Predicting blood–brain barrier penetration from molecular weight and number of polar atoms

The penetration of therapeutics across the blood-brain barrier: Classic case studies and clinical implications

The blood-brain barrier (BBB) plays central roles in the maintenance and health of the brain. Its mechanisms to safeguard the brain against xenobiotics and endogenous toxins also make the BBB the primary obstacle to the development of drugs for the central nervous system (CNS). Here, we review classic examples of the intersection of clinical medicine, drug delivery, and the BBB. We highlight the role of lipid solubility (heroin), saturable brain-to-blood (efflux: opiates) and blood-to-brain (influx: nutrients, vitamins, and minerals) transport systems, and adsorptive transcytosis (viruses and incretin receptor agonists). We examine how the disruption of the BBB that occurs in certain diseases (tumors) can also be modulated (osmotic agents and microbubbles) and used to deliver treatments, and the role of extracellular pathways in gaining access to the CNS (albumin and antibodies). In summary, this review provides a historical perspective of the key role of the BBB in delivery of drugs to the brain in health and disease.

In silico degradation of fluoroquinolones by a microalgae-based constructed wetland system

Fluoroquinolone antibiotics (FQs) have been used worldwide due to their extended antimicrobial spectrum. However, the overuse of FQs leads to frequent detection in the environment and cannot be efficiently removed. Microalgae-based constructed wetland systems have been proven to be a relatively proper method to treat FQs, mainly by microalgae, plants, microorganisms, and sediments. To improve the removal efficiency of microalgae-constructed wetland, a systematic molecular design, screening, functional, and risk evaluation method was developed using three-dimensional quantitative structure-activity relationship models, molecular dynamics simulation, molecular docking, and TOPKAT approaches. Five designed ciprofloxacin alternatives with improved bactericidal effects and lower human health risks were found to be more easily degraded by microalgae (16.11-167.88 %), plants (6.72-58.86 %), microorganisms (9.10-15.02 %), and sediments (435.83 %-1763.51 %) compared with ciprofloxacin. According to the mechanism analysis, the removal effect of the FQs can be affected via changes in the number, bond energy, and molecular descriptors of favorable and unfavorable amino acids. To the best of our knowledge, this is the first comprehensive study of improving the microalgae, plants, microorganisms, and sediment removal efficiency of FQs in constructed wetlands, which provides theoretical support for the treatment of FQ pollution.

Chromatographic Data in Statistical Analysis of BBB Permeability Indices

Blood-brain barrier (BBB) permeability is an essential phenomena when considering the treatment of neurological disorders as well as in the case of central nervous system (CNS) adverse effects caused by peripherally acting drugs. The presented work contains statistical analyses and the correlation assessment of the analyzed group of active pharmaceutical ingredients (APIs) with their BBB-permeability data collected from the literature (such as computational log BB; Kp,uu,brain, and CNS+/- groups). A number of regression models were constructed in order to observe the connections between the APIs' physicochemical properties in combination with their retention data from the chromatographic experiments (TLC and HPLC) and the indices of bioavailability in the CNS. Conducted analyses confirm that descriptors significant in BBB permeability modeling are hydrogen bond acceptors and donors, physiological charge, or energy of the lowest unoccupied molecular orbital. These molecular descriptors were the basis, along with the chromatographic data from the TLC in log BB regression analyses. Normal-phase TLC data showed a significant contribution to the creation of the log BB regression model using the multiple linear regression method. The model using them showed a good predictive value at the level of R² = 0.87. Models for Kp,uu,brain resulted in lower statistics: R² = 0.56 for the group of 23 APIs with the participation of k IAM.

Bidirectional selection of the functional properties and environmental friendliness of organophosphorus (OP) pesticide derivatives: Design, screening, and mechanism analysis

