QUANTUM MECHANICAL DESCRIPTORS OF NILOTINIB'S IMPURITIES

Objective: Mutagenic/genotoxic impurities in the clinically approved drugs have been a major concern for the pharmaceutical industry. Nilotinib (N), which is an approved drug of chronic leukemia, has a number of impurities (nilotinib impurity [NI]-3, NI-5, and NI-12). For drugs, either semi-empirical or quantum mechanical (QM) or topological molecular descriptors (MDs) have been popular for QSAR studies. However, details of MDs for impurities are yet to be established. Thus, the objective of the study has been to compute QM-based MDs for impurities of N and to compare them with that of approved drugs to identify MDs of the former in relation to their known genotoxic/mutagenic properties.Methods: Impurities are optimized by B3LYP/6-311G (d,p) level of theory and ionization potential (IP), electron affinity (EA), and other MDs are determined. Further, non-linear optical (NLO) descriptors such as dipole moment (DM) and polarizability (α) are also determined.Results: Impurities of N show much deviation of IP, EA, MD, α, and other properties from the reported mean values of approved drugs. Unlike NI-5 and NI-12, NI-3 shows increase in DM (~double) and α properties, which may point to its higher interactivity with cellular targets (like DNA/ RNA/protein), might be due to additional substituents, Ï€-conjugation, and planarity in its structure. The latter seems to be due to compensation of oppositely sensed dihedral properties of the structure of NI-3.Conclusion: The study identifies QM-based differential MDs for impurities of N, which seems to have a relationship with their genotoxicity/ mutagenicity properties. Similar studies can be done for other such systems.Â

Application of egg shell with fortified vermicompost in Capsicum cultivation: A strategy in waste management

Purpose Chicken eggshell (ES) is a global biowaste product of poultry industry and an enriched source of calcium required for plant growth. Therefore, the present study has been carried out to assess the potentiality of the combination of ES with vermicompost (VC) and chicken feather protein hydrolysate (CFPH) on growth and yield improvement of Capsicum plants.Method A field study was conducted through randomized block design (RBD) with eight treatments having three replicates for each. Principle Component Analysis (PCA) have performed to analyze the yield related parameters of plant. Nutritional components of VC and ES were also analyzed.Results The PCA analysis of the  field experiment data has indicated that the combination of ES, CFPH and VC (in a ratio of 100:10:3) remarkably increased the agronomic parameters of capsicum plant about four folds as compared to its chemical counterpart and control, while together VC and ES strongly influences the characteristics of fruits. The first two dimensions of first and second PCA analysis showed 88.39 and 66.91 percent of the overall dataset inertia respectively, explaining 88.39 and 66.91 percent of the total variability. These two values are higher than their respective reference values of 36.32 and 46.76 percent indicating substantial variability.Conclusion The co-application of ES, CFPH with VC could enhance the yield parameters of crops by enriching the soil with both micro and macronutrients. It also serves as a source of organic compost with concomitant reduction in the use of chemical fertilizers

Planned Marketing Adaptation and Multinationals' Choices Between Acquisitions and Greenfields

International marketing studies have extensively examined the antecedents of firms' marketing standardization/ adaptation decisions. However, it is unclear whether such decisions, once planned, codetermine the choice between buying and building foreign subsidiaries. Analyzing a sample of 150 foreign entries by Dutch firms, the authors find that the level of marketing adaptation planned for a wholly owned subsidiary is positively related to the likelihood that the subsidiary will be established through an acquisition rather than through a greenfield investment. Moreover, the authors find substantial evidence that this positive relationship is stronger for firms that (1) are establishing relatively larger subsidiaries, (2) have less experience with the industry entered, or (3) are entering less developed countries. The findings show that firms pursuing higher levels of marketing adaptation assign more value to the marketing adaptation advantages of acquisitions over greenfields, especially if the risks associated with implementing the planned adaptation level are high. In addition, firms typically strive for a fit between their international marketing strategy and their mode of foreign establishment. (authors' abstract

The Impact of Brand Quality on Shareholder Wealth

This study examines the impact of brand quality on three components of shareholder wealth: stock returns, systematic risk, and idiosyncratic risk. The study finds that brand quality enhances shareholder wealth insofar as unanticipated changes in brand quality are positively associated with stock returns and negatively related to changes in idiosyncratic risk. However, unanticipated changes in brand quality can also erode shareholder wealth because they have a positive association with changes in systematic risk. The study introduces a contingency theory view to the marketing-finance interface by analyzing the moderating role of two factors that are widely followed by investors. The results show an unanticipated increase (decrease) in current-period earnings enhances (depletes) the positive impact of unanticipated changes in brand quality on stock returns and mitigates (enhances) their deleterious effects on changes in systematic risk. Similarly, brand quality is more valuable for firms facing increasing competition (i.e., unanticipated decreases in industry concentration). The results are robust to endogeneity concerns and across alternative models. The authors conclude by discussing the nuanced implications of their findings for shareholder wealth, reporting brand quality to investors, and its use in employee evaluation

Metrics--When and Why Nonaveraging Statistics Work

Good metrics are well-defined formulae (often involving averaging) that transmute multiple measures of raw numerical performance (e.g., dollar sales, referrals, number of customers) to create informative summary statistics (e.g., average share of wallet, average customer tenure). Despite myriad uses (benchmarking, monitoring, allocating resources, diagnosing problems, explanatory variables), most uses require metrics that contain information summarizing multiple observations. On this criterion, we show empirically (with people data) that although averaging has remarkable theoretical properties, supposedly inferior nonaveraging metrics (e.g., maximum, variance) are often better. We explain theoretically (with exact proofs) and numerically (with simulations) when and why. For example, when the environment causes a correlation between observed sample sizes (e.g., number of past purchases, projects, observations) and latent underlying parameters (e.g., the likelihood of favorable outcomes), the maximum statistic is a better metric than the mean. We refer to this environmental effect as the Muth effect, which occurs when rational markets provide more opportunities (i.e., more observations) to individuals and organizations with greater innate ability. Moreover, when environments are adverse (e.g., failure-rich), nonaveraging metrics correctly overweight favorable outcomes. We refer to this environmental effect as the Anna Karenina effect, which occurs when less-favorable outcomes convey less information. These environmental effects impact metric construction, selection, and employment.metrics, metric selection, metric evaluation, summary statistics, environmental effects, natural correlations, forecasting, benchmarking, monitoring, statistical biases, choosing explanatory variables

Order Restricted Inference for Adaptive Progressively Censored Competing Risks Data

Under adaptive progressive Type-II censoring schemes, order restricted inference based on competing risks data is discussed in this article. The latent failure lifetimes for the competing causes are assumed to follow Weibull distributions, with an order restriction on the scale parameters of the distributions. The practical implication of this order restriction is that one of the risk factors is dominant, as often observed in competing risks scenarios. In this setting, likelihood estimation for the model parameters, along with bootstrap based techniques for constructing asymptotic confidence intervals are presented. Bayesian inferential methods for obtaining point estimates and credible intervals for the model parameters are also discussed. Through a detailed Monte Carlo simulation study, the performance of order restricted inferential methods are assessed. In addition, the results are also compared with the case when no order restriction is imposed on the estimation approach. The simulation study shows that order restricted inference is more efficient between the two, when this additional information is taken into consideration. A numerical example is provided for illustrative purpose.Comment: 26 pages, 1 figure