Computational prediction of CNS drug exposure based on a novel in vivo dataset

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.

Prediction Models for Brain Distribution of Drugs Based on Biomimetic Chromatographic Data

The development of high-throughput approaches for the valid estimation of brain disposition is of great importance in the early drug screening of drug candidates. However, the complexity of brain tissue, which is protected by a unique vasculature formation called the blood−brain barrier (BBB), complicates the development of robust in silico models. In addition, most computational approaches focus only on brain permeability data without considering the crucial factors of plasma and tissue binding. In the present study, we combined experimental data obtained by HPLC using three biomimetic columns, i.e., immobilized artificial membranes, human serum albumin, and α1-acid glycoprotein, with molecular descriptors to model brain disposition of drugs. Kp,uu,brain, as the ratio between the unbound drug concentration in the brain interstitial fluid to the corresponding plasma concentration, brain permeability, the unbound fraction in the brain, and the brain unbound volume of distribution, was collected from literature. Given the complexity of the investigated biological processes, the extracted models displayed high statistical quality (R2 > 0.6), while in the case of the brain fraction unbound, the models showed excellent performance (R2 > 0.9). All models were thoroughly validated, and their applicability domain was estimated. Our approach highlighted the importance of phospholipid, as well as tissue and protein, binding in balance with BBB permeability in brain disposition and suggests biomimetic chromatography as a rapid and simple technique to construct models with experimental evidence for the early evaluation of CNS drug candidates.

In Situ Surface Modification of Microfluidic Blood-Brain-Barriers for Improved Screening of Small Molecules and Nanoparticles

Here, we have developed and evaluated a microfluidic-based human blood-brain-barrier (μBBB) platform that models and predicts brain tissue uptake of small molecule drugs and nanoparticles (NPs) targeting the central nervous system. By using a photocrosslinkable copolymer that was prepared from monomers containing benzophenone and N-hydroxysuccinimide ester functional groups, we were able to evenly coat and functionalize μBBB chip channels in situ, providing a covalently attached homogenous layer of extracellular matrix proteins. This novel approach allowed the coculture of human endothelial cells, pericytes, and astrocytes and resulted in the formation of a mimic of cerebral endothelium expressing tight junction markers and efflux proteins, resembling the native BBB. The permeability coefficients of a number of compounds, including caffeine, nitrofurantoin, dextran, sucrose, glucose, and alanine, were measured on our μBBB platform and were found to agree with reported values. In addition, we successfully visualized the receptor-mediated uptake and transcytosis of transferrin-functionalized NPs. The BBB-penetrating NPs were able to target glioma cells cultured in 3D in the brain compartment of our μBBB. In conclusion, our μBBB was able to accurately predict the BBB permeability of both small molecule pharmaceuticals and nanovectors and allowed time-resolved visualization of transcytosis. Our versatile chip design accommodates different brain disease models and is expected to be exploited in further BBB studies, aiming at replacing animal experiments.

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.

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.

Tools for Early Prediction of Drug Loading in Lipid-Based Formulations

Identification of the usefulness of lipid-based formulations (LBFs) for delivery of poorly water-soluble drugs is at date mainly experimentally based. In this work we used a diverse drug data set, and more than 2,000 solubility measurements to develop experimental and computational tools to predict the loading capacity of LBFs. Computational models were developed to enable in silico prediction of solubility, and hence drug loading capacity, in the LBFs. Drug solubility in mixed mono-, di-, triglycerides (Maisine 35-1 and Capmul MCM EP) correlated (R² 0.89) as well as the drug solubility in Carbitol and other ethoxylated excipients (PEG400, R² 0.85; Polysorbate 80, R² 0.90; Cremophor EL, R² 0.93). A melting point below 150 °C was observed to result in a reasonable solubility in the glycerides. The loading capacity in LBFs was accurately calculated from solubility data in single excipients (R² 0.91). In silico models, without the demand of experimentally determined solubility, also gave good predictions of the loading capacity in these complex formulations (R² 0.79). The framework established here gives a better understanding of drug solubility in single excipients and of LBF loading capacity. The large data set studied revealed that experimental screening efforts can be rationalized by solubility measurements in key excipients or from solid state information. For the first time it was shown that loading capacity in complex formulations can be accurately predicted using molecular information extracted from calculated descriptors and thermal properties of the crystalline drug.

