Current Pharmaceutical Design - Volume 22, Issue 34, 2016

The search for new drug candidates in databases is of paramount importance in pharmaceutical chemistry. The selection of molecular subsets is greatly optimized and much more promising when potential drug-like molecules are detected a priori. In this work, about one hundred thousand molecules are ranked following a new methodology: a drug/non-drug classifier constructed by a consensual set of classification trees. The classification trees arise from the stochastic generation of training sets, which in turn are used to estimate probability factors of test molecules to be drug-like compounds. Molecules were represented by Topological Quantum Similarity Indices and their Graph Theoretical counterparts. The contribution of the present paper consists of presenting an effective ranking method able to improve the probability of finding drug-like substances by using these types of molecular descriptors.

Background: Leishmaniasis is a neglected disease that affects 15 million people around the world. Many limitations are associated to the treatment as high cost and toxicity. Several classes of natural substances with proven leishmanicidal activity were reported in the literature. Phytochemsitry study of Anaxagorea dolichocarpa (Annonacea) reported the isolation of aporphine alkaloids. Methods: In this study, we evaluate the potential activity of the azaphenanthrene alkaloids eupolaramine, imbiline 1, imbiline 4, sampangine, 3-metoxisampangine and 4- metoxisampangine, isolated from A. dolichocarpa, together with a homemade databank of 142 aporphynic alkaloids isolated from Annonaceae, through ligand-based and structurebased virtual screening (VS) against Leishmania donovani. A diverse set selected from CHEMBL databank of 1397 structures, with tested antileishmanial activity against promastigote L. donovani, were classified according pIC50 values in order to generate and validate Random Forest model that show higher statistical indices values. The structure of six different L. donovani enzymes were downloaded from PDB databank and alkaloids structures were submitted to molecular docking. Results: From the six azaphenanthrene alkaloids, sampangine, 3-methoxy, and 4-methoxy were indicated as potential actives by the RF model. Docking results gave similar values for all six azaphenanthrene alkaloids. So, we performed in vitro tests with sampangine, imbiline 1, imbiline 4, and eupolaramine, which are available in our laboratory, and that show significant values of pIC50 (> 5.26). Conclusion: Combined approach of VS allowed us to select that aporphynic alkaloid xyloguyelline as potential multitarget compound for leishmanial treatment, presenting activity against five strategic enzymes to treatment with probability of activity over 60% by RF model.

Background: Plants became the basis of traditional medicine system throughout the world for thousands of years and continue to provide mankind with new remedies. Annona muricata, the plant of Annonaceae family, is also known as sour sop or Graviola. In recent years, many compounds have been reported and have gained organic chemist’s and biochemist’s attention because of their novel structure and wide range of bioactivity. Local populations have used the bark, leaves, roots, fruit, seeds and flowers for thousands of years to treat everything from arthritis to liver problems. Annonaceous acetogenins found only in the Annonaceae family kill malignant cells of 12 different types of cancer including Breast, Ovarian, Colon, Prostate, Liver, Lung, Pancreatic and Lymphoma. Methods: Hence, the study was initiated to identify, fractionate and validate compounds of pharmaceutical importance from the leaf extract. Research was made for the phytochemical screening using different solvents and the metabolites were screened using TLC. Results: The presence of acetogenins was confirmed using PMA 5% spray. The anti-cancer activity studies were done for Breast Cancer cell lines, MCF-7 and the cell inhibition activity was 98%. We were curious to study the computational part of the aetogenins; hence, apart from the experimental studies, various computational studies were progressed using Schrödinger and other computational tools to validate the key target protein and the potent molecule “Coronin” and Annonaine. Conclusion: To predict and decipher the activity of various acetogenins in Annona muricata and its potent activity towards the inhibition of cancer cell lines, it is interesting to look out for the potent lead compound against the disease.

Background: Understanding the physicochemical basis and the different models of nanosystems is nowadays fundamental in a great number of scientific areas and industrial processes. Results: Here, we focus on nanosystems created by self-assembly, molecular or inorganic. The organization of single units at these scales is a challenging matter in light of the inherently small dimensions involved, the sensitivity of the system to small perturbations, and the problem of scaling up such a process for widespread use and implementation. Conclusion: This review examines the different self-assembly routes used to create nanostructures in both the equilibrium and non-equilibrium/dynamic systems and discusses their limits and applications. The connection to biomedicine and pharmaceutical design has been emphasized.

