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 (ntrees = 5) based on only four descriptors yields a validated accuracy of 88%

In vivo and in silico characterization of apocynin in reducing organ oxidative stress: a pharmacokinetic and pharmacodynamic study

Apocynin has been widely used in vivo as a Nox2-contaninig NADPH oxidase inhibitor. However, its time-dependent tissue distribution and inhibition on organ reactive oxygen species (ROS) production remained unclear. In this study, we examined apocynin pharmacokinetics and pharmacodynamics (PKPD) after iv injection (bolus, 5 mg/kg) of mice (CD1, 12-week). Apocynin was detected using a HPLC coupled to a linear ion-trap tandem mass spectrometer. Apocynin peak concentrations were detected in plasma at 1 min (5494±400 ng/mL) (t1/2=0.05 h, clearance=7.76 L/h/kg), in urine at 15 min (14942±5977 ng/mL), in liver at 5 min (2853±35 ng/g), in heart at 5 min (3161±309 ng/g) and in brain at 1 min (4603±208 ng/g) after iv injection. These were accompanied with reduction of ROS production in the liver, heart and brain homogenates. Diapocynin was not detected in these samples. Therapeutic effect of apocynin was examined using a mouse model (C57BL/6J) of high-fat diet (HFD, 16 weeks)-induced obesity and accelerated aging. Apocynin (5 mM) was supplied in drinking water during the HFD period and was detected at the end of treatment in the brain (5369±1612 ng/g), liver (4818±1340 ng/g) and heart (1795±1487 ng/g) along with significant reductions of ROS production in these organs. In conclusion, apocynin PKPD is characterized by a short half-life, rapid clearance, good distribution and inhibition of ROS production in major organs. Diapocynin is not a metabolite of apocynin in vivo. Apocynin crosses easily the blood-brain barrier and reduces brain oxidative stress associated with metabolic disorders and aging

Partial Least Square and Hierarchical Clustering in ADMET Modeling: Prediction of Blood - Brain Barrier Permeation of alpha-Adrenergic and Imidazoline Receptor Ligands

PURPOSE. Rate of brain penetration (logPS), brain/plasma equilibration rate (logPS-brain), and extent of blood-brain barrier permeation (logBB) of 29 alpha-adrenergic and imidazoline-receptors ligands were examined in Quantitative-Structure-Property Relationship (QSPR) study. METHODS. Experimentally determined chromatographic retention data (logKw at pH 4.4, slope (S) at pH 4.4, logKw at pH 7.4, slope (S) at pH 7.4, logKw at pH 9.1, and slope (S) at pH 9.1) and capillary electrophoresis migration parameters (mu(eff) at pH 4.4, mu(eff) at pH 7.4, and mu(eff) at pH 9.1), together with calculated molecular descriptors, were used as independent variables in the QSPR study by use of partial least square (PLS) methodology. RESULTS. Predictive potential of the formed QSPR models, QSPR(logPS), QSPR(logPS-brain), QSPR(logBB), was confirmed by cross- and external validation. Hydrophilicity (Hy) and H-indices (H7m) were selected as significant parameters negatively correlated with both logPS and logPS-brain, while topological polar surface area (TPSA(NO)) was chosen as molecular descriptor negatively correlated with both logPS and logBB. The principal component analysis (PCA) and hierarchical clustering analysis (HCA) were applied to cluster examined drugs based on their chromatographic, electrophoretic and molecular properties. Significant positive correlations were obtained between the slope (S) at pH 7.4 and logBB in A/B cluster and between the logKw at pH 9.1 and logPS in C/D cluster. CONCLUSIONS. Results of the QSPR, clustering and correlation studies could be used as novel tool for evaluation of blood-brain barrier permeation of related alpha-adrenergic/imidazoline receptor ligands

Building predictive unbound brain-to-plasma concentration ratio (Kp,uu,brain) models

