Current Drug Discovery Technologies (v.13, #4)

Meet Our Editorial Board Member by Julianna Kardos (188-188).

GENIUS In Silico Screening Technology for HCV Drug Discovery by Vaishali M. Patil, Neeraj Masand, Satya P. Gupta (189-198).
The various reported in silico screening protocols such as molecular docking are associated with various drawbacks as well as benefits. In molecular docking, on interaction with ligand, the protein or receptor molecule gets activated by adopting conformational changes. These conformational changes cannot be utilized to predict the 3D structure of a protein-ligand complex from unbound protein conformations rigid docking, which necessitates the demand for understanding protein flexibility. Therefore, efficiency and accuracy of docking should be achieved and various available/developed protocols may be adopted. One such protocol is GENIUS induced-fit docking and it is used effectively for the development of anti-HCV NS3-4A serine protease inhibitors. The present review elaborates the GENIUS docking protocol along with its benefits and drawbacks.

Phytochemistry and Phytotherapeutic Aspects of Elaeagnus angustifolia L. by Farid Niknam, Amir Azadi, Alireza Barzegar, Pouya Faridi, Nader Tanideh, Mohammad M. Zarshenas (199-210).
Persian or Russian olive (Elaeagnus angustifolia L.), from the family Elaeagnaceae, is a nitrogen-fixing thorny shrub extensively used in traditional medicine to alleviate pain and treat rheumatoid arthritis, osteoarthritis, gastrointestinal problems, fever, and asthma. The current review has tried to give a concise overview of the phytochemistry and pharmacological properties of the plant from published data. Accordingly, published English literatures on Elaeagnus angustifolia were gathered from popular databases such as PubMed, Scopus, Web of Science, and ScienceDirect up to 31 December 2015. In accordance with traditional and ethnopharmacological uses, different extracts of E. angustifolia have been known for their antioxidant, anti-inflammatory, anti-nociceptive, antimicrobial, muscle relaxant, anti-ulcer and wound-healing capabilities. Additionally, cardioprotective, antitumor and anti-mutagenic effects of the herb have been demonstrated by current assessments. Despite numerous experimental studies, there is a notable lack of profound and comprehensive clinical trials as well as critical research on E. angustifolia toxicity and teratogenicity. With reference to various pharmacological effects based on experimental and animal investigation, it is worthy to mention a frame for respective clinical studies in further studies.

Advances in Drug Discovery: Impact of Genomics and Role of Analytical Instrumentation by Ambavaram V.B. Reddy, Zulkifli Yusop, Jafariah Jaafar, Vemula Madhavi, Gajulapalle Madhavi (211-224).
Drug discovery is a highly complicated, tedious and potentially rewarding approach associated with great risk. Pharmaceutical companies literally spend millions of dollars to produce a single successful drug. The drug discovery process also need strict compliance to the directions on manufacturing and testing of new drug standards before their release into market. All these regulations created the necessity to develop advanced approaches in drug discovery. The contributions of advanced technologies including high resolution analytical instruments, 3-D biological printing, next-generation sequencing and bioinformatics have made positive impact on drug discovery & development. Fortunately, all these advanced technologies are evolving at the right time when new issues are rising in drug development process. In the present review, we have discussed the role of genomics and advanced analytical techniques in drug discovery. Further, we have also discussed the significant advances in drug discovery as case studies.

Peptides for Anti-Ebolavirus Vaccines by Darja Kanduc (225-231).
Background: Two main factors can affect the development of ebolavirus immunotherapeutics: the vast peptide commonality between ebolavirus and human proteins, and the high rate of spontaneous mutation of ebolavirus within its human host. Indeed, the viral versus human peptide overlap may represent a relevant source of autoimmune crossreactions following vaccination, while ebolavirus genome mutations can limit and/or nullify a vaccine response.
Methods: Aiming at defining safe and effective peptide-based vaccines to fight ebola disease, this study analyzed a recently described Ebola virus isolate (Hoenen et al., Emerging Infect Dis 2016, 22, 331) for sequences not shared with the human proteome and conserved among ebolaviruses.
Results: Using the pentapeptide as a minimal immune determinant, it was found that: 1) only 6.6% of the 4865 pentapeptides present in the Ebola virus isolate proteins are unique to the virus; 2) only 55 of the unique viral pentapeptides are conserved among 251 proteomes derived from the four ebolavirus species that may affect humans; and 3) none of the unique peptide signatures that mark Ebola virus isolate glycoprotein are 100% conserved.
Conclusions: The present findings pose the basis for the construction of viral polypeptide antigens able to induce non-crossreactive, specific and broadly protective immune responses against ebolavirus, and warn against immune therapeutic/preventive approaches exclusively focused on glycoprotein epitope(s).

