Ecopharmacognosy: Exploring The Chemical And Biological Potential Of Nature For Human Health

“Why didn’t they develop natural product drugs in a sustainable manner at the beginning of this century?â€Â  In 2035, when about 10.0 billion will inhabit Earth, will this be our legacy as the world contemplates the costs and availability of synthetic and gene-based products for primary health care?  Acknowledging the recent history of the relationship between humankind and the Earth, it is essential that the health care issues being left for our descendants be considered in terms of resources. For most people in the world, there are two vast health care “gapsâ€, access to quality drugs and the development of drugs for major global and local diseases.  Consequently for all of these people, plants, in their various forms, remain a primary source of health care.  In the developed countries, natural products derived from plants assume a relatively minor role in health care, as prescription and over-the-counter products, even with the widespread use of phytotherapeutical preparations.  Significantly, pharmaceutical companies have retrenched substantially in their disease areas of focus.  These research areas do not include the prevalent diseases of the middle- and lower-income countries, and important diseases of the developed world, such as drug resistance. What then is the vision for natural product research to maintain the choices of drug discovery and pharmaceutical development for future generations?  In this discussion some facets of how natural products must be involved globally, in a sustainable manner, for improving health care will be examined within the framework of the new term “ecopharmacognosyâ€, which invokes sustainability as the basis for research on biologically active natural products.  Access to the biome, the acquisition, analysis and dissemination of plant knowledge, natural product structure diversification, biotechnology development, strategies for natural product drug discovery, and aspects of multitarget therapy and synergy research will be discussed.  Options for the future will be presented which may be significant as countries decide how to develop approaches to relieve their own disease burden, and the needs of their population for improved access to medicinal agents

Robust techniques and applications in fuzzy clustering

This dissertation addresses issues central to frizzy classification. The issue of sensitivity to noise and outliers of least squares minimization based clustering techniques, such as Fuzzy c-Means (FCM) and its variants is addressed. In this work, two novel and robust clustering schemes are presented and analyzed in detail. They approach the problem of robustness from different perspectives. The first scheme scales down the FCM memberships of data points based on the distance of the points from the cluster centers. Scaling done on outliers reduces their membership in true clusters. This scheme, known as the Mega-clustering, defines a conceptual mega-cluster which is a collective cluster of all data points but views outliers and good points differently (as opposed to the concept of Dave\u27s Noise cluster). The scheme is presented and validated with experiments and similarities with Noise Clustering (NC) are also presented. The other scheme is based on the feasible solution algorithm that implements the Least Trimmed Squares (LTS) estimator. The LTS estimator is known to be resistant to noise and has a high breakdown point. The feasible solution approach also guarantees convergence of the solution set to a global optima. Experiments show the practicability of the proposed schemes in terms of computational requirements and in the attractiveness of their simplistic frameworks. The issue of validation of clustering results has often received less attention than clustering itself. Fuzzy and non-fuzzy cluster validation schemes are reviewed and a novel methodology for cluster validity using a test for random position hypothesis is developed. The random position hypothesis is tested against an alternative clustered hypothesis on every cluster produced by the partitioning algorithm. The Hopkins statistic is used as a basis to accept or reject the random position hypothesis, which is also the null hypothesis in this case. The Hopkins statistic is known to be a fair estimator of randomness in a data set. The concept is borrowed from the clustering tendency domain and its applicability to validating clusters is shown here. A unique feature selection procedure for use with large molecular conformational datasets with high dimensionality is also developed. The intelligent feature extraction scheme not only helps in reducing dimensionality of the feature space but also helps in eliminating contentious issues such as the ones associated with labeling of symmetric atoms in the molecule. The feature vector is converted to a proximity matrix, and is used as an input to the relational fuzzy clustering (FRC) algorithm with very promising results. Results are also validated using several cluster validity measures from literature. Another application of fuzzy clustering considered here is image segmentation. Image analysis on extremely noisy images is carried out as a precursor to the development of an automated real time condition state monitoring system for underground pipelines. A two-stage FCM with intelligent feature selection is implemented as the segmentation procedure and results on a test image are presented. A conceptual framework for automated condition state assessment is also developed

