Human P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter that confers resistance to a wide range of chemotherapeutic agents in cancer cells by active efflux of the drugs from cells. P-gp also plays a key role in limiting oral absorption and brain penetration and in facilitating biliary and renal elimination of structurally diverse drugs. Thus, identification of drugs or new molecular entities to be P-gp substrates is of vital importance for predicting the pharmacokinetics, efficacy, safety, or tissue levels of drugs or drug candidates. At present, publicly available, reliable in silico models predicting P-gp substrates are scarce. In this study, a support vector machine (SVM) method was developed to predict P-gp substrates and P-gp-substrate interactions, based on a training data set of 197 known P-gp substrates and non-substrates collected from the literature. We showed that the SVM method had a prediction accuracy of approximately 80% on an independent external validation data set of 32 compounds. A homology model of human P-gp based on the X-ray structure of mouse P-gp as a template has been constructed. We showed that molecular docking to the P-gp structures successfully predicted the geometry of P-gp-ligand complexes. Our SVM prediction and the molecular docking methods have been integrated into a free web server (http://pgp.althotas.com), which allows the users to predict whether a given compound is a P-gp substrate and how it binds to and interacts with P-gp. Utilization of such a web server may prove valuable for both rational drug design and screening.
Human serum albumin (HSA), the most abundant plasma protein is well known for its extraordinary binding capacity for both endogenous and exogenous substances, including a wide range of drugs. Interaction with the two principal binding sites of HSA in subdomain IIA (site 1) and in subdomain IIIA (site 2) controls the free, active concentration of a drug, provides a reservoir for a long duration of action and ultimately affects the ADME (absorption, distribution, metabolism, and excretion) profile. Due to the continuous demand to investigate HSA binding properties of novel drugs, drug candidates and drug-like compounds, a support vector machine (SVM) model was developed that efficiently predicts albumin binding. Our SVM model was integrated to a free, web-based prediction platform (http://albumin.althotas.com). Automated molecular docking calculations for prediction of complex geometry are also integrated into the web service. The platform enables the users (i) to predict if albumin binds the query ligand, (ii) to determine the probable ligand binding site (site 1 or site 2), (iii) to select the albumin X-ray structure which is complexed with the most similar ligand and (iv) to calculate complex geometry using molecular docking calculations. Our SVM model and the potential offered by the combined use of in silico calculation methods and experimental binding data is illustrated.
Cyclodextrins are cyclic oligosaccharides that are able to form water-soluble inclusion complexes with small molecules. Because of their complexing ability, they are widely applied in food, pharmaceutical and chemical industries. In this paper we describe the development of a free web-service, Cyclodextrin KnowledgeBase: ( http://www.cyclodextrin.net ). The database contains four modules: the Publication, Interaction, Chirality and Analysis Modules. In the Publication Module, almost 50,000 publication details are collected that can be retrieved by text search. In the Interaction and Chirality Modules relevant literature data on cyclodextrin complexation and chiral recognition are collected that can be retrieved by both text and structural searches. Moreover, in the Analysis Module, the geometries of small molecule-cyclodextrin complexes can be predicted using molecular docking tools in order to explore the structures and interaction energies of the inclusion complexes. Complex geometry prediction is made possible by the built-in database of 95 cyclodextrin derivatives, where the 3D structures as well as the partial charges are calculated and stored for further utilization. The use of the database is demonstrated by several examples.
Uncaria gambir (Ug) is the main ingredient for producing Gambir which is an international trading commodity that Indonesia has shared its production of 80 % in the world. This paper investigates the type of Ug cultivation system in West Sumatra and its contribution to farmers income security. Rapid rural appraisal was used for collecting data. Economic analysis is carried out consisting of Benefit and Cost ratio (B/C Ratio), net present value (NPV), internal rate of return (IRR), sensitivity test on the discount rate and Gambir production. Six Ug cultivation systems were found, namely Ug-Mono, Ug-Rubber, and Ug-Areca nut in Lima Puluh Kota regency (LPKR) and in Pesisir Selatan regency (PSR) Ug-Durian, Ug-Durian-Jengkol and Ug-Durian-Petai. In general, The Ug cultivation systems combined with Durian and Jengkol or Petai, that were found valuable additional crops, were more stable in income generation against to the fluctuation of Ug production and Gambir price. Among the six, the highest B/C Ratio was found in Ug-Durian-Jengkol (2.8) while the lowest was in Ug-Mono and Ug-Rubber (1.9). Moreover, Ug-Durian-Jengkol show better NPV and IRR in the most conditions of Gambir price from 10,000 to100,000 Rp kg 1 as well as Gambir production from 2,400 to 4,800 kg y 1 . On the other hand, NPV and IRR of Ug-Mono, -Rubber or -Areca nut systems sharply decreased with the decrease of Gambir price. These systems relied more on Ug production and Gambir price in the income generation. It exhibited the vulnerability of income structure of these systems. From the results, to secure farmers income from volatility of Ug production and Gambir price, this research suggested Ug cultivation systems combining with durian or other profitable cash crops in West Sumatra.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.