Chemoinformatics Models for Pharmaceutical Design, Part 2
Current Pharmaceutical Design, 2016, Vol. 22, No. 34
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Editorial Chemoinformatics Models for Pharmaceutical Design, Part 2 Anuraj Nayarisseri1, Danail Bonchev2, Subhash C. Basak3and Humberto González-Díaz4,5 1
Bioinformatics Research Laboratory, Eminent Biosciences, Vijaynagar, Indore 452010, India; 2Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, United States of América; 3Natural Resources Research Institute and Department of Chemistry & Biochemistry, University of Minnesota, Duluth, Duluth MN 55811 USA; 4Department of Organic Chemistry II, Faculty of Science and Technology, University of the Basque Country UPV/EHU, 48080, Leioa, Spain; 5IKERBASQUE, Basque Foundation for Science,48011, Bilbao, Spain We are glad to present the second part of the special issue of current pharmaceutical design. This issue emphasizes advancements on Cheminformatics, Complex NetAnuraj Nayarisseri works, and Data Science Models for Pharmaceutical Design. Both the special issues form an innovative collection of in-depth review papers shedding light on Quantitative Structure-Activity/Property Relationship(QSAR/QSPR) models, Perturbation theory, Markov chains theory, Graph and Complex Networks theory, Molecular Mechanics and Molecular Dynamics (MM/MD) calculations etc. The first issue- part 1, includes 8 research reviews while the part 2 includes (the present issue) 7 articles. Second part of the issue opens with investigation on discrimination of drug-like compounds by partition trees with quantum similarity indices and graph invariants proposed by de Julián-Ortiz, Gozalbes, and Besalú [1]. The search for new drug candidates in databases has always been 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 promising investigation, about one hundred thousand molecules were ranked following a latest methodology which involves construction of a drug/nondrug classifier by a consensual set of classification trees. The classification trees arise from the stochastic generation of training sets, which in turn can be used to estimate probability factors of test molecules to be drug-like compounds. Authors represent molecules by Topological Quantum Similarity Indices and their Graph Theoretical counterparts. The contribution of the this review consists of presenting an effective ranking method able to improve the probability of finding drug-like substances by using these types of molecular descriptors. In the second research review, Lorenzo et al. [2] focused on Structure- and ligand-based approaches to evaluate aporphynic alkaloids from Annonaceae as multi-target agent against Leishmaniadonovani. Leishmaniasis is a neglected disease that affects 15 million people around the world. Numerouslimitations are associated to the treatment as high cost and toxicity. In this review the author’s central theme lies in combined approach of VS allow to select aporphynic alkaloid xyloguyelline as potential multitarget compound for leishmanial treatment and presenting activity against five strategic enzymes for treatment. Prabhakaran et al. [3] in the third research article, discuss the study of biochemical and cytotoxic activity of polyketide natural products with computational and Bio-Programming methods. In recent years, many compounds have been reported and have gained attention of a biochemist because of their novel structure and wide range of bioactivity. Annonaceousacetogenins 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. Hence, the authors used experimental and computational methods to identify, fractionate and validate compounds of pharmaceutical Importance from the Annonaceous leaf extract for the possibility of tumour treatment. In the fourth article, Messina, Rial, and Ruso [4] reviewed computational models for self-assembly of nanoscale systems with biomedical applications. Understanding the physicochemical basis and the different models of nanosystems is nowadays fundamental in a great number or scientific areas and industrial processes. Here they focus on nanosystems created by self-assembly. 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. 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. Traditionally these systems are created by trial and error using experimental data. However, in most cases measure all the possible combinations represents an extensive work and almost always unaffordable. In the given setback, authors presenthere the theoretical concepts based on binary selfassembled systems and NaDC-DTAB binary system to predict different physicochemical properties of self-aggregation processes of mixed molecularsystems. Bandaru et al. [5] in the fifth article delve in identification of small molecule as a high affinity β2 agonist promiscuously targeting wild and mutated (thr164Ile) β2 adrenergic receptor in the treatment of bronchial asthma. They found the rationale behind the ineffective binding of Salbutamol in the mutated Thr164Ile receptor. In addition, here they present a novel molecule bestowed to target both wild and mutated beta 2 adrenergic receptor with similar affinity, thereby promises to overcome refractory problems observed in the patients with poor response to Salbutamol. In addition, the compound proposed can act as a “wide spectrum” beta 2 agonist which has a potential to target wild and mutated receptor indiscriminately.