Abstract The blood-brain barrier (BBB) constitutes a dynamic membrane primarily evolved to protect the brain from exposure to harmful xenobiotics. The distribution of synthesized drugs across the blood-brain barrier (BBB) is a vital parameter to consider in drug discovery projects involving a central nervous system (CNS) target, since the molecules should be capable of crossing the major hurdle, BBB. In contrast, the peripherally acting drugs have to be designed optimally to minimize brain exposure which could possibly result in undue side effects. It is thus important to establish the BBB permeability of molecules early in the drug discovery pipeline. Previously, most of the in-silico attempts for the prediction of brain exposure have relied on the total drug distribution between the blood plasma and the brain. However, it is now understood that the unbound brain-to-plasma concentration ratio ( Kp,uu,brain) is the parameter that precisely indicates the BBB availability of compounds. Kp,uu,brain describes the free drug concentration of the drug molecule in the brain, which, according to the free drug hypothesis, is the parameter that causes the relevant pharmacological response at the target site. Current work involves revisiting a model built in 2011 and uploaded in an in-house server and checking for its performance on the data collected since then. This gave a satisfying result showing the stability of the model. The old dataset was then further extended with the temporal dataset in order to update the model. This is important to maintain a substantial chemical space so as to ensure a good predictability with unknown data. Using other methods and descriptors not used in the previous study, a further improvement in the model performance was achieved. Attempts were also made in order to interpret the model by identifying the most influential descriptors in the model.Popular science summary: Predictive model for unbound brain-to-plasma concentration ratio Blood-brain barrier (BBB) is a dynamic interface evolved to protect the brain from exposure to toxic xenobiotics and to maintain homeostasis. Distribution of drugs across BBB is critical for any drug discovery project. A drug designed for a target in brain has to pass through the BBB in sufficient concentrations to elicit the desired therapeutic effect. On the other hand, a drug designed for a non-CNS target should be kept away from the brain to avoid fatal side effects. Unbound brain-to-plasma concentration ratio, Kp,uu,brain is a parameter that describes the distribution of a molecule across the BBB. It represents the free drug concentration in the brain, which is the fraction that elicits the pharmacological effect on the CNS. The experimental measurement of this parameter is time consuming and laborious. Computational prediction of such properties thus prove to be of a great utility in reducing the time and resources spent by aiding in the early elimination of compounds possessing undesirable qualities. This helps in reducing late stage compound attrition (failure rate) which has always been a major problem for pharmaceutical industries. Quantitative Structure Activity Relationship (QSAR) is an approach that attempts to establish a meaningful relationship between the chemical structure of a molecule and its chemical/biological activity. Once established, this relationship can be used to predict the activity of a new compound based on its chemical structure. In a typical QSAR experiment, the chemical structures are often represented in terms of numerical values called molecular descriptors. The thesis work utilized machine learning algorithm (Support Vector Machine and Random forest) to define the structure -activity relationship. A predictive model for estimating the unbound brain-to-plasma concentration ratio (Kp,uu,brain) was developed based on a training set of in-house compounds and was mounted in an in-house program (C-lab) in 2011 for routine use. The thesis project involved validating the existing model and updating the model by extending the dataset with the data collected since 2011. Different combinations of Machine Learning algorithms, modeling approaches and molecular descriptors (calculated numerical values representing of chemical structures) were used to build the models. Further, combining the prediction from these models, consensus models were built and validated. Two-class classification models were also evaluated based on categorizing compounds into BBB positive (crosses BBB) or negative (does not cross BBB). The validation of the old model using temporal test set (Kp,uu,brain data collected since 2011) gave a promising result showing stability and good predictive power. However, it is very important to keep the chemical space updated, which defines the purpose for updating the model. The new model (a consensus model with five components) shows a significant improvement in terms of the predictive power along with an improvement in the classification performance. This model will be uploaded to C-lab and will be accessible for use within AstraZeneca. Advisors: Hongming Chen, Ola Engkvist (Computational Chemistry, AstraZeneca R&D Mölndal) Master´s Degree Project 60 credits in Bioinformatics (2014) Department of Biology., Lund Universit

SIGMA 1 RECEPTOR (S1R) MODULATORS AS A THERAPEUTIC STRATEGY FOR PROMOTING NEUROPLASTICITY. DESIGN AND SYNTHESIS OF NOVEL MONO- AND BI-VALENT LIGANDS FOR SRS

