Pharmacovigilance in India: A Rising Concern towards Safe Medication Use

Pharmacovigilance deals with studying the safety and efficacy of new as well as already existing drugs with an objective to minimize the possibility of associated risk involved with drug use. Awareness regarding pharmacovigilance is rising in response to the new challenges it faces. With the modern computer aids and technology, rapid spread of information, increased communication across borders and easy access to variety of medicinal products is possible, thus increasing public expectation regarding safety of drugs in use. To meet the challenges, there is a need to plan a strategy. We need to conduct continuing medical education programs and dynamic progress of all aspects of pharmacovigilance with interdisciplinary approach by sharing information within and across the borders to improve public health and safety of humans

CHLOROQUINE AND HYDROXYCHLOROQUINE: A MAJOR BREAKTHROUGH FOR COVID-19

Coronavirus pandemic or COVID-19 is a global public health emergency at this period. Presently, no pharmacological treatment is known to treat this condition. Hydroxychloroquine (HCQ), a derivative of chloroquine (CQ), was first synthesized in 1946 by adding a hydroxyl group to CQ, which is much less toxic than CQ in animal studies. Other than being an anti-malarial drug, it was revealed to have various pharmacological effects and one of those is its anti-viral property. CQ, as well as HCQ, has been used in SARS (Severe Acute Respiratory Syndrome) coronavirus infection due to its antiviral properties. Even though various scientists have considered HCQ as a better therapeutic approach than CQ for the treatment of coronavirus infection, there are various adverse drug reactions associated with HCQ treatment in COVID-19 patients. In this paper, we review the anti-viral mechanism, various adverse drug reactions, and side effects of HCQ for COVID-19 treatment

Formulation of Ramipril Tablets Containing Solid Dispersion Employing Selective Polymers to Enhance Dissolution Rate

Objective: The present work based on formulation of Ramipril tablets containing solid dispersion employing selective polymers. The objective of the preparation is to prepare the solid dispersion of the Ramipril, which has more responsive value in terms of the dissolution rate. Method: Solid dispersion complex was prepared with two different carriers PEG 6000 and PVP K30. Nine formulations were developed and each formulation were subjected to pre compression and post compression parameters. Result and Discussion:  Pre-compression and post compression parameters were studied which had shown good flow property and compiled the standard data. In-vitro dissolution studies shows more than 90 % drug release in phosphate buffer pH 6.8 in 30 min. Out of all formulation F4 showed 92.55±0.67 % drug release with in 30min which was the best result rest of the formulation. Conclusion: Ramipril tablets were successfully prepared and evaluated. F4 formulation shows the greater dissolution rate in phosphate buffer pH 6.8 as compared to other formulations. When compared with marketed formulation it also shows better results. Therefore, Ramipril solid dispersion tablets enhanced the dissolution rate and can be more efficacious for improving oral bioavailability of Ramipril. Keywords: Solid dispersion, Ramipril, Solvent Evaporation Technique

Internet and its Impact on the Patient-Physician Relationship Patient Visiting Various Dental Clinics in Northern India

INTRODUCTION: Readily available health-related information over the internet has led to increased patient awareness, and this might be a possible factor straining the patient-physician relationship. AIM: To assess the impact of the internet on the patient-physician relationship amongst patient visiting various dental clinics in Northern India. MATERIALS AND METHODS: Of the 600 pre-tested online questionnaires distributed, a total of 456 (response rate 76%) adequately filled questionnaires were analysed for the impact of internet on the patient-physician relationship. Responses were subsequently tabulated and analysed using SPSS Version 21.0. Statistical significance was kept as p≤0.05. RESULTS: A statistically significant difference (p=.04) was seen amongst males and females regarding their internet usage with a higher proportion of health information being seeked by males. Most internet users (66.6%) followed their physician’s advice before they began using the internet with behavioural changes seen mostly in the 18-30 years age group (75.64%), yet only 14.38% of them informing their physician about such changes. CONCLUSION: It is important that people be advised about the potential risks of believing in sources from the internet with physicians also being advised to spend more quality time with their patients to alleviate them of their fears and doubts

