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%

Identification of cryptolepine metabolites in rat and human hepatocytes and metabolism and pharmacokinetics of cryptolepine in Sprague Dawley rats

YesBackground: This study aims at characterizing the in vitro metabolism of cryptolepine using human and rat hepatocytes, identifying metabolites in rat plasma and urine after a single cryptolepine dose, and evaluating the single-dose oral and intravenous pharmacokinetics of cryptolepine in male Sprague Dawley (SD) rats. Methods: The in vitro metabolic profiles of cryptolepine were determined by LC-MS/MS following incubation with rat and human hepatocytes. The in vivo metabolic profile of cryptolepine was determined in plasma and urine samples from Sprague Dawley rats following single-dose oral administration of cryptolepine. Pharmacokinetic parameters of cryptolepine were determined in plasma and urine from Sprague Dawley rats after single-dose intravenous and oral administration. Results: Nine metabolites were identified in human and rat hepatocytes, resulting from metabolic pathways involving oxidation (M2-M9) and glucuronidation (M1, M2, M4, M8, M9). All human metabolites were found in rat hepatocyte incubations except glucuronide M1. Several metabolites (M2, M6, M9) were also identified in the urine and plasma of rats following oral administration of cryptolepine. Unchanged cryptolepine detected in urine was negligible. The Pharmacokinetic profile of cryptolepine showed a very high plasma clearance and volume of distribution (Vss) resulting in a moderate average plasma half-life of 4.5 h. Oral absorption was fast and plasma exposure and oral bioavailability were low. Conclusions: Cryptolepine metabolism is similar in rat and human in vitro with the exception of direct glucuronidation in human. Clearance in rat and human is likely to include a significant metabolic contribution, with proposed primary human metabolism pathways hydroxylation, dihydrodiol formation and glucuronidation. Cryptolepine showed extensive distribution with a moderate half-life.Funded by Novartis Pharma under the Next Generation Scientist Program

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer

PDE 7 Inhibitors: New Potential Drugs for the Therapy of Spinal Cord Injury

BACKGROUND: Primary traumatic mechanical injury to the spinal cord (SCI) causes the death of a number of neurons that to date can neither be recovered nor regenerated. During the last years our group has been involved in the design, synthesis and evaluation of PDE7 inhibitors as new innovative drugs for several neurological disorders. Our working hypothesis is based on two different facts. Firstly, neuroinflammation is modulated by cAMP levels, thus the key role for phosphodiesterases (PDEs), which hydrolyze cAMP, is undoubtedly demonstrated. On the other hand, PDE7 is expressed simultaneously on leukocytes and on the brain, highlighting the potential crucial role of PDE7 as drug target for neuroinflammation. METHODOLOGY/PRINCIPAL FINDINGS: Here we present two chemically diverse families of PDE7 inhibitors, designed using computational techniques such as virtual screening and neuronal networks. We report their biological profile and their efficacy in an experimental SCI model induced by the application of vascular clips (force of 24 g) to the dura via a four-level T5-T8 laminectomy. We have selected two candidates, namely S14 and VP1.15, as PDE7 inhibitors. These compounds increase cAMP production both in macrophage and neuronal cell lines. Regarding drug-like properties, compounds were able to cross the blood brain barrier using parallel artificial membranes (PAMPA) methodology. SCI in mice resulted in severe trauma characterized by edema, neutrophil infiltration, and production of a range of inflammatory mediators, tissue damage, and apoptosis. Treatment of the mice with S14 and VP1.15, two PDE7 inhibitors, significantly reduced the degree of spinal cord inflammation, tissue injury (histological score), and TNF-α, IL-6, COX-2 and iNOS expression. CONCLUSIONS/SIGNIFICANCE: All these data together led us to propose PDE7 inhibitors, and specifically S14 and VP1.15, as potential drug candidates to be further studied for the treatment of SCI

A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinase-like chemical space

<p>Abstract</p> <p>Background</p> <p>The current chemical space of known small molecules is estimated to exceed 10⁶⁰structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinski's rule-of-five for drug-likeness and Oprea's criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors.</p> <p>Results</p> <p>The method employs a collection of 22,615 known kinase inhibitors from the ChEMBL database. A kinase-likeness score is computed using statistical analysis of nine key physicochemical descriptors for these inhibitors. Based on this score, the kinase-likeness of four publicly and commercially available databases, i.e., National Cancer Institute database (NCI), the Natural Products database (NPD), the National Institute of Health's Molecular Libraries Small Molecule Repository (MLSMR), and the World Drug Index (WDI) database, is analyzed. Three of these databases, i.e., NCI, NPD, and MLSMR are frequently used in the virtual screening of kinase inhibitors, while the fourth WDI database is for comparison since it covers a wide range of known chemical space. Based on the kinase-likeness score, a kinase-focused library is also developed and tested against three different kinase targets selected from three different branches of the human kinome tree.</p> <p>Conclusions</p> <p>Our proposed methodology is one of the first that explores how the narrow chemical space of kinase inhibitors and its relevant physicochemical information can be utilized to build kinase-focused libraries and prioritize pre-existing compound databases for screening. We have shown that focused libraries generated by filtering compounds using the kinase-likeness score have, on average, better docking scores than an equivalent number of randomly selected compounds. Beyond library design, our findings also impact the broader efforts to identify kinase inhibitors by screening pre-existing compound libraries. Currently, the NCI library is the most commonly used database for screening kinase inhibitors. Our research suggests that other libraries, such as MLSMR, are more kinase-like and should be given priority in kinase screenings.</p