During my three-years-project I focused on the development of novel mono- and bi-valent Sigma1 Receptor (S1R) modulators to address two main objectives: (i) the obtainment of multitarget-directed ligands (MTDLs) endowed with therapeutic potential for the treatment of neurodegenerative diseases; (ii) the preparation of a series of bivalent compounds to be used for the study of S1R oligomerization process. These two major topics are briefly discussed hereafter. (i) Neurodegeneration is a key event in many challenging disorders (e.g. Alzheimers diseases, Parkinsons disease, multiple sclerosis). Such pathologies involve the alteration of several molecular pathways, making the multi-target paradigm a promising strategy for new effective therapies. Among the numerous molecular targets that have been correlated with neurodegenerative disorders, S1R has gained great attention from the scientific community, and S1R agonists are considered viable pharmacological tools for their neuroprotective activity. Hence, we reasoned that coupling S1R agonism with modulation of other selected targets might afford new molecular entities more effective in counteracting neuropathies. The additional targets of our MTDLs include N-Methyl-D-Aspartate (NMDA) receptor, which plays a relevant role in synaptic plasticity, and acetylcholinesterase (AChE), which regulates acetylcholine levels in central nervous system. A structurally focused compound library was prepared through a divergent synthesis. The so-obtained compounds were tested for a preliminary biological evaluation, evaluating their affinity and selectivity towards S1R and NMDA receptor, the AChE inhibition and their antioxidant properties, since oxidative stress is considered a hallmark of neurodegeneration. A number of promising compounds, endowed with effective multitarget profile, was identified. These results will pave the way for further biological investigation and structure optimization in order to achieve viable tools for the treatment of neurodegenerative diseases. (ii) In the last decade numerous studies have supported the hypothesis that S1R can exist in multiple oligomeric forms. In detail, agonists seem to stabilize S1R monomers and dimers that act as chaperones, whereas antagonists bind to higher oligomer complexes, maintaining them in repository forms. Moreover, the recently disclosed crystal of S1R was obtained as a trimer. Nevertheless, the mechanism of generation, as well as the precise biological function of S1R oligomers, are still unknown. Accordingly, a series of homo- and hetero-bivalent S1R ligands was designed and synthetized to investigate S1R oligomerization process. Since S1R agonists are known to exert neuroprotective effects, and S1R can form homo-dimeric structures upon interaction with agonists, we reasoned that promoting dimerization through bivalent agonists might enhance ligands activity. The designed bivalent compounds consist in two units of (R)-RC-33 (a potent and selective S1R agonist developed by our group) joined by a linker. Different lengths, polarities and spatial constraints were explored for the linker. The key precursor of the synthesis is (R)-RC-33A, an aminic derivative of RC-33. For the obtainment of enantiopure (R)-RC-33A, three different synthetic approaches have been explored, resulting in the identification of an efficient pathway to access (R)-RC-33 derivatives with high yield and chiral purity. Once the designed ligands were obtained in sufficient amount and purity, they were tested in binding assays to assess their S1R affinity. Moreover, computational studies were performed on both mono- and bi-valent S1R modulators. In detail, docking into the crystals binding pocket served as basis for the development of a 3D-QSAR model and for the rationalization of experimental results. Molecular dynamics studies are ongoing, and future functional assays will contribute to shed light on the S1R oligomeric states

Pharmacophore analysis, design and in vitro testing of multi-target ligands as potentially effective therapeutics of complex neurological and mental disorders