Validation and Characterization of a Novel Peptide That Binds Monomeric and Aggregated β-Amyloid and Inhibits the Formation of Neurotoxic Oligomers

Although the formation of β-amyloid (Aβ) deposits in the brain is a hallmark of Alzheimer disease (AD), the soluble oligomers rather than the mature amyloid fibrils most likely contribute to Aβ toxicity and neurodegeneration. Thus, the discovery of agents targeting soluble Aβ oligomers is highly desirable for early diagnosis prior to the manifestation of a clinical AD phenotype and also more effective therapies. We have previously reported that a novel 15-amino acid peptide (15-mer), isolated via phage display screening, targeted Aβ and attenuated its neurotoxicity (Taddei, K., Laws, S. M., Verdile, G., Munns, S., D'Costa, K., Harvey, A. R., Martins, I. J., Hill, F., Levy, E., Shaw, J. E., and Martins, R. N. (2010) Neurobiol. Aging 31, 203-214). The aim of the current study was to generate and biochemically characterize analogues of this peptide with improved stability and therapeutic potential. We demonstrated that a stable analogue of the 15-amino acid peptide (15M S.A.) retained the activity and potency of the parent peptide and demonstrated improved proteolytic resistance in vitro (stable to t = 300 min, c.f. t = 30 min for the parent peptide). This candidate reduced the formation of soluble Aβ42 oligomers, with the concurrent generation of non-toxic, insoluble aggregates measuring up to 25-30 nm diameter as determined by atomic force microscopy. The 15M S.A. candidate directly interacted with oligomeric Aβ42, as shown by coimmunoprecipitation and surface plasmon resonance/Biacore analysis, with an affinity in the low micromolar range. Furthermore, this peptide bound fibrillar Aβ42 and also stained plaques ex vivo in brain tissue from AD model mice. Given its multifaceted ability to target monomeric and aggregated Aβ42 species, this candidate holds promise for novel preclinical AD imaging and therapeutic strategies.

A method to predict different mechanisms for blood-brain barrier permeability of CNS activity compounds in Chinese herbs using support vector machine

The blood-brain barrier (BBB), a highly selective barrier between central nervous system (CNS) and the blood stream, restricts and regulates the penetration of compounds from the blood into the brain. Drugs that affect the CNS interact with the BBB prior to their target site, so the prediction research on BBB permeability is a fundamental and significant research direction in neuropharmacology. In this study, we combed through the available data and then with the help of support vector machine (SVM), we established an experiment process for discovering potential CNS compounds and investigating the mechanisms of BBB permeability of them to advance the research in this field four types of prediction models, referring to CNS activity, BBB permeability, passive diffusion and efflux transport, were obtained in the experiment process. The first two models were used to discover compounds which may have CNS activity and also cross the BBB at the same time; the latter two were used to elucidate the mechanism of BBB permeability of those compounds. Three optimization parameter methods, Grid Search, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), were used to optimize the SVM models. Then, four optimal models were selected with excellent evaluation indexes (the accuracy, sensitivity and specificity of each model were all above 85%). Furthermore, discrimination models were utilized to study the BBB properties of the known CNS activity compounds in Chinese herbs and this may guide the CNS drug development. With the relatively systematic and quick approach, the application rationality of traditional Chinese medicines for treating nervous system disease in the clinical practice will be improved.

A tool to utilize adverse effect profiles to identify brain-active medications for repurposing

BACKGROUND

To shorten the time required to bring new treatments to clinics, recent efforts have focused on repurposing existing Food and Drug Administration (FDA)-approved drugs with established safety data for new indications. We hypothesized that adverse effect profiles might aid in prioritizing compounds for investigation in central nervous system (CNS) applications by providing an indication of their abilities to cross the blood-brain barrier.

METHODS

Data were drawn from an investigation of similarity of adverse effect profiles, utilizing pre- and post-marketing data. A panel of known CNS-active drugs was utilized to estimate aggregate similarity profiles for all other FDA drugs in the database. Permutations were used to test whether similarities for any given drug exceeded that expected under the null hypothesis. To estimate the performance of algorithms using such profiles, manually-curated lists of known CNS-active and -inactive medications were classified using logistic regression. Algorithms with and without this similarity data were compared for prediction of CNS penetrance.

RESULTS

Models incorporating adverse effect similarity data exhibited greater discrimination of brain-penetrant and non-penetrant drugs than models without this data. A visualization tool was developed to allow any medication to be evaluated for adverse effect similarity to the CNS panel or a custom panel.

CONCLUSIONS

Consideration of adverse effect profiles allows in silico prioritization of compounds for follow-up investigation for CNS indications. In concert with chemical screening approaches, this may accelerate repurposing efforts for putative CNS-active medications.

Synthesis and pharmacological evaluation of dual acting ligands targeting the adenosine A2A and dopamine D2 receptors for the potential treatment of Parkinson's disease

A relatively new strategy in drug discovery is the development of dual acting ligands. These molecules are potentially able to interact at two orthosteric binding sites of a heterodimer simultaneously, possibly resulting in enhanced subtype selectivity, higher affinity, enhanced or modified physiological response, and reduced reliance on multiple drug administration regimens. In this study, we have successfully synthesized a series of classical heterobivalent ligands as well as a series of more integrated and "drug-like" dual acting molecules, incorporating ropinirole as a dopamine D2 receptor agonist and ZM 241385 as an adenosine A2A receptor antagonist. The best compounds of our series maintained the potency of the original pharmacophores at both receptors (adenosine A2A and dopamine D2). In addition, the integrated dual acting ligands also showed promising results in preliminary blood-brain barrier permeability tests, whereas the classical heterobivalent ligands are potentially more suited as pharmacological tools.

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.

