Artificial Neural Networks in ADMET Modeling: Prediction of Blood-Brain Barrier Permeation

A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds

MOTIVATION

Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate.

RESULTS

Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates.

AVAILABILITY AND IMPLEMENTATION

Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

New Myrtenal–Adamantane Conjugates Alleviate Alzheimer’s-Type Dementia in Rat Model

Alzheimer’s disease (AD) is a neurodegenerative disease associated with memory impairment and other central nervous system (CNS) symptoms. Two myrtenal–adamantane conjugates (MACs) showed excellent CNS potential against Alzheimer’s models. Adamantane is a common pharmacophore for drug design, and myrtenal (M) demonstrated neuroprotective effects in our previous studies. The aim of this study is to evaluate the MACs’ neuroprotective properties in dementia. Methods: Scopolamine (Scop) was applied intraperitoneally in Wistar rats for 11 days, simultaneously with MACs or M as a referent, respectively. Brain acetylcholine esterase (AChE) activity, noradrenaline and serotonin levels, and oxidative brain status determination followed behavioral tests on memory abilities. Molecular descriptors and docking analyses for AChE activity center affinity were performed. Results: M derivatives have favorable physicochemical parameters to enter the CNS. Both MACs restored memory damaged by Scop, showing significant AChE-inhibitory activity in the cortex, in contrast to M, supported by the modeling analysis. Moderate antioxidant properties were manifested by glutathione elevation and catalase activity modulation. MACs also altered noradrenaline and serotonin content in the hippocampus. Conclusion: For the first time, neuroprotective properties of two MACs in a rat dementia model were observed. They were stronger than the natural M effects, which makes the substances promising candidates for AD treatment.

DeepBBBP: High accuracy Blood-Brain-Barrier Permeability Prediction with a Mixed Deep Learning Model

Blood-brain-barrier permeability (BBBP) is an important property that is used to establish the drug-likeness of a molecule, as it establishes whether the molecule can cross the BBB when desired. It also eliminates those molecules which are not supposed to cross the barrier, as doing so would lead to toxicity. BBBP can be measured in vivo, in vitro or in silico. With the advent and subsequent rise of in silico methods for virtual drug screening, quite a bit of work has been done to predict this feature using statistical machine learning (ML) and deep learning (DL) based methods. In this work a mixed DL-based model, consisting of a Multi-layer Perceptron (MLP) and Convolutional Neural Network layers, has been paired with Mol2vec. Mol2vec is a convenient and unsupervised machine learning technique which produces high-dimensional vector representations of molecules and its molecular substructures. These succinct vector representations are utilized as inputs to the mixed DL model that is used for BBBP predictions. Several well-known benchmarks incorporating BBBP data have been used for supervised training and prediction by our mixed DL model which demonstrates superior results when compared to existing ML and DL techniques used for predicting BBBP.

The Application of in silico Methods for Prediction of Blood-Brain Barrier Permeability of Small Molecule PET Tracers

Designing positron emission tomography (PET) tracers for targets in the central nervous system (CNS) is challenging. Besides showing high affinity and high selectivity for their intended target, these tracers have to be able to cross the blood-brain barrier (BBB). Since only a small fraction of small molecules is estimated to be able to cross the BBB, tools that can predict permeability at an early stage during the development are of great importance. One such tool is in silico models for predicting BBB-permeability. Thus far, such models have been built based on CNS drugs, with one exception. Herein, we sought to discuss and analyze if in silico predictions that have been built based on CNS drugs can be applied for CNS PET tracers as well, or if dedicated models are needed for the latter. Depending on what is taken into account in the prediction, i.e., passive diffusion or also active influx/efflux, there may be a need for a model build on CNS PET tracers. Following a brief introduction, an overview of a few selected in silico BBB-permeability predictions is provided along with a short historical background to the topic. In addition, a combination of previously reported CNS PET tracer datasets were assessed in a couple of selected models and guidelines for predicting BBB-permeability. The selected models were either predicting only passive diffusion or also the influence of ADME (absorption, distribution, metabolism and excretion) parameters. To conclude, we discuss the potential need of a prediction model dedicated for CNS PET tracers and present the key issues in respect to setting up a such a model.

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.

