Create 2024-comparing-lipinskis-rule-of-5-and-machine-learning-predic…

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content/publication/2024-comparing-lipinskis-rule-of-5-and-machine-learning-prediction-fraction-absorbed.md

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+bibtex_type = "article"
+author="Fagerholm U, Hellberg S, Alvarsson J, Ekmefjord M and Spjuth O."
+title="Comparing Lipinskis Rule of 5 and Machine Learning Based Prediction of Fraction Absorbed for Assessing Oral Absorption in Humans"
+journal="bioRxiv"
+year="2024"
+date="2024-08-23T00:00:00+01:55"
+volume="2024.08.20.608791"
+number=""
+preprint = true
+pages=""
+abstract="Background: The influential Lipinski’s Rule of 5 (Ro5) describes molecular properties important for oral absorption in humans. According to Ro5, poor absorption is more likely when 2 or more of its criteria (molecular weight (MW) >500 g/mol, calculated octanol-water partition coefficient (clog P) >5, >5 hydrogen bond donors (HBD) and >10 hydrogen bond acceptors (HBA)) are violated. Earlier evaluations have shown that many drugs are sufficiently well absorbed into the systemic circulation despite many Ro5-violations. No evaluation of Ro5 vs fraction absorbed (fa) has, however, been done. Methods: Datasets of orally administered drugs violating and not violating Ro5 and with available human clinical fa-values were assembled, and contrasted to machine learning based predictions using the ANDROMEDA prediction software having a major MW-domain of 150-750 g/mol. Results: 129 Ro5-violent compounds (29 with MW>1000 g/mol) were found, 59 of which had fa-values (42 % mean fa). 34 % and 66 % of compounds were predicted as having fa≤10% and >10-30 % respectively, which was in good agreement with measured fa of 37 % and 63 %. The fa for all compounds with fa≤5 and ≤10 % were correctly predicted. For compounds with fa>30 %, 81 % were predicted to have a fa>30 %, but none were predicted to have a fa<10 %. The Q2 for predicted vs observed fa was 0.64. For a set of 77 compounds without Ro5 violation (80 % mean fa), all compounds were correctly predicted to have a fa below or above 30 % (Q2=0.56). Among these are compounds with poor uptake (<1 % to 7 %). Conclusion: We show that machine learning based predictions of fa are superior to Ro5 for assessing oral absorption obstacles in humans. Too strict reliance on Ro5 may hence constitute a risk. ANDROMEDA predicts fa well, easily and quickly, and also differentiates well between poor and adequate oral uptake for compounds violating and not-violating Ro5. This makes it a valid and useful tool capable of predicting oral absorption in humans with good accuracy and replacing Ro5 for oral absorption assessments."
+doi="10.1101/2024.08.20.608791"
+url_html=""
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