Disfunkcija serotoninske i dopaminske neurotransmisije u mozgu je u osnovi patofiziologijebrojnih neuroloških i mentalnih oboljenja. Definisanje protokola koji integriše in silico i in vitrometode, u cilju prouĉavanja farmakofore multi-potentnih jedinjenja koja deluju na nivou centralnognervnog sistema (CNS), predstavlja vaţan korak u racionalizaciji procesa otkrivanja novih lekova.Primenom simulacija molekulske dinamike i molekulskog dokinga, kao i analize kvantitativnogodnosa strukture i aktivnosti (eng. 3D-Quantitative Structure Activity Relationship, 3D-QSAR)definisane su kljuĉne strukturne karakteristike dualnih antagonista 5-HT2A i D2 receptora, sasmanjenim afinitetom za H1 receptor. Na osnovu dobijenih rezultata izvršeno je pretraţivanje bazafragmenata primenom metode virtuelnog skrininga (eng. Virtual Screening, VS) u cilju dizajniranjapotencijalno bezbednijih i efikasnijih liganada sa višestrukim delovanjem (eng. multi-target),pruţajući smernice za razvoj novih lekova u terapiji sloţenih CNS oboljenja. 3D-QSAR analizombicikliĉnih α-iminofosfonata definisana je struktura farmakofore selektivnih liganadaimidazolinskih I2 receptora, kao potencijalno novih lekova za leĉenje kognitivnih poremećaja. Invitro paralelni test permeabilnosti na veštaĉkim membranama (eng. Parallel Artificial MembranePermeability Assay, PAMPA) je korišćen za odreĊivanje efektivne permeabilnosti (logPe) krozkrvno-moţdanu barijeru (KMB) jedinjenja koja utiĉu na modulaciju aktivnosti serotoninskog idopaminskog sistema u mozgu. Dobijeni rezultati su korišćeni u analizi kvantitativnog odnosastrukture i osobina (eng. Quantitative Structure-Property Relationship, QSPR) u cilju razumevanjastrukturnih karakteristika koje najviše utiĉu na prolazak jedinjenja kroz KMB. Model formiranprimenom metode podrţavajućih vektora (eng. Support-Vector Machine, SVM) i validiranopseţnom statistiĉkom analizom, je korišćen za predviĊanje logPe vrednosti dizajniranih dualnihantagonista i liganada I2 receptora, svrstavajući ih u grupu visoko permeabilnih jedinjenja. Sa ciljemda se dodatno analizira i vizuelizuje proces permeabilnosti centralnodelujućih jedinjenja kroz KMBna molekulskom nivou, korišćene su simulacije usmerene molekulske dinamike (eng. SteeredMolecular Dynamics, SMD).Disturbances in serotoninergic and dopaminergic neurotransmissions in the central nervoussystem (CNS) play a key role in the pathophysiology of various neurological and mental disorders.Developing an integrative approach through application of in silico and in vitro methods, in order toanalyse pharmacophore of multi-target neuroactive compounds, presents a promising strategy inrationalization of drug design process. Molecular dynamics simulations and molecular dockingmethods in combination with 3D-quantitative structure activity relationship analysis (3D-QSAR)were used to evaluate crucial structural features of potent dual antagonists of 5-HT2A i D2 receptors,with lower antagonistic activity on H1 receptors. The virtual screening of the available fragmentlibraries was performed with the aim to design novel multi-target compounds with a more effectiveand safer profile, laying a good foundation for the therapy of complex brain diseases. Moreover,3D-QSAR analysis of bicyclic α-iminophosphonates was used to reveal the pharmacophorestructure of selective imidazoline I2 receptor (I2-IR) ligands, as potentially new drugs for thetreatment of cognitive disorders. In vitro parallel artificial membrane permeability assay (PAMPA)was further employed to examine the effective permeability (logPe) through blood brain barrier(BBB) of compounds that affect serotonin and dopamine levels in the CNS. Based on the obtainedresults, quantitative structure-property relationship (QSPR) analysis was performed with the aim todefine structural features that mostly affect the permeability of compounds through BBB. Support-vector machine (SVM) method was used to create predictable and reliable QSPR model that wasfurther employed to predict logPe values of new designed dual antagonists of 5-HT2A/D2 receptorsand I2-IR ligands, classifying them into a group of highly permeable compounds. Steered moleculardynamics (SMD) simulations have been carried out to additionally explain and visualize the entireBBB permeation pathway at the molecular level

Pharmacophore analysis, design and in vitro testing of multi-target ligands as potentially effective therapeutics of complex neurological and mental disorders

