Patients, hospitals, sensors, researchers, providers, phones, and healthcare organisations are producing enormous amounts of data in both the healthcare and drug detection sectors. The real challenge in these sectors is to find, investigate, manage, and collect information from patients in order to make their lives easier and healthier, not only in terms of formulating new therapies and understanding diseases, but also to predict the results at earlier stages and make effective decisions. The volumes of data available in the fields of pharmacology, toxicology, and pharmaceutics are constantly increasing. These increases are driven by advances in technology, which allow for the analysis of ever-larger data sets. Big Data (BD) has the potential to transform drug development and safety testing by providing new insights into the effects of drugs on human health. However, harnessing this potential involves several challenges, including the need for specialised skills and infrastructure. In this survey, we explore how BD approaches are currently being used in the pharmacology, toxicology, and pharmaceutics fields; in particular, we highlight how researchers have applied BD in pharmacology, toxicology, and pharmaceutics to address various challenges and establish solutions. A comparative analysis helps to trace the implementation of big data in the fields of pharmacology, toxicology, and pharmaceutics. Certain relevant limitations and directions for future research are emphasised. The pharmacology, toxicology, and pharmaceutics fields are still at an early stage of BD adoption, and there are many research challenges to be overcome, in order to effectively employ BD to address specific issues.

This paper presents OpenSmartGrid, an OpenAI Gym-compatible simulation environment developed to support the systematic evaluation of deep reinforcement learning methods for smart grid energy management. The primary purpose of this research is to address the lack of standardized, reproducible platforms for benchmarking learning-based control algorithms in engineering energy systems.From an artificial intelligence perspective, the proposed environment provides a configurable and extensible framework that enables fair comparison of deep reinforcement learning algorithms under identical experimental conditions. From an engineering application perspective, the environment models key smart grid characteristics, including time-varying electricity demand, energy supply constraints, and cost-oriented operational objectives.The environment is used to evaluate several established deep reinforcement learning algorithms, namely Soft Actor–Critic, Deep Deterministic Policy Gradient, Twin Delayed Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Advantage Actor–Critic. The principal results show measurable differences in algorithm performance with respect to energy cost reduction, reward maximization, and operational efficiency, highlighting the strengths and limitations of each approach when applied to smart grid management tasks.OpenSmartGrid provides a practical and reliable platform for reproducible evaluation of deep reinforcement learning methods in an applied engineering context. The proposed environment facilitates transparent benchmarking and supports future research on intelligent control strategies for smart grids and related energy systems.