FORMULATION AND EVALUATION OF HERBAL OIL-BASED ITRACONAZOLE CREAM FOR FUNGAL INFECTION

Objective: The current study was to formulate and to evaluate itraconazole herbal oil-based cream for fungal infection. Methods: Six herbal oils were used for the formulation of itraconazole creams, namely, mustard oil (MO), olive oil (OO), wheat germ oil (WGO), jojoba oil (JO), tea tree oil (TTO), and combined oil (CO). Creams were formulated by the trituration method. Each herbal oils containing three different formulations of different concentrations of oils. Results: All the prepared formulation was evaluated successfully. Out of all the formulations from each herbal oils the best two formulations were finalized, that is, MO2 and CO2 which show the greater in vitro diffusion rate among all. Formulation MO2 shows 55.45±0.10% and CO2 showed 59.43±1.18% within 480 min, respectively. Optimized formulations were also compared with the marketed formulation, which results in formulation CO2 as the best formulation among all. Conclusion: It can be concluded that herbal oil-based cream proved better alternate than oral preparation and improve patient compliance, ease of administration, local bioavailability, and better proves for fungal infected patients

Robust Few-shot Learning Without Using any Adversarial Samples

The high cost of acquiring and annotating samples has made the `few-shot' learning problem of prime importance. Existing works mainly focus on improving performance on clean data and overlook robustness concerns on the data perturbed with adversarial noise. Recently, a few efforts have been made to combine the few-shot problem with the robustness objective using sophisticated Meta-Learning techniques. These methods rely on the generation of adversarial samples in every episode of training, which further adds a computational burden. To avoid such time-consuming and complicated procedures, we propose a simple but effective alternative that does not require any adversarial samples. Inspired by the cognitive decision-making process in humans, we enforce high-level feature matching between the base class data and their corresponding low-frequency samples in the pretraining stage via self distillation. The model is then fine-tuned on the samples of novel classes where we additionally improve the discriminability of low-frequency query set features via cosine similarity. On a 1-shot setting of the CIFAR-FS dataset, our method yields a massive improvement of $60.55\%$ & $62.05\%$ in adversarial accuracy on the PGD and state-of-the-art Auto Attack, respectively, with a minor drop in clean accuracy compared to the baseline. Moreover, our method only takes $1.69\times$ of the standard training time while being $\approx$ $5\times$ faster than state-of-the-art adversarial meta-learning methods. The code is available at https://github.com/vcl-iisc/robust-few-shot-learning.Comment: TNNLS Submission (Under Review

Data-free Defense of Black Box Models Against Adversarial Attacks

Several companies often safeguard their trained deep models (i.e. details of architecture, learnt weights, training details etc.) from third-party users by exposing them only as black boxes through APIs. Moreover, they may not even provide access to the training data due to proprietary reasons or sensitivity concerns. We make the first attempt to provide adversarial robustness to the black box models in a data-free set up. We construct synthetic data via generative model and train surrogate network using model stealing techniques. To minimize adversarial contamination on perturbed samples, we propose `wavelet noise remover' (WNR) that performs discrete wavelet decomposition on input images and carefully select only a few important coefficients determined by our `wavelet coefficient selection module' (WCSM). To recover the high-frequency content of the image after noise removal via WNR, we further train a `regenerator' network with an objective to retrieve the coefficients such that the reconstructed image yields similar to original predictions on the surrogate model. At test time, WNR combined with trained regenerator network is prepended to the black box network, resulting in a high boost in adversarial accuracy. Our method improves the adversarial accuracy on CIFAR-10 by 38.98% and 32.01% on state-of-the-art Auto Attack compared to baseline, even when the attacker uses surrogate architecture (Alexnet-half and Alexnet) similar to the black box architecture (Alexnet) with same model stealing strategy as defender. The code is available at https://github.com/vcl-iisc/data-free-black-box-defenseComment: TIFS Submission (Under Review