GENERIC DRUG: PRESCRIBER’S PERSPECTIVE

Objective: Knowledge of doctors and their understanding of generic drugs could facilitate in recognizing potential barriers to larger generic medicine prescriptions. Hence, the primary objective of this study was focused to explore knowledge, attitude, and practice (KAP) of doctors toward generic medicines. Methods: It is a cross-sectional questionnaire-based study. The study participants are the doctors working in the hospital during the study period (2016–2017). The questionnaire designed for this study comprised of thirty-five questions related to the knowledge, attitude, and practice (KAP) of generic medicine and about demographic details of the participants. Results: A total of 86 questionnaires were distributed among the health care professionals and the response rate is 37%. The majority of doctors who participated in this survey perceived that generic medicine is effective, safe and need to have the same active component, dose and bioequivalent as the brand name medicines. Most of the doctors (72%) were of the view that generic drugs were manufactured in poor quality than branded medicines. More than three-quarters of doctors (78%) prescribed generic drugs. Conclusion: Majority of the participants had an honest angle about the efficaciousness and safety of generic and though they sometimes prescribe generic medicine, however a high range of doctors (72%) were of the opinion that generic was of poorer quality than brand medicine. To have a better understanding of the generic drug, the doctor must be well informed about the generics during their academic career resulting in savings to healthcare budgets

DRUG PRESCRIBING PATTERN IN DERMATOLOGY IN A TEACHING HOSPITAL IN WESTERN NEPAL

The aim was to monitor drug prescribing for patients attending the dermatology services (OPD) of Manipal Teaching Hospital (MTH). 292 prescriptions of patients attending the dermatology OPD of MTH attached to MCOMS, Nepal were collected by a random once weekly survey between July 2000 to June 2001. This information was analysed in consultation with clinical collaborators and critically evaluated using WHO guidelines. The average number of drugs prescribed was 2.42 drugs/prescription. Only 13%(91 out of 708) of the drugs were prescribed in generic names. The most commonly prescribed topical drugs were topical steroids and their combinations (28%) followed by topical antifungal agents (12.5%). The most commonly prescribed systemic agents were antihistamines (47.6%) followed by antimicrobials (20%) and antifungal agents (12%). Frequency of administration and site of application were specified in the majority of the prescriptions (82%) of topically administered drugs but dose/ strength of topical preparations were specified in only 11 prescriptions. The duration was not specified for 9 of the systemically prescribed drugs and 341 of all the topical drugs prescribed. In two patients, 2 medium or 2 high potency topical steroids were prescribed together with systemic steroid administration. Patients who received systemic antiiungal agents (34) also got a topical one simultaneously. In this study, a great majority of the drugs were prescribed in brand names (87 %). Drugs should be prescribed in their generic names to * avoid confusion and to minimize the costs. In this study, on some occasions, prescriptions may have been written imprecisely/ inappropriately. Key Words: Prescribing pattern, Dermatology, Nepal

Promise of the NLRP3 Inflammasome Inhibitors in In Vivo Disease Models

Nucleotide-binding oligomerization domain NOD-like receptors (NLRs) are conserved cytosolic pattern recognition receptors (PRRs) that track the intracellular milieu for the existence of infection, disease-causing microbes, as well as metabolic distresses. The NLRP3 inflammasome agglomerates are consequent to sensing a wide spectrum of pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs). Certain members of the NLR family have been documented to lump into multimolecular conglomerates called inflammasomes, which are inherently linked to stimulation of the cysteine protease caspase-1. Following activation, caspase-1 severs the proinflammatory cytokines interleukin (IL)-1β and IL-18 to their biologically active forms, with consequent commencement of caspase-1-associated pyroptosis. This type of cell death by pyroptosis epitomizes a leading pathway of inflammation. Accumulating scientific documentation has recorded overstimulation of NLRP3 (NOD-like receptor protein 3) inflammasome involvement in a wide array of inflammatory conditions. IL-1β is an archetypic inflammatory cytokine implicated in multiple types of inflammatory maladies. Approaches to impede IL-1β’s actions are possible, and their therapeutic effects have been clinically demonstrated; nevertheless, such strategies are associated with certain constraints. For instance, treatments that focus on systemically negating IL-1β (i.e., anakinra, rilonacept, and canakinumab) have been reported to result in an escalated peril of infections. Therefore, given the therapeutic promise of an NLRP3 inhibitor, the concerted escalated venture of the scientific sorority in the advancement of small molecules focusing on direct NLRP3 inflammasome inhibition is quite predictable