Computational prediction of drug solubility in fasted simulated and aspirated human intestinal fluid

Purpose

To develop predictive models of apparent solubility (S_app) of lipophilic drugs in fasted state simulated intestinal fluid (FaSSIF) and aspirated human intestinal fluid (HIF).

Methods

Measured S_app values in FaSSIF, HIF and phosphate buffer pH 6.5 (PhB_pH6.5) for 86 lipophilic drugs were compiled and divided into training (Tr) and test (Te) sets. Projection to latent structure (PLS) models were developed through variable selection of calculated molecular descriptors. Experimentally determined properties were included to investigate their contribution to the predictions.

Results

Modest relationships between S_app in PhB_pH6.5 and FaSSIF (R² = 0.61) or HIF (R² = 0.62) were found. As expected, there was a stronger correlation obtained between FaSSIF and HIF (R² = 0.78). Computational models were developed using calculated descriptors alone (FaSSIF, R² = 0.69 and RMSE_te of 0.77; HIF, R² = 0.84 and RMSE_te of 0.81). Accuracy improved when solubility in PhB_pH6.5 was added as a descriptor (FaSSIF, R² = 0.76 and RMSE_Te of 0.65; HIF, R² = 0.86 and RMSE_Te of 0.69), whereas no improvement was seen when melting point (Tm) or logD_{pH 6.5} were included in the models.

Conclusion

Computational models were developed, that reliably predicted S_app of lipophilic compounds in intestinal fluid, from molecular structures alone. If experimentally determined pH-dependent solubility values were available, this further improved the accuracy of the predictions.

Prediction of drug disposition on the basis of its chemical structure

The chemical structure of any drug determines its pharmacokinetics and pharmacodynamics. Detailed understanding of relationships between the drug chemical structure and individual disposition pathways (i.e., distribution and elimination) is required for efficient use of existing drugs and effective development of new drugs. Different approaches have been developed for this purpose, ranging from statistics-based quantitative structure-property (or structure-pharmacokinetic) relationships (QSPR) analysis to physiologically based pharmacokinetic (PBPK) models. This review critically analyzes currently available approaches for analysis and prediction of drug disposition on the basis of chemical structure. Models that can be used to predict different aspects of disposition are presented, including: (a) value of the individual pharmacokinetic parameter (e.g., clearance or volume of distribution), (b) efficiency of the specific disposition pathway (e.g., biliary drug excretion or cytochrome P450 3A4 metabolism), (c) accumulation in a specific organ or tissue (e.g., permeability of the placenta or accumulation in the brain), and (d) the whole-body disposition in the individual patients. Examples of presented pharmacological agents include "classical" low-molecular-weight compounds, biopharmaceuticals, and drugs encapsulated in specialized drug-delivery systems. The clinical efficiency of agents from all these groups can be suboptimal, because of inefficient permeability of the drug to the site of action and/or excessive accumulation in other organs and tissues. Therefore, robust and reliable approaches for chemical structure-based prediction of drug disposition are required to overcome these limitations. PBPK models are increasingly being used for prediction of drug disposition. These models can reflect the complex interplay of factors that determine drug disposition in a mechanistically correct fashion and can be combined with other approaches, for example QSPR-based prediction of drug permeability and metabolism, pharmacogenomic data and tools, pharmacokinetic-pharmacodynamic modeling approaches, etc. Moreover, the PBPK models enable detailed analysis of clinically relevant scenarios, for example the effect of the specific conditions on the time course of the analyzed drug in the individual organs and tissues, including the site of action. It is expected that further development of such combined approaches will increase their precision, enhance the effectiveness of drugs, and lead to individualized drug therapy for different patient populations (geriatric, pediatric, specific diseases, etc.).

Computational prediction of drug solubility in lipid based formulation excipients

PURPOSE

To investigate if drug solubility in pharmaceutical excipients used in lipid based formulations (LBFs) can be predicted from physicochemical properties.

METHODS

Solubility was measured for 30 structurally diverse drug molecules in soybean oil (SBO, long-chain triglyceride; TGLC), Captex355 (medium-chain triglyceride; TGMC), polysorbate 80 (PS80; surfactant) and PEG400 co-solvent and used as responses during PLS model development. Melting point and calculated molecular descriptors were used as variables and the PLS models were validated with test sets and permutation tests.

RESULTS

Solvation capacity of SBO and Captex355 was equal on a mol per mol scale (R (2) = 0.98). A strong correlation was also found between PS80 and PEG400 (R (2) = 0.85), identifying the significant contribution of the ethoxylation for the solvation capacity of PS80. In silico models based on calculated descriptors were successfully developed for drug solubility in SBO (R (2) = 0.81, Q (2) = 0.76) and Captex355 (R (2) = 0.84, Q (2) = 0.80). However, solubility in PS80 and PEG400 were not possible to quantitatively predict from molecular structure.

CONCLUSION

Solubility measured in one excipient can be used to predict solubility in another, herein exemplified with TGMC versus TGLC, and PS80 versus PEG400. We also show, for the first time, that solubility in TGMC and TGLC can be predicted from rapidly calculated molecular descriptors.