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Knotted peptides present a wealth of structurally diverse, biologically active molecules, with the inhibitor cystine knot/knottin class among the most ecologically common ones. Many of these natural products interact with extracellular targets such as voltage-gated ion channels with exquisite selectivity and potency, making them intriguing therapeutic modalities. Such compounds are often produced in low concentrations by intractable organisms, making structural and biological characterization challenging, which is frequently overcome by various expression strategies. Here, we sought to test a biosynthetic route for the expression and study of knotted peptides. We screened expression constructs for a biosynthesized knotted peptide to determine the most influential parameters for successful disulfide folding and used NMR spectroscopic fingerprinting to validate topological structures. We performed pharmacokinetic characterization, which indicated that the interlocking disulfide structure minimizes liabilities of linear peptide sequences, and propose a mechanism by which knotted peptides are cleared. We then developed an assay to monitor solution folding in real time, providing a strategy for studying the folding process during maturation, which provided direct evidence for the importance of backbone organization as the driving force for topology formation.

User influence is an important topic of research for online social networks. Recent work has shown that a user's influence is not directly related with node in-degree. However, the definition of what is an influential or relevant user is still an open subject. In general, we can say that an influential user has the ability to produce incoming activity in an interaction graph. For this reason we have focused our attention on the user's incoming activity and the search for which factors are related with this indicator. We have studied a Facebook dataset and have found that outgoing activity is highly correlated with incoming activity. This characteristic is valid not only for users with a low level of activity, but it is also valid for high activity users. This result hasn't been reported before and appears to be an important factor of user behavior. To contrast this finding, we have compared it against a popular e-mail dataset and a Twitter dataset. The result was that we found a similar behavior for the Twitter Dataset, but for the e-mail dataset the correlation was lower. Hence, we conjecture that the high correlation may be a distinctive property of social networks. In our future work, we propose to extend this study to other social network platforms.

Advances in the design of permeable peptides and in the synthesis of large arrays of macrocyclic peptides with diverse amino acids have evolved on parallel but independent tracks. Less precedent combines their respective attributes, thereby limiting the potential to identify permeable peptide ligands for key targets. Herein, we present novel 6-, 7-, and 8-mer cyclic peptides (MW 774-1076 g·mol-1) with passive permeability and oral exposure that feature the amino acids and thioether ring-closing common to large array formats, including DNA- and RNA-templated synthesis. Each oral peptide herein, selected from virtual libraries of partially N-methylated peptides using in silico methods, reflects the subset consistent with low energy conformations, low desolvation penalties, and passive permeability. We envision that, by retaining the backbone N-methylation pattern and consequent bias toward permeability, one can generate large peptide arrays with sufficient side chain diversity to identify permeability-biased ligands to a variety of protein targets.

Elevated LDL-C is a major risk factor for atherosclerotic cardiovascular disease, the leading cause of death worldwide. Proprotein convertase subtilisin/kexin type 9 (PCSK9) has pronounced effects on LDL-C levels via its modulation of hepatic LDL receptors (LDLR), the main pathway for cholesterol removal from the circulation. The epidermal growth factor precursor homology domain A (EGF-A) of the LDLR serves as a primary contact with PCSK9 via a flat interface, presenting a challenge for identifying small molecule PCSK9-LDLR disruptors. We employed an affinity-based screen of 10 13 in vitro -translated macrocyclic peptides to identify high affinity PCSK9 ligands that utilize a novel, induced-fit pocket and partially disrupt the PCSK9-LDLR interaction (Hit 1 - PCSK9-LDLR FRET, IC 50 2 nM, A max 41%). Structure-based design led to 13 PCSK9i , a molecule with enhanced in vitro function (PCSK9-LDLR FRET, IC 50 2 nM, A max 78%) and pharmacokinetic properties suitable for in vivo evaluation. To determine if 13 PCSK9i ’s functional activity in vitro would translate in vivo , C57BL/6 mice were dosed with vehicle or 13 PCSK9i via subcutaneous injection twice-daily for 3 days, and blood and liver were collected for the measurement of plasma total cholesterol (TC) and hepatic LDLR density. A PCSK9 antibody was included as a positive control. As illustrated in the figure, 13 PCSK9i increased hepatic LDLR density in a dose-dependent manner. Despite being 1/100 th its size, 13 PCSK9i (MW = 1.65 kDa) increased hepatic LDLR density on par with a PCSK9 antibody (MW = 144 kDa). Plasma TC levels were significantly reduced versus vehicle in all 13 PCSK9i dose groups, with similar reductions noted in the 13 PCSK9i 30 mg/kg and PCSK9 antibody groups (44% and 48%, respectively). In summary, 13 PCSK9i breaks new ground with its previously undescribed allosteric mechanism and, in turn, as the smallest molecule identified to date with in vivo PCSK9-LDLR disruptor function.