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Not AvailableThe ultimate role of prokaryote (bacteria and archaea), the decomposer of the wetland ecosystem, depends on its community structure and its interaction with the environment. The present study has used three universal prokaryote primers to compare prokaryote community structure and diversity of three distinctly different wetlands. The study results revealed that α-diversity indices and phylogenetic differential abundance patterns did not differ significantly among primers, but they did differ significantly across wetlands. Microbial community composition revealed a distinct pattern for each primer in each wetland. Overall comparison of prokaryote communities in sediments of three wetlands revealed the highest prokaryote richness and diversity in Bhomra (freshwater wetland) followed by Malencho (brackish-water wetland) and East Kolkata wetland (EKW) (sewage-fed wetland). Indicator genus analysis identified 21, 4, and 29 unique indicator genera, having preferential abode for Bhomra, EKW, and Malencho, respectively. Prediction of potential roles of these microbes revealed a preference for sulfate-reducing microbes in Malencho and methanogens in Bhomra. The distinct phylogenetic differential abundance pattern, microbial abode preference, and their potential functional role predict ecosystem variables shaping microbial diversity. The variation in community composition of prokaryotes in response to ecosystem variables can serve as the most sensitive bioindicator of wetland ecosystem assessment and management.Not Availabl

PATTERN OF MEDICATION USE AMONG ELDERLY INPATIENTS IN A TERTIARY CARE HOSPITAL OF NORTHEAST INDIA

Objectives: The objective of the present study was to assess the pattern of medication use among elderly inpatients of internal Medicine Wards and to evaluate inappropriate prescribing with the help of Beers criteria 2019. Methods: It was a retrospective hospital data-based study. Data were obtained from treatment charts of elderly inpatients stored in the Medical Records Department. Total 236 treatment record charts of patients ≥60 years of either sex was obtained from the period of July 2015 to December 2015 and the information were noted in predesigned pro forma. Results: The mean±SD age of the patients was 69.07±7.72 years with male preponderance (58.5%). Maximum number of patients were having respiratory disorders (57.6%), followed by kidney diseases (20.8%), cardiovascular diseases (18.6%), and so on. A total of 2683 drugs were prescribed with average number of 10.68±4.74 drugs per prescription. Only 363 formulations were prescribed by their generic names and 29.1% drugs were prescribed as fixed dose combinations. Polypharmacy was seen in 91.5% and 39%, respectively, in hospital stay and during discharge. About 60.5% drugs were prescribed from the National list of essential medicine. Total 57 drugs were found to be potentially inappropriate. About 22% patients received at least one drug which was potentially inappropriate according to Beers criteria and around 14% drugs were prescribed inappropriately. Conclusion: This study suggests that use of potentially inappropriate medications is common in elderly patients, some of them associated with high degree of risk in terms of worsening of the co-morbidity or drug-drug interactions. There is a need for nationwide assessment and strategies that may reduce or overcome such high prevalence

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Not AvailableThe ultimate role of prokaryote (bacteria and archaea), the decomposer of the wetland ecosystem, depends on its community structure and its interaction with the environment. The present study has used three universal prokaryote primers to compare prokaryote community structure and diversity of three distinctly different wetlands. The study results revealed that a-diversity indices and phylogenetic differential abundance patterns did not differ significantly among primers, but they did differ significantly across wetlands. Microbial community composition revealed a distinct pattern for each primer in each wetland. Overall comparison of prokaryote communities in sediments of three wetlands revealed the highest prokaryote richness and diversity in Bhomra (freshwater wetland) followed by Malencho (brackish-water wetland) and East Kolkata wetland (EKW) (sewage-fed wetland). Indicator genus analysis identified 21, 4, and 29 unique indicator genera, having preferential abode for Bhomra, EKW, and Malencho, respectively. Prediction of potential roles of these microbes revealed a preference for sulfate-reducing microbes in Malencho and methanogens in Bhomra. The distinct phylogenetic differential abundance pattern, microbial abode preference, and their potential functional role predict ecosystem variables shaping microbial diversity. The variation in community composition of prokaryotes in response to ecosystem variables can serve as the most sensitive bioindicator of wetland ecosystem assessment and management.Not Availabl

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

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