Organophosphorus pesticides (OPs) are widely used in agricultural production, but the resulting pollution and drug resistance have sparked widespread concern. Therefore, this paper built a model to design OP substitute molecules with high functionality and environmental friendliness, as well as conducted various human health and ecological environment evaluations, synthetic accessibility screening, and easy detection screening. The functionality of the two OP substitute molecules, DIM-100 and DIM-164, increased by 22.79 % and 22.18 %, respectively, and the environmental friendliness increased by 18.07 % and 24.02 %, respectively. The human health risk and ecological, environmental risks were significantly reduced. Both molecules are easy to synthesize, and their detection sensitivity is 9.85 % and 11.24 % higher than that of the target molecule, respectively. Furthermore, significant changes in the distribution of electrons and holes near the C8 and S1 atoms of the OP substitute molecule resulted in easier breakage of the C8-S1 bond, enhancing its photodegradation ability. The charge transfer ability between the atoms of the molecule (as increasing the electron-withdrawing group led to an increase in charge of the P atom) and the volume of the cholinesterase active pocket both affect the functionality of the DIM substitute molecule. That is, the volume of the cholinesterase active pocket of the bee is smaller than that of the brown planthopper and is more affected by the volume of the OP molecule. Furthermore, the mutual verification analysis of the bidirectional selectivity effect of OP substitute molecules between the BayesianRidge model and the 3D-QS(A² + ∀³)R model reveals that the overall charge transfer degree of DIM substitute molecules is the main reason for the increase in the bidirectional selectivity effect.

Development of QSAR models to predict blood-brain barrier permeability

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington's Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80-83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70-72% in negative predictivity, and 78-86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

Molecular design of high-efficacy and high drug safety Fluoroquinolones suitable for a variety of aerobic biodegradation bacteria

The present study attempted to improve the biodegradation removal rate of Fluoroquinolones (FQs) in sewage treatment plants. The similarity index analysis (CoMSIA) model for combined biodegradability was constructed, and 33 kinds of molecular derivatives of FQs suitable for a variety of aerobic biodegradation microorganisms were designed. Further, derivative-20 and derivative-28, with high drug efficiency, drug safety, and environmental friendliness were selected through pharmacokinetics (ADMET), toxicokinetics (TOPKAT), FQs functional characteristics, and environmental friendliness evaluations. Compared with the target molecules, the combined biodegradability of the above two FQ-derivative molecules were increased by 193.57 % and 205.07 %, respectively, while their environment-friendly characteristics were improved to a certain degree. Through molecular docking and molecular dynamic simulation analysis, it showed that van der Waals force (decreased by 2.73 %-61.74 %) was the main factor influencing the binding ability of the modified FQ molecules to the receptor proteins. In addition, the relationship among the non-bonding interaction resultant force, the binding effect of the FQ-derivative molecules, and the receptor protein-related amino acid residues were studied for the first time. It was observed that the higher the value of the non-bonding interaction resultant force, the better was the binding effect, which demonstrating the significantly improved biodegradability of the designed FQ-derivative molecules.

A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors

The highly-selective blood-brain barrier (BBB) prevents neurotoxic substances in blood from crossing into the extracellular fluid of the central nervous system (CNS). As such, the BBB has a close relationship with CNS disease development and treatment, so predicting whether a substance crosses the BBB is a key task in lead discovery for CNS drugs. Machine learning (ML) is a promising strategy for predicting the BBB permeability, but existing studies have been limited by small datasets with limited chemical diversity. To mitigate this issue, we present a large benchmark dataset, B3DB, complied from 50 published resources and categorized based on experimental uncertainty. A subset of the molecules in B3DB has numerical log BB values (1058 compounds), while the whole dataset has categorical (BBB+ or BBB-) BBB permeability labels (7807). The dataset is freely available at https://github.com/theochem/B3DB and https://doi.org/10.6084/m9.figshare.15634230.v3 (version 3). We also provide some physicochemical properties of the molecules. By analyzing these properties, we can demonstrate some physiochemical similarities and differences between BBB+ and BBB- compounds.

Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds

Permeation through the blood–brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds. Using the fragmental (substructural) descriptors representing the occurrence number of various substructures, as well as the artificial neural network approach and the double cross-validation procedure, we have developed a predictive in silico LogBB model based on an extensive and verified dataset (529 compounds), which is applicable to diverse drugs and drug-like compounds. The model has good predictivity parameters (Q2=0.815, RMSEcv=0.318) that are similar to or better than those of the most reliable models available in the literature. Larger datasets, and perhaps more sophisticated network architectures, are required to realize the full potential of deep neural networks. The analysis of fragment contributions reveals patterns of influence consistent with the known concepts of structural characteristics that affect the BBB permeability of organic compounds. The external validation of the model confirms good agreement between the predicted and experimental LogBB values for most of the compounds. The model enables the evaluation and optimization of the BBB permeability of potential neuroactive agents and other drug compounds.