Characterization of Rhodamine-123 as a Tracer Dye for Use In In vitro Drug Transport Assays

Fluorescent tracer dyes represent an important class of sub-cellular probes and allow the examination of cellular processes in real-time with minimal impact upon these processes. Such tracer dyes are becoming increasingly used for the examination of membrane transport processes, as they are easy-to-use, cost effective probe substrates for a number of membrane protein transporters. Rhodamine 123, a member of the rhodamine family of flurone dyes, has been used to examine membrane transport by the ABCB1 gene product, MDR1. MDR1 is viewed as the archetypal drug transport protein, and is able to efflux a large number of clinically relevant drugs. In addition, ectopic activity of MDR1 has been associated with the development of multiple drug resistance phenotype, which results in a poor patient response to therapeutic intervention. It is thus important to be able to examine the potential for novel compounds to be MDR1 substrates. Given the increasing use rhodamine 123 as a tracer dye for MDR1, a full characterisation of its spectral properties in a range of in vitro assay-relevant media is warranted. Herein, we determine λmax for excitation and emission or rhodamine 123 and its metabolite rhodamine 110 in commonly used solvents and extraction buffers, demonstrating that fluorescence is highly dependent on the chemical environment: Optimal parameters are 1% (v/v) methanol in HBSS, with λex = 505 nm, λem = 525 nm. We characterise the uptake of rhodamine 123 into cells, via both passive and active processes, and demonstrate that this occurs primarily through OATP1A2-mediated facilitated transport at concentrations below 2 µM, and via micelle-mediated passive diffusion above this. Finally, we quantify the intracellular sequestration and metabolism of rhodamine 123, demonstrating that these are both cell line-dependent factors that may influence the interpretation of transport assays

Drug Discovery for Duchenne Muscular Dystrophy via Utrophin Promoter Activation Screening

Background: Duchenne muscular dystrophy (DMD) is a devastating muscle wasting disease caused by mutations in dystrophin, a muscle cytoskeletal protein. Utrophin is a homologue of dystrophin that can functionally compensate for its absence when expressed at increased levels in the myofibre, as shown by studies in dystrophin-deficient mice. Utrophin upregulation is therefore a promising therapeutic approach for DMD. The use of a small, drug-like molecule to achieve utrophin upregulation offers obvious advantages in terms of delivery and bioavailability. Furthermore, much of the time and expense involved in the development of a new drug can be eliminated by screening molecules that are already approved for clinical use. Methodology/Principal Findings: We developed and validated a cell-based, high-throughput screening assay for utrophin promoter activation, and used it to screen the Prestwick Chemical Library of marketed drugs and natural compounds. Initial screening produced 20 hit molecules, 14 of which exhibited dose-dependent activation of the utrophin promoter and were confirmed as hits. Independent validation demonstrated that one of these compounds, nabumetone, is able to upregulate endogenous utrophin mRNA and protein, in C2C12 muscle cells. Conclusions/Significance: We have developed a cell-based, high-throughput screening utrophin promoter assay. Using this assay, we identified and validated a utrophin promoter-activating drug, nabumetone, for which pharmacokinetics an

Ovarian endometrioid adenocarcinoma diagnosed in pregnancy. a case report

The prevalence of adnexal masses pregnancy is about 0.19-8.8%. The malignancy rate is around 1-6%, and indeed most cases are benign masses. During pregnancy adnexal masses should be accurately evaluated to identify the patients who need surgery from those who need a 'wait-and-see' strategy. The authors report a case of 36-year-old woman (gravida 2, para 2, two vaginal deliveries) referred to the Gynecologic Department with diagnosis of endometrioid ovarian adenocarcinoma. The patient underwent laparotomic surgery with left salpingo-oophorectomy, total hysterectomy, pelvic and para-aortic lymphadenectomy, and peritoneal washing for optimal surgery staging. No ascites or residual tumour in the abdominal cavity were found macroscopically. Histopathology was negative for residual ovarian cancer. Currently the treatment and the management of ovarian cancer are not well established because scientific evidence is limited