Summary Proprotein convertase subtilisin/kexin type 9 (PCSK9) regulates plasma low-density lipoprotein cholesterol (LDL-C) levels by promoting hepatic LDL receptor (LDLR) degradation. Therapeutic antibodies that disrupt PCSK9-LDLR binding reduce LDL-C concentrations and cardiovascular disease risk. The epidermal growth factor precursor homology domain A (EGF-A) of the LDLR serves as a primary contact with PCSK9 via a flat interface, presenting a challenge for identifying small molecule PCSK9-LDLR disruptors. We employ an affinity-based screen of 1013 in vitro-translated macrocyclic peptides to identify high-affinity PCSK9 ligands that utilize a unique, induced-fit pocket and partially disrupt the PCSK9-LDLR interaction. Structure-based design led to molecules with enhanced function and pharmacokinetic properties (e.g., 13PCSK9i). In mice, 13PCSK9i reduces plasma cholesterol levels and increases hepatic LDLR density in a dose-dependent manner. 13PCSK9i functions by a unique, allosteric mechanism and is the smallest molecule identified to date with in vivo PCSK9-LDLR disruptor function.

The first examples of biologically active monocyclic 1,2-azaborines have been synthesized and demonstrated to exhibit not only improved in vitro aqueous solubility in comparison with their corresponding carbonaceous analogues, but in the context of a CDK2 inhibitor, also improved biological activity and better in vivo oral bioavailability. This proof-of-concept study establishes the viability of monocyclic 1,2-azaborines as a novel pharmacophore with distinct pharmacological profiles that can help address challenges associated with solubility in drug development research.

Los datos de los archivos de registro de actividades Web (Web log data) han sido la base del analisis del comportamiento de los usuarios durante muchos años, sin embargo, estos datos tienen algunos defectos importantes, el principal es que no permiten saber con exactitud lo que el usuario está haciendo.

Evaluation of the potential of a drug candidate to inhibit or inactivate cytochrome P450 (CYP) enzymes remains an important part of pharmaceutical drug Discovery and Development programs. CYP enzymes are considered to be one of the most important enzyme families involved in the metabolic clearance of the vast majority of prescribed drugs. Clinical drug-drug interactions (DDI) involving inhibition or time-dependent inactivation of these enzymes can result in dangerous side effects resulting from reduced clearance/increased exposure of the drug being affected (the 'victim' drug). In this regard, pharmaceutical companies have become quite vigilant in mitigating CYP inhibition/inactivation liabilities of drug candidates early in Discovery including continued risk assessment throughout Development. In this review, common strategies and decision making processes for the assessment of DDI risk in the different stages of pharmaceutical development are discussed. In addition, in vitro study designs, analysis, and interpretation of CYP inhibition and inactivation data are described in stage appropriate context. The in vitro tools and knowledge available now enable the Discovery Chemist to place the potential CYP DDI liability of a drug candidate into perspective and to aid in the optimization of chemical drug design to further mitigate this risk.

Nicotinamide (2) is a potent and selective inhibitor of the PDE4D isozyme and as a chemical tool selectively blocks eosinophil mediator release and chemotaxis thus linking the role of PDE4D to eosinophil function.