Disfunkcija serotoninske i dopaminske neurotransmisije u mozgu je u osnovi patofiziologije brojnih neuroloških i mentalnih oboljenja. Definisanje protokola koji integriše in silico i in vitro metode, u cilju prouĉavanja farmakofore multi-potentnih jedinjenja koja deluju na nivou centralnog nervnog sistema (CNS), predstavlja vaţan korak u racionalizaciji procesa otkrivanja novih lekova. Primenom simulacija molekulske dinamike i molekulskog dokinga, kao i analize kvantitativnog odnosa strukture i aktivnosti (eng. 3D-Quantitative Structure Activity Relationship, 3D-QSAR) definisane su kljuĉne strukturne karakteristike dualnih antagonista 5-HT2A i D2 receptora, sa smanjenim afinitetom za H1 receptor. Na osnovu dobijenih rezultata izvršeno je pretraţivanje baza fragmenata primenom metode virtuelnog skrininga (eng. Virtual Screening, VS) u cilju dizajniranja potencijalno bezbednijih i efikasnijih liganada sa višestrukim delovanjem (eng. multi-target), pruţajući smernice za razvoj novih lekova u terapiji sloţenih CNS oboljenja. 3D-QSAR analizom bicikliĉnih α-iminofosfonata definisana je struktura farmakofore selektivnih liganada imidazolinskih I2 receptora, kao potencijalno novih lekova za leĉenje kognitivnih poremećaja. In vitro paralelni test permeabilnosti na veštaĉkim membranama (eng. Parallel Artificial Membrane Permeability Assay, PAMPA) je korišćen za odreĊivanje efektivne permeabilnosti (logPe) kroz krvno-moţdanu barijeru (KMB) jedinjenja koja utiĉu na modulaciju aktivnosti serotoninskog i dopaminskog sistema u mozgu. Dobijeni rezultati su korišćeni u analizi kvantitativnog odnosa strukture i osobina (eng. Quantitative Structure-Property Relationship, QSPR) u cilju razumevanja strukturnih karakteristika koje najviše utiĉu na prolazak jedinjenja kroz KMB. Model formiran primenom metode podrţavajućih vektora (eng. Support-Vector Machine, SVM) i validiran opseţnom statistiĉkom analizom, je korišćen za predviĊanje logPe vrednosti dizajniranih dualnih antagonista i liganada I2 receptora, svrstavajući ih u grupu visoko permeabilnih jedinjenja. Sa ciljem da se dodatno analizira i vizuelizuje proces permeabilnosti centralnodelujućih jedinjenja kroz KMB na molekulskom nivou, korišćene su simulacije usmerene molekulske dinamike (eng. Steered Molecular Dynamics, SMD).Disturbances in serotoninergic and dopaminergic neurotransmissions in the central nervous system (CNS) play a key role in the pathophysiology of various neurological and mental disorders. Developing an integrative approach through application of in silico and in vitro methods, in order to analyse pharmacophore of multi-target neuroactive compounds, presents a promising strategy in rationalization of drug design process. Molecular dynamics simulations and molecular docking methods in combination with 3D-quantitative structure activity relationship analysis (3D-QSAR) were used to evaluate crucial structural features of potent dual antagonists of 5-HT2A i D2 receptors, with lower antagonistic activity on H1 receptors. The virtual screening of the available fragment libraries was performed with the aim to design novel multi-target compounds with a more effective and safer profile, laying a good foundation for the therapy of complex brain diseases. Moreover, 3D-QSAR analysis of bicyclic α-iminophosphonates was used to reveal the pharmacophore structure of selective imidazoline I2 receptor (I2-IR) ligands, as potentially new drugs for the treatment of cognitive disorders. In vitro parallel artificial membrane permeability assay (PAMPA) was further employed to examine the effective permeability (logPe) through blood brain barrier (BBB) of compounds that affect serotonin and dopamine levels in the CNS. Based on the obtained results, quantitative structure-property relationship (QSPR) analysis was performed with the aim to define structural features that mostly affect the permeability of compounds through BBB. Support- vector machine (SVM) method was used to create predictable and reliable QSPR model that was further employed to predict logPe values of new designed dual antagonists of 5-HT2A/D2 receptors and I2-IR ligands, classifying them into a group of highly permeable compounds. Steered molecular dynamics (SMD) simulations have been carried out to additionally explain and visualize the entire BBB permeation pathway at the molecular level