Tetrahydroindoles as Multipurpose Screening Compounds and Novel Sirtuin Inhibitors

Indoles are privileged structures in medicinal and bioorganic chemistry that are particularly well suited to serve as platforms for diversity. Among many other therapeutic areas, the indole scaffold has been used to design aromatic compounds useful to interfere with enzymes engaged in the regulation of substrate acylation status, such as sirtuins. However, the planarity of the indole ring is not necessarily optimal for all target enzymes, especially when functionalization with aromatic side chains is required. Replacement of flat scaffolds by nonplanar molecular cores dominated by sp³ hybridization is a common strategy to avoid the disadvantages associated with poor solubility and high promiscuity, while covering less-well-explored areas of chemical space. Thus, we synthesized fragment-like tetrahydroindoles suitable for fragment-based drug discovery as well as a well-characterized small library intended as multipurpose screening compounds. For proof of principle, these compounds were screened against sirtuins 1–3, enzymes known to be addressable by indoles. We found that 2,6,6-trimethyl-4-oxo-4,5,6,7-tetrahydro-1H-indole-3-carboxamides are potent and selective SIRT2 inhibitors. Compound 16 t displayed an IC₅₀ value of 0.98 μm and could serve as exquisite starting point for hit-to-lead profiling.     

Reoptimization framework and policy analysis for maritime inventory routing under uncertainty

We study a maritime inventory routing problem, in which shipments between production and consumption nodes are carried out by a fleet of vessels. The vessels have specific capacities and can be chartered under different agreements. The inventory levels of all consumption nodes and some production nodes should be maintained within specified bounds; for the remaining production nodes, orders should be picked up within pre-defined time windows. We propose a discrete-time mixed-integer programming model. In the face of new information and uncertainty, this optimization model has to be re-solved, as the horizon is rolled forward. We discuss how to account for different sources of uncertainty. We present a rolling-horizon reoptimization framework that allows us to study different policies that impact the quality of the implemented solution, so we can identify the optimal set of policies.     

Oxidative degradation of polyamide reverse osmosis membranes: Studies of molecular model compounds and selected membranes

Selected aromatic amides were used to model the chemical reactivity of aromatic polyamides found in thin‐film composite reverse osmosis (RO) membranes. Chlorination and possible amide bond cleavage of aromatic amides upon exposure to aqueous chlorine, which can lead to membrane failure, were investigated. Correlations are made of the available chlorine concentration, pH, and exposure time with chemical changes in the model compounds. From the observed reactivity trends, insights are obtained into the mechanism of RO membrane performance loss upon chlorine exposure. Two chemical pathways for degradation are shown, one at constant pH and another that is pH‐history dependent. An alternative strategy is presented for the design of chlorine‐resistant RO membranes, and an initial performance study of RO membranes incorporating this strategy is reported. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 90: 1173–1184, 2003     

Knowledge representation for mathematical discovery: Three experiments in graph theory

This paper describes the nature of mathematical discovery (including concept definition and exploration, example generation, and theorem conjecture and proof), and considers how such an intelligent process can be simulated by a machine. Although the material is drawn primarily from graph theory, the results are immediately relevant to research in mathematical discovery and learning.The thought experiment, a protocol paradigm for the empirical study of mathematical discovery, highlights behavioral objectives for machine simulation. This thought experiment provides an insightful account of the discovery process, motivates a framework for describing mathematical knowledge in terms of object classes, and is a rich source of advice on the design of a system to perform discovery in graph theory. The evaluation criteria for a discovery system, it is argued, must include both a set of behavior to display (behavioral objectives) and a target set of facts to be discovered (factual knowledge).Cues from the thought experiment are used to formulate two hierarchies of representational languages for graphy theory. The first hierarchy is based on the superficial terminology and knowledge of the thought experiment. Generated by formal grammars with set-theoretic semantics, this eminently reasonable approach ultimately fails to meet the factual knowledge criteria. The second hierarchy uses declarative expressions, each of which has a semantic interpretation as a stylized, recursive algorithm that defines a class by generating it correctly and completely. A simple version of one such representation is validated by a successful, implemented system called Graph Theorist (GT) for mathematical research in graph theory. GT generates correct examples, defines and explores new graph theory properties, and conjectures and proves theorems.Several themes run through this paper. The first is the dual goals, behavioral objectives and factural knowledge to be discovered, and the multiplicity of their demands on a discovery system. The second theme is the central role of object classes to knowledge representation. The third is the increased power and flexibility of a constructive (generator) definition over the more traditional predicate (tester) definition. The final theme is the importance of examples and recursion in mathematical knowledge. The results provide important guidance for further research in the simulation of mathematical discovery.     