Growth hormone secretagogues (GHSs) represent attractive therapeutic alternatives to recombinant growth hormone (GH), given their ability to amplify pulsatile hormone secretion in a relatively physiologic manner. CP-424,391 (391) is a novel, orally active pyrazolinone-piperidine GHS. In rat pituitary cell cultures, 391 stimulated GH release with an EC50=3 nM. The addition of 391 to rat pituitary cells activated intracellular calcium signaling but did not elevate intracellular cyclic adenosine monophosphate (cAMP). 391 also modulated the effects of GH-releasing hormone and somatostatin on pituitary cell GH-release and intracellular signaling. In nonpituitary cell lines, the ability of 391 to stimulate intracellular signaling was dependent on the expression of recombinant human GHS receptor. Acute administration of 391 to anesthetized rats or to conscious dogs induced pulsatile release of GH in a dose-dependent manner. Plasma insulin-like growth factor-1 (IGF-1) was elevated progressively over a 5-d course of daily oral dosing in dogs. Chronic oral administration of 391 augmented body weight gain in rats and dogs. Thus, the peptidomimetic GHS 391 has potential utility for the treatment of clinical conditions that could benefit from systemic augmentation of GH and IGF-1 levels.

This chapter discusses various types of sampling such as random sampling and sampling based on business criteria (age of customer, time as client, etc.). It also discusses extracting train and test datasets for specific business objectives and considers the issue of Big Data, given that it is currently a hot topic.

Creating and deploying a data model is the last step of the data mining process, having already defined a good business objective; extracted and prepared the data; guaranteed its quality; and analyzed, segmented, and created new indicators and factors with greater information value. This chapter defines what is meant by a data model and introduces key associated concepts such as supervised and unsupervised learning, cross-validation, and how to evaluate the precision of the modeling results. A variety of techniques for modeling data are considered, from AI (Artificial Intelligence) approaches, such as neural networks and rule induction, to statistical techniques such as regression. The chapter goes on to discuss which techniques to use for specific modeling scenarios, how to apply models to real-world production data, and how to evaluate and use the results. Finally, guidelines are given for how to perform and reiterate the modeling phase, especially when the initial results are not the desired or optimum ones.

This chapter discusses the ways data can be represented in order to facilitate its interpretation and visualization. The chapter is divided into two sections: basic representation and advanced representation. The first section includes a discussion of the basic data types; representation, comparison and processing of different types of variables; visualization methods; and normalization, distributions, atypical values, and outliers. Visualization methods include pie charts, histograms, graph plots, and radar diagrams. The advanced data representation section discusses higher level ways of representing data, which are required for some special types of data and data that has a more complex structure. The section then briefly presents four advanced types of representation: hierarchies, semantic networks, graphs, and fuzzy. Indications of implementation details for data entry and data processing are given for each type.

One of the fundamental uses of information technology is data storage. The simplest way to store data is in the form of a flat file, text file, spreadsheet, or table. However, many businesses have more complex relational database systems based on data structures that reflect the relations between the key data entities and their relations, using primary indexes/keys, secondary indexes, normalized data structures, and so on. This chapter discusses the concepts of data mart and data warehouse and comments on how the informational data is separated from the operational data. Then the chapter follows an example of extracting data from an operational environment into a data mart and finally into a unique file that can be used as the starting point for data mining.

This chapter discusses simple and advanced text processing and text analysis: the basic processing considers format-checking based on pattern identification; the advanced techniques consider named entity recognition, concept identification based on synonyms and hyponyms, and information retrieval concepts.

The area of CRM (Customer Relationship Management) has attracted a lot of attention, and many businesses who are end users of IT solutions have spent considerable amounts of money on implementing CRM systems integrated to a greater or lesser extent with their operational and business processes. However, what should be kept in mind is that CRM is a basic, common sense idea that can be put into practice with nothing more than a spreadsheet and a modest database. This chapter introduces the reader to CRM in terms of recency, frequency, and latency of customer activity, and in terms of the client life cycle: capturing new clients, potentiating and retaining existing clients, and winning back ex-clients. The chapter then discusses the relation of data analysis to each of the CRM phases and considers customer satisfaction and integrated CRM systems. Next, it briefly describes the characteristics of commercial CRM software products, and finally, the chapter examines example screens and functionality from a simple CRM application.

