In this work, we developed a general Perturbation Theory model for prediction of B-cell epitopes in vaccine design. The method predicts the epitope activity ε_q(c_qj) of one query peptide (q-peptide) in a set of experimental query conditions (c_qj). The model proposed here is able to classify 1,048,190 pairs of query and reference peptide sequences reported on IEDB database with perturbations in sequence or assay conditions. The model has accuracy, sensitivity, and specificity between 71% and 80% for training and external validation series. The model may become a useful tool for epitope selection towards vaccine design. The theoretic-experimental results on Bm86 protein may help on the future design of a new vaccine based on this protein. Ref: J Proteome Res. 2017 Sep 18. doi: 10.1021/acs.jproteome.7b00477. 

Inga multinervis, a little-known species, is being used in agroforestry systems for nitrogen fixation and soil improvement. The aim of this research was to characterize the water relations of the species I. multinervis from pressure–volume measurements. The results indicated that the species has the capacity for osmotic and elastic adjustment, given to the low solute potentials and elasticity of the cell walls, thus its use is recommended in degraded forest systems with low water levels in the soil.

The ultimate goal of medicinal chemistry is to find most effective ways to treat various diseases and extend human beings’ life as long as possible. In fact, Medicinal chemistry is currently undergoing an unprecedented revolution. Accompanied with such a revolution is the emergence of many new concepts, terminologies, approaches, and techniques. In a recent review, Chou discuss these processes from several different aspects. Ref: Current Topics in Medicinal Chemistry, 2017, 17, 2337-2358. See: http://www.eurekaselect.com/151622/article

In this paper we generalized QSAR models to predict the biological activity of antifungal drugs against 87 fungi species. The data was processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active. The model correctly classifies 338 out of 368 active compounds (91.85%) and 89 out of 123 non-active compounds (72.36%). Overall training predictability was 86.97% (427 out of 491 compounds). Validation of the model was carried out by means of Leave-Species-Out (LSO) procedure. After elimination step-by-step of all drugs tested against one specific species we record the percentage of good classification of leave-out compounds (LSO-predictability).

Mansoa Alliacea is a native plant from Amazonian basin and has great ancestral value for the local communities. M. alliacea is a traditional medicine for healers and shamans and has multiple uses due to the presence of several chemical constituents with important pharmacological properties. Plant derivate are used as: antiseptic, diuretic, analgesic, antipyretic. Folk medicine is related also to the treatment of many diseases such as: reduction of blood pressure against atherosclerosis, arthritis and rheumatism. Researches have also been proven an appreciable antioxidant property, which revalue it for cosmetic purposes.  Chemical composition includes: alidil-sulfoxide, allylindyloxide, allina, allein, allicin, disulfide, propylallyl, divinyl sulfide, diallyl sulfide, dimethyl sulfide, daucosterol, beta-sitosterol, fucosterol, stigmasterol, iridoides and isothiocyanates, naphthoquinones, alkaloids, saponins, flavones, vitamin E, vitamin C and minerals such as chromium and selenium. The present review includes ethnobotanical and pharmacological data that are related to the chemical composition of M. alliacea species.

Protein c-Myb is a therapeutic target. Some sesquiterpene lactones suppress Myb-dependent gene expression, which results in a mechanism for their potential anti-cancer activity. Database ChEMBL is representative of lactones for physicochemical and physiochemical properties. Data presented for 31 natural lactones are discussed in terms of quantitative structure–activity relationships, which objective is to predict inhibitors of Myb-induced gene expression. Several constitutional descriptors are related to structure–activity. a‑Methylene‑g‑lactone groups enhance while OH functions worsen potency. The latter feature is in agreement  with the fact that the more lipophilic the lactone, the greater the cytotoxicity because of the ability to cross lipoidal biomembranes. In general, numbers of p-systems and atoms, and polarizability enhance activity. Linear and nonlinear structure–activity models are developed, between lactones of a great structural diversity, to predict inhibitors of Myb-induced gene expression. Four variables (ML, UNC, TCO+OCOR, UNC+UNA) related to ATOM show a positive correlation because of the partial anionic and H-acceptor characters of O-atom. In most, CO group is conjugated. Term OH shows negative coefficients because of the partial cationic quality of H-atom and because OH forms H-bonds with CO, causing them to be less H-acceptor. s‑trans‑s‑trans‑Germacranolide structure is the most active. Coefficients standard errors result acceptable in almost all equations. After cross-validation, linear equations for lactones, pseudoguaianolides and germacranolides are the most predictive. Most descriptors are constitutional variables.