Implementing software metrics — the critical success factors

This paper reviews the current state of industry with regard to the introduction of software metrics. It discusses the benefits that organizations have derived from metrication, it looks at why organizations have sought to introduce metrics programmes, how they have gone about introducing those programmes and the problems that they have encountered during implementation. The review found that, on the whole, only the sanitized aspects of metrics experiences have actually been published. This seems to be especially the case with respect to practitioner resistance. Very few organizations admit to having encountered resistance during the introduction of a metrics programme. The paper also includes the results of a pilot study, conducted by the first author, which examines the attitudes that developers hold towards the introduction of software metrics. The key findings of this pilot study are that positive attitudes to metrics correlate highly with levels of education and to job satisfaction.     

On the geometrical material structure of anelasticity

Summary G-structures are the geometric backbone of the theory of material uniformity in continuum mechanics. Within this geometric framework, anelasticity is seen as a result of evolving distributions of inhomogeneity reflected as material nonintegrability. Constitutive principles governing thetime evolution of the G-structure underlying the finite-strain theory of anelasticity (e.g., plasticity) are proposed. The material Eshelby stress tensor is shown to be thedriving force behind this evolution. This should allow for a thermodynamically admissible formulation of anelasticity viewed as a G-structure evolution.     

HLA-DR mismatching correlates with early cardiac allograft rejection, incidence, and graft survival when high-confidence-level serological DR typing is used

To determine if cardiac allograft outcome is improved among patients with fewer HLA-DR mismatches with their donors, we studied 132 recipients of a primary cardiac allograft who were transplanted between December 1985 and December 1991. These recipients and their donors all had high-confidence-level serological HLA-DR typing, previously shown to correlate highly with DNA DR typing. Patients were divided in two groups based on the HLA-DR mismatch with their donors. Group I consisted of 78 patients with 1 or zero DR mismatch and group II of 54 patients with 2 DR mismatches. Allograft outcome measurements included incidence of moderate rejection, incidence of allograft vasculopathy at 12 months, cardiac function measured as left ventricular ejection fraction (LVEF) and cardiac index (CI), and actuarial graft survival up to 7 years. Groups I and group II were not different with regard to recipient age, donor age, ischemia time, pulmonary vascular resistance, sex, or PRA greater than 0%. Group II had a higher incidence of moderate rejection on the first-week biopsy (47% vs. 25%, P = 0.019), and during the first month (84% vs. 58%, P = 0.006), but no difference was found in frequency of rejection from months 2 to 12. LVEF was not different in the groups at any point. CI was better in group I at 12 months (2.76 vs. 2.5, P = 0.03). No statistically significant difference was found in incidence of allograft vasculopathy (17% vs. 26%, P = 0.204). Actual graft survival at 1 year was better for group I (91% vs. 74%, P = 0.008), and actuarial graft survival at 6 years also favored group I (76% vs. 56%, P = 0.04). Using high-confidence-level serological HLA-DR typing assignments we demonstrated that HLA-DR mismatching correlates highly with cardiac allograft outcome. Implications are that heart transplant survival could be improved if prospective matching were feasible and prioritized or if immunosuppression were tailored to the HLA-DR match.