Rajesh Kumar Tiwari

Application of microarray in breast cancer: An overview

There are more than 1.15 million cases of breast cancer diagnosed worldwide annually. At present, only small numbers of accurate prognostic and predictive factors are used clinically for managing the patients with breast cancer. DNA microarrays have the potential to assess the expression of thousands of genes simultaneously. Recent preliminary researches indicate that gene expression profiling based on DNA microarray can offer potential and independent prognostic information in patients with newly diagnosed breast cancer. In this paper, an overview upon the applications of microarray techniques in breast cancer is presented.

Measurement of Scientific Productivity in R&D Sector: Changing paradigm

Scientific Productivity is a demand of policy makers for a judicious utilization of massive R&D budget allocated and utilized. A huge mass of intellectual assets is employed, which after investing manpower, infrastructure and lab consumables demand for a major outcome which contributes towards building nations economy. Scientific productivity was only measured through publications or patents. Patents, earmarked as a strong parameter for innovation generation, where, Word Intellectual Property Organisation generated a data on applications for the top 20 offices for patents, where Australia, Brazil and Canada occupied top 3 positions. India ranked 9th with the total patent applications rising from 39762 (2010) to 42854 (2014) i.e. 15%, whereas, it contributes around 2% Patents (innovative productivity) on global scale. Many studies have come forward interestingly within scientific and academic domains in the form of measurement of scientific performance, however, development of productivity indicators and calculation of Scientific Productivity (SP) as a holistic evaluation system is a significant demand. SP, a herculean task is envisaged for productivity analysis and would submit significant factors towards fabricating an effective measurement engine in a holistic manner viable for an individual and organization, being supplementary to each other. This review projects the significance of performance measurement system in R&D through identification and standardization of key parameters. It also includes emphasis on inclusion of standardized parameters, effective for performance measurement which is applicable for scientists, technical staff as well as lab as a facility. This review aims at providing an insight to the evaluators, policy makers, and high level scientific panels to stimulate the scientific intellects on identified indicators so that their work proceeds to generate productive outcome contributing to the economic growth.

GMO and Food Security

Abstract With the exponential rise in the human population, the biggest challenge to society is food security. Food security simply means access to sufficient, safe, and nutritious food to all the people of the world. However, for many developing countries and underdeveloped countries access to food security is a big issue. A report of the Food and Agriculture Organization (FAO) of the United Nations (2012) says that over 900 million people in the world are undernourished or suffering from one or more diseases just because of deficiency of important vitamins and minerals in food. It is well understood that agricultural land cannot be increased with the growth of population. In fact, due to extensive urbanization agricultural land is decreasing day by day. Thus, the biggest challenge is to multiply food production more than twofold, without affecting ecology and biodiversity. Farmers of developing countries are facing more challenges as they are dependent on natural resources in traditional ways to meet out agricultural requirements. Not only scientific advancement but social and ethical factors are required to be taken into consideration while strategizing about the food security issues. Increased active involvement of social community awareness toward modern technology is a must. Currently, realizing the exponential growth of the world population and recent scientific advancement, one method seems to be impressive—genetically modified (GM) technology. Genetically modified crops are needed to feed a huge human population with limited resources. Currently, people are divided in their opinions about GM technology: some people think that this technology will reduce hunger from the world while others consider it as a technology that risks food security. Some ethical issues, raised by many nongovernmental organizations (NGOs) also pose hurdles. However, concrete evidence to support any of these views has yet to be scientifically validated. GM technology comes with many promises as well as associated risks of health and environment. This technology is adopted by many developed countries for enhancing yield. The latest FAO report (2012–2014) reported a decline in the global hunger, as now about 809 million people are undernourished as compared to the 2012 report. However, these are merely statistical data to present the broader picture. In the present scenario, with the help of GM technology, we will be able to meet the shortcomings in the productivity so that the future world is secured and safe regarding food.

Prediction of Metabolism of Drugs using Artificial Intelligence: How far have we reached?