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© 2016 Bentham Science Publishers
5178 Current Pharmaceutical Design, 2016, Vol. 22, No. 34
Chemoinformatics Models for Pharmaceutical Design, Part 2
Yadav et al. [6] in the sixth article focused on the Systems Medicine approaches to improve understanding, treatment, and clinical management of Neuroendocrine Prostate Cancer (NEPC). Prostate cancer is emerging as 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. The present study includes gene-set analyses, network analyses, genomics and phenomics aided drug development, microRNA and peptide-based therapeutics, pathway modeling, drug repositioning and cancer immunotherapies. The authors also discuss in this article about the application of cancer risk estimations and mining of electronic medical records to develop personalized risk predictions models for NEPC. In the last article, Messina et al. [7], investigates the formation of nanoparticles of miscelles. 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. Traditionally, these systems are created by trial and error using experimental data. However, in most cases to measure all the possible combinations is a daunting task and almost always unaffordable. The authors present the investigation in two parts. First, they carried out an analysis on the new results on the applications andexperimental-theoretical studies of binary self-assembled systems. In the second, they reportfor the first time, a new experimental theoretical study of the NaDC-DTAB binary system. For this purpose, they have combined experimental procedures plus physicochemical thermodynamic framework with the PT-LFER model. REFERENCES [1] [2] [3] [4] [5] [6] [7]
de Julián-Ortiz JV, Gozalbes R, Besalú E. Discriminating drug-like compounds by partition trees with quantum similarity indices and graph invariants. Curr Pharm Des 2016; 22(34): 5179-95. Lorenzo VP, CarneiroLúcio ASS, Scotti L, et al. Structure- and ligand-based approaches to evaluate aporphynic alkaloids from Annonaceae as multitarget agent against leishmaniadonovani. Curr Pharm Des 2016; 22(34): 5196-203. Prabhakaran K, Ramasamy G, Doraisamy U, Mannu J, Rajamani K, RajamaniMurugesan J. Polyketide natural products, acetogenins from Graviola (Annonamuricata L), its biochemical, cytotoxic activity and various analyses through computational and bio-programming methods. Curr Pharm Des 2016; 22(34): 5204-10. Messina PV, Rial R, Ruso JM. Models for Self-assembly of nanoscale systems with biomedical applications. Curr Pharm Des 2016; 22(34): 5211-20. Bandaru S, Alvala M, Akka J, et al. Mundluru. Identification of small molecule as a high affinity β 2 agonist promiscuously targeting wild and mutated (Thr164Ile)β2 adrenergic receptor in the treatment of bronchial asthma. Curr Pharm Des 2016; 22(34): 5221-33. Yadav KK, Khader S, Readhead B, et al. Systems Medicine approaches to improving understanding, treatment, and clinical management of neuroendocrine prostate cancer. Curr Pharm Des 2016; 22(34): 5234-48. Messina PV, Besada-Porto JM, Rial R, González-Díaz H, Ruso JM. Computational modeling and experimental facts of mixed self-assembly systems. Curr Pharm Des 2016; 22(34): 5249-56.
Subhash C. Basak
Anuraj Nayarisseri
Natural Resources Research Institute and
Bioinformatics Research Laboratory,
Department of Chemistry & Biochemistry,
Eminent Biosciences, Vijaynagar, Indore - 452010, India.
University of Minnesota Duluth, Duluth MN 55811 USA.
E-mail:
[email protected]
E-mail:
[email protected]
Humberto González-Díaz, IKERBASQUE
Danail Bonchev
(1) Department of Organic Chemistry II, Faculty of Science and Technology,
Center for the Study of Biological Complexity,
University of the Basque Country UPV/EHU, 48080, Leioa, Spain.
Virginia Commonwealth University, Richmond,
(2) IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain,
Virginia, United States of America.
E-mail:
[email protected]
E-mail:
[email protected]