Background: A subset of asthmatics shows refractoriness to Salbutamol owing to ADRB2 gene C→T polymorphism (rs 1800888) that substitutes Thr to Ile at the position 164 in the β2 adrenergic receptor leading to sub-optimal binding of Salbutamol. The present study aims to associate the Salbutamol (200 mcg) refractoriness with the polymorphism and select the best existing agonist with optimal binding affinity against wild and mutated receptor and further identify high affinity compound, irrespectively targeting wild and mutated receptor through virtual screening methods. Methods: Responders to Salbutamol were categorized, if percentage reversibility was greater than or equal to 12% in them, while those showing reversibility less than 12% were non-responders. The genotyping for polymorphism was performed by ARMS PCR method. Established agonists with consistent binding affinity against wild and mutated receptors formed query compound to identify high affinity molecule from Phase database through 7 point pharmacophore based screening. Results: Polymorphism was significantly associated with non-responders (p= < 0.05) demonstrating it as a major factor of Salbutamol refractoriness. Results from Glide Docking showed that Fenoterol had highest affinity for mutated receptor and stood as second best (after Salbutamol) high affinity agonist for wild receptor among the established β2 agonists. Therefore Fenoterol formed a query molecule (7 point pharmacophore) in identification of high affinity compound for virtual screening process. Conclusion: Compound CACPD2011a-0001278239 identified through virtual screening against 4 million compounds in phase database was shown to irrespectively target both wild and mutated β2 adrenergic receptor with high and consistent affinity which was par greater than established β2agonists.

Background: Prostate cancer is the most commonly diagnosed cancer in men. More than 200,000 new cases are added each year in the US, translating to a lifetime risk of 1 in 7 men. Neuroendocrine prostate cancer (NEPC) is an aggressive and treatmentresistant form of prostate cancer. A subset of patients treated with aggressive androgen deprivation therapy (ADT) present with NEPC. Patients with NEPC have a reduced 5-year overall survival rate of 12.6%. Knowledge integration from genetic, epigenetic, biochemical and therapeutic studies suggests NEPC as an indicative mechanism of resistance development to various forms of therapy. Methods: In this perspective, we discuss various experimental, computational and risk prediction methodologies that can be utilized to identify novel therapies against NEPC. We reviewed literature from PubMed and computationally analyzed publicly available genomics data to present different possibilities for developing systems medicine based therapeutic and curative models to understand and target prostate cancer and NEPC. Results: We discuss strategies including gene-set analyses, network analyses, genomics and phenomics aided drug development, microRNA and peptide-based therapeutics, pathway modeling, drug repositioning and cancer immunotherapies. We also discuss the application of cancer risk estimations and mining of electronic medical records to develop personalized risk predictions models for NEPC. Preemptive stratification of patients who are at risk of evolving NEPC phenotypes using predictive models could also help to design and deliver better therapies. Conclusion: Collectively, understanding the mechanism of NEPC evolution from prostate cancer using systems biology approaches would help in devising better treatment strategies and is critical and unmet clinical need.

The formation of liposomes, nanoparticle micelles, and related systems by mixtures of drugs and/or surfactants is of major relevance for the design of drug delivery systems. We can design new systems using different compounds. Traditionally these systems are created by trial and error using experimental data. However, in most cases measuring all the possible combinations represents a extensive work and almost always unaffordable. In this sense, we can use theoretical concepts and develop computational models to predict different physicochemical properties of self-aggregation processes of mixed molecular systems. In a previous work, we developed a new PT-LFER model (Linear Free Energy Relationships, LFER, combined with Perturbation Theory, PT, ideas) for binary systems. The best PT-LFER model found predicted the effects of 25000 perturbations over nine different properties of binary systems. The present work has two parts. Firstly, we carry out an analysis on the new results on the applications and experimental-theoretical studies of binary selfassembled systems. In the second part, we report for the first time, a new experimental-theoretic study of the NaDC-DTAB binary system. For this purpose, we have combined experimental procedures plus physicochemical thermodynamic framework with the PT-LFER model reported in our previous work.