Information about drug metabolism is an essential component of drug development. Modeling the drug metabolism requires identification of the involved enzymes, rate and extent of metabolism, the sites of metabolism etc. There has been continuous attempts in the prediction of metabolism of drugs using artificial intelligence in effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are number of predictive models available for metabolism using Support vector machines, Artificial neural networks, Bayesian classifiers etc. There is an urgent need to review their progress so far and address the existing challenges in prediction of metabolism. In this attempt, we are presenting the currently available literature models and some of the critical issues regarding prediction of drug metabolism.

Genetic Engineering Potential of Hairy Roots of Poppy ( Papaver spp.) for Production of Secondary Metabolites, Phytochemistry, and In Silico Approaches

Opium poppy is one of the most important medicinal plants, because of its secondary metabolites (alkaloids). Opium as such is an important product, which has many uses and abuses. Its alkaloids are widely used in modern pharmacopeia. Agrobacterium rhizogenes (hairy roots), mediated hairy root culture, is also used for secondary metabolite production under in vitro conditions. Hairy roots are able to grow fast without phytohormones and to produce the metabolites of the mother plant. India is the only country where UN has given license to produce opium from latex. The application of opiate alkaloids, mainly in hydrochloride, sulfate, and phosphate forms, is restricted in some well-defined therapeutic fields. A major component among alkaloids is morphine, having analgesic in nature and used mainly to control severe pain and sedative effects. Poppy seeds have been described as tonic and aphrodisiac, promote luster of the body, enhance capacity to muscular work, and allay nervous excitement. Plant of such economic importance is affected by various biotic and abiotic factors leading to yield loss. Biotic factors include fungi, bacteria, viruses, nematodes, and birds too. This important plant has huge prospects in pharma industry, and on other hand, it is facing lots of challenges in the form of illicit trade, drug abuse, and biotic and abiotic stresses.

Promises of Machine Learning Approaches in Prediction of Absorption of Compounds

The Machine Learning (ML) is one of the fastest developing techniques in the prediction and evaluation of important pharmacokinetic properties such as absorption, distribution, metabolism and excretion. The availability of a large number of robust validation techniques for prediction models devoted to pharmacokinetics has significantly enhanced the trust and authenticity in ML approaches. There is a series of prediction models generated and used for rapid screening of compounds on the basis of absorption in last one decade. Prediction of absorption of compounds using ML models has great potential across the pharmaceutical industry as a non-animal alternative to predict absorption. However, these prediction models still have to go far ahead to develop the confidence similar to conventional experimental methods for estimation of drug absorption. Some of the general concerns are selection of appropriate ML methods and validation techniques in addition to selecting relevant descriptors and authentic data sets for the generation of prediction models. The current review explores published models of ML for the prediction of absorption using physicochemical properties as descriptors and their important conclusions. In addition, some critical challenges in acceptance of ML models for absorption are also discussed.

Status of research, regulations and challenges for genetically modified crops in India

A large number of genetically modified (GM) crops, including both food and non-food crops carrying novel traits have been developed and released for commercial agriculture production. Soybean, maize, canola and cotton for the traits insect resistance and herbicide tolerance are the main crops under commercial cultivation worldwide. In addition, many other GM crops are under development and not yet released commercially. Food and Agriculture Organization (FAO) in its report, the State of Food Security and Nutrition in the World 2017, highlights the severity of food security and malnourishment problem in most of the Asian and developing countries. GM crops could be an option for nutrients enhancement and yield increase in major crops and solve the problem of malnourishment and food security. India has progressed tremendously in GM crops research, evaluation and monitoring in last two decades but regulatory system impeded gravely due to lack of coordination and common stand on GM technology across different governments, ministries and departments. The increasing cultivation of genetically modified crops has raised a wide range of concerns with respect to food safety, environmental effects and socio-economic issues. Here, we discussed the current status of GM crops research, regulatory framework, and challenges involved with transgenic plants research in India.

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques

BACKGROUND Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. METHODS Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. RESULTS Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. CONCLUSION Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development.

Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms

AIM AND OBJECTIVE Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. MATERIALS AND METHODS In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. RESULTS The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. CONCLUSION This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development.