Predicting the Blood-Brain Barrier Permeability of New Drug-Like Compounds via HPLC with Various Stationary Phases

The permeation of the blood-brain barrier is a very important consideration for new drug candidate molecules. In this research, the reversed-phase liquid chromatography with different columns (Purosphere RP-18e, IAM.PC.DD2 and Cosmosil Cholester) was used to predict the penetration of the blood-brain barrier by 65 newly-synthesized drug-like compounds. The linear free energy relationships (LFERs) model (log BB = c + eE + sS + aA + bB + vV) was established for a training set of 23 congeneric biologically active azole compounds with known experimental log BB (BB = C_blood/C_brain) values (R² = 0.9039). The reliability and predictive potency of the model were confirmed by leave-one-out cross validation as well as leave-50%-out cross validation. Multiple linear regression (MLR) was used to develop the quantitative structure-activity relationships (QSARs) to predict the log BB values of compounds that were tested, taking into account the chromatographic lipophilicity (log k_w), polarizability and topological polar surface area. The excellent statistics of the developed MLR equations (R² > 0.8 for all columns) showed that it is possible to use the HPLC technique and retention data to produce reliable blood-brain barrier permeability models and to predict the log BB values of our pharmaceutically important molecules.

RP-18 HPLC Analysis of Drugs’ Ability to Cross the Blood-Brain Barrier

One hundred ten compounds of diverse structures (actives and excipients used in pharmaceutical preparations) were studied by RP-18 HPLC with acetonitrile-pH 7.4 phosphate buffer 1 : 1 (v/v) as the mobile phase. The relationships between the BBB permeation coefficients and the chromatographic parameters log k and (log k)/PSA were compared to those between the blood-brain barrier (BBB) permeation parameters and the RP-18 TLC descriptors Rf and Rf/PSA known from our earlier studies. It was found that the correlations between the BBB permeability and the HPLC data are slightly worse than those achieved for the thin-layer chromatographic data. MLR analysis based upon the physicochemical data confirmed the value of the molecular descriptors, related to the CNS bioavailability. These variables, combined with the HPLC data, made it possible to generate computational models, explaining 70–96% of the total variance of the CNS bioavailability. Contrary to TLC Rf, the advantage of the modification of HPLC log k with PSA (polar surface area) has not been confirmed and the results obtained with log k are superior to those obtained after a novel (log k)/PSA parameter has been introduced. Establishing a firm threshold limit of (log k)/PSA, log k, or even k and k/PSA to distinguish between the CNS+ and CNS− compounds was impossible. On the other hand, discriminant function analyses involving log k and (log k)/PSA as discriminating variables separated the CNS+ and CNS− compounds with the success rate ca. 90%. On the basis of these results, it was concluded that the RP-18 HPLC analytical models are entirely successful in studies and predictions of the BBB permeability.

A novel PEG–haloperidol conjugate with a non-degradable linker shows the feasibility of using polymer–drug conjugates in a non-prodrug fashion

A PEG–haloperidol conjugate was synthesised, which retains binding to the dopamine D₂receptor, showing the possibility of using polymer-drug conjugates as drugsper se' rather than as prodrugs.

Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set

In the present report, the challenging task of drug delivery across the blood-brain barrier (BBB) is addressed via a computational approach. The BBB passage was modeled using classification and regression schemes on a novel extensive and curated data set (the largest to the best of our knowledge) in terms of log BB. Prior to the model development, steps of data analysis that comprise chemical data curation, structural, cutoff and cluster analysis (CA) were conducted. Linear Discriminant Analysis (LDA) and Multiple Linear Regression (MLR) were used to fit classification and correlation functions. The best LDA-based model showed overall accuracies over 85 % and 83 % for the training and test sets, respectively. Also a MLR-based model with acceptable explanation of more than 69 % of the variance in the experimental log BB was developed. A brief and general interpretation of proposed models allowed the estimation on how 'near' our computational approach is to the factors that determine the passage of molecules through the BBB. In a final effort some popular and powerful Machine Learning methods were considered. Comparable or similar performance was observed respect to the simpler linear techniques. Most of the compounds with anomalous behavior were put aside into a set denoted as controversial set and discussion regarding to these compounds is provided. Finally, our results were compared with methodologies previously reported in the literature showing comparable to better results. The results could represent useful tools available and reproducible by all scientific community in the early stages of neuropharmaceutical drug discovery/development projects.

