Prediction of blood–brain barrier permeation of α-adrenergic and imidazoline receptor ligands using PAMPA technique and quantitative-structure permeability relationship analysis

Design, synthesis and biological evaluation of N-salicyloyl tryptamine derivatives as multifunctional neuroprotectants for the treatment of ischemic stroke

Ischemic stroke (IS) is a disease of high death and disability worldwide with few medications in clinical treatment. Neuroinflammation and oxidative stress are considered as crucial factors in the progression of IS. In our previous studies, N-salicyloyl tryptamine derivative (NST) L7 exhibited promising anti-inflammatory properties and is considered a potential clinical therapy for IS but had limited antioxidant capacity. Here, we have designed, synthesized, and biologically evaluated 30 novel NSTs for their neuroprotective effects against cerebral ischemia-reperfusion (CI/R) injury. To identify a multifunctional neuroprotectant with enhanced antioxidant and anti-inflammatory capacity, as well as an effective therapeutic agent for CI/R damage. Among them, M11 exhibited synergistic highly anti-oxidant, anti-inflammatory, anti-ferroptosis, and anti-apoptosis effects and surpassed the parent compound L7. Further studies demonstrated that the synergistic and efficient neuroprotective role of M11 was mainly achieved by activating Nrf2 and stimulating its downstream target HO-1/GCLC/NQO1/GPX4. In addition, M11 possessed good blood-brain barrier permeability. Moreover, M11 effectively reduced cerebral infarct volume and improved neurological deficits in MCAO/R mice. Its hydrochloride form, M11·HCl, exhibited better pharmacokinetic properties, high safety, and a significant reduction in infarct volume, which is comparable to Edaravone. In conclusion, our findings suggested that M11 capable of activating Nrf2, could represent a promising candidate agent for IS.

First report on pesticide sub-chronic and chronic toxicities against dogs using QSAR and chemical read-across

Excessive use of chemicals is the outcome of the industrialization of agricultural sectors which leads to disturbance of ecological balance. Various agrochemicals are widely used in agricultural fields, urban green areas, and to protect from various pest-associated diseases. Due to their long-term health and environmental hazards, chronic toxicity assessment is crucial. Since in vivo and in vitro toxicity assessments are costly, lengthy, and require a large number of animal experiments, in silico toxicity approaches are better alternatives to save time, cost, and animal experimentation. We have developed the first regression-based 2D-QSAR models using different sub-chronic and chronic toxicity data of pesticides against dogs employing 2D descriptors. From the statistical results (n train = 53 - 62 ,   r 2  = 0.614 to 0.754, Q L O O 2  = 0.501 to 0.703 and   Q F 1 2  = 0.531 to 0.718, Q F 2 2 = 0.523 - 0.713 ), it was concluded that the models are robust, reliable, interpretable, and predictive. Similarity-based read-across algorithm was also used to improve the predictivity (Q F 1 2 = 0.595 - 0.813 , Q F 2 2 = 0.573 - 0.809 ) of the models. 5132 chemicals obtained from the CPDat and 1694 pesticides obtained from the PPDB database were also screened using the developed models, and their predictivity and reliability were checked. Thus, these models will be helpful for eco-toxicological data-gap filling, toxicity prediction of untested pesticides, and development of novel, safer & eco-friendly pesticides.

Design, Synthesis, In Silico Studies and In Vitro Evaluation of New Indole- and/or Donepezil-like Hybrids as Multitarget-Directed Agents for Alzheimer's Disease

Alzheimer's disease (AD) is considered a complex neurodegenerative condition which warrants the development of multitargeted drugs to tackle the key pathogenetic mechanisms of the disease. In this study, two novel series of melatonin- and donepezil-based hybrid molecules with hydrazone (3a-r) or sulfonyl hydrazone (5a-l) fragments were designed, synthesized, and evaluated as multifunctional ligands against AD-related neurodegenerative mechanisms. Two lead compounds (3c and 3d) exhibited a well-balanced multifunctional profile, demonstrating intriguing acetylcholinesterase (AChE) inhibition, promising antioxidant activity assessed by DPPH, ABTS, and FRAP methods, as well as the inhibition of lipid peroxidation in the linoleic acid system. Compound 3n, possessing two indole scaffolds, showed the highest activity against butyrylcholinesterase (BChE) and a high selectivity index (SI = 47.34), as well as a pronounced protective effect in H2O2-induced oxidative stress in SH-SY5Y cells. Moreover, compounds 3c, 3d, and 3n showed low neurotoxicity against malignant neuroblastoma cell lines of human (SH-SY5Y) and murine (Neuro-2a) origin, as well as normal murine fibroblast cells (CCL-1) that indicate the in vitro biocompatibility of the experimental compounds. Furthermore, compounds 3c, 3d, and 3n were capable of penetrating the blood-brain barrier (BBB) in the experimental PAMPA-BBB study. The molecular docking showed that compound 3c could act as a ligand to both MT1 and MT2 receptors, as well as to AchE and BchE enzymes. Taken together, those results outline compounds 3c, 3d, and 3n as promising prototypes in the search of innovative compounds for the treatment of AD-associated neurodegeneration with oxidative stress. This study demonstrates that hydrazone derivatives with melatonin and donepezil are appropriate for further development of new AChE/BChE inhibitory agents.