Quantitative structure-activity relationship (QSAR) modeling has travelled a long journey in drug discovery process as well as in prediction of property and/or toxicity data of diverse chemicals in order to fill the data gaps. The goodness-of-fit and quality of a  model and its prediction capability for untested compounds are assessed through diverse validation metrics. There is a constant endeavor among QSAR researchers to get better the quality of predictions for lowering the predicted residuals for external compounds. The objective of the present study has been to improve the prediction quality for external compounds with implication of “intelligent” consensus modeling approach. Three different forms of consensus models were developed for six different datasets to explore their prediction capability on query chemicals. The types are average of predictions from all qualifying individual models (CM1), weighted average predictions from all qualifying individual models (CM2), and best selection of predictions (compound-wise) from individual models (CM3). Among three consensus models, newer strategies like CM2 and CM3 are evolved as the “winners” considering prediction errors of query compounds for the studied six data sets irrespective of diverse responses, number of data points as well as dissimilar modeling algorithm. We have also developed a tool named “Intelligent Consensus Predictor” which is freely accessible via the web http://teqip.jdvu.ac.in/QSAR_Tools/ and http://dtclab.webs.com/software-tools. The details of this work have been presented in Conferentia Chemometrica http://cc2017.ttk.mta.hu/ in Hungary during September 3-6, 2017.

References

1. Dearden JC. The history and development of quantitative structure-activity relationships (QSARs). IJQSPR. 2016;1(1):1–44.
2. Roy K, Kar S, Das RN. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic press. 2015.
3. http://teqip.jdvu.ac.in/QSAR_Tools/
4. http://dtclab.webs.com/software-tools
5. Roy K, Das RN, Ambure P, Aher RB. Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom Intell Lab Syst. 2016;152:18-33.
6. Roy K, Ambure P, Kar S, Ojha PK, Is it possible to improve the quality of predictions from an intelligent” use of multiple QSAR/QSPR/QSTR models? J Chemom 2017 (Submitted)

In the present work the fruits availability that contribute to a healthy diet in the markets of the city of the Puyo, that affects the low products production with value added are calculated. In the literature consulted, there are authors who identify the availability indicator for sale as basic for the competitiveness of agrifood chains, as well as the need of high nutritional value products for better health in consumers. Interviews were conducted to identify the ancestral knowledge about the fruits and plants use and their availability in inventories for the client, as well as on the presence of basic market elements, define the dimensions of competitiveness in customer, economic, technical, market, medium environment, social and financial. A sample of nine markets in the city of puyo was made, the fundamental characteristic being that they are the largest markets of the city, although they cannot be classified according to the international classification. 140 products were studied in the Puyo city markets. The results showed the products availability per family, where the highest value with 40% is represented by banana and cassava, 16% vegetables, 18% fruits, 17% grains, 16% processed non-meat products and 16 % processed meat products. It is concluded that there is a lack of Amazonian products in the markets of the city of Puyo due to the low production of tangibles typical of the area.

Leishmaniasis is a neglected tropical disease responsible for the ninth largest disease burden in the world. Excipients are necessary for ensuring the stability and bioavailability of currently available antileishmaniasis drugs. In a recent work, we have evaluated the in vitro activity of 30 commercially available excipients against different Leishmania spp., their cytotoxicity and potential use for inclusion in novel formulations. Ref: Curr Top Med Chem. 2017 Jul 19. https://www.ncbi.nlm.nih.gov/pubmed/28730958

Dengue, zika and chikungunya have severe public health concerns in several countries. Human modification of the natural environment continues to create habitats in which mosquitoes, vectors of a wide variety of human and animal pathogens, thrive which can bring about enormous negative impact on public health if not controlled properly. Quantitative Structure–Activity Relationship (QSAR) modeling was applied in this work with the aim to explore features contributing to promising larvicidal and insecticidal property against the vector Aedes aegypti (Diptera:Culicidae). A dataset of 62 plant derived compounds obtained from the previous literatures was used in this present study where Genetic Algorithm (GA) was used for model development employing Double Cross Validation (DCV) tool. Simple topological descriptors like Extended Topochemical Atom (ETA) indices developed by the present authors’ group were used for model development. A number of models were generated by the GA method and the descriptors obtained were pooled for Best Subset Selection method (BSS). Further, the best model obtained from BSS was used for Partial Least Square (PLS) regression to obtain the final model. The model was validated extensively using different validation metrics to check the robustness and predictivity of the model for regulatory acceptance and enhancing confidence in QSAR predictions. Based on the insights obtained from the PLS model, we can conclude that presence of hydrogen bond acceptor atoms, presence of multiple bonds as well as sufficient lipophilicity and limited polar surface area play crucial roles in regulating the activity of the compounds.