Background: Alzheimer's disease is the most common form of dementia, characterized by loss of neurons and synapses in the cerebral cortex and certain subcortical regions, leading to altered and unsuitable activities. In this study, we focused on the influence of rndom selection (RS) and SOMs (self-organizing maps) data splitting on the external predictivity of quantitative structure-property relationship (QSPR) models of some N-ary derivatives as butyrylcholinesterase (BChE) inhibitors. A QSPR model relates molecular descriptors to a chemical property can save time and money in drug discovery and development. Model validation is a critical step in QSPR model generation; for this purpose, it is necessary to carry out the data splitting on original data set. The GA is very useful for finding global minima for high dimensionality of the problem (e.g. variable selection in QSPR) when response surface has many local optima.
Methods: The molecular structures and experimental values for inhibition constants of BChE catalytic activities obtained from the literature. In this study, total number of 88 compounds was divided into training and test set by means of RS and SOM methods. The Chem3D module was used in order to create the 3D structures of compounds; geometry optimization, using the Polak-Ribiere algorithm. Using Dragon package over 1145 molecular descriptors such as 3D-MoRSE, GETAWAY and WHIM descriptors were derived to characterize the structures of ChEs inhibitors derivatives, properly. The constant variables, variables which have low correlation with response and collinear descriptors were omitted, and the number of descriptors was reduced to 422 in the data set. The QSAR models were constructed using stepwise-MLR and GA-MLR.
Results: The best MLR models with four, five and six variables were built to obtain the best QSAR model. The best multivariate linear models in both stepwise-MLR and GA-MLR methods had five variables. The best significant relationships, using comparison of Q2 of models, for logki values of BChE catalytic activity inhibition in the models obtained in S-MLR and GA-MLR methods are presented for all of the random sets and SOM set.
Conclusion: The results of this study showed that a GA-MLR generally performs better than stepwise-MLR. The five variable models were chosen as the best models after evaluating the other models in both GA-MLR and S-MLR methods. The Q2 results indicate that the test set consists of compounds that are evenly distributed within the chemical space; hence, in QSPR modeling, rational splitting methods such as SOM rather than random selection should be used. Also, the Q2 comparison of GA-MLR and stepwise-MLR methods highlights the power of GA-MLR for feature selection. According to the interpretation of QSPR descriptors indicates that, QSAR equation can be useful in designing new N-aryl derivatives as butyrylcholinesterase inhibitors compounds with improved inhibition catalytic activity

Background: Curcumin has been shown to possess strong cytotoxic effect against various cancer cell lines. However, curcumin has not applied as a drug for treatment of cancer yet due to low solubility in water and low bioavailability. The aims of this study were to prepare a new polyethylene glycol (PEG) conjugated curcumin and to evaluate its antitumor activity in vitro.
Methods: PEG-CUR was prepared by the reaction between curcumin and PEG. PEG-CUR which was characterized by SEM, TEM, FTIR, DSC and 1H NMR analysis. The physicochemical parameters of PEG-CUR such as zeta potential, size distribution, solubility and percentage of curcumin were also investigated.
Results: Our results showed that the percentage of curcumin in PEG-CUR was 13.26 ± 1.25 %. PEG-CUR has nanosize values of 96.3 nm and the zeta potential values of - 48.4 mV. The PEG-CUR showed significantly increasing curcumin's solubility in water and another medium such as in 0,1 N HCl, phosphate buffer pH 4.5 and pH 6.8 solution and n-octanol. Our data also have shown cytotoxicity effect of PEG-CUR was much greater than curcumin-free in two different HepG2 and HCT116 cancer cell lines.
Conclusion: It could be concluded from our results that the PEG-CUR may be a potential candidate for cancer treatment. Further studies are needed to evaluate the antitumor efficacy of PEG-CUR in vivo.