A deep learning approach to predict blood-brain barrier permeability

The blood-brain barrier plays a crucial role in regulating the passage of 98% of the compounds that enter the central nervous system (CNS). Compounds with high permeability must be identified to enable the synthesis of brain medications for the treatment of various brain diseases, such as Parkinson's, Alzheimer's, and brain tumors. Throughout the years, several models have been developed to solve this problem and have achieved acceptable accuracy scores in predicting compounds that penetrate the blood-brain barrier. However, predicting compounds with "low" permeability has been a challenging task. In this study, we present a deep learning (DL) classification model to predict blood-brain barrier permeability. The proposed model addresses the fundamental issues presented in former models: high dimensionality, class imbalances, and low specificity scores. We address these issues to enhance the high-dimensional, imbalanced dataset before developing the classification model: the imbalanced dataset is addressed using oversampling techniques and the high dimensionality using a non-linear dimensionality reduction technique known as kernel principal component analysis (KPCA). This technique transforms the high-dimensional dataset into a low-dimensional Euclidean space while retaining invaluable information. For the classification task, we developed an enhanced feed-forward deep learning model and a convolutional neural network model. In terms of specificity scores (i.e., predicting compounds with low permeability), the results obtained by the enhanced feed-forward deep learning model outperformed those obtained by other models in the literature that were developed using the same technique. In addition, the proposed convolutional neural network model surpassed models used in other studies in multiple accuracy measures, including overall accuracy and specificity. The proposed approach solves the problem inevitably faced with obtaining low specificity resulting in high false positive rate.

LightBBB: computational prediction model of blood-brain-barrier penetration based on LightGBM

MOTIVATION

Identification of blood-brain barrier (BBB) permeability of a compound is a major challenge in neurotherapeutic drug discovery. Conventional approaches for BBB permeability measurement are expensive, time-consuming and labor-intensive. BBB permeability is associated with diverse chemical properties of compounds. However, BBB permeability prediction models have been developed using small datasets and limited features, which are usually not practical due to their low coverage of chemical diversity of compounds. Aim of this study is to develop a BBB permeability prediction model using a large dataset for practical applications. This model can be used for facilitated compound screening in the early stage of brain drug discovery.

RESULTS

A dataset of 7162 compounds with BBB permeability (5453 BBB+ and 1709 BBB-) was compiled from the literature, where BBB+ and BBB- denote BBB-permeable and non-permeable compounds, respectively. We trained a machine learning model based on Light Gradient Boosting Machine (LightGBM) algorithm and achieved an overall accuracy of 89%, an area under the curve (AUC) of 0.93, specificity of 0.77 and sensitivity of 0.93, when 10-fold cross-validation was performed. The model was further evaluated using 74 central nerve system compounds (39 BBB+ and 35 BBB-) obtained from the literature and showed an accuracy of 90%, sensitivity of 0.85 and specificity of 0.94. Our model outperforms over existing BBB permeability prediction models.

AVAILABILITYAND IMPLEMENTATION

The prediction server is available at http://ssbio.cau.ac.kr/software/bbb.

A Recurrent Neural Network model to predict blood-brain barrier permeability

The rapid development of computational methods and the increasing volume of chemical and biological data have contributed to an immense growth in chemical research. This field of study is known as "chemoinformatics," which is a discipline that uses machine-learning techniques to extract, process, and extrapolate data from chemical structures. One of the significant lines of research in chemoinformatics is the study of blood-brain barrier (BBB) permeability, which aims to identify drug penetration into the central nervous system (CNS). In this research, we attempt to solve the problem of BBB permeability by predicting compounds penetration to the CNS. To accomplish this goal: (i) First, an overview is provided to the field of chemoinformatics, its definition, applications, and challenges, (ii) Second, a broad view is taken to investigate previous machine-learning and deep-learning computational models to solve BBB permeability. Based on the analysis of previous models, three main challenges that collectively affect the classifier performance are identified, which we define as "the triple constraints"; subsequently, we map each constraint to a proposed solution, (iii) Finally, we conclude this endeavor by proposing a deep learning based Recurrent Neural Network model, to predict BBB permeability (RNN-BBB model). Our model outperformed other studies from the literature by scoring an overall accuracy of 96.53%, and a specificity score of 98.08%. The obtained results confirm that addressing the triple constraints substantially improves the classification model capability specifically when predicting compounds with low penetration.