Emerging Chemical Patterns for Virtual Screening and Knowledge Discovery

The adaptation and evaluation of contemporary data mining methods to chemical and biological problems is one of major areas of research in chemoinformatics. Currently, large databases containing millions of small organic compounds are publicly available, and the need for advanced methods to analyze these data increases. Most methods used in chemoinformatics, e.g. quantitative structure activity relationship (QSAR) modeling, decision trees and similarity searching, depend on the availability of large high-quality training data sets. However, in biological settings, the availability of these training sets is rather limited. This is especially true for early stages of drug discovery projects where typically only few active molecules are available. The ability of chemoinformatic methods to generalize from small training sets and accurately predict compound properties such as activity, ADME or toxicity is thus crucially important. Additionally, biological data such as results from high-throughput screening (HTS) campaigns is heavily biased towards inactive compounds. This bias presents an additional challenge for the adaptation of data mining methods and distinguishes chemoinformatics data from the standard benchmark scenarios in the data mining community. Even if a highly accurate classifier would be available, it is still necessary to evaluate the predictions experimentally. These experiments are both costly and time-consuming and the need to optimize resources has driven the development of integrated screening protocols which try to minimize experimental efforts but still reaching high hit rates of active compounds. This integration, termed “sequential screening” benefits from the complementary nature of experimental HTS and computational virtual screening (VS) methods. In this thesis, a current data mining framework based on class-specific nominal combinations of attributes (emerging patterns) is adapted to chemoinformatic problems and thoroughly evaluated. Combining emerging pattern methodology and the well-known notion of chemical descriptors, emerging chemical patterns (ECP) are defined as class- specific descriptor value range combinations. Each pattern can be thought of as a region in chemical space which is dominated by compounds from one class only. Based on chemical patterns, several experiments are presented which evaluate the performance of pattern-based knowledge mining, property prediction, compound ranking and sequential screening. ECP-based classification is implemented and evaluated on four activity classes for the prediction of compound potency levels. Compared to decision trees and a Bayesian binary QSAR method, ECP-based classification produces high accuracy in positive and negative classes even on the basis of very small training set, a result especially valuable to chemoinformatic problems. The simple nature of ECPs as class-specific descriptor value range combinations makes them easily interpretable. This is used to related ECPs to changes in the interaction network of protein-ligand complexes when the binding conformation is replaced by a computer-modeled conformation in a knowledge mining experiment. ECPs capture well-known energetic differences between binding and energy-minimized conformations and additionally present new insight into these differences on a class level analysis. Finally, the integration of ECPs and HTS is evaluated in simulated lead-optimization and sequential screening experiments. The high accuracy on very small training sets is exploited to design an iterative simulated lead optimization experiment based on experimental evaluation of randomly selected small training sets. In each iteration, all compounds predicted to be weakly active are removed and the remaining compound set is enriched with highly potent compounds. On this basis, a simulated sequential screening experiment shows that ECP-based ranking recovers 19% of available compounds while reducing the “experimental” effort to 0.2%. These findings illustrate the potential of sequential screening protocols and hopefully increase the popularity of this relatively new methodology

Ligand-based design of dopamine reuptake inhibitors : fuzzy relational clustering and 2-D and 3-D QSAR modleing

As the three-dimensional structure of the dopamine transporter (DAT) remains undiscovered, any attempt to model the binding of drug-like ligands to this protein must necessarily include strategies that use ligand information. For flexible ligands that bind to the DAT, the identification of the binding conformation becomes an important but challenging task. In the first part of this work, the selection of a few representative structures as putative binding conformations from a large collection of conformations of a flexible GBR 12909 analogue was demonstrated by cluster analysis. Novel structurebased features that can be easily generalized to other molecules were developed and used for clustering. Since the feature space may or may not be Euclidean, a recently-developed fuzzy relational clustering algorithm capable of handling such data was used. Both superposition-dependent and superposition-independent features were used along with region-specific clustering that focused on separate pharmacophore elements in the molecule. Separate sets of representative structures were identified for the superpositiondependent and superposition-independent analyses. In the second part of this work, several QSAR models were developed for a series of analogues of methylphenidate (MP), another potent dopamine reuptake inhibitor. In a novel method, the Electrotopological-state (B-state) indices for atoms of the scaffold common to all 80 compounds were used to develop an effective test set spanning both the structure space as well as the activity space. The utility of B-state indices in modeling a series of analogues with a common scaffold was demonstrated. Several models were developed using various combinations of 2-D and 3-D descriptors in the Molconn-Z and MOE descriptor sets. The models derived from CoMFA descriptors were found to be the most predictive and explanatory. Progressive scrambling of all models indicated several stable models. The best models were used to predict the activity of the test set analogues and were found to produce reasonable residuals. Substitutions in the phenyl ring of MP, especially at the 3- and 4-positions, were found to be the most important for DATbinding. It was predicted that for better DAT-binding the substituents at these positions should be relatively bulky, electron-rich atoms or groups

