Medications probably are the single most important health care technology in preventing illness, disability, and death in the geriatric population. Age-related changes in drug disposition and pharmacodynamic responses have significant clinical implications; increased use of a number of medications raises the risk that medicine-related problems may occur. The relationship between increased use of drugs including the prescription medication and elderly is well established. Majority of ADRs (80%) causing admission or occurring in hospital are type A reactions. Although less common occurring in elderly, type B ADRs may sometimes cause serious toxicity. Studies have correlated the integral association between old age and increased rate of adverse drug reactions arising out of confounding association between age and polypharmacy contributed by age-related changes in pharmacodynamics and pharmacokinetics at least for some medical conditions. A drug combination may sometimes cause synergistic toxicity which is greater than the sum of the risks of toxicity of either agent used alone. But, strategies to increase opportunities for identifying ADRs and related problems have not been emphasised in current international policy responses especially in India to the increase in elderly population and chronic conditions. Careful epidemiological studies that encompass large numbers of elderly drug users are required to obtain this information as increased knowledge of the frequency and cost of adverse drug reactions is important in enabling both more rational therapeutic decisions by individual clinicians and more optimal social policy.

Background: An adverse drug reaction (ADR) is defined by World Health Organization (WHO) as 'Any response to a drug which is noxious, unintended and occurs at doses used in man for prophylaxis, diagnosis or therapy'. ADRs associated with cancer chemotherapy warrant analysis on their severity and preventability. The outcome would create awareness among health care providers and prevent their recurrence. We have performed a hospital-based prospective observational study designed to analyze the pattern of ADRs to chemotherapeutic agents in cancer patients of a tertiary care hospital. Methods: A total of 119 cancer patients were monitored for suspected ADRs during the course of chemotherapy from November 2014 to December 2015. Clinical events were recorded and analyzed with regard to the demographics and drug details of the patients. Results: A total of 106 ADRs were recorded from 119 cases. The ADRs commonly encountered included constipation, nausea, vomiting, alopecia and hematological changes. Cisplatin, cyclophosphamide, paclitaxel and 5-FU were used for the treatment of commonly found cancers in this region affecting the lungs, esophagus and lymphomas. Naranjo's causality assessment showed 86.7% possible (score 4) and 13.2% probable (score 5-6). Severity of adverse reactions showed 77.4% mild, 18.9% moderate and 3.8% severe. A total of 45.3% of ADRs were preventable reactions such as nausea, vomiting and constipation. Conclusions: This study highlights the role of active monitoring as an important tool for early detection, assessment and timely management of ADRs in patients undergoing cancer chemotherapy. The observed ADRs were preventable although ADRs such as hiccough, anemia, neutropenia and alopecia were not preventable.

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as “message-passing paradigms”, “spatial-symmetry-preserving networks”, “hybrid de novo design”, and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

This study prospectively quantified wastage of cancer chemotherapeutic drugs in an oncology unit to find the associated cost in 3 months. Retrospective analysis of drug usage for 12 months was also conducted to determine the expected drug loss in 1 year. The effect of vial sharing was evaluated under the assumption of sharing. A significant drug wastage of 19.72% (95% confidence interval [CI], 14.52-24.93%) in 3 months and 17.14% (95% CI 14.69-19.59%) in 1 year occurred in our oncology unit. Number of vials purchased (r = 0.362, p < 0.01), weight (r = −0.146, P < .01) and body surface area (r = −0.26, P < .01) correlated with the drug wasted. Vial sharing assumption showed a 9% (95% CI, 2.5-15.5%) reduction in cost in 1 year.

This study is aimed at assessing the patients’ waiting time in the neurology outpatient department of a tertiary health care institution in North East India. This is a prospective observational study where duration of patients’ waiting time, doctor consultation time and total waiting time were noted and recorded for each patient. The participants were enrolled randomly in this study and total number of participants was 80 including men and women from different age ranges. The results included data records of (1) The mean waiting time which was found to be 110.86 minutes (2) Mean consultation time was 5.34 minutes and (3) Mean total waiting time was found to be 116.20 minutes. From the findings of this study, it can be concluded that efforts need to be taken to reduce the long waiting time; which is identified by the WHO as an important index for determining the quality and satisfactory health services; in order to help improve patient’s satisfaction, winning their trust as well as their willingness and promptness to acquire basic healthcare needs and services.