In Silico Prediction of Blood-Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

The blood-brain barrier (BBB) as a part of absorption protects the central nervous system by separating the brain tissue from the bloodstream. In recent years, BBB permeability has become a critical issue in chemical ADMET prediction, but almost all models were built using imbalanced data sets, which caused a high false-positive rate. Therefore, we tried to solve the problem of biased data sets and built a reliable classification model with 2358 compounds. Machine learning and resampling methods were used simultaneously for the refinement of models with both 2 D molecular descriptors and molecular fingerprints to represent the chemicals. Through a series of evaluation, we realized that resampling methods such as Synthetic Minority Oversampling Technique (SMOTE) and SMOTE+edited nearest neighbor could effectively solve the problem of imbalanced data sets and that MACCS fingerprint combined with support vector machine performed the best. After the final construction of a consensus model, the overall accuracy rate was increased to 0.966 for the final external data set. Also, the accuracy rate of the model for the test set was 0.919, with an excellent balanced capacity of 0.925 (sensitivity) to predict BBB-positive compounds and of 0.899 (specificity) to predict BBB-negative compounds. Compared with other BBB classification models, our models reduced the rate of false positives and were more robust in prediction of BBB-positive as well as BBB-negative compounds, which would be quite helpful in early drug discovery.

Activation of the Cannabinoid Type 2 Receptor by a Novel Indazole Derivative Normalizes the Survival Pattern of Lymphoblasts from Patients with Late-Onset Alzheimer's Disease

BACKGROUND

Alzheimer's disease is a multifactorial disorder for which there is no disease-modifying treatment yet. CB2 receptors have emerged as a promising therapeutic target for Alzheimer's disease because they are expressed in neuronal and glial cells and their activation has no psychoactive effects.

OBJECTIVE

The aim of this study was to investigate whether activation of the CB2 receptor would restore the aberrant enhanced proliferative activity characteristic of immortalized lymphocytes from patients with late-onset Alzheimer's disease. It is assumed that cell-cycle dysfunction occurs in both peripheral cells and neurons in patients with Alzheimer's disease, contributing to the instigation of the disease.

METHODS

Lymphoblastoid cell lines from patients with Alzheimer's disease and age-matched control individuals were treated with a new, in-house-designed dual drug PGN33, which behaves as a CB2 agonist and butyrylcholinesterase inhibitor. We analyzed the effects of this compound on the rate of cell proliferation and levels of key regulatory proteins. In addition, we investigated the potential neuroprotective action of PGN33 in β-amyloid-treated neuronal cells.

RESULTS

We report here that PGN33 normalized the increased proliferative activity of Alzheimer's disease lymphoblasts. The compound blunted the calmodulin-dependent overactivation of the PI3K/Akt pathway, by restoring the cyclin-dependent kinase inhibitor p27 levels, which in turn reduced the activity of the cyclin-dependent kinase/pRb cascade. Moreover, this CB2 agonist prevented β-amyloid-induced cell death in neuronal cells.

CONCLUSION

Our results suggest that the activation of CB2 receptors could be considered a useful therapeutic approach for Alzheimer's disease.

Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery

Quantitative structure-activity relationship modeling using machine learning techniques constitutes a complex computational problem, where the identification of the most informative molecular descriptors for predicting a specific target property plays a critical role. Two main general approaches can be used for this modeling procedure: feature selection and feature learning. In this paper, a performance comparative study of two state-of-art methods related to these two approaches is carried out. In particular, regression and classification models for three different issues are inferred using both methods under different experimental scenarios: two drug-like properties, such as blood-brain-barrier and human intestinal absorption, and enantiomeric excess, as a measurement of purity used for chiral substances. Beyond the contrastive analysis of feature selection and feature learning methods as competitive approaches, the hybridization of these strategies is also evaluated based on previous results obtained in material sciences. From the experimental results, it can be concluded that there is not a clear winner between both approaches because the performance depends on the characteristics of the compound databases used for modeling. Nevertheless, in several cases, it was observed that the accuracy of the models can be improved by combining both approaches when the molecular descriptor sets provided by feature selection and feature learning contain complementary information.