Chemomechanical regulation of integrin activation and cellular processes in acidic extracellular pH

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 162-176).It is well established that extracellular pH (pHe) becomes acidic in several important physiological and pathological contexts, including the tumor and wound microenvironments. Although it is known that acidic pHe can have profound effects on cell adhesion and migration processes integral to tumor progression and wound healing, the molecular mechanisms underlying the cellular responses to acidic pHe are largely unknown. Transmembrane integrin receptors form a physical linkage between cells and the extracellular matrix, and are thus capable of modulating cell adhesion and migration in response to extracellular conditions. In this thesis, computational and experimental approaches are used to investigate the role of acidic extracellular pH in regulating activation and binding of integrin [alpha]v[beta]3, and to characterize the consequences for downstream subcellular- and cellular-scale processes. Molecular dynamics simulations demonstrate that opening of the integrin [alpha]v[beta]3 headpiece occurs more frequently in acidic pHe than in normal pHe, and that this increased headpiece opening can be partially attributed to protonation of ASP[beta]127 in acidic pHe. These computational data indicate that acidic pHe can promote activation of integrin [alpha]v[beta]3. This is consistent with flow cytometry and atomic force microscope-enabled molecular force spectroscopy experiments, which demonstrate that there are more activated [alpha]v[beta]3 receptors on live [alpha]v[beta]3 CHO-B2 cell surfaces at acidic pHe than at normal pHe 7.4. Put together, these atomistic- and molecular-level data suggest a novel mechanism of outside-in integrin activation regulation by acidic extracellular pH. Next, the consequences of acid-induced integrin activation for subcellular- and cellular-scale processes are investigated. Kymography experiments show that [alpha]v[beta]3 CHO-B2 cell membrane protrusion lifetime is increased and protrusion velocity is decreased for cells in pHe 6.5, compared to cells in pHe 7.4. Furthermore, [alpha]v[beta]3 CHO-B2 cells in pHe 6.5 form more actin-integrin adhesion complexes than cells in pHe 7.4, and acidic extracellular pH results in increased cell area and decreased cell circularity. Cell migration measurements demonstrate that [alpha]v[beta]3 CHO-B2 cells in pHe 6.5 migrate slower than cells in pHe 7.4, and that the fibronectin ligand density required for peak migration speed is lower for cells in pHe 6.5. Together, these data show that acidic pHe affects subcellular- and cellular-scale processes in a manner that is consistent with increased integrin activation in this condition. Finally, the migration behavior of [alpha]v[beta]3 CHO-B2 cells, bovine retinal microvascular endothelial cells, and NIH-3T3 fibroblasts in an extracellular pH gradient is investigated. Results demonstrate that NIH-3T3 fibroblasts do not exhibit directional preferences in the pHe gradient, but that [alpha]v[beta]3 CHO-B2 cells and bovine retinal microvascular endothelial cells migrate preferentially toward the acidic end of the gradient. These data suggest that acidic extracellular pH may serve as a cue that directs migration of angiogenic endothelial cells to poorly vascularized regions of tumors and wounds. Overall, this thesis research results in multiscale, in-depth understanding of extracellular pH as a critical regulator of cell function, with associated implications for tumor growth, wound healing, and the role of proton pumps in cell migration.by Ranjani Krishnan Paradise.Ph.D