Naphazoline and oxymetazoline are superior to epinephrine in enhancing the cutaneous analgesia of lidocaine in rats

This study observed the cutaneous analgesic effect of adrenergic agonists when combined with lidocaine. We aimed at the usefulness of four adrenergic agonists and epinephrine as analgesics or as tools to prolong the effect of local anesthetics using a model of cutaneous trunci muscle reflex (pinprick pain) in rats. We showed that subcutaneous four adrenergic agonists and epinephrine, as well as the local anesthetic bupivacaine and lidocaine, developed a concentration-dependent cutaneous analgesia. The rank order of the efficacy of different compounds (ED₅₀ ; median effective dose) was epinephrine [0.013 (0.012-0.014) μmol] > oxymetazoline [0.25 (0.22-0.28) μmol] > naphazoline [0.42 (0.34-0.53) μmol] = bupivacaine [0.43 (0.37-0.50) μmol] > xylometazoline [1.34 (1.25-1.45) μmol] > lidocaine [5.86 (5.11-6.72) μmol] > tetrahydrozoline [6.76 (6.21-7.36) μmol]. The duration of full recovery caused by tetrahydrozoline, oxymetazoline, or xylometazoline was greater (P < 0.01) than that induced via epinephrine, bupivacaine, lidocaine, or naphazoline at equianesthetic doses (ED₂₅ , ED₅₀ , and ED₇₅ ). Co-administration of lidocaine (ED₅₀ ) with four adrenergic agonists or epinephrine enhanced the cutaneous analgesic effect. We observed that four adrenergic agonists and epinephrine induce analgesia by themselves, and such an effect has a longer duration than local anesthetics. Co-administration of lidocaine with the adrenergic agonist enhances the analgesic effect, and the cutaneous analgesic effect of lidocaine plus naphazoline (or oxymetazoline) is greater than that of lidocaine plus epinephrine.

Natural Alkaloids as Multi-Target Compounds towards Factors Implicated in Alzheimer's Disease

Alzheimer's disease (AD) is the most common cause of dementia in elderly people; currently, there is no efficient treatment. Considering the increase in life expectancy worldwide AD rates are predicted to increase enormously, and thus the search for new AD drugs is urgently needed. A great amount of experimental and clinical evidence indicated that AD is a complex disorder characterized by widespread neurodegeneration of the CNS, with major involvement of the cholinergic system, causing progressive cognitive decline and dementia. The current treatment, based on the cholinergic hypothesis, is only symptomatic and mainly involves the restoration of acetylcholine (ACh) levels through the inhibition of acetylcholinesterase (AChE). Since the introduction of the Amaryllidaceae alkaloid galanthamine as an antidementia drug in 2001, alkaloids have been one of the most attractive groups for searching for new AD drugs. The present review aims to comprehensively summarize alkaloids of various origins as multi-target compounds for AD. From this point of view, the most promising compounds seem to be the β-carboline alkaloid harmine and several isoquinoline alkaloids since they can simultaneously inhibit several key enzymes of AD's pathophysiology. However, this topic remains open for further research on detailed mechanisms of action and the synthesis of potentially better semi-synthetic analogues.