Hepatocellular carcinoma (HCC) is one of the most common types of malignancies worldwide. There is little information on the mechanisms involved in the pathogenesis of this disease. Diagnosis of HCC at early stages would be crucial for increasing the survival of patients. Circulating miRNAs have emerged as one of the most attractive tools for an early diagnosis of cancers. Various studies have shown that there is an aberrant expression of miRNAs such as miR-25, miR-375, miR-206, miR-223, miR- 92a, miR-222, miR-1, let- 7f and miR-21 in HCC. Circulating and tissue miRNAs have also key roles in the pathogenesis of HCC by affecting several biologically important pathways such as p53, p21, PTEN, PI3K-AKT, c-Myc and STAT3. In this review, we summarize the current knowledge on the role of miRNAs in diagnosis, prognosis, and treatment of HCC.

The snake is the symbol of medicine due to its association with Asclepius, the Greek God of medicine, and so with good reasons. More than 725 species of venomous snakes have toxins specifically evolved to exert potent bioactivity in prey or victims, and snakebites constitute a public health hazard of high impact in Asia, Africa, Latin America, and parts of Oceania. Parenteral administration of antivenoms is the mainstay in snakebite envenoming therapy. However, despite well-demonstrated efficacy and safety of many antivenoms worldwide, they are still being produced by traditional animal immunization procedures, and therefore present a number of drawbacks. Technological advances within biopharmaceutical development and medicinal chemistry could pave the way for rational drug design approaches against snake toxins. This could minimize the use of animals and bring forward more effective therapies for snakebite envenomings. In this review, current stateof- the-art in biopharmaceutical antitoxin development is presented together with an overview of available bioinformatics and structural data on snake venom toxins. This growing body of scientific and technological tools could define the basis for introducing a rational drug design approach into the field of snakebite envenoming therapy.

Background: Hemolytic Uremic Syndrome (HUS) caused by infections with Shiga toxin (Stx)-producing E. coli is a life-threatening complication characterized by acute renal failure, thrombocytopenia and hemolytic anemia. Stx is the main pathogenic factor. Therefore, the mouse model by intravenous administration of a single lethal dose of Stx is often used to explore its pathogenic mechanisms. Objective: The aim of this work was to develop an alternative mouse model of Stx type 2 (Stx2) intoxication to evaluate new therapeutic strategies. Methods and Results: One lethal dose of Stx2 was divided in four daily doses. We observed a dose-dependent toxicity characterized by neutrophilia, leukocytopenia and renal damage. Most importantly, we demonstrated that the polyclonal anti-Stx2 serum was able to protect mice from fatal evolution even when administered together the third dose of Stx2. Conclusion: This model would provide an advantage for evaluation of therapeutic strategies. Furthermore, the results presented herein suggest that appropriate treatment with anti-Stx2 agents following the appearance of initial clinical signs may block the ongoing outcome or may alleviate disease in patients who have just been diagnosed with HUS. However, the delay in the onset of therapy would be unsafe.

Lung cancer (LC) remains the leading cause of cancer mortality worldwide, and non-small cell LC (NSCLC) represents 80% of all LC. Oxidative stress and inflammation, autophagy, ubiquitin-proteasome system, nuclear factor (NF)-ΚB, and mitogen activated protein kinases (MAPK) participate in LC pathophysiology. Currently available treatment for LC is limited and in vivo models are lacking. We hypothesized that antioxidants and NF- ΚB, MAPK, and proteasome inhibitors may exert an antitumoral response through attenuation of several key biological mechanisms that promote tumorigenesis and cancer cell growth. Body and tumor weights, oxidative stress, antioxidants, inflammation, NF-ΚB p65 expression, fibulins, apoptosis, autophagy, tumor and stroma histology were evaluated in the subcutaneous tumor of LC (LP07 adenocarcinoma) BALB/c mice, with and without concomitant treatment with NF-ΚB (sulfasalazine), MEK (U0126), and proteasome (bortezomib) inhibitors, and N-acetyl cysteine (NAC). Compared to LC control mice, in subcutanous tumors, the four pharmacological agents reduced oxidative stress markers and tumor proliferation (ki-67). Inflammation and NF-ΚB p65 expression were attenuated by NF-ΚB and MAPK inhibitors, and the latter also enhanced apoptotic markers. Catalase was induced by the three inhibitors, while bortezomib also promoted superoxide dismutase expression. NF-ΚB and MEK inhibitors significantly reduced tumor burden through several biological mechanisms that favored tumor degradation and attenuated tumor proliferation. These two pharmacological agents may enhance the anti-tumor activity of selectively targeted therapeutic strategies for LC. Proteasomal inhibition using bortezomib rather promotes tumor degradation, while treatment with antioxidants cannot be recommended. This experimental model supports the use of adjuvant drugs for the improvement of LC treatment.