Novel Imidazo[4,5-c][1,2,6]thiadiazine 2,2-dioxides as antiproliferative trypanosoma cruzi drugs: Computational screening from neural network, synthesis and in vivo biological properties

A new family of imidazo[4,5-c][1,2,6]thiadiazine 2,2-dioxide with antiproliferative Trypanosoma cruzi properties was identified from a neural network model published by our group. The synthesis and evaluation of this new class of trypanocidal agents are described. These compounds inhibit the growth of Trypanosoma cruzi, comparable with benznidazole or nifurtimox. In vitro assays were performed to study their effects on the growth of the epimastigote form of the Tulahuen 2 strain, as well as the epimastigote and amastigote forms of CL clone B5 of Trypanosoma cruzi. To verify selectivity towards parasite cells, the non-specific cytotoxicity of the most relevant compounds was studied in mammalian cells, i.e. J774 murine macrophages and NCTC clone 929 fibroblasts. Furthermore, these compounds were assayed regarding the inhibition of cruzipain. In vivo studies revealed that one of the compounds, 19, showed interesting trypanocidal activity, and could be a very promising candidate for the treatment of Chagas disease.

The role of multidrug resistance protein (MRP-1) as an active efflux transporter on blood-brain barrier (BBB) permeability

Drugs acting on central nervous system (CNS) may take longer duration to reach the market as these compounds have a higher attrition rate in clinical trials due to the complexity of the brain, side effects, and poor blood-brain barrier (BBB) permeability compared to non-CNS-acting compounds. The roles of active efflux transporters with BBB are still unclear. The aim of the present work was to develop a predictive model for BBB permeability that includes the MRP-1 transporter, which is considered as an active efflux transporter. A support vector machine model was developed for the classification of MRP-1 substrates and non-substrates, which was validated with an external data set and Y-randomization method. An artificial neural network model has been developed to evaluate the role of MRP-1 on BBB permeation. A total of nine descriptors were selected, which included molecular weight, topological polar surface area, ClogP, number of hydrogen bond donors, number of hydrogen bond acceptors, number of rotatable bonds, P-gp, BCRP, and MRP-1 substrate probabilities for model development. We identified 5 molecules that fulfilled all criteria required for passive permeation of BBB, but they all have a low logBB value, which suggested that the molecules were effluxed by the MRP-1 transporter.

Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

INTRODUCTION

Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery.

AREAS COVERED

In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field.

EXPERT OPINION

The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction

Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration.

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.

Role of breast cancer resistance protein (BCRP) as active efflux transporter on blood-brain barrier (BBB) permeability

Nowadays most of the CNS acting therapeutic molecules are failing in clinical trials due to efflux transporters at the blood brain barrier (BBB) which imparts resistance and poor ADMET properties of these molecules. CNS acting drug molecules interact with the BBB prior to their target site, so there is a need to develop predictive models for BBB permeability which can be used in the initial phases of drug discovery process. Most of the drug molecules are transported to the brain via passive diffusion which is explored extensively; on the other hand, the role of active efflux transporters in BBB permeability is unclear. Our aim is to develop predictive models for BBB permeability that include active efflux transporters. An in silico model has been developed to assess the role of BCRP on BBB permeation. Eight descriptors were selected, which also include BCRP substrate probabilities used for model development and show a relationship between BCRP and logBB. From our analysis, it was found that 11 molecules satisfied all criteria required for BBB permeation but have low logBB values. These 11 molecules are predicted as BCRP substrates from the model developed, suggesting that the molecules are effluxed by the BCRP transporter. This predictive ability was further validated by docking of these 11 molecules into BCRP protein. This study provides a new mechanistic insight into correlation of low logBB values and efflux mechanism of BCRP in BBB.

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.

Selection of test methods to be included in a testing strategy to predict acute oral toxicity: an approach based on statistical analysis of data collected in phase 1 of the ACuteTox project

More than 50 different in vitro and in silico methods assessing specific organ- and system-toxicity, such as haemato-, neuro-, nephro- and hepatotoxicity, as well as intestinal absorption, distribution and metabolism, have been used in the first phase of the ACuteTox project to test a common set of 57 chemicals. This paper describes the methods used for statistical evaluation of concentration-response data collected for each of the endpoint assays, and for the development of a testing strategy applicable for acute toxicity classification of chemicals based on the achieved results of the concentration-response analysis. A final list of in vitro test methods considered to be promising candidates for building blocks of the testing strategy is presented. Only these selected test methods were further investigated in the prevalidation phase of the project. The test methods were chosen according to their reproducibility and reliability and most importantly, according to their potential to classify chemicals into the official acute oral toxicity categories of the EU Classification, Labelling and Packaging (CLP) Regulation. The potential of the test methods to correctly classify the chemicals was assessed by Classification and Regression Trees (CART) analysis.

