Effects of low temperature on blood‐to‐plasma ratio measurement

Novak, Jonathan J.; Burchett, Woodrow; Di, Li

doi:10.1002/bdd.2265

Cited by 8 publications

(2 citation statements)

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“…Human blood-to-plasma ratio ( R b/p ) was measured using a method that has been described previously . Briefly, test compounds (1 μM) were added to fresh human blood and incubated at 37 °C for both 1 and 3 h in an incubator (90% humidity, 5% CO 2 /air) on a shaker (450 rpm).…”

Section: Experimental Sectionmentioning

confidence: 99%

“…Human blood-to-plasma ratio (R b/p ) was measured using a method that has been described previously. 36 Briefly, test compounds (1 μM) were added to fresh human blood and incubated at 37 °C for both 1 and 3 h in an incubator (90% humidity, 5% CO 2 /air) on a shaker (450 rpm). At the end of the incubations, plasma samples were separated from red blood cells, and blood and plasma samples were matrix-matched and quenched with cold acetonitrile containing IS.…”

Section: ■ Experimental Sectionmentioning

confidence: 99%

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The Comparison of Machine Learning and Mechanistic In Vitro–In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance

Keefer,

Chang,

et al. 2023

Mol. Pharmaceutics

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Accurate prediction of human pharmacokinetics (PK) remains one of the key objectives of drug metabolism and PK (DMPK) scientists in drug discovery projects. This is typically performed by using in vitro−in vivo extrapolation (IVIVE) based on mechanistic PK models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes to predict future events, has gained increased popularity in application to absorption, distribution, metabolism, and excretion (ADME) sciences. This study compares the performance of various ML and mechanistic models for the prediction of human IV clearance for a large (645) set of diverse compounds with literature human IV PK data, as well as measured relevant in vitro end points. ML models were built using multiple approaches for the descriptors: (1) calculated physical properties and structural descriptors based on chemical structure alone (classical QSAR/QSPR); (2) in vitro measured inputs only with no structure-based descriptors (ML IVIVE); and (3) in silico ML IVIVE using in silico model predictions for the in vitro inputs. For the mechanistic models, well-stirred and parallel-tube liver models were considered with and without the use of empirical scaling factors and with and without renal clearance. The best ML model for the prediction of in vivo human intrinsic clearance (CL int ) was an in vitro ML IVIVE model using only six in vitro inputs with an average absolute fold error (AAFE) of 2.5. The best mechanistic model used the parallel-tube liver model, with empirical scaling factors resulting in an AAFE of 2.8. The corresponding mechanistic model with full in silico inputs achieved an AAFE of 3.3. These relative performances of the models were confirmed with the prediction of 16 Pfizer drug candidates that were not part of the original data set. Results show that ML IVIVE models are comparable to or superior to their best mechanistic counterparts. We also show that ML IVIVE models can be used to derive insights into factors for the improvement of mechanistic PK prediction.

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Section: Experimental Sectionmentioning

confidence: 99%

Section: ■ Experimental Sectionmentioning

confidence: 99%