Parallel evolution of tetrodotoxin resistance in three voltage-gated sodium channel genes in the garter snake Thamnophis sirtalis

Members of a gene family expressed in a single species often experience common selection pressures. Consequently, the molecular basis of complex adaptations may be expected to involve parallel evolutionary changes in multiple paralogs. Here, we use bacterial artificial chromosome library scans to investigate the evolution of the voltage-gated sodium channel (Na_v) family in the garter snake Thamnophis sirtalis, a predator of highly toxic Taricha newts. Newts possess tetrodotoxin (TTX), which blocks Na_v’s, arresting action potentials in nerves and muscle. Some Thamnophis populations have evolved resistance to extremely high levels of TTX. Previous work has identified amino acid sites in the skeletal muscle sodium channel Na_v1.4 that confer resistance to TTX and vary across populations. We identify parallel evolution of TTX resistance in two additional Na_v paralogs, Na_v1.6 and 1.7, which are known to be expressed in the peripheral nervous system and should thus be exposed to ingested TTX. Each paralog contains at least one TTX-resistant substitution identical to a substitution previously identified in Na_v1.4. These sites are fixed across populations, suggesting that the resistant peripheral nerves antedate resistant muscle. In contrast, three sodium channels expressed solely in the central nervous system (Na_v1.1–1.3) showed no evidence of TTX resistance, consistent with protection from toxins by the blood–brain barrier. We also report the exon–intron structure of six Na_v paralogs, the first such analysis for snake genes. Our results demonstrate that the molecular basis of adaptation may be both repeatable across members of a gene family and predictable based on functional considerations.

Evaluation of sphingomyelin, cholester, and phosphatidylcholine-based immobilized artificial membrane liquid chromatography to predict drug penetration across the blood-brain barrier

Over the past decades, several in vitro methods have been tested for their ability to predict drug penetration across the blood-brain barrier. So far, in high-performance liquid chromatography, most attention has been paid to micellar liquid chromatography and immobilized artificial membrane (IAM) LC. IAMLC has been described as a viable approach, since the stationary phase emulates the lipid environment of a cell membrane. However, research in IAMLC has almost exclusively been limited to phosphatidylcholine (PC)-based stationary phases, even though PC is only one of the lipids present in cell membranes. In this article, sphingomyelin and cholester stationary phases have been tested for the first time towards their ability to predict drug penetration across the blood-brain barrier. Upon comparison with the PC stationary phase, the sphingomyelin- and cholester-based columns depict similar predictive performance. Combining data from the different stationary phases did not lead to improvements of the models.

A method to predict blood-brain barrier permeability of drug-like compounds using molecular dynamics simulations

The blood-brain barrier (BBB) is formed by specialized tight junctions between endothelial cells that line brain capillaries to create a highly selective barrier between the brain and the rest of the body. A major problem to overcome in drug design is the ability of the compound in question to cross the BBB. Neuroactive drugs are required to cross the BBB to function. Conversely, drugs that target other parts of the body ideally should not cross the BBB to avoid possible psychotropic side effects. Thus, the task of predicting the BBB permeability of new compounds is of great importance. Two gold-standard experimental measures of BBB permeability are logBB (the concentration of drug in the brain divided by concentration in the blood) and logPS (permeability surface-area product). Both methods are time-consuming and expensive, and although logPS is considered the more informative measure, it is lower throughput and more resource intensive. With continual increases in computer power and improvements in molecular simulations, in silico methods may provide viable alternatives. Computational predictions of these two parameters for a sample of 12 small molecule compounds were performed. The potential of mean force for each compound through a 1,2-dioleoyl-sn-glycero-3-phosphocholine bilayer is determined by molecular dynamics simulations. This system setup is often used as a simple BBB mimetic. Additionally, one-dimensional position-dependent diffusion coefficients are calculated from the molecular dynamics trajectories. The diffusion coefficient is combined with the free energy landscape to calculate the effective permeability (Peff) for each sample compound. The relative values of these permeabilities are compared to experimentally determined logBB and logPS values. Our computational predictions correlate remarkably well with both logBB (R(2) = 0.94) and logPS (R(2) = 0.90). Thus, we have demonstrated that this approach may have the potential to provide reliable, quantitatively predictive BBB permeability, using a relatively quick, inexpensive method.

Computer modeling of blood brain barrier permeability of physiologically active compounds

At present work discusses the current level of computer modeling the relationship structure of organic compounds and drugs and their ability to penetrate the BBB. All descriptors that influence to this permeability within classification and regression QSAR models are generalized and analyzed. The crucial role of H-bond in processes both passive, and active transport across BBB is observed. It is concluded that further research should be focused on interpretation the spatial structure of a full-size P-glycoprotein molecule with high resolution and the creation of QSAR models describing the quantitative relationship between structure and active transport of substances across BBB.