Identification of drugs inducing phospholipidosis by novel in vitro data

Drug-induced phospholipidosis (PLD) is a lysosomal storage disorder characterized by the accumulation of phospholipids within the lysosome. This adverse drug effect can occur in various tissues and is suspected to impact cellular viability. Therefore, it is important to test chemical compounds for their potential to induce PLD during the drug design process. PLD has been reported to be a side effect of many commonly used drugs, especially those with cationic amphiphilic properties. To predict drug-induced PLD in silico, we established a high-throughput cell-culture-based method to quantitatively determine the induction of PLD by chemical compounds. Using this assay, we tested 297 drug-like compounds at two different concentrations (2.5 μM and 5.0 μM). We were able to identify 28 previously unknown PLD-inducing agents. Furthermore, our experimental results enabled the development of a binary classification model to predict PLD-inducing agents based on their molecular properties. This random forest prediction system yields a bootstrapped validated accuracy of 86 %. PLD-inducing agents overlap with those that target similar biological processes; a high degree of concordance with PLD-inducing agents was identified for cationic amphiphilic compounds, small molecules that inhibit acid sphingomyelinase, compounds that cross the blood-brain barrier, and compounds that violate Lipinski's rule of five. Furthermore, we were able to show that PLD-inducing compounds applied in combination additively induce PLD.

Identification in silico and experimental validation of novel phosphodiesterase 7 inhibitors with efficacy in experimental autoimmune encephalomyelitis mice

A neural network model has been developed to predict the inhibitory capacity of any chemical structure to be a phosphodiesterase 7 (PDE7) inhibitor, a new promising kind of drugs for the treatment of neurological disorders. The numerical definition of the structures was achieved using CODES program. Through the validation of this neural network model, a novel family of 5-imino-1,2,4-thiadiazoles (ITDZs) has been identified as inhibitors of PDE7. Experimental extensive biological studies have demonstrated the ability of ITDZs to inhibit PDE7 and to increase intracellular levels of cAMP. Among them, the derivative 15 showed a high in vitro potency with desirable pharmacokinetic profile (safe genotoxicity and blood brain barrier penetration). Administration of ITDZ 15 in an experimental autoimmune encephalomyelitis (EAE) mouse model results in a significant attenuation of clinical symptoms, showing the potential of ITDZs, especially compound 15, for the effective treatment of multiple sclerosis.

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.

CurlySMILES: a chemical language to customize and annotate encodings of molecular and nanodevice structures

CurlySMILES is a chemical line notation which extends SMILES with annotations for storage, retrieval and modeling of interlinked, coordinated, assembled and adsorbed molecules in supramolecular structures and nanodevices. Annotations are enclosed in curly braces and anchored to an atomic node or at the end of the molecular graph depending on the annotation type. CurlySMILES includes predefined annotations for stereogenicity, electron delocalization charges, extra-molecular interactions and connectivity, surface attachment, solutions, and crystal structures and allows extensions for domain-specific annotations. CurlySMILES provides a shorthand format to encode molecules with repetitive substructural parts or motifs such as monomer units in macromolecules and amino acids in peptide chains. CurlySMILES further accommodates special formats for non-molecular materials that are commonly denoted by composition of atoms or substructures rather than complete atom connectivity.

Synthesis, structural analysis, and biological evaluation of thioxoquinazoline derivatives as phosphodiesterase 7 inhibitors

PDE7 inhibitors regulate pro-inflammatory and immune T-cell functions, and are a potentially novel class of drugs especially useful in the treatment of a wide variety of immune and inflammatory disorders. Starting from our lead family of thioxoquinazolines, we designed, synthesized, and characterized a novel series of thioxoquinazoline derivatives. Many of these compounds showed inhibitory potencies at sub-micromolar levels against the catalytic domain of PDE7A1 and at the micromolar level against PDE4D2. Cell-based studies showed that these compounds not only increased intracellular cAMP levels, but also had interesting anti-inflammatory properties within a therapeutic window. The in silico data predict that these compounds are capable of the crossing the blood-brain barrier. The X-ray crystal structure of the PDE7A1 catalytic domain in complex with compound 15 at a resolution of 2.4 A demonstrated that hydrophobic interactions at the active site pocket are a key feature. This structure, together with molecular modeling, provides insight into the selectivity of the PDE inhibitors and a template for the discovery of new PDE7 or PDE7/PDE4 dual inhibitors.