Partial Least Squares Enhances Genomic Prediction of New Environments

In plant breeding, the need to improve the prediction of future seasons or new locations and/or environments, also denoted as “leave one environment out,” is of paramount importance to increase the genetic gain in breeding programs and contribute to food and nutrition security worldwide. Genomic selection (GS) has the potential to increase the accuracy of future seasons or new locations because it is a predictive methodology. However, most statistical machine learning methods used for the task of predicting a new environment or season struggle to produce moderate or high prediction accuracies. For this reason, in this study we explore the use of the partial least squares (PLS) regression methodology for this specific task, and we benchmark its performance with the Bayesian Genomic Best Linear Unbiased Predictor (GBLUP) method. The benchmarking process was done with 14 real datasets. We found that in all datasets the PLS method outperformed the popular GBLUP method by margins between 0% (in the Indica data) and 228.28% (in the Disease data) across traits, environments, and types of predictors. Our results show great empirical evidence of the power of the PLS methodology for the prediction of future seasons or new environments.

I2-Imidazoline Ligand CR4056 Improves Memory, Increases ApoE Expression and Reduces BBB Leakage in 5xFAD Mice

Recent evidence suggests that I2-imidazoline ligands have neuroprotective properties in animal models of neurodegeneration, such as Alzheimer's disease (AD). We recently demonstrated that the I2-ligand BU224 reversed memory impairments in AD transgenic mice and this effect was not because of reductions in amyloid-β (Aβ) deposition. In this study, our aim was to determine the therapeutic potential of the powerful analgesic I2-imidazoline ligand CR4056 in the 5xFAD model of AD, since this ligand has been proven to be safely tolerated in humans. Sub-chronic oral administration of CR4056 (30 mg/kg for 10 days) led to an improvement in recognition memory in 6-month-old 5xFAD mice, but not in wild-type littermates, without affecting Aβ levels or deposition. Our results also revealed a change in the profile of microglia by CR4056, resulting in a suppression of pro-inflammatory activated microglia, but increased the density of astrocytes and the expression of ApoE, which is mainly produced by these glial cells. In addition, CR4056 restored fibrinogen extravasation, affecting the distribution of markers of astrocytic end feet in blood vessels. Therefore, these results suggest that CR4056 protects against Aβ-mediated neuroinflammation and vascular damage, and offers therapeutic potential at any stage of AD.

Application of in vitro PAMPA technique and in silico computational methods for blood-brain barrier permeability prediction of novel CNS drug candidates

Permeability assessment of small molecules through the blood-brain barrier (BBB) plays a significant role in the development of effective central nervous system (CNS) drug candidates. Since in vivo methods for BBB permeability estimation require a lot of time and resources, in silico and in vitro approaches are becoming increasingly popular nowadays for faster and more economical predictions in early phases of drug discovery. In this work, through application of in vitro parallel artificial membrane permeability assay (PAMPA-BBB) and in silico computational methods we aimed to examine the passive permeability of eighteen compounds, which affect serotonin and dopamine levels in the CNS. The data set was consisted of novel six human dopamine transporter (hDAT) substrates that were previously identified as the most promising lead compounds for further optimisation to achieve neuroprotective effect, twelve approved CNS drugs, and their related compounds. Firstly, PAMPA methods was used to experimentally determine effective BBB permeability (P_e) for all studied compounds and obtained results were further submitted for quantitative structure permeability relationship (QSPR) analysis. QSPR models were built by using three different statistical methods: stepwise multiple linear regression (MLR), partial least square (PLS), and support-vector machine (SVM), while their predictive capability was tested through internal and external validation. Obtained statistical parameters (MLR- R²_pred=-0.10; PLS- R²_pred=0.64, r²_m=0.69, r^/2_m=0.44; SVM- R²_pred=0.57, r²_m=0.72, r^/2_m=0.55) indicated that the SVM model is superior over others. The most important molecular descriptors (H₀p and SolvEMt_3D) were identified and used to propose structural modifications of the examined compounds in order to improve their BBB permeability. Moreover, steered molecular dynamics (SMD) simulation was employed to comprehensively investigate the permeability pathway of compounds through a lipid bilayer. Taken together, the created QSPR model could be used as a reliable and fast pre-screening tool for BBB permeability prediction of structurally related CNS compounds, while performed MD simulations provide a good foundation for future in silico examination.