This chapter discusses data sources that can be accessed for a commercial data analysis project. One way of enriching the information available about a business’s environment and activity is to fuse together various sources of information and data. The chapter begins with a discussion of internal data, that is, data about a business’s products, services, and customers, together with feedback on business activities from surveys, questionnaires, and loyalty and customer cards. The chapter then considers external data—which affects a business and its customers in different ambits—such as demographic and census data, macro-economic data, data about competitors, and data relating to stocks, shares, and investments. Examples are given for each source and where and how the data could be obtained.

This chapter discusses the definition of a data mining project, including its initial concept, motivation, objective, viability, estimated costs, and expected benefit (returns). Key considerations are defined, and a way of quantifying the cost and benefit is presented in terms of the factors that most influence the project. Two case studies illustrate how the cost/benefit evaluation can be applied to real-world projects.

This is the second of four chapters dealing with the analysis of data on the Internet and the online environment. This chapter gives an in-depth insight into the Internet’s structure and how users search the web. The first section considers basic ideas about the Internet and Internet search categories. The second section follows an example of processing, analyzing, and modeling a user search log in order to evaluate the search experience quality of a business’s users (customers, employees, etc.).

This chapter deals with one of the aspects of commercial data analysis that may be more difficult for newcomers: selecting and deriving key factors from a large number of variables. On the one hand, the novice analyst may think that this is a totally intuitive process performed by business experts. On the other hand, the novice may consider the process to be completely statistical. The reality is found somewhere between the two approaches. From a practical point of view, two different starting points can be considered: (i) What data do I have and what can I do with it? and (ii) I know the final goal or result that I am interested in and I am prepared to obtain the necessary data to achieve it. The first two sections of the chapter consider the approaches from these two starting points and present some of the basic statistical techniques for selecting variables and deriving factors. The third section discusses how to use data mining techniques for selecting the most relevant variables. The final section considers the alternative of obtaining a packaged or proprietary solution of preselected variables and factors for a specific business area. In practice, one or more of these approaches can be employed in order to guarantee that the best possible data and variable selection for the business objective is obtained.

This chapter discusses “the other side of the coin” to data mining—which is not often dealt with in data mining books—namely, data privacy and privacy-preserving data publishing. The chapter first considers how some popular Internet applications deal with data privacy, followed by a brief look at some of the legal aspects. Then it looks at “privacy-preserving data publishing,” which is perhaps the area of most concern for data miners: concepts, anonymization techniques, and document sanitization.

This chapter reviews the various ways that the results of data mining, such as predictive models, rules, and analysis results, can be presented in a way that allows a business to feed those results directly into its decision-making and operative processes. First, simple graphic and reporting formats are addressed, followed by more sophisticated executive information system interfaces and a discussion of how the results can be integrated into the business operations, such as CRM systems and call centers. Finally, the chapter looks at more complex applications, such as expert systems and case-based systems.

This is the third of four chapters dealing with the analysis of data on the Internet and the online environment. This chapter gives an insight into the analysis of online social networks (OSNs). Throughout the chapter the exploitation of information for commercial use is addressed and a selection of references for OSN analysis application tools is given.

This is the fourth and final chapter dealing with the analysis of data on the Internet and the online environment. This chapter gives an insight into how to analyze search keyword tendencies over time using the Google Trends application. The chapter is divided into two main sections. The first section examines practical examples from Google Trends, showing the changing frequencies of Internet search terms over time. Then a classification of four major trends types is defined, using real-world examples of search terms for each tendency type. The second section follows the processing steps for the trend data, defining a set of descriptive factors for the time series, and then applying a clustering technique and a predictive modeling technique to classify the example query trends into one of the four categories defined in the first section. There are comments throughout the chapter on how the information can be exploited for commercial uses, such as Internet marketing and brand and sentiment analysis.

This chapter discusses two approaches to using computer software programs for commercial data analysis. There are the powerful, integrated tools with higher price tags, which tend to be used by multinational companies, banks, insurance companies, large retail stores, large online application providers, and so on. These tools often require the assistance of consultants or the manufacturers as support for utilizing them and are best for high data volumes and data base integration. The low-cost, artisanal approach consists of acquiring ad hoc, or open source, software tools, which tend to be more technique-specific, but which can offer greater independence for analyzing commercial data.

