Research on Credit Assessment and Prediction Based on Deep Learning

The huge pressure of market demand and competitive environment makes supply chain finance the choice of most enterprises. The emergence of public health emergencies such as the COVID-19 epidemic has made it particularly urgent to improve the risk management capabilities of the pharmaceutical industry's supply chain in a transitional period. In-depth exploration of the key factors affecting the financial credit risk of pharmaceutical companies' supply chain, and the construction of a high-accuracy forecast model is of great significance to the stability of the macroeconomy. Combining the characteristics of the pharmaceutical manufacturing industry, this paper builds a financial credit risk assessment system for the pharmaceutical supply chain. On the basis of Factor Analysis and Random Forest variable screening, the AdaBoost algorithm is used to build the prediction model. By comparing basic machine learning models such as SVM model, decision tree, logistic regression, Bayesian classifier, BP neural network, and integrated learning models such as Random Forest, Bagging meta-estimator, GBM, and XGBoost, the study found that the AdaBoost model has higher accuracy. And through the data forecast in 2020, the superiority and effectiveness of the model for credit risk assessment in the pharmaceutical industry are further verified. According to the prediction results, this paper finds that the epidemic has no obvious negative impact on pharmaceutical manufacturing enterprises and proposes suggestions from the perspectives of the government and enterprises for reference. © 2022 SPIE.

An Evaluation of Local Pharmaceutical Manufacturing in Zimbabwe: How Prepared is Zimbabwe to Produce COVID-19 Vaccines?

Globally, the COVID-19 pandemic has had a significant impact, given the rise in the demand for novel therapeutics such as vaccines that can be used in the treatment of COVID-19 patients. Compared to other regions of the world, gross vaccine inequity exists in Africa due to several factors tied to the acute lack of vaccines in the region. As a result, efforts are currently being made to ramp up the production of COVID-19 vaccines in the region. However, there are concerns that most countries in Africa lack the adequate pharmaceutical manufacturing capacity required to produce COVID-19 vaccines, and Zimbabwe is not an exception. This article, therefore, aims to evaluate the preparedness and readiness efforts of the Zimbabwean pharmaceutical industry in the production of COVID-19 vaccines.

Stochastic ice nucleation governs the freezing process of biopharmaceuticals in vials.

Biopharmaceuticals commonly require freezing to ensure the stability of the active pharmaceutical ingredients (APIs). At commercial scale, freezing is typically carried out over the course of days in pallets comprising tens of thousands of vials. The selected process conditions have to ensure both complete freezing in all vials and a satisfactory manufacturing throughput. Current process design, however, is mainly experimental, since no mechanistic understanding of pallet freezing and its underlying phenomena has been achieved so far. Within this work, we derive a mechanistic modeling framework and compare the model predictions with engineering run data from the Janssen COVID-19 vaccine. The model qualitatively reproduced all observed trends and reveals that stochastic ice nucleation governs both process duration and batch heterogeneity. Knowledge on the ice nucleation kinetics of the formulation to be frozen thus is required to identify suitable freezing process conditions. The findings of this work pave the way towards a more rational design of pallet freezing, from which a plethora of frozen drug products may benefit. For this reason, we provide open source access to the model in the form of a python package (Deck et al., 2021).

Impact of Monetary Policy Uncertainty on R&D Investment Smoothing Behavior of Pharmaceutical Manufacturing Enterprises: Empirical Research Based on a Threshold Regression Model.

R&D investment is the source of technological innovation of pharmaceutical enterprises, but it will be restricted by the funding level, especially in the context of major public health emergencies occurring more frequently, therefore exploring the impact of monetary policy uncertainty on the R&D investment smoothing behavior of pharmaceutical manufacturing enterprises has important theoretical and practical value. Based on the relevant data of Chinese pharmaceutical manufacturing enterprises from 2012 to 2018, this paper studies the impact of monetary policy uncertainty on R&D investment smoothing behavior of pharmaceutical enterprises, and investigates whether there is a threshold effect. First, our results demonstrate that the empirical test results of this article support the hypothesis of R&D investment smoothing behavior of pharmaceutical manufacturing enterprises. Second, there is a negative correlation between monetary policy uncertainty and R&D investment smoothing behavior, and the shorter the period is, the higher the financing constraints of pharmaceutical enterprises are, and the more obvious the negative correlation is. Third, financing constraints have a single threshold effect on the R&D investment smoothing behavior of pharmaceutical manufacturing enterprises, with a threshold of -13.7693. Moreover, this conclusion can better promote the virtuous circle of the real economy of financial and pharmaceutical manufacturing enterprises. It is recommended that pharmaceutical manufacturing enterprises establish and improve the enterprise R&D reserve system, reduce the risk of R&D investment, play the role of R&D smoothing, and realize the sustainable development of enterprise R&D.