Current computer-aided drug design methodologies in discovery of novel drug candidates for neuropsychiatric and inflammatory diseases

Drug discovery and development is a very challenging, expensive and time-consuming process. Impressive technological advances in computer sciences and molecular biology have made it possible to use computer-aided drug design (CADD) methods in various stages of the drug discovery and development pipeline. Nowadays, CADD presents an efficacious and indispensable tool, widely used in medicinal chemistry, to lead rational drug design and synthesis of novel compounds. In this article, an overview of commonly used CADD approaches from hit identification to lead optimization was presented. Moreover, different aspects of design of multitarget ligands for neuropsychiatric and anti-inflammatory diseases were summarized. Apparently, designing multi-target directed ligands for treatment of various complex diseases may offer better efficacy, and fewer side effects. Antipsychotics that act through aminergic G protein-coupled receptors (GPCRs), especially Dopamine D2 and serotonin 5-HT2A receptors, are the best option for treatment of various symptoms associated with neuropsychiatric disorders. Furthermore, multi-target directed cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors are also a successful approach to aid the discovery of new anti-inflammatory drugs with fewer side effects. Overall, employing CADD approaches in the process of rational drug design provides a great opportunity for future development, allowing rapid identification of compounds with the optimal polypharmacological profile.

In Silico Studies on Triterpenoid Saponins Permeation through the Blood-Brain Barrier Combined with Postmortem Research on the Brain Tissues of Mice Affected by Astragaloside IV Administration

As the number of central nervous system (CNS) drug candidates is constantly growing, there is a strong need for precise a priori prediction of whether an administered compound is able to cross the blood–brain barrier (BBB). The aim of this study was to evaluate the ability to cross the BBB of triterpenoid saponins occurring in Astragalus mongholicus roots. The research was carried out using in silico methods combined with postmortem studies on the brain tissues of mice treated with isolated astragaloside IV (AIV). Firstly, to estimate the ability to cross the BBB by the tested saponins, new quantitative structure–activity relationship (QSAR) models were established. The reliability and predictability of the model based on the values of the blood–brain barrier penetration descriptor (logBB), the difference between the n-octanol/water and cyclohexane/water logP (ΔlogP), the logarithm of n-octanol/water partition coefficient (logP_ow), and the excess molar refraction (E) were both confirmed using the applicability domain (AD). The critical leverage value h* was found to be 0.128. The relationships between the standardized residuals and the leverages were investigated here. The application of an in vitro acetylcholinesterase-inhibition test showed that AIV can be recognized as the strongest inhibitor among the tested compounds. Therefore, it was isolated for the postmortem studies on brain tissues and blood using semi-preparative HPLC with the mobile phase composed of water, methanol, and ethyl acetate (1.7:2.1:16.2 v/v/v). The results of the postmortem studies on the brain tissues show a regular dependence of the final concentration of AIV in the analyzed brain samples of animals treated with 12.5 and 25 mg/kg b.w. of AIV (0.00012299 and 0.0002306 mg, respectively, per one brain). Moreover, the AIV logBB value was experimentally determined and found to be equal to 0.49 ± 0.03.

Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes

One of the most challenging goals in modern pharmaceutical research is to develop models that can predict drugs’ behavior, particularly permeability in human tissues. Since the permeability is closely related to the molecular properties, numerous characteristics are necessary in order to develop a reliable predictive tool. The present study attempts to decode the permeability by correlating the apparent permeability coefficient (P_app) of 33 steroids with their properties (physicochemical and structural). The P_app of the molecules was determined by in vitro experiments and the results were plotted as Y variable on a Partial Least Squares (PLS) model, while 37 pharmacokinetic and structural properties were used as X descriptors. The developed model was subjected to internal validation and it tends to be robust with good predictive potential (R²Y = 0.902, RMSEE = 0.00265379, Q²Y = 0.722, RMSEP = 0.0077). Based on the results specific properties (logS, logP, logD, PSA and VD_ss) were proved to be more important than others in terms of drugs P_app. The models can be utilized to predict the permeability of a new candidate drug avoiding needless animal experiments, as well as time and material consuming experiments.

Models for skin and brain penetration of major components from essential oils used in aromatherapy for dementia patients

Aromatherapy with essential oils (EOs) has been linked to improvement of cognitive function in patients with dementia. In order to act systemically, active EO components must be absorbed through the skin, enter the systemic circulation, and cross the blood brain barrier (BBB). Thus, the aim of this work was to develop quantitative structure activity relationships (QSARs), to predict skin and blood barrier penetrative abilities of 119 terpenoids from EOs used in aromatherapy. The first model was based on experimentally measured skin permeability for 162 molecules, and the second model on BBB permeability for 138 molecules. Each molecule was encoded with 63 calculated molecular descriptors and an artificial neural network was used to correlate molecular descriptors to permeabilities. Developed QSAR models confirm that EOs components penetrate through the skin and across the BBB. Some well-known descriptors, such as log P (lipophilicity), molecular size and shape, dominated the QSAR model for BBB permeability. Compounds with the highest predicted BBB penetration were hydrocarbon terpenes with the smallest molecular size and highest lipophilicity. Thus, molecular size is a limiting factor for penetration. Compounds with the highest skin permeability have slightly higher molecular size, high lipophilicity and low polarity. Our work shows that a major disadvantage of novel multitarget compounds developed for the treatment of Alzheimer's disease is the size of molecules, which cause problems in their delivery to the brain. Therefore, there is a need for smaller compounds, which possess more desirable physicochemical properties and pharmacokinetics, in addition to targeted biological effects.Communicated by Ramaswamy H. Sarma.