This is the first of four chapters that deal with the analysis of data on the Internet and in an online environment. This chapter gives an introduction to website analysis and Internet search using two contrasting case studies: first, the chapter discusses how to analyze the transactional data from customer visits to a business’s website, and second, it explores how Internet search can be used as a market research tool. The examples serve to illustrate how the Internet can be used as a tool for individual marketing, mass marketing, and marketing sentiment surveys. The examples also illustrate the following two business objectives: (i) analyzing activity on a website to adapt the website’s commercial offering at both the general and individual levels, and (ii) gathering commercial information on the Internet from a diversity of sources in order to analyze and understand the marketplace. From a data mining perspective (and recalling the data sources in Chapter 3 ), throughout this chapter the Internet could be considered as a meta data source born from a company's Internet presence. Following each case study, details are given of which technical techniques are relevant and which software applications could be used for the examples.

This chapter discusses data quality, which is a preliminary consideration for any commercial data analysis project; the definition of quality includes the availability or accessibility of data. The chapter examines typical problems that can occur with data, including errors in the data content (textual and numerical data) and the relevance and reliability of the data, as well as how to quantitatively evaluate data quality. Finally, some typical errors due to data extraction and how to avoid them are discussed by examining a practical case study.

The transient kinetics of glucose binding to glucokinase (GK) was studied using stopped-flow fluorescence spectrophotometry to investigate the underlying mechanism of positive cooperativity of monomeric GK with glucose. Glucose binding to GK was shown to display biphasic kinetics that fit best to a reversible two-step mechanism. GK initially binds glucose to form a transient intermediate, namely, E* x glucose, followed by a conformational change to a catalytically competent E x glucose complex. The microscopic rate constants for each step were determined as follows: on rate k1 of 557 M(-1) s(-1) and off rate k(-1) of 8.1 s(-1) for E* x glucose formation, and forward rate k2 of 0.45 s(-1) and reverse rate k(-2) of 0.28 s(-1) for the conformational change from E* x glucose to E x glucose. These results suggest that the enzyme conformational change induced by glucose binding is a reversible, slow event that occurs outside the catalytic cycle (kcat = 38 s(-1)). This slow transition between the two enzyme conformations modulated by glucose likely forms the kinetic foundation for the allosteric regulation. Furthermore, the kinetics of the enzyme conformational change was altered in favor of E x glucose formation in D2O, accompanied by a decrease in cooperativity with glucose (Hill slope of 1.3 in D2O vs 1.7 in H2O). The deuterium solvent isotope effects confirm the role of the conformational change in the magnitude of glucose cooperativity. Similar studies were conducted with GK activating mutation Y214C at the allosteric activator site that is likely involved in the protein domain rearrangement associated with glucose binding. The mutation enhanced equilibrium glucose binding by a combination of effects on both the formation of E* x glucose and an enzyme conformational change to E x glucose. Kinetic simulation by KINSIM supports the conclusion that the kinetic cooperativity of GK arises from slow glucose-induced conformational changes in GK.

Higher-throughput ADME programs in early drug discovery are becoming common throughout the pharmaceutical industry as companies strive to reduce their compound attrition in later-stage development. Many of the ADME assays developed into higher-throughput formats rely on LC/MS analyses. Since the biological aspects of the assay are amenable to parallel processes using dense plate formats, the number of samples generated from these assays produce a large analysis load for serial LC/MS. Presented in this report are two novel strategies, including a sample pooling method and a two time-point method, that could be used in drug discovery to reduce the number of samples generated during multiple time-point in-vitro ADME assays. One hundred and sixty-three compounds were subjected to human microsomal incubations with full time-point method samples taken at t = 0, 5, 15, 30, and 45 min. The ER data correlation (R(2)) between the full time-point method and the pooling method and two time-point methods were 0.98 and 0.97, respectively. Both methods have the potential to: 1. produce data of similar quality to traditional high throughput ADME assays, 2. be easily implemented, 3. shorten analytical run times, and 4. be reproducible and robust.