Improving the prediction of drug disposition in the brain

INTRODUCTION

Ability to cross the blood-brain barrier is one of the key ADME characteristics of all drug candidates regardless of their target location in the body. While good brain penetration is essential for CNS drugs, it may lead to serious side effects in case of peripherally-targeted molecules. Despite a high demand of computational methods for estimating brain transport early in drug discovery, achieving good prediction accuracy still remains a challenging task.

AREAS COVERED

This article reviews various measures employed to quantify brain delivery and recent advances in QSAR approaches for predicting these properties from the compound's structure. Additionally, the authors discuss the classification models attempting to distinguish between permeable and impermeable chemicals.

EXPERT OPINION

Recent research in the field of brain penetration modeling showed an increasing understanding of the processes involved in drug disposition, although most models of brain/plasma partitioning still rely on purely statistical considerations. Preferably, new models should incorporate mechanistic knowledge since it is the prerequisite for guiding drug design efforts in the desired direction. To increase the efficiency of computational tools, a broader view is necessary, involving rate and extent of brain penetration, as well as plasma and brain tissue binding strength, instead of relying on any single property.

Prediction of pharmacokinetic parameters

In silico tools specifically developed for prediction of pharmacokinetic parameters are of particular interest to pharmaceutical industry because of the high potential of discarding inappropriate molecules during an early stage of drug development itself with consequent saving of vital resources and valuable time. The ultimate goal of the in silico models of absorption, distribution, metabolism, and excretion (ADME) properties is the accurate prediction of the in vivo pharmacokinetics of a potential drug molecule in man, whilst it exists only as a virtual structure. Various types of in silico models developed for successful prediction of the ADME parameters like oral absorption, bioavailability, plasma protein binding, tissue distribution, clearance, half-life, etc. have been briefly described in this chapter.

Qualitative prediction of blood-brain barrier permeability on a large and refined dataset

The prediction of blood-brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood-brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood-brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (n(trees) = 5) based on only four descriptors yields a validated accuracy of 88%.

Identification of novel functional inhibitors of acid sphingomyelinase

We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 µM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans.

In vitro primary human and animal cell-based blood-brain barrier models as a screening tool in drug discovery

Brain penetration is characterized by its extent and rate and is influenced by drug physicochemical properties, plasma exposure, plasma and brain protein binding and BBB permeability. This raises questions related to physiology, interspecies differences and in vitro/in vivo extrapolation. We herein discuss the use of in vitro human and animal BBB model as a tool to improve CNS compound selection. These cell-based BBB models are characterized by low paracellular permeation, well-developed tight junctions and functional efflux transporters. A study of twenty drugs shows similar compound ranking between rat and human models although with a 2-fold higher permeability in rat. cLogP < 5, PSA < 120 Å, MW < 450 were confirmed as essential for CNS drugs. An in vitro/in vivo correlation in rat (R² = 0.67; P = 2 × 10⁻⁴) was highlighted when in vitro permeability and efflux were considered together with plasma exposure and free fraction. The cell-based BBB model is suitable to optimize CNS-drug selection, to study interspecies differences and then to support human brain exposure prediction.

Water-solvent partition coefficients and Delta Log P values as predictors for blood-brain distribution; application of the Akaike information criterion

It is shown that log P values for water-alkane or water-cyclohexane partitions, and the corresponding Delta log P values when used as descriptors for blood-brain distribution, as log BB, yield equations with very poor correlation coefficients but very good standard deviations, S from 0.25 to 0.33 log units. Using quite large data sets, we have verified that similar S-values apply to predictions of log BB. A suggested model, based on log P for water-dodecane and water-hexadecane partition coefficients, has 109 data points and a fitted S = 0.254 log units. It is essential to include in the model an indicator variable for volatile compounds, and an indicator variable for drugs that contain the carboxylic group. A similar equation based on water-chloroform partition coefficients has 83 data points and a fitted S = 0.287 log units. We can find no causal connection between these log P values and log BB in terms of correlation or in terms of chemical similarity, but conclude that the log P descriptor will yield excellent predictions of log BB provided that predictions are within the chemical space of the compounds used to set up the model. We also show that model based on log P(octanol) and an Abraham descriptor provides a simple and easy method of predicting log BB with an error of no more than 0.31 log units. We have used the Akaike information criterion to investigate the most economic models for log BB.