Neuropharmacokinetics: a bridging tool between CNS drug development and therapeutic outcome

WHO classified neurological disorders to be among 6.3% of the global disease burden. Among the most central aspects of CNS drug development is the ability of novel molecules to cross the blood-brain barrier (BBB) to reach the target site over a desired time period for therapeutic action. Based on various aspects, brain pharmacokinetics is considered to be one of the foremost perspectives for the higher attrition rate of CNS biologics. Although drug traits are important, the BBB and blood-cerebrospinal fluid barrier together with transporters become the mechanistic approach behind CNS drug delivery. The present review emphasizes neuropharmacokinetic parameters, their importance, an assessment approach and the vast effect of transporters to brain drug distribution for CNS drug discovery.

Antiangiogenic Activity and Chemical Derivatization of the Neurotoxic Acetogenin Annonacin Isolated from Asimina triloba

Annonacin (1) was isolated from the North American pawpaw ( Asimina triloba), as reported earlier from these laboratories. Natural 1 was submitted to the rat aortic ring bioassay for evaluation of antiangiogenic activity and was found to inhibit microvessel growth (IC₅₀ value of 3 μM). 4,10,15,20-Tetraazido derivatives of 1 were prepared by permesylation followed by azide displacement or by iodination followed by azide displacement. The tetraazide derived from mesylation/azidation was antiangiogenic, while that derived from iodination/azidation exhibited no appreciable activity. The membrane permeability of natural 1 was evaluated using the parallel artificial membrane permeability assay and was found to be marginally permeable as compared to several clinically relevant compounds.

BBB penetration-targeting physicochemical lead selection: Ecdysteroids as chemo-sensitizers against CNS tumors

The anticancer potential of ecdysteroids, especially their chemo-sensitizing activity has recently gained a substantial scientific interest. A comprehensive physicochemical profiling was performed for a set of natural or semi-synthetic ecdysteroids (N=37) to identify a lead compound against central nervous system (CNS) tumors. Calculated properties, such as lipophilicity (clogP), topological polar surface area (TPSA), brain-to-plasma ratio (clogBB) along with the measured blood-brain barrier specific in vitro permeability (logP_e) were evaluated in parallel. Compounds with the highest CNS-availability predicted (clogBB>0.0 and logP_e>-6.0) showed moderate to high lipophilicity (clogP=3.89-5.25), relatively low TPSA (94.45Ų), and shared a common apolar 2,3- and 20,22-diacetonide motif (25, 30-33). These ecdysteroids were selected for testing their capacity to sensitize SH-SY5Y neuroblastoma cells to vincristine. All of the five tested compounds exerted a remarkably strong, dose dependent chemo-sensitizing activity: at 2.5-10.0μM ecdysteroids increased the cytotoxic activity of vincristine one to three orders of magnitude in (e.g., from IC₅₀=39.5±2.9nM to as low as 0.056±0.03nM). Moreover, analysis of the combination index (CI) revealed outstanding synergism between ecdysteroids and vincristine (CI₅₀=0.072-0.444). Thus, based on drug-likeness, physchem character and in vitro CNS activity, compound 25 was proposed as a lead for further in vivo studies.

Molecular aspects of monoamine oxidase B

Monoamine oxidases (MAO) influence the monoamine levels in brain by virtue of their role in neurotransmitter breakdown. MAO B is the predominant form in glial cells and in platelets. MAO B structure, function and kinetics are described as a background for the effect of alterations in its activity on behavior. The need to inhibit MAO B to combat decreased brain amines continues to drive the search for new drugs. Reversible and irreversible inhibitors are now designed using data-mining, computational screening, docking and molecular dynamics. Multi-target ligands designed to combat the elevated activity of MAO B in Alzheimer's and Parkinson's Diseases incorporate MAO inhibition (usually irreversible) as well as iron chelation, antioxidant or neuroprotective properties. The main focus of drug design is the catalytic activity of MAO, but the imidazoline I2 site in the entrance cavity of MAO B is also a pharmacological target. Endogenous regulation of MAO B expression is discussed briefly in light of new studies measuring mRNA, protein, or activity in healthy and degenerative samples, including the effect of DNA methylation on the expression. Overall, this review focuses on examples of recent research on the molecular aspects of the expression, activity, and inhibition of MAO B.

