Purpose India accounts for the third-largest Muslim population in the world after Indonesia and Pakistan. The previous studies about halal consumption have focused on the “food and money industry” only. Muslim consumers are prohibited from using alcohol, pork and other items in any form; the rising awareness among Muslims has led to the rapid growth in demand of halal cosmetic products around the globe. This paper aims to present a framework of halal consumers’ purchase and explores the factors that Indian consumers consider while buying halal cosmetics. Design/methodology/approach The authors carried out qualitative research (focus group discussion and in-depth interviews) in Delhi, Mumbai and Hyderabad to gain deeper insight from the respondents. Findings The study found that religiosity and increasing awareness about halal products acts as an influencer for individuals’ halal products consumption along with halal certification and growing education level of Muslim consumers. Originality/value The paper has been developed based on the original research work carried out among the halal Muslim consumers in the major Muslim population in metropolitan cities of Hyderabad, Mumbai and Delhi over the past year.

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).

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