #DrugSafety #PBK #BSEP #QSP #SystemsToxicology

Big thanks to Ahenk Zeynep Sayin, Lars Kuepfer, Stephan Schaller for the great teamwork and discussions that made this possible.

…and provide a case study showing how mathematical models (#QSP) enable deeper understanding of in vivo biological effects.

As if this was not enough, our analysis also gives new insights into why DILI often manifests in an “idiosyncratic” fashion. And on top of that, we also gain new insights into the in vivo relevance of #BSEP inhibition…

And this in a way that is fully mechanistic and transparent.

But using high-throughput PBK modelling allows us to circumvent this problem and to generate such predictions from chemical structure alone. Thereby, we can show that already today available in vitro and in silico methods allow accurate predictions of DILI.

...combining in vitro toxicity measurements and PK predicts DILI risks with up to 96% ROC AUC, all without animal testing. Traditionally, the prospective utility of this prediction approach would have been limited by the need for in vivo PK data from human studies.

In this new study, we collected a huge dataset of 241 drugs with known clinical DILI outcomes, pharmacokinetic data, as well as in vitro hepatotoxicity data from 17 in vitro assays. This allowed us to show that…

“Integration of In Vitro and In Silico Approaches Enables Prediction of Drug-Induced Liver Injury” is now available on #bioRxiv:

Still thinking that Drug-Induced Liver Injury (DILI) is hard to foresee? That LogP and Dose are good DILI predictors? Wondering why DILI often manifests idiosyncratically? Then you should read our new preprint! 📄👇

🤝 Interested in applying PSA to your own chemical portfolio? Let’s connect and chat! #OpenAccess #ExposureLed #NGRA #ChemicalSafety #NAMs #PBK #RiskAssessment #Toxicology #RegulatoryScience

Huge thanks to my co-authors Andrew Worth, Alicia Paini, Lars Kuepfer and Stephan Schaller, and to everyone who debated these ideas at the NAM Designathon workshop in Ispra 🇮🇹. Your insights were invaluable!

• However, most substances fall into a “medium PSA concern” band meaning bioactivity and external exposure still matter and it is not possible to (de)prioritise the majority of chemicals on their ADME alone.