A new PAMPA model using an in-house brain lipid extract for screening the blood-brain barrier permeability of drug candidates

The determination of the permeability of drug candidates across the blood-brain barrier (BBB) is a fundamental step during drug discovery programs. The parallel artificial membrane permeability assay (PAMPA) is a high throughput screening tool applied to evaluate the passive permeability and adapted to predict BBB penetration. Herein, a new PAMPA model was developed using an in-house brain lipid extract capable of discriminating BBB permeable from non-permeable compounds. The apparent permeability (Papp) of 18 reference molecules and 10 test compounds was assessed and compared with phosphatidylcholine and commercial porcine polar brain lipid (PBL). The physicochemical selectivity of the in-house brain lipid extract was demonstrated by correlating Papp values with physicochemical properties and its predictive capacity estimated by establishing in vitro-in vivo correlations. The strong correlations achieved between 2% (w/v) in-house lipid extract and PBL for reference (r(2)=0.77) and test compounds (r(2)=0.94) support an equivalent discriminatory capacity and validate the presented model. Moreover, PAMPA studies performed with PBL and in-house lipid extract exhibited a higher correlation with the in vivo parameter logBB (r(2)=0.76 and r(2)=0.72, respectively) than phosphatidylcholine (r(2)=0.51). Overall, the applied lipid extraction process was reproducible, economical and provided lipid extracts that can be used to reliably assess BBB permeation.

Tuning the predictive capacity of the PAMPA-BBB model

Due to its robustness and versatility, several variations of the blood-brain barrier specific parallel artificial membrane permeability assay (PAMPA-BBB) have been reported in the central nervous system (CNS) drug discovery practice. In this study, the impact of the main assay parameters on the predictive power of PAMPA-BBB was thoroughly investigated with 27, passively BBB-transported drug molecules with in vivo logBB data. The single and combined effects of the following variables were systematically studied and optimized: incubation time and temperature (4 vs. 18h, RT vs. 37°C), type of the read-out (UV-reader vs. HPLC), solvent composition (n-dodecane/n-hexane), lipid concentration (0-10w/v % PBLE), cholesterol content (0-1.66w/v %), and thickness of the lipid membrane, and the DMSO cosolvent content (5-20v/v %), respectively. Based on our results, solvent-driven and lipid-driven mechanisms of diffusion were identified in different assay conditions. Moreover, the analysis of membrane retention (MR%; the mole fraction of solute "lost" to the membrane) data obtained at various membrane compositions (volume of solvent and concentration of phospholipids) revealed the compound-specific nature of this parameter. The optimized conditions for the PAMPA-BBB were the following: 4h incubation at 37°C, detection by HPLC-DAD, iso-pH conditions (pH=7.4) with 5v/v % DMSO content in buffer solutions, and PBLE (10w/v %; without cholesterol) as membrane dissolved in the mixture of n-hexane:n-dodecane 3:1.

Prediction of placental barrier permeability: a model based on partial least squares variable selection procedure

Assessing the human placental barrier permeability of drugs is very important to guarantee drug safety during pregnancy. Quantitative structure–activity relationship (QSAR) method was used as an effective assessing tool for the placental transfer study of drugs, while in vitro human placental perfusion is the most widely used method. In this study, the partial least squares (PLS) variable selection and modeling procedure was used to pick out optimal descriptors from a pool of 620 descriptors of 65 compounds and to simultaneously develop a QSAR model between the descriptors and the placental barrier permeability expressed by the clearance indices (CI). The model was subjected to internal validation by cross-validation and y-randomization and to external validation by predicting CI values of 19 compounds. It was shown that the model developed is robust and has a good predictive potential (r² = 0.9064, RMSE = 0.09, q² = 0.7323, r_p² = 0.7656, RMSP = 0.14). The mechanistic interpretation of the final model was given by the high variable importance in projection values of descriptors. Using PLS procedure, we can rapidly and effectively select optimal descriptors and thus construct a model with good stability and predictability. This analysis can provide an effective tool for the high-throughput screening of the placental barrier permeability of drugs.