IMPACT OF ACTIVE PHARMACEUTICAL INGREDIENT (API) SCARCITY IN PHARMACEUTICAL SECTORS AMIDST COVID-19 PANDEMIC

The novel coronavirus disease 2019 (COVID-19) was characterized as a global pandemic by the World Health Organization (WHO) on March 11, 2020. The present pandemic has caused an intolerable impact on the health structure as well as the pharmaceutical sector, which in ultimatum has created enormous issues in the everyday lives of the patient community. On the other hand, the situation may appear in short and long-term time-horizon and need identification along with appropriate planning to reduce their socio-economic burden. The Indian pharmaceutical industry is the world's third-largest drug producer by volume. India supplies affordable and low-cost generic drugs to millions of people around the globe and operates more than 250 United States Food and Drug Administration (USFDA) and United Kingdom Medicine and Healthcare products Regulatory Agency (UKMHRA) approved plants. Given the Indian pharmaceutical industry, the source of Active Pharmaceutical Ingredients (APIs) for multiple diseases is much crucial part of the Pharma industry’s strategic plan to combat the COVID-19 pandemic. China is the top global producer and exporter of APIs by volume and Indian pharmaceutical industries are also rely heavily on APIs from China for the production of their medicine formulations by importing around 70 percent of the total requirement. However, the present pandemic situation has exposed the world's over-reliance on China in terms of API import and bound world leaders to fig. out sustainable alternatives

Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network

Based on an analysis of the short range chemical environment of each atom in a system, standard machine learning based approaches to the construction of interatomic potentials aim at determining directly the central quantity which is the total energy. This prevents for instance an accurate description of the energetics of systems where long range charge transfer is important as well as of ionized systems. We propose therefore not to target directly with machine learning methods the total energy but an intermediate physical quantity namely the charge density, which then in turn allows to determine the total energy. By allowing the electronic charge to distribute itself in an optimal way over the system, we can describe not only neutral but also ionized systems with unprecedented accuracy. We demonstrate the power of our approach for both neutral and ionized NaCl clusters where charge redistribution plays a decisive role for the energetics. We are able to obtain chemical accuracy, i.e. errors of less than a milli Hartree per atom compared to the reference density functional results. The introduction of physically motivated quantities which are determined by the short range atomic environment via a neural network leads also to an increased stability of the machine learning process and transferability of the potential.Comment: 4 figure