Aug 2026· Pharmaceutics· Vol 18, pp. 967· 0 citations· 16 references
Medicine
TL;DR
These findings suggest that the commonly applied TPSA threshold of 90 Å2 may be overly lenient, and experimentally grounded rules offer a practical framework for the early-stage design of CNS leads.
Abstract
Background/Objectives: Rules for predicting blood–brain barrier (BBB) permeability, including the CNS multiparameter optimization (CNS MPO) score, Lipinski’s Rule of Five, and Veber’s rules, were developed using relatively limited datasets and have not been systematically re-evaluated using modern large-scale experimental databases. Using the B3DB database, which contains 1058 experimentally measured logBB values, we derived quantitative, data-driven structural thresholds for BBB permeability and benchmarked them against established heuristic rules. Methods: Six key physicochemical properties were calculated for all compounds, and optimal classification thresholds were identified through exhaustive optimizations. Decision trees and scaffold analyses were used to generate interpretable medicinal chemistry guidelines. Results: The topological polar surface area (TPSA) emerged as the strongest single predictor of BBB permeability, with an optimal threshold of 66.8 Å2 (AUC = 0.731, 95% CI: 0.689–0.771). This threshold outperformed the approximated CNS MPO ≥ 4 (AUC = 0.625), Lipinski’s Rule of Five (AUC = 0.546), and Veber rules (AUC = 0.566). A simple two-parameter rule combining TPSA < 67 Å2 and H-bond donors ≤ 1 achieved 96.6% precision for BBB-permeable compounds while maintaining an AUC of 0.720. Decision tree analysis further confirmed TPSA as the dominant determinant of BBB permeability, whereas scaffold analysis identified the molecular frameworks associated with highly permeable and impermeable compounds. External validation provided preliminary support for the improved specificity of the proposed rule, compared with existing approaches. Conclusions: These findings suggest that the commonly applied TPSA threshold of 90 Å2 may be overly lenient. A data-driven threshold of approximately 67 Å2 substantially improved the discrimination of BBB permeability across the entire dataset. Compounds with TPSA values between 67 and 90 Å2 should be assessed on a case-by-case basis, considering the ionization state and active transport potential, rather than being automatically classified as BBB-permeable. These experimentally grounded rules offer a practical framework for the early-stage design of CNS leads.
Blood-brain barrier (BBB) permeability is a critical determinant in the development of central nervous system therapeutics because it directly influences the ability of drug candidates to reach their target sites within the brain. In this study, an explainable machine learning framework was developed to predict BBB per...
Evidence that LogP-BBB associations vary systematically across chemical space is insufficient underscores that feature representation and model architecture are coupled design choices, and that unorthogonalized causal forests risk overstating genuine treatment effect heterogeneity.
Tshemollo Rapolai, S. Makgai, Mohammad Arashi· 0 citations
BACKGROUND
Predicting blood-brain barrier (BBB) permeability remains a central problem in central nervous system (CNS) drug design. Conventional rules such as Lipinski's rule of five and related CNS guidelines describe physicochemical boundaries but do not guarantee permeability.
OBJECTIVE
We examined how a set of mo...
Tatsuki Onishi· European Journal of Pharmace...· 0 citations
Background/Objectives: Blood–brain barrier (BBB) permeability prediction is important for central nervous system drug discovery. Many models rely mainly on chemical structure, omit transport information, and may report optimistic performance when related compounds occur across training and test sets without adequate le...
Yu Shin, Sahith Mada, S. Dakshanamurthy· Pharmaceuticals· 0 citations
A next generation CNS-MPO framework, Aragen-iMPO, (A-iMPO) is introduced which was developed using 5,129 curated compounds through an integrated workflow combining explainable machine learning, rigorous descriptor selection, and low-dimensional discriminant mapping.
S. Phukan, Ashis Nandy, M. Ramarao· ACS Medicinal Chemistry Lett...· 0 citations
An integrated platform linking molecular structure, physicochemical properties, and BBB permeability to support the rational evaluation and prioritisation of compounds within the investigated KYNA derivative series is established.
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